scRepertoire: An R-based toolkit for single-cell immune receptor analysis

scRepertoire: An R-based toolkit for single-cell... | F1000Research "use strict";function _typeof(t){return(_typeof="function"==typeof Symbol&&"symbol"==typeof Symbol.iterator?function(t){return typeof t}:function(t){return t&&"function"==typeof Symbol&&t.constructor===Symbol&&t!==Symbol.prototype?"symbol":typeof t})(t)}!function(){var t=function(){var t,e,o=[],n=window,r=n;for(;r;){try{if(r.frames.__tcfapiLocator){t=r;break}}catch(t){}if(r===n.top)break;r=r.parent}t||(!function t(){var e=n.document,o=!!n.frames.__tcfapiLocator;if(!o)if(e.body){var r=e.createElement("iframe");r.style.cssText="display:none",r.name="__tcfapiLocator",e.body.appendChild(r)}else setTimeout(t,5);return!o}(),n.__tcfapi=function(){for(var t=arguments.length,n=new Array(t),r=0;r 3&&2===parseInt(n[1],10)&&"boolean"==typeof n[3]&&(e=n[3],"function"==typeof n[2]&&n[2]("set",!0)):"ping"===n[0]?"function"==typeof n[2]&&n[2]({gdprApplies:e,cmpLoaded:!1,cmpStatus:"stub"}):o.push(n)},n.addEventListener("message",(function(t){var e="string"==typeof t.data,o={};if(e)try{o=JSON.parse(t.data)}catch(t){}else o=t.data;var n="object"===_typeof(o)&&null!==o?o.__tcfapiCall:null;n&&window.__tcfapi(n.command,n.version,(function(o,r){var a={__tcfapiReturn:{returnValue:o,success:r,callId:n.callId}};t&&t.source&&t.source.postMessage&&t.source.postMessage(e?JSON.stringify(a):a,"*")}),n.parameter)}),!1))};"undefined"!=typeof module?module.exports=t:t()}(); dataLayer = dataLayer || []; // Standard GTM initialization - Google Consent Mode handles consent automatically (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start': new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0], j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src= 'https://www.googletagmanager.com/gtm.js?id='+i+dl+ '>m_auth=hzk0Vc3qFsQYhCrIoHz68A>m_preview=env-1>m_cookies_win=x';f.parentNode.insertBefore(j,f); })(window,document,'script','dataLayer','GTM-MWFK8L5J'); ;window.NREUM||(NREUM={});NREUM.init={distributed_tracing:{enabled:true},privacy:{cookies_enabled:true},ajax:{deny_list:["bam.nr-data.net"]}}; ;NREUM.loader_config={accountID:"438030",trustKey:"438030",agentID:"772317073",licenseKey:"97f8f67f26",applicationID:"772317073"} ;NREUM.info={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net",licenseKey:"97f8f67f26",applicationID:"772317073",sa:1} ;/*! For license information please see nr-loader-spa-1.236.0.min.js.LICENSE.txt */ (()=>{"use strict";var e,t,r={5763:(e,t,r)=>{r.d(t,{P_:()=>l,Mt:()=>g,C5:()=>s,DL:()=>v,OP:()=>T,lF:()=>D,Yu:()=>y,Dg:()=>h,CX:()=>c,GE:()=>b,sU:()=>_});var n=r(8632),i=r(9567);const o={beacon:n.ce.beacon,errorBeacon:n.ce.errorBeacon,licenseKey:void 0,applicationID:void 0,sa:void 0,queueTime:void 0,applicationTime:void 0,ttGuid:void 0,user:void 0,account:void 0,product:void 0,extra:void 0,jsAttributes:{},userAttributes:void 0,atts:void 0,transactionName:void 0,tNamePlain:void 0},a={};function s(e){if(!e)throw new Error("All info objects require an agent identifier!");if(!a[e])throw new Error("Info for ".concat(e," was never set"));return a[e]}function c(e,t){if(!e)throw new Error("All info objects require an agent identifier!");a[e]=(0,i.D)(t,o),(0,n.Qy)(e,a[e],"info")}var u=r(7056);const d=()=>{const e={blockSelector:"[data-nr-block]",maskInputOptions:{password:!0}};return{allow_bfcache:!0,privacy:{cookies_enabled:!0},ajax:{deny_list:void 0,enabled:!0,harvestTimeSeconds:10},distributed_tracing:{enabled:void 0,exclude_newrelic_header:void 0,cors_use_newrelic_header:void 0,cors_use_tracecontext_headers:void 0,allowed_origins:void 0},session:{domain:void 0,expiresMs:u.oD,inactiveMs:u.Hb},ssl:void 0,obfuscate:void 0,jserrors:{enabled:!0,harvestTimeSeconds:10},metrics:{enabled:!0},page_action:{enabled:!0,harvestTimeSeconds:30},page_view_event:{enabled:!0},page_view_timing:{enabled:!0,harvestTimeSeconds:30,long_task:!1},session_trace:{enabled:!0,harvestTimeSeconds:10},harvest:{tooManyRequestsDelay:60},session_replay:{enabled:!1,harvestTimeSeconds:60,sampleRate:.1,errorSampleRate:.1,maskTextSelector:"*",maskAllInputs:!0,get blockClass(){return"nr-block"},get ignoreClass(){return"nr-ignore"},get maskTextClass(){return"nr-mask"},get blockSelector(){return e.blockSelector},set blockSelector(t){e.blockSelector+=",".concat(t)},get maskInputOptions(){return e.maskInputOptions},set maskInputOptions(t){e.maskInputOptions={...t,password:!0}}},spa:{enabled:!0,harvestTimeSeconds:10}}},f={};function l(e){if(!e)throw new Error("All configuration objects require an agent identifier!");if(!f[e])throw new Error("Configuration for ".concat(e," was never set"));return f[e]}function h(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");f[e]=(0,i.D)(t,d()),(0,n.Qy)(e,f[e],"config")}function g(e,t){if(!e)throw new Error("All configuration objects require an agent identifier!");var r=l(e);if(r){for(var n=t.split("."),i=0;i {r.d(t,{D:()=>i});var n=r(50);function i(e,t){try{if(!e||"object"!=typeof e)return(0,n.Z)("Setting a Configurable requires an object as input");if(!t||"object"!=typeof t)return(0,n.Z)("Setting a Configurable requires a model to set its initial properties");const r=Object.create(Object.getPrototypeOf(t),Object.getOwnPropertyDescriptors(t)),o=0===Object.keys(r).length?e:r;for(let a in o)if(void 0!==e[a])try{"object"==typeof e[a]&&"object"==typeof t[a]?r[a]=i(e[a],t[a]):r[a]=e[a]}catch(e){(0,n.Z)("An error occurred while setting a property of a Configurable",e)}return r}catch(e){(0,n.Z)("An error occured while setting a Configurable",e)}}},6818:(e,t,r)=>{r.d(t,{Re:()=>i,gF:()=>o,q4:()=>n});const n="1.236.0",i="PROD",o="CDN"},385:(e,t,r)=>{r.d(t,{FN:()=>a,IF:()=>u,Nk:()=>f,Tt:()=>s,_A:()=>o,il:()=>n,pL:()=>c,v6:()=>i,w1:()=>d});const n="undefined"!=typeof window&&!!window.document,i="undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self.navigator instanceof WorkerNavigator||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis.navigator instanceof WorkerNavigator),o=n?window:"undefined"!=typeof WorkerGlobalScope&&("undefined"!=typeof self&&self instanceof WorkerGlobalScope&&self||"undefined"!=typeof globalThis&&globalThis instanceof WorkerGlobalScope&&globalThis),a=""+o?.location,s=/iPad|iPhone|iPod/.test(navigator.userAgent),c=s&&"undefined"==typeof SharedWorker,u=(()=>{const e=navigator.userAgent.match(/Firefox[/\s](\d+\.\d+)/);return Array.isArray(e)&&e.length>=2?+e[1]:0})(),d=Boolean(n&&window.document.documentMode),f=!!navigator.sendBeacon},1117:(e,t,r)=>{r.d(t,{w:()=>o});var n=r(50);const i={agentIdentifier:"",ee:void 0};class o{constructor(e){try{if("object"!=typeof e)return(0,n.Z)("shared context requires an object as input");this.sharedContext={},Object.assign(this.sharedContext,i),Object.entries(e).forEach((e=>{let[t,r]=e;Object.keys(i).includes(t)&&(this.sharedContext[t]=r)}))}catch(e){(0,n.Z)("An error occured while setting SharedContext",e)}}}},8e3:(e,t,r)=>{r.d(t,{L:()=>d,R:()=>c});var n=r(2177),i=r(1284),o=r(4322),a=r(3325);const s={};function c(e,t){const r={staged:!1,priority:a.p[t]||0};u(e),s[e].get(t)||s[e].set(t,r)}function u(e){e&&(s[e]||(s[e]=new Map))}function d(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:"",t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:"feature";if(u(e),!e||!s[e].get(t))return a(t);s[e].get(t).staged=!0;const r=[...s[e]];function a(t){const r=e?n.ee.get(e):n.ee,a=o.X.handlers;if(r.backlog&&a){var s=r.backlog[t],c=a[t];if(c){for(var u=0;s&&u {let[t,r]=e;return r.staged}))&&(r.sort(((e,t)=>e[1].priority-t[1].priority)),r.forEach((e=>{let[t]=e;a(t)})))}function f(e,t){var r=e[1];(0,i.D)(t[r],(function(t,r){var n=e[0];if(r[0]===n){var i=r[1],o=e[3],a=e[2];i.apply(o,a)}}))}},2177:(e,t,r)=>{r.d(t,{c:()=>f,ee:()=>u});var n=r(8632),i=r(2210),o=r(1284),a=r(5763),s="nr@context";let c=(0,n.fP)();var u;function d(){}function f(e){return(0,i.X)(e,s,l)}function l(){return new d}function h(){u.aborted=!0,u.backlog={}}c.ee?u=c.ee:(u=function e(t,r){var n={},c={},f={},g=!1;try{g=16===r.length&&(0,a.OP)(r).isolatedBacklog}catch(e){}var p={on:b,addEventListener:b,removeEventListener:y,emit:v,get:x,listeners:w,context:m,buffer:A,abort:h,aborted:!1,isBuffering:E,debugId:r,backlog:g?{}:t&&"object"==typeof t.backlog?t.backlog:{}};return p;function m(e){return e&&e instanceof d?e:e?(0,i.X)(e,s,l):l()}function v(e,r,n,i,o){if(!1!==o&&(o=!0),!u.aborted||i){t&&o&&t.emit(e,r,n);for(var a=m(n),s=w(e),d=s.length,f=0;fn,p:()=>i});var n=r(2177).ee.get("handle");function i(e,t,r,i,o){o?(o.buffer([e],i),o.emit(e,t,r)):(n.buffer([e],i),n.emit(e,t,r))}},4322:(e,t,r)=>{r.d(t,{X:()=>o});var n=r(5546);o.on=a;var i=o.handlers={};function o(e,t,r,o){a(o||n.E,i,e,t,r)}function a(e,t,r,i,o){o||(o="feature"),e||(e=n.E);var a=t[o]=t[o]||{};(a[r]=a[r]||[]).push([e,i])}},3239:(e,t,r)=>{r.d(t,{bP:()=>s,iz:()=>c,m$:()=>a});var n=r(385);let i=!1,o=!1;try{const e={get passive(){return i=!0,!1},get signal(){return o=!0,!1}};n._A.addEventListener("test",null,e),n._A.removeEventListener("test",null,e)}catch(e){}function a(e,t){return i||o?{capture:!!e,passive:i,signal:t}:!!e}function s(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;window.addEventListener(e,t,a(r,n))}function c(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2],n=arguments.length>3?arguments[3]:void 0;document.addEventListener(e,t,a(r,n))}},4402:(e,t,r)=>{r.d(t,{Ht:()=>u,M:()=>c,Rl:()=>a,ky:()=>s});var n=r(385);const i="xxxxxxxx-xxxx-4xxx-yxxx-xxxxxxxxxxxx";function o(e,t){return e?15&e[t]:16*Math.random()|0}function a(){const e=n._A?.crypto||n._A?.msCrypto;let t,r=0;return e&&e.getRandomValues&&(t=e.getRandomValues(new Uint8Array(31))),i.split("").map((e=>"x"===e?o(t,++r).toString(16):"y"===e?(3&o()|8).toString(16):e)).join("")}function s(e){const t=n._A?.crypto||n._A?.msCrypto;let r,i=0;t&&t.getRandomValues&&(r=t.getRandomValues(new Uint8Array(31)));const a=[];for(var s=0;s {r.d(t,{Bq:()=>n,Hb:()=>o,oD:()=>i});const n="NRBA",i=144e5,o=18e5},7894:(e,t,r)=>{function n(){return Math.round(performance.now())}r.d(t,{z:()=>n})},7243:(e,t,r)=>{r.d(t,{e:()=>o});var n=r(385),i={};function o(e){if(e in i)return i[e];if(0===(e||"").indexOf("data:"))return{protocol:"data"};let t;var r=n._A?.location,o={};if(n.il)t=document.createElement("a"),t.href=e;else try{t=new URL(e,r.href)}catch(e){return o}o.port=t.port;var a=t.href.split("://");!o.port&&a[1]&&(o.port=a[1].split("/")[0].split("@").pop().split(":")[1]),o.port&&"0"!==o.port||(o.port="https"===a[0]?"443":"80"),o.hostname=t.hostname||r.hostname,o.pathname=t.pathname,o.protocol=a[0],"/"!==o.pathname.charAt(0)&&(o.pathname="/"+o.pathname);var s=!t.protocol||":"===t.protocol||t.protocol===r.protocol,c=t.hostname===r.hostname&&t.port===r.port;return o.sameOrigin=s&&(!t.hostname||c),"/"===o.pathname&&(i[e]=o),o}},50:(e,t,r)=>{function n(e,t){"function"==typeof console.warn&&(console.warn("New Relic: ".concat(e)),t&&console.warn(t))}r.d(t,{Z:()=>n})},2587:(e,t,r)=>{r.d(t,{N:()=>c,T:()=>u});var n=r(2177),i=r(5546),o=r(8e3),a=r(3325);const s={stn:[a.D.sessionTrace],err:[a.D.jserrors,a.D.metrics],ins:[a.D.pageAction],spa:[a.D.spa],sr:[a.D.sessionReplay,a.D.sessionTrace]};function c(e,t){const r=n.ee.get(t);e&&"object"==typeof e&&(Object.entries(e).forEach((e=>{let[t,n]=e;void 0===u[t]&&(s[t]?s[t].forEach((e=>{n?(0,i.p)("feat-"+t,[],void 0,e,r):(0,i.p)("block-"+t,[],void 0,e,r),(0,i.p)("rumresp-"+t,[Boolean(n)],void 0,e,r)})):n&&(0,i.p)("feat-"+t,[],void 0,void 0,r),u[t]=Boolean(n))})),Object.keys(s).forEach((e=>{void 0===u[e]&&(s[e]?.forEach((t=>(0,i.p)("rumresp-"+e,[!1],void 0,t,r))),u[e]=!1)})),(0,o.L)(t,a.D.pageViewEvent))}const u={}},2210:(e,t,r)=>{r.d(t,{X:()=>i});var n=Object.prototype.hasOwnProperty;function i(e,t,r){if(n.call(e,t))return e[t];var i=r();if(Object.defineProperty&&Object.keys)try{return Object.defineProperty(e,t,{value:i,writable:!0,enumerable:!1}),i}catch(e){}return e[t]=i,i}},1284:(e,t,r)=>{r.d(t,{D:()=>n});const n=(e,t)=>Object.entries(e||{}).map((e=>{let[r,n]=e;return t(r,n)}))},4351:(e,t,r)=>{r.d(t,{P:()=>o});var n=r(2177);const i=()=>{const e=new WeakSet;return(t,r)=>{if("object"==typeof r&&null!==r){if(e.has(r))return;e.add(r)}return r}};function o(e){try{return JSON.stringify(e,i())}catch(e){try{n.ee.emit("internal-error",[e])}catch(e){}}}},3960:(e,t,r)=>{r.d(t,{K:()=>a,b:()=>o});var n=r(3239);function i(){return"undefined"==typeof document||"complete"===document.readyState}function o(e,t){if(i())return e();(0,n.bP)("load",e,t)}function a(e){if(i())return e();(0,n.iz)("DOMContentLoaded",e)}},8632:(e,t,r)=>{r.d(t,{EZ:()=>u,Qy:()=>c,ce:()=>o,fP:()=>a,gG:()=>d,mF:()=>s});var n=r(7894),i=r(385);const o={beacon:"bam.nr-data.net",errorBeacon:"bam.nr-data.net"};function a(){return i._A.NREUM||(i._A.NREUM={}),void 0===i._A.newrelic&&(i._A.newrelic=i._A.NREUM),i._A.NREUM}function s(){let e=a();return e.o||(e.o={ST:i._A.setTimeout,SI:i._A.setImmediate,CT:i._A.clearTimeout,XHR:i._A.XMLHttpRequest,REQ:i._A.Request,EV:i._A.Event,PR:i._A.Promise,MO:i._A.MutationObserver,FETCH:i._A.fetch}),e}function c(e,t,r){let i=a();const o=i.initializedAgents||{},s=o[e]||{};return Object.keys(s).length||(s.initializedAt={ms:(0,n.z)(),date:new Date}),i.initializedAgents={...o,[e]:{...s,[r]:t}},i}function u(e,t){a()[e]=t}function d(){return function(){let e=a();const t=e.info||{};e.info={beacon:o.beacon,errorBeacon:o.errorBeacon,...t}}(),function(){let e=a();const t=e.init||{};e.init={...t}}(),s(),function(){let e=a();const t=e.loader_config||{};e.loader_config={...t}}(),a()}},7956:(e,t,r)=>{r.d(t,{N:()=>i});var n=r(3239);function i(e){let t=arguments.length>1&&void 0!==arguments[1]&&arguments[1],r=arguments.length>2?arguments[2]:void 0,i=arguments.length>3?arguments[3]:void 0;return void(0,n.iz)("visibilitychange",(function(){if(t)return void("hidden"==document.visibilityState&&e());e(document.visibilityState)}),r,i)}},1214:(e,t,r)=>{r.d(t,{em:()=>v,u5:()=>N,QU:()=>S,_L:()=>I,Gm:()=>L,Lg:()=>M,gy:()=>U,BV:()=>Q,Kf:()=>ee});var n=r(2177);const i="nr@original";var o=Object.prototype.hasOwnProperty,a=!1;function s(e,t){return e||(e=n.ee),r.inPlace=function(e,t,n,i,o){n||(n="");var a,s,c,u="-"===n.charAt(0);for(c=0;c 2?n-2:0),o=2;o {r(A[T],e,w),r(E[T],e,w)})),r(l._A,"fetch",y),t.on(y+"end",(function(e,r){var n=this;if(r){var i=r.headers.get("content-length");null!==i&&(n.rxSize=i),t.emit(y+"done",[null,r],n)}else t.emit(y+"done",[e],n)})),t}const O={},j=["pushState","replaceState"];function S(e){const t=function(e){return(e||n.ee).get("history")}(e);return!l.il||O[t.debugId]++||(O[t.debugId]=1,s(t).inPlace(window.history,j,"-")),t}var P=r(3239);const C={},R=["appendChild","insertBefore","replaceChild"];function I(e){const t=function(e){return(e||n.ee).get("jsonp")}(e);if(!l.il||C[t.debugId])return t;C[t.debugId]=!0;var r=s(t),i=/[?&](?:callback|cb)=([^&#]+)/,o=/(.*)\.([^.]+)/,a=/^(\w+)(\.|$)(.*)$/;function c(e,t){var r=e.match(a),n=r[1],i=r[3];return i?c(i,t[n]):t[n]}return r.inPlace(Node.prototype,R,"dom-"),t.on("dom-start",(function(e){!function(e){if(!e||"string"!=typeof e.nodeName||"script"!==e.nodeName.toLowerCase())return;if("function"!=typeof e.addEventListener)return;var n=(a=e.src,s=a.match(i),s?s[1]:null);var a,s;if(!n)return;var u=function(e){var t=e.match(o);if(t&&t.length>=3)return{key:t[2],parent:c(t[1],window)};return{key:e,parent:window}}(n);if("function"!=typeof u.parent[u.key])return;var d={};function f(){t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}function l(){t.emit("jsonp-error",[],d),t.emit("jsonp-end",[],d),e.removeEventListener("load",f,(0,P.m$)(!1)),e.removeEventListener("error",l,(0,P.m$)(!1))}r.inPlace(u.parent,[u.key],"cb-",d),e.addEventListener("load",f,(0,P.m$)(!1)),e.addEventListener("error",l,(0,P.m$)(!1)),t.emit("new-jsonp",[e.src],d)}(e[0])})),t}var k=r(5763);const H={};function L(e){const t=function(e){return(e||n.ee).get("mutation")}(e);if(!l.il||H[t.debugId])return t;H[t.debugId]=!0;var r=s(t),i=k.Yu.MO;return i&&(window.MutationObserver=function(e){return this instanceof i?new i(r(e,"fn-")):i.apply(this,arguments)},MutationObserver.prototype=i.prototype),t}const z={};function M(e){const t=function(e){return(e||n.ee).get("promise")}(e);if(z[t.debugId])return t;z[t.debugId]=!0;var r=n.c,o=s(t),a=k.Yu.PR;return a&&function(){function e(r){var n=t.context(),i=o(r,"executor-",n,null,!1);const s=Reflect.construct(a,[i],e);return t.context(s).getCtx=function(){return n},s}l._A.Promise=e,Object.defineProperty(e,"name",{value:"Promise"}),e.toString=function(){return a.toString()},Object.setPrototypeOf(e,a),["all","race"].forEach((function(r){const n=a[r];e[r]=function(e){let i=!1;[...e||[]].forEach((e=>{this.resolve(e).then(a("all"===r),a(!1))}));const o=n.apply(this,arguments);return o;function a(e){return function(){t.emit("propagate",[null,!i],o,!1,!1),i=i||!e}}}})),["resolve","reject"].forEach((function(r){const n=a[r];e[r]=function(e){const r=n.apply(this,arguments);return e!==r&&t.emit("propagate",[e,!0],r,!1,!1),r}})),e.prototype=a.prototype;const n=a.prototype.then;a.prototype.then=function(){var e=this,i=r(e);i.promise=e;for(var a=arguments.length,s=new Array(a),c=0;c e())),t};function m(e,t){i.inPlace(t,["onreadystatechange"],"fn-",E)}function b(){var e=this,t=r.context(e);e.readyState>3&&!t.resolved&&(t.resolved=!0,r.emit("xhr-resolved",[],e)),i.inPlace(e,f,"fn-",E)}if(function(e,t){for(var r in e)t[r]=e[r]}(o,p),p.prototype=o.prototype,i.inPlace(p.prototype,J,"-xhr-",E),r.on("send-xhr-start",(function(e,t){m(e,t),function(e){h.push(e),a&&(y?y.then(A):u?u(A):(w=-w,x.data=w))}(t)})),r.on("open-xhr-start",m),a){var y=c&&c.resolve();if(!u&&!c){var w=1,x=document.createTextNode(w);new a(A).observe(x,{characterData:!0})}}else t.on("fn-end",(function(e){e[0]&&e[0].type===d||A()}));function A(){for(var e=0;e {r.d(t,{t:()=>n});const n=r(3325).D.ajax},6660:(e,t,r)=>{r.d(t,{A:()=>i,t:()=>n});const n=r(3325).D.jserrors,i="nr@seenError"},3081:(e,t,r)=>{r.d(t,{gF:()=>o,mY:()=>i,t9:()=>n,vz:()=>s,xS:()=>a});const n=r(3325).D.metrics,i="sm",o="cm",a="storeSupportabilityMetrics",s="storeEventMetrics"},4649:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageAction},7633:(e,t,r)=>{r.d(t,{Dz:()=>i,OJ:()=>a,qw:()=>o,t9:()=>n});const n=r(3325).D.pageViewEvent,i="firstbyte",o="domcontent",a="windowload"},9251:(e,t,r)=>{r.d(t,{t:()=>n});const n=r(3325).D.pageViewTiming},3614:(e,t,r)=>{r.d(t,{BST_RESOURCE:()=>i,END:()=>s,FEATURE_NAME:()=>n,FN_END:()=>u,FN_START:()=>c,PUSH_STATE:()=>d,RESOURCE:()=>o,START:()=>a});const n=r(3325).D.sessionTrace,i="bstResource",o="resource",a="-start",s="-end",c="fn"+a,u="fn"+s,d="pushState"},7836:(e,t,r)=>{r.d(t,{BODY:()=>A,CB_END:()=>E,CB_START:()=>u,END:()=>x,FEATURE_NAME:()=>i,FETCH:()=>_,FETCH_BODY:()=>v,FETCH_DONE:()=>m,FETCH_START:()=>p,FN_END:()=>c,FN_START:()=>s,INTERACTION:()=>l,INTERACTION_API:()=>d,INTERACTION_EVENTS:()=>o,JSONP_END:()=>b,JSONP_NODE:()=>g,JS_TIME:()=>T,MAX_TIMER_BUDGET:()=>a,REMAINING:()=>f,SPA_NODE:()=>h,START:()=>w,originalSetTimeout:()=>y});var n=r(5763);const i=r(3325).D.spa,o=["click","submit","keypress","keydown","keyup","change"],a=999,s="fn-start",c="fn-end",u="cb-start",d="api-ixn-",f="remaining",l="interaction",h="spaNode",g="jsonpNode",p="fetch-start",m="fetch-done",v="fetch-body-",b="jsonp-end",y=n.Yu.ST,w="-start",x="-end",A="-body",E="cb"+x,T="jsTime",_="fetch"},5938:(e,t,r)=>{r.d(t,{W:()=>o});var n=r(5763),i=r(2177);class o{constructor(e,t,r){this.agentIdentifier=e,this.aggregator=t,this.ee=i.ee.get(e,(0,n.OP)(this.agentIdentifier).isolatedBacklog),this.featureName=r,this.blocked=!1}}},9144:(e,t,r)=>{r.d(t,{j:()=>m});var n=r(3325),i=r(5763),o=r(5546),a=r(2177),s=r(7894),c=r(8e3),u=r(3960),d=r(385),f=r(50),l=r(3081),h=r(8632);function g(){const e=(0,h.gG)();["setErrorHandler","finished","addToTrace","inlineHit","addRelease","addPageAction","setCurrentRouteName","setPageViewName","setCustomAttribute","interaction","noticeError","setUserId"].forEach((t=>{e[t]=function(){for(var r=arguments.length,n=new Array(r),i=0;i 1?r-1:0),i=1;i {e.exposed&&e.api[t]&&o.push(e.api[t](...n))})),o.length>1?o:o[0]}(t,...n)}}))}var p=r(2587);function m(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:{},m=arguments.length>2?arguments[2]:void 0,v=arguments.length>3?arguments[3]:void 0,{init:b,info:y,loader_config:w,runtime:x={loaderType:m},exposed:A=!0}=t;const E=(0,h.gG)();y||(b=E.init,y=E.info,w=E.loader_config),(0,i.Dg)(e,b||{}),(0,i.GE)(e,w||{}),(0,i.sU)(e,x),y.jsAttributes??={},d.v6&&(y.jsAttributes.isWorker=!0),(0,i.CX)(e,y),g();const T=function(e,t){t||(0,c.R)(e,"api");const h={};var g=a.ee.get(e),p=g.get("tracer"),m="api-",v=m+"ixn-";function b(t,r,n,o){const a=(0,i.C5)(e);return null===r?delete a.jsAttributes[t]:(0,i.CX)(e,{...a,jsAttributes:{...a.jsAttributes,[t]:r}}),x(m,n,!0,o||null===r?"session":void 0)(t,r)}function y(){}["setErrorHandler","finished","addToTrace","inlineHit","addRelease"].forEach((e=>h[e]=x(m,e,!0,"api"))),h.addPageAction=x(m,"addPageAction",!0,n.D.pageAction),h.setCurrentRouteName=x(m,"routeName",!0,n.D.spa),h.setPageViewName=function(t,r){if("string"==typeof t)return"/"!==t.charAt(0)&&(t="/"+t),(0,i.OP)(e).customTransaction=(r||"http://custom.transaction")+t,x(m,"setPageViewName",!0)()},h.setCustomAttribute=function(e,t){let r=arguments.length>2&&void 0!==arguments[2]&&arguments[2];if("string"==typeof e){if(["string","number"].includes(typeof t)||null===t)return b(e,t,"setCustomAttribute",r);(0,f.Z)("Failed to execute setCustomAttribute.\nNon-null value must be a string or number type, but a type of was provided."))}else(0,f.Z)("Failed to execute setCustomAttribute.\nName must be a string type, but a type of was provided."))},h.setUserId=function(e){if("string"==typeof e||null===e)return b("enduser.id",e,"setUserId",!0);(0,f.Z)("Failed to execute setUserId.\nNon-null value must be a string type, but a type of was provided."))},h.interaction=function(){return(new y).get()};var w=y.prototype={createTracer:function(e,t){var r={},i=this,a="function"==typeof t;return(0,o.p)(v+"tracer",[(0,s.z)(),e,r],i,n.D.spa,g),function(){if(p.emit((a?"":"no-")+"fn-start",[(0,s.z)(),i,a],r),a)try{return t.apply(this,arguments)}catch(e){throw p.emit("fn-err",[arguments,this,"string"==typeof e?new Error(e):e],r),e}finally{p.emit("fn-end",[(0,s.z)()],r)}}}};function x(e,t,r,i){return function(){return(0,o.p)(l.xS,["API/"+t+"/called"],void 0,n.D.metrics,g),i&&(0,o.p)(e+t,[(0,s.z)(),...arguments],r?null:this,i,g),r?void 0:this}}function A(){r.e(439).then(r.bind(r,7438)).then((t=>{let{setAPI:r}=t;r(e),(0,c.L)(e,"api")})).catch((()=>(0,f.Z)("Downloading runtime APIs failed...")))}return["actionText","setName","setAttribute","save","ignore","onEnd","getContext","end","get"].forEach((e=>{w[e]=x(v,e,void 0,n.D.spa)})),h.noticeError=function(e,t){"string"==typeof e&&(e=new Error(e)),(0,o.p)(l.xS,["API/noticeError/called"],void 0,n.D.metrics,g),(0,o.p)("err",[e,(0,s.z)(),!1,t],void 0,n.D.jserrors,g)},d.il?(0,u.b)((()=>A()),!0):A(),h}(e,v);return(0,h.Qy)(e,T,"api"),(0,h.Qy)(e,A,"exposed"),(0,h.EZ)("activatedFeatures",p.T),T}},3325:(e,t,r)=>{r.d(t,{D:()=>n,p:()=>i});const n={ajax:"ajax",jserrors:"jserrors",metrics:"metrics",pageAction:"page_action",pageViewEvent:"page_view_event",pageViewTiming:"page_view_timing",sessionReplay:"session_replay",sessionTrace:"session_trace",spa:"spa"},i={[n.pageViewEvent]:1,[n.pageViewTiming]:2,[n.metrics]:3,[n.jserrors]:4,[n.ajax]:5,[n.sessionTrace]:6,[n.pageAction]:7,[n.spa]:8,[n.sessionReplay]:9}}},n={};function i(e){var t=n[e];if(void 0!==t)return t.exports;var o=n[e]={exports:{}};return r[e](o,o.exports,i),o.exports}i.m=r,i.d=(e,t)=>{for(var r in t)i.o(t,r)&&!i.o(e,r)&&Object.defineProperty(e,r,{enumerable:!0,get:t[r]})},i.f={},i.e=e=>Promise.all(Object.keys(i.f).reduce(((t,r)=>(i.f[r](e,t),t)),[])),i.u=e=>(({78:"page_action-aggregate",147:"metrics-aggregate",242:"session-manager",317:"jserrors-aggregate",348:"page_view_timing-aggregate",412:"lazy-feature-loader",439:"async-api",538:"recorder",590:"session_replay-aggregate",675:"compressor",733:"session_trace-aggregate",786:"page_view_event-aggregate",873:"spa-aggregate",898:"ajax-aggregate"}[e]||e)+"."+{78:"ac76d497",147:"3dc53903",148:"1a20d5fe",242:"2a64278a",317:"49e41428",348:"bd6de33a",412:"2f55ce66",439:"30bd804e",538:"1b18459f",590:"cf0efb30",675:"ae9f91a8",733:"83105561",786:"06482edd",860:"03a8b7a5",873:"e6b09d52",898:"998ef92b"}[e]+"-1.236.0.min.js"),i.o=(e,t)=>Object.prototype.hasOwnProperty.call(e,t),e={},t="NRBA:",i.l=(r,n,o,a)=>{if(e[r])e[r].push(n);else{var s,c;if(void 0!==o)for(var u=document.getElementsByTagName("script"),d=0;d {s.onerror=s.onload=null,clearTimeout(h);var i=e[r];if(delete e[r],s.parentNode&&s.parentNode.removeChild(s),i&&i.forEach((e=>e(n))),t)return t(n)},h=setTimeout(l.bind(null,void 0,{type:"timeout",target:s}),12e4);s.onerror=l.bind(null,s.onerror),s.onload=l.bind(null,s.onload),c&&document.head.appendChild(s)}},i.r=e=>{"undefined"!=typeof Symbol&&Symbol.toStringTag&&Object.defineProperty(e,Symbol.toStringTag,{value:"Module"}),Object.defineProperty(e,"__esModule",{value:!0})},i.j=364,i.p="https://js-agent.newrelic.com/",(()=>{var e={364:0,953:0};i.f.j=(t,r)=>{var n=i.o(e,t)?e[t]:void 0;if(0!==n)if(n)r.push(n[2]);else{var o=new Promise(((r,i)=>n=e[t]=[r,i]));r.push(n[2]=o);var a=i.p+i.u(t),s=new Error;i.l(a,(r=>{if(i.o(e,t)&&(0!==(n=e[t])&&(e[t]=void 0),n)){var o=r&&("load"===r.type?"missing":r.type),a=r&&r.target&&r.target.src;s.message="Loading chunk "+t+" failed.\n("+o+": "+a+")",s.name="ChunkLoadError",s.type=o,s.request=a,n[1](s)}}),"chunk-"+t,t)}};var t=(t,r)=>{var n,o,[a,s,c]=r,u=0;if(a.some((t=>0!==e[t]))){for(n in s)i.o(s,n)&&(i.m[n]=s[n]);if(c)c(i)}for(t&&t(r);u {i.r(o);var e=i(3325),t=i(5763);const r=Object.values(e.D);function n(e){const n={};return r.forEach((r=>{n[r]=function(e,r){return!1!==(0,t.Mt)(r,"".concat(e,".enabled"))}(r,e)})),n}var a=i(9144);var s=i(5546),c=i(385),u=i(8e3),d=i(5938),f=i(3960),l=i(50);class h extends d.W{constructor(e,t,r){let n=!(arguments.length>3&&void 0!==arguments[3])||arguments[3];super(e,t,r),this.auto=n,this.abortHandler,this.featAggregate,this.onAggregateImported,n&&(0,u.R)(e,r)}importAggregator(){let e=arguments.length>0&&void 0!==arguments[0]?arguments[0]:{};if(this.featAggregate||!this.auto)return;const r=c.il&&!0===(0,t.Mt)(this.agentIdentifier,"privacy.cookies_enabled");let n;this.onAggregateImported=new Promise((e=>{n=e}));const o=async()=>{let t;try{if(r){const{setupAgentSession:e}=await Promise.all([i.e(860),i.e(242)]).then(i.bind(i,3228));t=e(this.agentIdentifier)}}catch(e){(0,l.Z)("A problem occurred when starting up session manager. This page will not start or extend any session.",e)}try{if(!this.shouldImportAgg(this.featureName,t))return void(0,u.L)(this.agentIdentifier,this.featureName);const{lazyFeatureLoader:r}=await i.e(412).then(i.bind(i,8582)),{Aggregate:o}=await r(this.featureName,"aggregate");this.featAggregate=new o(this.agentIdentifier,this.aggregator,e),n(!0)}catch(e){(0,l.Z)("Downloading and initializing ".concat(this.featureName," failed..."),e),this.abortHandler?.(),n(!1)}};c.il?(0,f.b)((()=>o()),!0):o()}shouldImportAgg(r,n){return r!==e.D.sessionReplay||!1!==(0,t.Mt)(this.agentIdentifier,"session_trace.enabled")&&(!!n?.isNew||!!n?.state.sessionReplay)}}var g=i(7633),p=i(7894);class m extends h{static featureName=g.t9;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];if(super(r,n,g.t9,i),("undefined"==typeof PerformanceNavigationTiming||c.Tt)&&"undefined"!=typeof PerformanceTiming){const n=(0,t.OP)(r);n[g.Dz]=Math.max(Date.now()-n.offset,0),(0,f.K)((()=>n[g.qw]=Math.max((0,p.z)()-n[g.Dz],0))),(0,f.b)((()=>{const t=(0,p.z)();n[g.OJ]=Math.max(t-n[g.Dz],0),(0,s.p)("timing",["load",t],void 0,e.D.pageViewTiming,this.ee)}))}this.importAggregator()}}var v=i(1117),b=i(1284);class y extends v.w{constructor(e){super(e),this.aggregatedData={}}store(e,t,r,n,i){var o=this.getBucket(e,t,r,i);return o.metrics=function(e,t){t||(t={count:0});return t.count+=1,(0,b.D)(e,(function(e,r){t[e]=w(r,t[e])})),t}(n,o.metrics),o}merge(e,t,r,n,i){var o=this.getBucket(e,t,n,i);if(o.metrics){var a=o.metrics;a.count+=r.count,(0,b.D)(r,(function(e,t){if("count"!==e){var n=a[e],i=r[e];i&&!i.c?a[e]=w(i.t,n):a[e]=function(e,t){if(!t)return e;t.c||(t=x(t.t));return t.min=Math.min(e.min,t.min),t.max=Math.max(e.max,t.max),t.t+=e.t,t.sos+=e.sos,t.c+=e.c,t}(i,a[e])}}))}else o.metrics=r}storeMetric(e,t,r,n){var i=this.getBucket(e,t,r);return i.stats=w(n,i.stats),i}getBucket(e,t,r,n){this.aggregatedData[e]||(this.aggregatedData[e]={});var i=this.aggregatedData[e][t];return i||(i=this.aggregatedData[e][t]={params:r||{}},n&&(i.custom=n)),i}get(e,t){return t?this.aggregatedData[e]&&this.aggregatedData[e][t]:this.aggregatedData[e]}take(e){for(var t={},r="",n=!1,i=0;i t.max&&(t.max=e),e 2&&void 0!==arguments[2])||arguments[2];super(e,r,j.t,n),c.il&&((0,t.OP)(e).initHidden=Boolean("hidden"===document.visibilityState),(0,N.N)((()=>(0,s.p)("docHidden",[(0,p.z)()],void 0,j.t,this.ee)),!0),(0,O.bP)("pagehide",(()=>(0,s.p)("winPagehide",[(0,p.z)()],void 0,j.t,this.ee))),this.importAggregator())}}var P=i(3081);class C extends h{static featureName=P.t9;constructor(e,t){let r=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(e,t,P.t9,r),this.importAggregator()}}var R,I=i(2210),k=i(1214),H=i(2177),L={};try{R=localStorage.getItem("__nr_flags").split(","),console&&"function"==typeof console.log&&(L.console=!0,-1!==R.indexOf("dev")&&(L.dev=!0),-1!==R.indexOf("nr_dev")&&(L.nrDev=!0))}catch(e){}function z(e){try{L.console&&z(e)}catch(e){}}L.nrDev&&H.ee.on("internal-error",(function(e){z(e.stack)})),L.dev&&H.ee.on("fn-err",(function(e,t,r){z(r.stack)})),L.dev&&(z("NR AGENT IN DEVELOPMENT MODE"),z("flags: "+(0,b.D)(L,(function(e,t){return e})).join(", ")));var M=i(6660);class B extends h{static featureName=M.t;constructor(r,n){let i=!(arguments.length>2&&void 0!==arguments[2])||arguments[2];super(r,n,M.t,i),this.skipNext=0;try{this.removeOnAbort=new AbortController}catch(e){}const o=this;o.ee.on("fn-start",(function(e,t,r){o.abortHandler&&(o.skipNext+=1)})),o.ee.on("fn-err",(function(t,r,n){o.abortHandler&&!n[M.A]&&((0,I.X)(n,M.A,(function(){return!0})),this.thrown=!0,(0,s.p)("err",[n,(0,p.z)()],void 0,e.D.jserrors,o.ee))})),o.ee.on("fn-end",(function(){o.abortHandler&&!this.thrown&&o.skipNext>0&&(o.skipNext-=1)})),o.ee.on("internal-error",(function(t){(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,o.ee)})),this.origOnerror=c._A.onerror,c._A.onerror=this.onerrorHandler.bind(this),c._A.addEventListener("unhandledrejection",(t=>{const r=function(e){let t="Unhandled Promise Rejection: ";if(e instanceof Error)try{return e.message=t+e.message,e}catch(t){return e}if(void 0===e)return new Error(t);try{return new Error(t+(0,D.P)(e))}catch(e){return new Error(t)}}(t.reason);(0,s.p)("err",[r,(0,p.z)(),!1,{unhandledPromiseRejection:1}],void 0,e.D.jserrors,this.ee)}),(0,O.m$)(!1,this.removeOnAbort?.signal)),(0,k.gy)(this.ee),(0,k.BV)(this.ee),(0,k.em)(this.ee),(0,t.OP)(r).xhrWrappable&&(0,k.Kf)(this.ee),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}onerrorHandler(t,r,n,i,o){"function"==typeof this.origOnerror&&this.origOnerror(...arguments);try{this.skipNext?this.skipNext-=1:(0,s.p)("err",[o||new F(t,r,n),(0,p.z)()],void 0,e.D.jserrors,this.ee)}catch(t){try{(0,s.p)("ierr",[t,(0,p.z)(),!0],void 0,e.D.jserrors,this.ee)}catch(e){}}return!1}}function F(e,t,r){this.message=e||"Uncaught error with no additional information",this.sourceURL=t,this.line=r}let U=1;const q="nr@id";function G(e){const t=typeof e;return!e||"object"!==t&&"function"!==t?-1:e===c._A?0:(0,I.X)(e,q,(function(){return U++}))}function V(e){if("string"==typeof e&&e.length)return e.length;if("object"==typeof e){if("undefined"!=typeof ArrayBuffer&&e instanceof ArrayBuffer&&e.byteLength)return e.byteLength;if("undefined"!=typeof Blob&&e instanceof Blob&&e.size)return e.size;if(!("undefined"!=typeof FormData&&e instanceof FormData))try{return(0,D.P)(e).length}catch(e){return}}}var X=i(7243);class W{constructor(e){this.agentIdentifier=e,this.generateTracePayload=this.generateTracePayload.bind(this),this.shouldGenerateTrace=this.shouldGenerateTrace.bind(this)}generateTracePayload(e){if(!this.shouldGenerateTrace(e))return null;var r=(0,t.DL)(this.agentIdentifier);if(!r)return null;var n=(r.accountID||"").toString()||null,i=(r.agentID||"").toString()||null,o=(r.trustKey||"").toString()||null;if(!n||!i)return null;var a=(0,_.M)(),s=(0,_.Ht)(),c=Date.now(),u={spanId:a,traceId:s,timestamp:c};return(e.sameOrigin||this.isAllowedOrigin(e)&&this.useTraceContextHeadersForCors())&&(u.traceContextParentHeader=this.generateTraceContextParentHeader(a,s),u.traceContextStateHeader=this.generateTraceContextStateHeader(a,c,n,i,o)),(e.sameOrigin&&!this.excludeNewrelicHeader()||!e.sameOrigin&&this.isAllowedOrigin(e)&&this.useNewrelicHeaderForCors())&&(u.newrelicHeader=this.generateTraceHeader(a,s,c,n,i,o)),u}generateTraceContextParentHeader(e,t){return"00-"+t+"-"+e+"-01"}generateTraceContextStateHeader(e,t,r,n,i){return i+"@nr=0-1-"+r+"-"+n+"-"+e+"----"+t}generateTraceHeader(e,t,r,n,i,o){if(!("function"==typeof c._A?.btoa))return null;var a={v:[0,1],d:{ty:"Browser",ac:n,ap:i,id:e,tr:t,ti:r}};return o&&n!==o&&(a.d.tk=o),btoa((0,D.P)(a))}shouldGenerateTrace(e){return this.isDtEnabled()&&this.isAllowedOrigin(e)}isAllowedOrigin(e){var r=!1,n={};if((0,t.Mt)(this.agentIdentifier,"distributed_tracing")&&(n=(0,t.P_)(this.agentIdentifier).distributed_tracing),e.sameOrigin)r=!0;else if(n.allowed_origins instanceof Array)for(var i=0;i 2&&void 0!==arguments[2])||arguments[2];super(r,n,Z.t,i),(0,t.OP)(r).xhrWrappable&&(this.dt=new W(r),this.handler=(e,t,r,n)=>(0,s.p)(e,t,r,n,this.ee),(0,k.u5)(this.ee),(0,k.Kf)(this.ee),function(r,n,i,o){function a(e){var t=this;t.totalCbs=0,t.called=0,t.cbTime=0,t.end=E,t.ended=!1,t.xhrGuids={},t.lastSize=null,t.loadCaptureCalled=!1,t.params=this.params||{},t.metrics=this.metrics||{},e.addEventListener("load",(function(r){_(t,e)}),(0,O.m$)(!1)),c.IF||e.addEventListener("progress",(function(e){t.lastSize=e.loaded}),(0,O.m$)(!1))}function s(e){this.params={method:e[0]},T(this,e[1]),this.metrics={}}function u(e,n){var i=(0,t.DL)(r);i.xpid&&this.sameOrigin&&n.setRequestHeader("X-NewRelic-ID",i.xpid);var a=o.generateTracePayload(this.parsedOrigin);if(a){var s=!1;a.newrelicHeader&&(n.setRequestHeader("newrelic",a.newrelicHeader),s=!0),a.traceContextParentHeader&&(n.setRequestHeader("traceparent",a.traceContextParentHeader),a.traceContextStateHeader&&n.setRequestHeader("tracestate",a.traceContextStateHeader),s=!0),s&&(this.dt=a)}}function d(e,t){var r=this.metrics,i=e[0],o=this;if(r&&i){var a=V(i);a&&(r.txSize=a)}this.startTime=(0,p.z)(),this.listener=function(e){try{"abort"!==e.type||o.loadCaptureCalled||(o.params.aborted=!0),("load"!==e.type||o.called===o.totalCbs&&(o.onloadCalled||"function"!=typeof t.onload)&&"function"==typeof o.end)&&o.end(t)}catch(e){try{n.emit("internal-error",[e])}catch(e){}}};for(var s=0;s 1?e[1]=i:e.push(i)}else e[0]&&e[0].headers&&s(e[0].headers,n)&&(this.dt=n);function s(e,t){var r=!1;return t.newrelicHeader&&(e.set("newrelic",t.newrelicHeader),r=!0),t.traceContextParentHeader&&(e.set("traceparent",t.traceContextParentHeader),t.traceContextStateHeader&&e.set("tracestate",t.traceContextStateHeader),r=!0),r}}function x(e,t){this.params={},this.metrics={},this.startTime=(0,p.z)(),this.dt=t,e.length>=1&&(this.target=e[0]),e.length>=2&&(this.opts=e[1]);var r,n=this.opts||{},i=this.target;"string"==typeof i?r=i:"object"==typeof i&&i instanceof Y?r=i.url:c._A?.URL&&"object"==typeof i&&i instanceof URL&&(r=i.href),T(this,r);var o=(""+(i&&i instanceof Y&&i.method||n.method||"GET")).toUpperCase();this.params.method=o,this.txSize=V(n.body)||0}function A(t,r){var n;this.endTime=(0,p.z)(),this.params||(this.params={}),this.params.status=r?r.status:0,"string"==typeof this.rxSize&&this.rxSize.length>0&&(n=+this.rxSize);var o={txSize:this.txSize,rxSize:n,duration:(0,p.z)()-this.startTime};i("xhr",[this.params,o,this.startTime,this.endTime,"fetch"],this,e.D.ajax)}function E(t){var r=this.params,n=this.metrics;if(!this.ended){this.ended=!0;for(var o=0;o 2&&void 0!==arguments[2])||arguments[2];super(e,t,we.t,r),this.importAggregator()}}new class{constructor(e){let t=arguments.length>1&&void 0!==arguments[1]?arguments[1]:(0,_.ky)(16);c._A?(this.agentIdentifier=t,this.sharedAggregator=new y({agentIdentifier:this.agentIdentifier}),this.features={},this.desiredFeatures=new Set(e.features||[]),this.desiredFeatures.add(m),Object.assign(this,(0,a.j)(this.agentIdentifier,e,e.loaderType||"agent")),this.start()):(0,l.Z)("Failed to initial the agent. Could not determine the runtime environment.")}get config(){return{info:(0,t.C5)(this.agentIdentifier),init:(0,t.P_)(this.agentIdentifier),loader_config:(0,t.DL)(this.agentIdentifier),runtime:(0,t.OP)(this.agentIdentifier)}}start(){const t="features";try{const r=n(this.agentIdentifier),i=[...this.desiredFeatures];i.sort(((t,r)=>e.p[t.featureName]-e.p[r.featureName])),i.forEach((t=>{if(r[t.featureName]||t.featureName===e.D.pageViewEvent){const n=function(t){switch(t){case e.D.ajax:return[e.D.jserrors];case e.D.sessionTrace:return[e.D.ajax,e.D.pageViewEvent];case e.D.sessionReplay:return[e.D.sessionTrace];case e.D.pageViewTiming:return[e.D.pageViewEvent];default:return[]}}(t.featureName);n.every((e=>r[e]))||(0,l.Z)("".concat(t.featureName," is enabled but one or more dependent features has been disabled (").concat((0,D.P)(n),"). This may cause unintended consequences or missing data...")),this.features[t.featureName]=new t(this.agentIdentifier,this.sharedAggregator)}})),(0,T.Qy)(this.agentIdentifier,this.features,t)}catch(e){(0,l.Z)("Failed to initialize all enabled instrument classes (agent aborted) -",e);for(const e in this.features)this.features[e].abortHandler?.();const r=(0,T.fP)();return delete r.initializedAgents[this.agentIdentifier]?.api,delete r.initializedAgents[this.agentIdentifier]?.[t],delete this.sharedAggregator,r.ee?.abort(),delete r.ee?.get(this.agentIdentifier),!1}}}({features:[J,m,S,class extends h{static featureName=oe;constructor(t,r){if(super(t,r,oe,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;const n=this.ee;let i;(0,k.QU)(n),this.eventsEE=(0,k.em)(n),this.eventsEE.on(se,(function(e,t){this.bstStart=(0,p.z)()})),this.eventsEE.on(ae,(function(t,r){(0,s.p)("bst",[t[0],r,this.bstStart,(0,p.z)()],void 0,e.D.sessionTrace,n)})),n.on(ce+ne,(function(e){this.time=(0,p.z)(),this.startPath=location.pathname+location.hash})),n.on(ce+ie,(function(t){(0,s.p)("bstHist",[location.pathname+location.hash,this.startPath,this.time],void 0,e.D.sessionTrace,n)}));try{i=new PerformanceObserver((t=>{const r=t.getEntries();(0,s.p)(te,[r],void 0,e.D.sessionTrace,n)})),i.observe({type:re,buffered:!0})}catch(e){}this.importAggregator({resourceObserver:i})}},C,xe,B,class extends h{static featureName=de;constructor(e,r){if(super(e,r,de,!(arguments.length>2&&void 0!==arguments[2])||arguments[2]),!c.il)return;if(!(0,t.OP)(e).xhrWrappable)return;try{this.removeOnAbort=new AbortController}catch(e){}let n,i=0;const o=this.ee.get("tracer"),a=(0,k._L)(this.ee),s=(0,k.Lg)(this.ee),u=(0,k.BV)(this.ee),d=(0,k.Kf)(this.ee),f=this.ee.get("events"),l=(0,k.u5)(this.ee),h=(0,k.QU)(this.ee),g=(0,k.Gm)(this.ee);function m(e,t){h.emit("newURL",[""+window.location,t])}function v(){i++,n=window.location.hash,this[ve]=(0,p.z)()}function b(){i--,window.location.hash!==n&&m(0,!0);var e=(0,p.z)();this[pe]=~~this[pe]+e-this[ve],this[ye]=e}function y(e,t){e.on(t,(function(){this[t]=(0,p.z)()}))}this.ee.on(ve,v),s.on(be,v),a.on(be,v),this.ee.on(ye,b),s.on(ge,b),a.on(ge,b),this.ee.buffer([ve,ye,"xhr-resolved"],this.featureName),f.buffer([ve],this.featureName),u.buffer(["setTimeout"+le,"clearTimeout"+fe,ve],this.featureName),d.buffer([ve,"new-xhr","send-xhr"+fe],this.featureName),l.buffer([me+fe,me+"-done",me+he+fe,me+he+le],this.featureName),h.buffer(["newURL"],this.featureName),g.buffer([ve],this.featureName),s.buffer(["propagate",be,ge,"executor-err","resolve"+fe],this.featureName),o.buffer([ve,"no-"+ve],this.featureName),a.buffer(["new-jsonp","cb-start","jsonp-error","jsonp-end"],this.featureName),y(l,me+fe),y(l,me+"-done"),y(a,"new-jsonp"),y(a,"jsonp-end"),y(a,"cb-start"),h.on("pushState-end",m),h.on("replaceState-end",m),window.addEventListener("hashchange",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("load",m,(0,O.m$)(!0,this.removeOnAbort?.signal)),window.addEventListener("popstate",(function(){m(0,i>1)}),(0,O.m$)(!0,this.removeOnAbort?.signal)),this.abortHandler=this.#e,this.importAggregator()}#e(){this.removeOnAbort?.abort(),this.abortHandler=void 0}}],loaderType:"spa"})})(),window.NRBA=o})(); window.jQuery || document.write(' ') CKEDITOR_BASEPATH='https://f1000research.com/js/vendor/ckeditor/' window.reactTheme = 'research'; window.MathJax = { CommonHTML: { linebreaks: { automatic: true } }, 'HTML-CSS': { linebreaks: { automatic: true } }, SVG: { linebreaks: { automatic: true } }, AuthorInit: function() { MathJax.Hub.Register.MessageHook('End Process', function () { let timeout = false; // holder for timeout id const delay = 250; // delay after event is "complete" to run callback const reflowMath = function() { const dispFormulas = document.querySelectorAll('.disp-formula.panel'); if (!dispFormulas) { return; } for (const dispFormula of dispFormulas) { const child = dispFormula.querySelector('.MathJax_Preview').nextSibling.firstChild; const isMultiline = MathJax.Hub.getAllJax(dispFormula)[0].root.isMultiline; if (dispFormula.offsetWidth < child.offsetWidth || isMultiline) { MathJax.Hub.Queue(['Rerender', MathJax.Hub, dispFormula]); } } }; window.addEventListener('resize', function() { clearTimeout(timeout); // clear the timeout timeout = setTimeout(reflowMath, delay); // start timing for event "completion" }); }); }, }; if (window.location.hash == '#_=_'){ window.location = window.location.href.split('#')[0] } !function(f,b,e,v,n,t,s){if(f.fbq)return;n=f.fbq=function() {n.callMethod? n.callMethod.apply(n,arguments):n.queue.push(arguments)} ;if(!f._fbq)f._fbq=n; n.push=n;n.loaded=!0;n.version='2.0';n.queue=[];t=b.createElement(e);t.async=!0; t.src=v;s=b.getElementsByTagName(e)[0];s.parentNode.insertBefore(t,s)}(window, document,'script','https://connect.facebook.net/en_US/fbevents.js'); fbq('init', '1641728616063202'); fbq('track', "PixelInitialized", {}); (function(h,o,t,j,a,r){ h.hj=h.hj||function(){(h.hj.q=h.hj.q||[]).push(arguments)}; h._hjSettings={hjid:2318163,hjsv:6}; a=o.getElementsByTagName('head')[0]; r=o.createElement('script');r.async=1; r.src=t+h._hjSettings.hjid+j+h._hjSettings.hjsv; a.appendChild(r); })(window,document,'https://static.hotjar.com/c/hotjar-','.js?sv='); search file_upload Submit your research search menu close search Browse Gateways & Collections How to Publish Submit your Research My Submissions Article Guidelines Article Guidelines (New Versions) Open Data, Software and Code Guidelines Open Data and Accessible Source Materials Guidelines (HSS) Open Data, Software and Code Guidelines (PSE) Prepublication Checks Production Process Posters and Slides Guidelines Document Guidelines Article Processing Charges Peer Review Finding Article Reviewers About How it Works For Reviewers Our Advisors Policies Glossary FAQs For Developers Newsroom Contact My Research Submissions Content and Tracking Alerts My Details Sign In file_upload Submit your research { "@context": "https://schema.org", "@type": "ScholarlyArticle", "mainEntityOfPage": { "@type": "WebPage", "@id": "https://f1000research.com/articles/9-47" }, "headline": "scRepertoire: An R-based toolkit for single-cell immune receptor analysis", "datePublished": "2020-01-27T12:30:18", "dateModified": "2020-06-15T16:13:39", "author": [ { "@type": "Person", "name": "Nicholas Borcherding" }, { "@type": "Person", "name": "Nicholas L. Bormann" }, { "@type": "Person", "name": "Gloria Kraus" } ], "publisher": { "@type": "Organization", "name": "F1000Research", "logo": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 480, "width": 60 } }, "image": { "@type": "ImageObject", "url": "https://f1000research.com/img/AMP/F1000Research_image.png", "height": 1200, "width": 150 }, "description": "Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine mRNA and immune profiling, scRepertoire was built to process data derived from 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with a number of popular R packages for single-cell expression, such as Seurat. The scRepertoire R package and processed data are open source and available on GitHub and provides in-depth tutorials on the capability of the package." } { "@context": "http://schema.org", "@type": "BreadcrumbList", "itemListElement": [ { "@type": "ListItem", "position": "1", "item": { "@id": "https://f1000research.com/", "name": "Home" } }, { "@type": "ListItem", "position": "2", "item": { "@id": "https://f1000research.com/browse/articles", "name": "Browse" } }, { "@type": "ListItem", "position": "3", "item": { "@id": "https://f1000research.com/articles/9-47", "name": "scRepertoire: An R-based toolkit for single-cell immune receptor analysis" } } ] } Home Browse scRepertoire: An R-based toolkit for single-cell immune receptor analysis ALL Metrics - Views Downloads Get PDF Get XML Cite How to cite this article Borcherding N, Bormann NL and Kraus G. scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.12688/f1000research.22139.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Software Tool Article Revised scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] Nicholas Borcherding https://orcid.org/0000-0003-1427-6342 1-4 , Nicholas L. Bormann https://orcid.org/0000-0001-8847-8727 5 , Gloria Kraus 6 Nicholas Borcherding https://orcid.org/0000-0003-1427-6342 1-4 , Nicholas L. Bormann https://orcid.org/0000-0001-8847-8727 5 , Gloria Kraus 6 PUBLISHED 15 Jun 2020 Author details Author details 1 Department of Pathology, University of Iowa, Iowa City, IA, USA 2 Medical Scientist Training Program, University of Iowa, Iowa City, IA, USA 3 Cancer Biology Graduate Program, University of Iowa, Iowa City, IA, USA 4 Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA 5 Department of Psychiatry, University of Iowa, Iowa ity, IA, USA 6 Faculty of Medicine, Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany Nicholas Borcherding Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Methodology, Software, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Nicholas L. Bormann Roles: Methodology, Software Gloria Kraus Roles: Methodology, Software OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the RPackage gateway. Abstract Single-cell sequencing is an emerging technology in the field of immunology and oncology that allows researchers to couple RNA quantification and other modalities, like immune cell receptor profiling at the level of an individual cell. A number of workflows and software packages have been created to process and analyze single-cell transcriptomic data. These packages allow users to take the vast dimensionality of the data generated in single-cell-based experiments and distill the data into novel insights. Unlike the transcriptomic field, there is a lack of options for software that allow for single-cell immune receptor profiling. Enabling users to easily combine mRNA and immune profiling, scRepertoire was built to process data derived from 10x Genomics Chromium Immune Profiling for both T-cell receptor (TCR) and immunoglobulin (Ig) enrichment workflows and subsequently interacts with a number of popular R packages for single-cell expression, such as Seurat. The scRepertoire R package and processed data are open source and available on GitHub and provides in-depth tutorials on the capability of the package. READ ALL READ LESS Keywords Single-cell RNA sequencing, immune receptor profiling, R, clonotypic analysis Corresponding Author(s) Nicholas Borcherding ( [email protected] ) Close Corresponding author: Nicholas Borcherding Competing interests: No competing interests were disclosed. Grant information: Funding for this project was provided from National Institute of Health F30 fellowship CA206255. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2020 Borcherding N et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Borcherding N, Bormann NL and Kraus G. scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.12688/f1000research.22139.2 ) First published: 27 Jan 2020, 9 :47 ( https://doi.org/10.12688/f1000research.22139.1 ) Latest published: 15 Jun 2020, 9 :47 ( https://doi.org/10.12688/f1000research.22139.2 ) Revised Amendments from Version 1 After receiving the very generous reviews, the new version of the manuscript and software reflects our attempts at improving expanding the usability of scRepertoire as a whole. As Drs. Drufuca, Bonnal, and Pagani suggested we have added scRepertoire interaction with a number of R packages and changed the definition of clonotype for B cells. Per the comments made by Dr. Stuart, we have extensively modified the code and accompanying documentation. In addition, we are in the process of submitting the package to Bioconductor. We have added an author, Gloria Kraus, who assisted in the development of the software after the paper was initially submitted. We want to thank both reviews for the suggestions, as well as a number of users, for immensely improving the scRepertoire package. After receiving the very generous reviews, the new version of the manuscript and software reflects our attempts at improving expanding the usability of scRepertoire as a whole. As Drs. Drufuca, Bonnal, and Pagani suggested we have added scRepertoire interaction with a number of R packages and changed the definition of clonotype for B cells. Per the comments made by Dr. Stuart, we have extensively modified the code and accompanying documentation. In addition, we are in the process of submitting the package to Bioconductor. We have added an author, Gloria Kraus, who assisted in the development of the software after the paper was initially submitted. We want to thank both reviews for the suggestions, as well as a number of users, for immensely improving the scRepertoire package. See the authors' detailed response to the review by Lorenzo Drufuca, Raoul Jean Pierre Bonnal and Massimiliano Pagani See the authors' detailed response to the review by Tim Stuart READ REVIEWER RESPONSES Introduction The molecular resolution offered by single-cell sequencing (SCS) technologies has led to extensive investigations in the realms of developmental biology, oncology, and immunology. In terms of the latter field, SCS offers the ability to couple the exploration of transcriptomic heterogeneity in immune cells along a disease process with clonality 1 . A number of methods exist for dimensional reduction of mRNA data, reviewed by Chen et al. 2 that have been implemented into R packages to assist in processing and analysis of SCS experiments. However, a gap exists in the processing of V(D)J sequencing, descriptive statistics, clonal comparisons, and repertoire diversity with the current SCS R packages. With these limitations in mind, scRepertoire 3 was generated ( Figure 1 ). Built using R, scRepertoire is a toolkit to assist in the analysis of immune profiles for both B and T cells, while interacting with the popular Seurat pipeline 4 – 6 , as well as SingleCellExperiment and monocle3 class expression objects. scRepertoire also includes processed single-cell mRNA and V(D)J sequencing data of 12,911 tumor-infiltrating and peripheral-blood T cells derived from three renal clear cell carcinoma patient, which is characterized below to demonstrate the capabilities of the package. Figure 1. A general workflow for single-cell data analysis involving scRepertoire. The analysis starts with the single-cell immune and mRNA sequencing and Cell Ranger-based alignment with the 10x Genomics pipeline. With the TCR or Ig sequencing, scRepertoire can import the filtered overlapping DNA segments, or contigs. The alignments are filtered by cell type of interest and combined using the individual cell barcodes. Clonotypes can be called using the gene sequence of the immune receptor loci, CDR3 nucleotide sequence or CDR3 amino acid sequence. After clonotype assignment, more extensive clonotypic analysis can be performed at the individual sample level or across all samples. General outputs from scRepertoire can be imported into a number of single-cell expression formats to visualize clonotype data overlaid onto the cell clustering. Likewise, metadata from the expression objects can be imported into scRepertoire to analyze clonotypes by assigned clusters. Methods Operation System requirements for running scRepertoire 3 include the installation of R v3.5.1 and the the Seurat R package (v3.1.2). Utilization of scRepertoire is dependent on the total number of single-cells being processed, with a base estimate of 1 Gb of random-access memory and a modern CPU. Data The isolation and processing of the 10x-Genomics-based single-cell mRNA and V(D)J Chromium sequencing data for immune cells has previously been described 7 , 8 . In addition, T cells were identified using expression values for canonical T cell markers: CD3D , CD4 , CD8A , CD8B1 and previous clustering. T cells were isolated and reclustered using the integration method from the Seurat R package (v3.1.2) with 20 principal components and a resolution of 0.5 4 . All code used to generate the figures appearing in the manuscript is available at https://github.com/ncborcherding/scRepertoire . Implementation The scRepertoire was built and tested in R v3.5.1. Analysis for scRepertoire was inspired from the bulk immune profiling tcR (v2.2.4) R package without derivations in code 9 . Clonotypes can be called using the combination of immune loci genes, a more sensitive approach, or the nucleotide/amino acid sequence of the complementary-determining region 3 (CDR3). In addition to the base functions in R, data processing was performed using the dplyr (v0.8.3) and reshape2 (v1.4.3) R packages. Visualizations are generated using the ggplot2 (v3.2.1) and ggalluvial (v0.11.1) R packages with color pallets derived from the use of colorRamps (v2.3) and RColorBrewer (v1.1.2) R packages. Diversity metrics are calculated using the vegan (v2.5-6) R package. Visual outputs of functions are stored as layers of geometric or statistical ggplot layering, allowing users to easily modify presentation. Results Clonal analysis scRepertoire 3 can be used to call clonotypes using the CDR3 amino acid/nucleotide sequences, by gene usage, or by the combination of CDR3 nucleotide sequences and genes. Using the quantContig function, unique clonotypes can be visualized as raw values or scaled to the size of the library for samples or by type ( Figure 2A ). The total abundance of clonotypes can also be visualized calling abundanceContig ( Figure 2B ) or relative abundance of clonotypes ( Figure 2C ). Additionally, the distribution of CDR3 nucleotide or amino acid sequences for clonotypes can be visualized with lengthContig ( Figure 2D ). More advance distribution analysis is also available using the clonesizeDistribution function based on recent work using Jensen-Shannon divergence. Figure 2. Basic clonotypic analysis functions in scRepertoire. ( A ) Scaled unique clonotypes by total number of TCRs sequenced by patient and type of sample (peripheral, P; tumor, T), using the quantContig function. ( B ) Total abundance of clonotypes by sample and type using the abundanceContig function. ( C ) Relative abundance of clonotypes using density comparing peripheral blood to tumor samples. ( D ) CDR3 nucleotide length analysis by sample using the lengthContig function. The bimodal nature of the curve is a function of calling clonotypes for cells with both one and two immune receptors sequenced. Proportional analysis and diversity measures More in depth analysis of clonal architecture is available. Within the framework of scRepertoire, analysis of clonal homeostasis, or the clonal space occupied by clonotypes of specific proportions, can be visualized by clonalHomeostasis function ( Figure 3A ). Similarly, clonalProportion can be called to look at the proportion of clonal space occupied by specific clonotypes ( Figure 3B ). Overlap between the samples can be calculated and visualized with clonalOverlap , using either the overlap coefficient or Morisita index methods ( Figure 3C ). Measured of diversity across samples or groups can be quantified with the clonalDiversity function, demonstrating an overall reduction in clonal diversity in tumor samples ( Figure 3D ). Figure 3. Advanced clonal measures between samples. ( A ) Clonal homeostatic space representations across all six samples using the gene and CDR3 AA sequence for clonotype calling. ( B ) Relative proportional space occupied by specific clonotypes across all six samples using the gene and CDR3 AA sequence for clonotype calling. ( C ) Morisita overlap quantifications for clonotypes across all six samples. ( D ) Diversity measures based on clonotypes by sample type using Shannon, Inverse Simpson, Chao, and abundance-based coverage estimator (ACE) indices. Expression interaction After the processing and analysis of the TCR repertoire with the base features, the next step is using scRepertoire to interact with the single-cell mRNA data. The expression data for the 12,911 cells built into the package have already been clusters ( Figure 4A ), with a clear distribution of the clusters into peripheral-blood- versus tumor-predominant ( Figure 4B ). Using the combineExpression function in scRepertoire, we can look at the clonotypic frequencies of cells that comprise the UMAP-based clusters ( Figure 4C ). This function also works with the SingleCellExperiment and monocle3 class of expression objects. ( Figure 4D ). In addition to clonal distribution, we can also use highlightClonotypes to set specific sequences of clonotypes to be visualized ( Figure 4D ), with clonotype 1 referring to the amino acid sequence “CAVNGGSQGNLIF_CSAEREDTDTQYF” and clonotype 2 for the amino acid sequence "NA_CATSATLRVVAEKLFF". Interesting clonotype 2 is restricted to a subcluster of the C6 cluster ( Figure 4D ). After combining both the clonotype and expression data, interaction between categories, such as cluster label and clonotype frequency can be visualized with the alluvialClonotypes function ( Figure 4E ). This function can also be used to examine the dynamics of single or multiple expanded clonotypes across the categorical variables ( Figure 4E ). Further, after the attachment of the expression information to a single-cell expression object, the function, expression2List() allows users generate analyses based on any categorical variable in the meta data.. Figure 4. Interaction of scRepertoire with the single-cell expression R packages. ( A ) UMAP projection from Seurat of the ccRCC T cells (n=12,911) into 12 distinct clusters. ( B ) UMAP projection with peripheral blood (red) and tumor (blued) populations highlighted and an accompanying relative proportion composition of each cluster, scaled by the total number of peripheral blood and tumor cells, respectively. ( C ) Using the combineExpression function places individual cells into groups by the number of clonotypes, which then can be displayed overlaid with the UMAP projection. ( D ) After combining the clonotype information with the Seurat object, highlightClonotypes can be used to specifically highlight the individual clonotypes of interest using the sequence information. ( E ) Interaction of clonotypes between multiple categories can be examined using the alluvialClonotypes function. Conclusions scRepertoire 3 is a R-based toolkit for the analysis of single-cell immune receptor profiling. The package is able to take the annotated filtered outputs from the 10x Genomics Cell Ranger platform and provide analysis a number of modalities, including calling clonotypes, clonal space/homeostasis, clonal diversity, and repertoire overlap between samples. Outputs from scRepertoire can combined with dimensional reduction strategies for single-cell RNA quantifications, allowing users to analyze mRNA and immune profiles together. Visualization functions in scRepertoire have a parameter, exportTable, allowing users to examine the quantifications underlying the generation of the graphs. Under the creative commons v4.0 license, the scRepertoire package is freely available from the GitHub repository and is extensively annotated to assist in implementation and modification. Data availability Source data Zenodo: scRepertoire. https://doi.org/10.5281/zenodo.3856827 3 . Folder ‘Data’ contains all data required to run the vignettes described in the Results . This is also available on GitHub . Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Software availability Source code is available from GitHub: https://github.com/ncborcherding/scRepertoire . Archived source code at the time of publication: https://doi.org/10.5281/zenodo.3856827 3 . License: Creative Commons Attribution 4.0 International . Acknowledgements We would like to thank Davide Angeletti of the University of Gothenburg and Jae Seung Moon of Stanford University for extensive beta testing and suggestions for improvement. Faculty Opinions recommended References 1. Papalexi E, Satija R: Single-cell RNA sequencing to explore immune cell heterogeneity. Nat Rev Immunol. 2018; 18 (1): 35–45. PubMed Abstract | Publisher Full Text 2. Chen G, Ning B, Shi T: Single-Cell RNA-Seq Technologies and Related Computational Data Analysis. Front Genet. 2019; 10 : 317. PubMed Abstract | Publisher Full Text | Free Full Text 3. Borcherding N, Bormann NL: scRepertoire (Version 1.2.0). Zenodo. 2020. http://www.doi.org/10.5281/zenodo.3856827 4. Stuart T, Butler A, Hoffman P, et al. : Comprehensive Integration of Single-Cell Data. Cell. 2019; 177 (7): 1888–1902.e21. PubMed Abstract | Publisher Full Text | Free Full Text 5. Macosko EZ, Basu A, Satija R, et al. : Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets. Cell. 2015; 161 (5): 1202–14. PubMed Abstract | Publisher Full Text | Free Full Text 6. Butler A, Hoffman P, Smibert P, et al. : Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 2018; 36 (5): 411–420. PubMed Abstract | Publisher Full Text | Free Full Text 7. Borcherding N, Ahmed KK, Voigt AP, et al. : Transcriptional heterogeneity in cancer-associated regulatory T cells is predictive of survival. bioRxiv. 2018; 478628. Publisher Full Text 8. Vishwakarma A, Bocherding N, Chimenti MS, et al. : Mapping the Immune Landscape of Clear Cell Renal Cell Carcinoma by Single-Cell RNA-seq. bioRxiv. 2019; 824482. Publisher Full Text 9. Nazarov VI, Pogorelyy MV, Komech EA, et al. : tcR: an R package for T cell receptor repertoire advanced data analysis. BMC Bioinformatics. 2015; 16 : 175. PubMed Abstract | Publisher Full Text | Free Full Text Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 27 Jan 2020 ADD YOUR COMMENT Comment Author details Author details 1 Department of Pathology, University of Iowa, Iowa City, IA, USA 2 Medical Scientist Training Program, University of Iowa, Iowa City, IA, USA 3 Cancer Biology Graduate Program, University of Iowa, Iowa City, IA, USA 4 Holden Comprehensive Cancer Center, University of Iowa, Iowa City, IA, USA 5 Department of Psychiatry, University of Iowa, Iowa ity, IA, USA 6 Faculty of Medicine, Center for Regenerative Therapies Dresden, Technische Universität Dresden, Dresden, Germany Nicholas Borcherding Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Methodology, Software, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing Nicholas L. Bormann Roles: Methodology, Software Gloria Kraus Roles: Methodology, Software Competing interests No competing interests were disclosed. Grant information Funding for this project was provided from National Institute of Health F30 fellowship CA206255. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (2) version 2 Revised Published: 15 Jun 2020, 9:47 https://doi.org/10.12688/f1000research.22139.2 version 1 Published: 27 Jan 2020, 9:47 https://doi.org/10.12688/f1000research.22139.1 Copyright © 2020 Borcherding N et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Borcherding N, Bormann NL and Kraus G. scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.12688/f1000research.22139.2 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 2 VERSION 2 PUBLISHED 15 Jun 2020 Revised Views 0 Cite How to cite this report: Bonnal RJP, Drufuca L and Pagani M. Reviewer Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.27282.r64820 ) The direct URL for this report is: https://f1000research.com/articles/9-47/v2#referee-response-64820 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Aug 2020 Raoul Jean Pierre Bonnal , National Institute of Molecular Genetics, Milan, Italy Lorenzo Drufuca , School of Medicine and Surgery, Università degli Studi di Milano Bicocca, Milan, Italy Massimiliano Pagani , National Institute of Molecular Genetics, Milan, Italy Approved VIEWS 0 https://doi.org/10.5256/f1000research.27282.r64820 I can confirm that the authors addressed all the ... Continue reading READ ALL I can confirm that the authors addressed all the issues and the paper is now suitable for indexing. Competing Interests: No competing interests were disclosed. We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Bonnal RJP, Drufuca L and Pagani M. Reviewer Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.27282.r64820 ) The direct URL for this report is: https://f1000research.com/articles/9-47/v2#referee-response-64820 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Stuart T. Reviewer Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.27282.r64821 ) The direct URL for this report is: https://f1000research.com/articles/9-47/v2#referee-response-64821 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Jul 2020 Tim Stuart , New York Genome Center, New York City, NY, USA Approved VIEWS 0 https://doi.org/10.5256/f1000research.27282.r64821 The authors have now ... Continue reading READ ALL The authors have now addressed all my original comments. Competing Interests: No competing interests were disclosed. Reviewer Expertise: Single-cell genomics, bioinformatics, epigenomics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Stuart T. Reviewer Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.27282.r64821 ) The direct URL for this report is: https://f1000research.com/articles/9-47/v2#referee-response-64821 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 27 Jan 2020 Views 0 Cite How to cite this report: Stuart T. Reviewer Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.24415.r60873 ) The direct URL for this report is: https://f1000research.com/articles/9-47/v1#referee-response-60873 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Mar 2020 Tim Stuart , New York Genome Center, New York City, NY, USA Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.24415.r60873 This paper presents a new R package for the analysis of T-cell and B-cell clonotypes inferred from V(D)J recombination events. The package is designed for the analysis of data generated using the 10x Genomics V(D)J kit that allows clonotypes and ... Continue reading READ ALL This paper presents a new R package for the analysis of T-cell and B-cell clonotypes inferred from V(D)J recombination events. The package is designed for the analysis of data generated using the 10x Genomics V(D)J kit that allows clonotypes and transcriptomes to be co-assayed in the same individual cells. Overall the package appears to fill an unmet need in the analysis of single-cell V(D)J data, and works nicely with the existing Seurat package for single-cell analysis. While the focus of the paper is on the software, a more detailed explanation of the biology of V(D)J recombination and how this information is detected in single cells would improve the clarity of the manuscript. A description of the major existing challenges and goals of immune clonotype analysis could also be added. I do not have extensive expertise in the immunology and so my review focuses on the software implementation. I leave discussion of the value and impact of the software to other reviewers. However I will reiterate comments from other reviewers in saying that there should be a distinction made between T and B cell clonotypes, due to the Ig class switching that occurs in B cells. The paper also states that “The expression data for the 12,911 cells built into the package”. I believe the clonotype information is included in the package in the data directory, but I don’t think the expression data is included. Including large gene expression matrix within the R package is not advisable anyway, and the authors should make it available through other means (NCBI GEO, for example). Code review I would encourage the authors to submit the package to CRAN or another R repository. This assist in the distribution of the package, and the process of getting the package accepted on CRAN will likely greatly improve the code quality. At a minimum, the authors should try to get the package to pass R CMD check with no notes, warnings, or errors. Currently the package includes all rendered figures in the vignettes folder. I would recommend removing these files, and also removing the `ggsave` function calls within the vignette to prevent these files being written while compiling the vignette. This will reduce the size of the package. Several functions in the package use scoping assignment to assign variables in the global environment. This is considered bad practice and should be avoided in all cases. Extra files such as .DS_Store should be removed from the git repository and from the package. Use the .gitignore and .Rbuildignore files for this. Avoid importing code within R functions, for example `require(ggplot2)` calls. Instead, document the dependencies using roxygen2, for example `@importFrom ggplot2 ggplot`. To access data in a Seurat object, I highly recommend using the functions defined in Seurat for this purpose rather than accessing the slots directly. For example, use obj[[]] to access metadata rather than [email protected] and Idents(obj) rather than [email protected] In general the documentation of functions can be greatly improved. Try to include a text description of each function, a detailed description of the parameters, document the returned values, and include an executable example. It is generally not advisable to overwrite functions in base R or other packages with variable names, for example the `call` variable in `clonalDiversity` overwrites the base R `call` function. Replace code like `class(df)[1] == “Seurat”` with `inherits(x = df, what = "Seurat”)` In plotting functions such as clonalOverlap, consider returning the ggplot object rather than printing the object. For example, replace `suppressWarnings(print(plot))` with `return(plot)`. This will allow users to modify the plot that is generated. Some code sections are duplicated, for example L91:104 and L131:144 in seuratFunctions.R. Consider putting duplicated code into functions. Imported functions should be added to the namespace. Documenting the imports using roxygen2 (as has been done for parameters and exports) will take care of this. The highlightClonotypes function is a bit redundant with existing functions in Seurat, ie DimPlot function with the cells.highlight parameter. Is the rationale for developing the new software tool clearly explained? Partly Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Single-cell genomics, bioinformatics, epigenomics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Stuart T. Reviewer Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.24415.r60873 ) The direct URL for this report is: https://f1000research.com/articles/9-47/v1#referee-response-60873 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 15 Jun 2020 Nicholas Borcherding , Cancer Biology Graduate Program, University of Iowa, Iowa City, USA 15 Jun 2020 Author Response This paper presents a new R package for the analysis of T-cell and B-cell clonotypes inferred from V(D)J recombination events. The package is designed for the analysis of data generated ... Continue reading This paper presents a new R package for the analysis of T-cell and B-cell clonotypes inferred from V(D)J recombination events. The package is designed for the analysis of data generated using the 10x Genomics V(D)J kit that allows clonotypes and transcriptomes to be co-assayed in the same individual cells. Overall the package appears to fill an unmet need in the analysis of single-cell V(D)J data, and works nicely with the existing Seurat package for single-cell analysis. While the focus of the paper is on the software, a more detailed explanation of the biology of V(D)J recombination and how this information is detected in single cells would improve the clarity of the manuscript. A description of the major existing challenges and goals of immune clonotype analysis could also be added. The authors agree that a more extensive explanation of VDJ biology would be a benefit, however, due to the limitations of less than 1000 words and 10 citations, we are limited by the structure of the F1000Research. We have added citations to the readme file to help users. I do not have extensive expertise in the immunology and so my review focuses on the software implementation. I leave discussion of the value and impact of the software to other reviewers. However I will reiterate comments from other reviewers in saying that there should be a distinction made between T and B cell clonotypes, due to the Ig class switching that occurs in B cells. The paper also states that “The expression data for the 12,911 cells built into the package”. I believe the clonotype information is included in the package in the data directory, but I don’t think the expression data is included. Including large gene expression matrix within the R package is not advisable anyway, and the authors should make it available through other means (NCBI GEO, for example). The seurat object with the package is available in the zenodo repository and linked in the github repository. The raw data has been deposited in GEO and more information can be found in the two preprints cited. Code review I would encourage the authors to submit the package to CRAN or another R repository. This assist in the distribution of the package, and the process of getting the package accepted on CRAN will likely greatly improve the code quality. At a minimum, the authors should try to get the package to pass R CMD check with no notes, warnings, or errors. Thank you for the suggestions, the authors believe this point helped the structure of the package immensely. We have used the devtools check function and as of the version in the github repository and zenodo at resubmission, there is no warnings, errors, or notes. We have begun the process of submitting the package to Bioconductor and have passed the build phase of the process. Currently the package includes all rendered figures in the vignettes folder. I would recommend removing these files, and also removing the `ggsave` function calls within the vignette to prevent these files being written while compiling the vignette. This will reduce the size of the package. Thank you for the suggestion, this has been completed. Several functions in the package use scoping assignment to assign variables in the global environment. This is considered bad practice and should be avoided in all cases. Scoping assignments in these instances are to give users the options of exporting the tables or matrices that form the visualization. We have modified the activity of the exportTable parameter to return a data frame used if TRUE or visualization if FALSE (default). Extra files such as .DS_Store should be removed from the git repository and from the package. Use the .gitignore and .Rbuildignore files for this. Thank you for the suggestion, we have removed .DS_Store. Avoid importing code within R functions, for example `require(ggplot2)` calls. Instead, document the dependencies using roxygen2, for example `@importFrom ggplot2 ggplot`. Thank you for the suggestion, we have made this change. To access data in a Seurat object, I highly recommend using the functions defined in Seurat for this purpose rather than accessing the slots directly. For example, use obj[[]] to access metadata rather than [email protected] and Idents(obj) rather than [email protected] Thanks for the suggestion, we have changed this in the new version of the R package. In general the documentation of functions can be greatly improved. Try to include a text description of each function, a detailed description of the parameters, document the returned values, and include an executable example. Both reviewers have made this excellent suggestion, the new version of scRepertoire has more comprehensive documentation, including descriptions and working examples. It is generally not advisable to overwrite functions in base R or other packages with variable names, for example the `call` variable in `clonalDiversity` overwrites the base R `call` function. Thank you for the suggestion, we have modified this for all functions. Replace code like `class(df)[1] == “Seurat”` with `inherits(x = df, what = "Seurat”)` We have made this change and also replace all class() evaluations. In plotting functions such as clonalOverlap, consider returning the ggplot object rather than printing the object. For example, replace `suppressWarnings(print(plot))` with `return(plot)`. This will allow users to modify the plot that is generated. Excellent suggestion and this has been fixed. Some code sections are duplicated, for example L91:104 and L131:144 in seuratFunctions.R. Consider putting duplicated code into functions. Thank you for the suggestion, part of the change in the “call” to cloneCall variable in the functions also including creating a function to deal with the user selection. These lines were consistently repeated across multiple functions, but now have been reduced. We have also extensively modularized the code and added these to utils.R . Imported functions should be added to the namespace. Documenting the imports using roxygen2 (as has been done for parameters and exports) will take care of this. This has been completed. The highlightClonotypes function is a bit redundant with existing functions in Seurat, ie DimPlot function with the cells.highlight parameter. Agreed, although redundant, this was a request from an early beta tester, we plan to keep the function in. This paper presents a new R package for the analysis of T-cell and B-cell clonotypes inferred from V(D)J recombination events. The package is designed for the analysis of data generated using the 10x Genomics V(D)J kit that allows clonotypes and transcriptomes to be co-assayed in the same individual cells. Overall the package appears to fill an unmet need in the analysis of single-cell V(D)J data, and works nicely with the existing Seurat package for single-cell analysis. While the focus of the paper is on the software, a more detailed explanation of the biology of V(D)J recombination and how this information is detected in single cells would improve the clarity of the manuscript. A description of the major existing challenges and goals of immune clonotype analysis could also be added. The authors agree that a more extensive explanation of VDJ biology would be a benefit, however, due to the limitations of less than 1000 words and 10 citations, we are limited by the structure of the F1000Research. We have added citations to the readme file to help users. I do not have extensive expertise in the immunology and so my review focuses on the software implementation. I leave discussion of the value and impact of the software to other reviewers. However I will reiterate comments from other reviewers in saying that there should be a distinction made between T and B cell clonotypes, due to the Ig class switching that occurs in B cells. The paper also states that “The expression data for the 12,911 cells built into the package”. I believe the clonotype information is included in the package in the data directory, but I don’t think the expression data is included. Including large gene expression matrix within the R package is not advisable anyway, and the authors should make it available through other means (NCBI GEO, for example). The seurat object with the package is available in the zenodo repository and linked in the github repository. The raw data has been deposited in GEO and more information can be found in the two preprints cited. Code review I would encourage the authors to submit the package to CRAN or another R repository. This assist in the distribution of the package, and the process of getting the package accepted on CRAN will likely greatly improve the code quality. At a minimum, the authors should try to get the package to pass R CMD check with no notes, warnings, or errors. Thank you for the suggestions, the authors believe this point helped the structure of the package immensely. We have used the devtools check function and as of the version in the github repository and zenodo at resubmission, there is no warnings, errors, or notes. We have begun the process of submitting the package to Bioconductor and have passed the build phase of the process. Currently the package includes all rendered figures in the vignettes folder. I would recommend removing these files, and also removing the `ggsave` function calls within the vignette to prevent these files being written while compiling the vignette. This will reduce the size of the package. Thank you for the suggestion, this has been completed. Several functions in the package use scoping assignment to assign variables in the global environment. This is considered bad practice and should be avoided in all cases. Scoping assignments in these instances are to give users the options of exporting the tables or matrices that form the visualization. We have modified the activity of the exportTable parameter to return a data frame used if TRUE or visualization if FALSE (default). Extra files such as .DS_Store should be removed from the git repository and from the package. Use the .gitignore and .Rbuildignore files for this. Thank you for the suggestion, we have removed .DS_Store. Avoid importing code within R functions, for example `require(ggplot2)` calls. Instead, document the dependencies using roxygen2, for example `@importFrom ggplot2 ggplot`. Thank you for the suggestion, we have made this change. To access data in a Seurat object, I highly recommend using the functions defined in Seurat for this purpose rather than accessing the slots directly. For example, use obj[[]] to access metadata rather than [email protected] and Idents(obj) rather than [email protected] Thanks for the suggestion, we have changed this in the new version of the R package. In general the documentation of functions can be greatly improved. Try to include a text description of each function, a detailed description of the parameters, document the returned values, and include an executable example. Both reviewers have made this excellent suggestion, the new version of scRepertoire has more comprehensive documentation, including descriptions and working examples. It is generally not advisable to overwrite functions in base R or other packages with variable names, for example the `call` variable in `clonalDiversity` overwrites the base R `call` function. Thank you for the suggestion, we have modified this for all functions. Replace code like `class(df)[1] == “Seurat”` with `inherits(x = df, what = "Seurat”)` We have made this change and also replace all class() evaluations. In plotting functions such as clonalOverlap, consider returning the ggplot object rather than printing the object. For example, replace `suppressWarnings(print(plot))` with `return(plot)`. This will allow users to modify the plot that is generated. Excellent suggestion and this has been fixed. Some code sections are duplicated, for example L91:104 and L131:144 in seuratFunctions.R. Consider putting duplicated code into functions. Thank you for the suggestion, part of the change in the “call” to cloneCall variable in the functions also including creating a function to deal with the user selection. These lines were consistently repeated across multiple functions, but now have been reduced. We have also extensively modularized the code and added these to utils.R . Imported functions should be added to the namespace. Documenting the imports using roxygen2 (as has been done for parameters and exports) will take care of this. This has been completed. The highlightClonotypes function is a bit redundant with existing functions in Seurat, ie DimPlot function with the cells.highlight parameter. Agreed, although redundant, this was a request from an early beta tester, we plan to keep the function in. Competing Interests: None Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 15 Jun 2020 Nicholas Borcherding , Cancer Biology Graduate Program, University of Iowa, Iowa City, USA 15 Jun 2020 Author Response This paper presents a new R package for the analysis of T-cell and B-cell clonotypes inferred from V(D)J recombination events. The package is designed for the analysis of data generated ... Continue reading This paper presents a new R package for the analysis of T-cell and B-cell clonotypes inferred from V(D)J recombination events. The package is designed for the analysis of data generated using the 10x Genomics V(D)J kit that allows clonotypes and transcriptomes to be co-assayed in the same individual cells. Overall the package appears to fill an unmet need in the analysis of single-cell V(D)J data, and works nicely with the existing Seurat package for single-cell analysis. While the focus of the paper is on the software, a more detailed explanation of the biology of V(D)J recombination and how this information is detected in single cells would improve the clarity of the manuscript. A description of the major existing challenges and goals of immune clonotype analysis could also be added. The authors agree that a more extensive explanation of VDJ biology would be a benefit, however, due to the limitations of less than 1000 words and 10 citations, we are limited by the structure of the F1000Research. We have added citations to the readme file to help users. I do not have extensive expertise in the immunology and so my review focuses on the software implementation. I leave discussion of the value and impact of the software to other reviewers. However I will reiterate comments from other reviewers in saying that there should be a distinction made between T and B cell clonotypes, due to the Ig class switching that occurs in B cells. The paper also states that “The expression data for the 12,911 cells built into the package”. I believe the clonotype information is included in the package in the data directory, but I don’t think the expression data is included. Including large gene expression matrix within the R package is not advisable anyway, and the authors should make it available through other means (NCBI GEO, for example). The seurat object with the package is available in the zenodo repository and linked in the github repository. The raw data has been deposited in GEO and more information can be found in the two preprints cited. Code review I would encourage the authors to submit the package to CRAN or another R repository. This assist in the distribution of the package, and the process of getting the package accepted on CRAN will likely greatly improve the code quality. At a minimum, the authors should try to get the package to pass R CMD check with no notes, warnings, or errors. Thank you for the suggestions, the authors believe this point helped the structure of the package immensely. We have used the devtools check function and as of the version in the github repository and zenodo at resubmission, there is no warnings, errors, or notes. We have begun the process of submitting the package to Bioconductor and have passed the build phase of the process. Currently the package includes all rendered figures in the vignettes folder. I would recommend removing these files, and also removing the `ggsave` function calls within the vignette to prevent these files being written while compiling the vignette. This will reduce the size of the package. Thank you for the suggestion, this has been completed. Several functions in the package use scoping assignment to assign variables in the global environment. This is considered bad practice and should be avoided in all cases. Scoping assignments in these instances are to give users the options of exporting the tables or matrices that form the visualization. We have modified the activity of the exportTable parameter to return a data frame used if TRUE or visualization if FALSE (default). Extra files such as .DS_Store should be removed from the git repository and from the package. Use the .gitignore and .Rbuildignore files for this. Thank you for the suggestion, we have removed .DS_Store. Avoid importing code within R functions, for example `require(ggplot2)` calls. Instead, document the dependencies using roxygen2, for example `@importFrom ggplot2 ggplot`. Thank you for the suggestion, we have made this change. To access data in a Seurat object, I highly recommend using the functions defined in Seurat for this purpose rather than accessing the slots directly. For example, use obj[[]] to access metadata rather than [email protected] and Idents(obj) rather than [email protected] Thanks for the suggestion, we have changed this in the new version of the R package. In general the documentation of functions can be greatly improved. Try to include a text description of each function, a detailed description of the parameters, document the returned values, and include an executable example. Both reviewers have made this excellent suggestion, the new version of scRepertoire has more comprehensive documentation, including descriptions and working examples. It is generally not advisable to overwrite functions in base R or other packages with variable names, for example the `call` variable in `clonalDiversity` overwrites the base R `call` function. Thank you for the suggestion, we have modified this for all functions. Replace code like `class(df)[1] == “Seurat”` with `inherits(x = df, what = "Seurat”)` We have made this change and also replace all class() evaluations. In plotting functions such as clonalOverlap, consider returning the ggplot object rather than printing the object. For example, replace `suppressWarnings(print(plot))` with `return(plot)`. This will allow users to modify the plot that is generated. Excellent suggestion and this has been fixed. Some code sections are duplicated, for example L91:104 and L131:144 in seuratFunctions.R. Consider putting duplicated code into functions. Thank you for the suggestion, part of the change in the “call” to cloneCall variable in the functions also including creating a function to deal with the user selection. These lines were consistently repeated across multiple functions, but now have been reduced. We have also extensively modularized the code and added these to utils.R . Imported functions should be added to the namespace. Documenting the imports using roxygen2 (as has been done for parameters and exports) will take care of this. This has been completed. The highlightClonotypes function is a bit redundant with existing functions in Seurat, ie DimPlot function with the cells.highlight parameter. Agreed, although redundant, this was a request from an early beta tester, we plan to keep the function in. This paper presents a new R package for the analysis of T-cell and B-cell clonotypes inferred from V(D)J recombination events. The package is designed for the analysis of data generated using the 10x Genomics V(D)J kit that allows clonotypes and transcriptomes to be co-assayed in the same individual cells. Overall the package appears to fill an unmet need in the analysis of single-cell V(D)J data, and works nicely with the existing Seurat package for single-cell analysis. While the focus of the paper is on the software, a more detailed explanation of the biology of V(D)J recombination and how this information is detected in single cells would improve the clarity of the manuscript. A description of the major existing challenges and goals of immune clonotype analysis could also be added. The authors agree that a more extensive explanation of VDJ biology would be a benefit, however, due to the limitations of less than 1000 words and 10 citations, we are limited by the structure of the F1000Research. We have added citations to the readme file to help users. I do not have extensive expertise in the immunology and so my review focuses on the software implementation. I leave discussion of the value and impact of the software to other reviewers. However I will reiterate comments from other reviewers in saying that there should be a distinction made between T and B cell clonotypes, due to the Ig class switching that occurs in B cells. The paper also states that “The expression data for the 12,911 cells built into the package”. I believe the clonotype information is included in the package in the data directory, but I don’t think the expression data is included. Including large gene expression matrix within the R package is not advisable anyway, and the authors should make it available through other means (NCBI GEO, for example). The seurat object with the package is available in the zenodo repository and linked in the github repository. The raw data has been deposited in GEO and more information can be found in the two preprints cited. Code review I would encourage the authors to submit the package to CRAN or another R repository. This assist in the distribution of the package, and the process of getting the package accepted on CRAN will likely greatly improve the code quality. At a minimum, the authors should try to get the package to pass R CMD check with no notes, warnings, or errors. Thank you for the suggestions, the authors believe this point helped the structure of the package immensely. We have used the devtools check function and as of the version in the github repository and zenodo at resubmission, there is no warnings, errors, or notes. We have begun the process of submitting the package to Bioconductor and have passed the build phase of the process. Currently the package includes all rendered figures in the vignettes folder. I would recommend removing these files, and also removing the `ggsave` function calls within the vignette to prevent these files being written while compiling the vignette. This will reduce the size of the package. Thank you for the suggestion, this has been completed. Several functions in the package use scoping assignment to assign variables in the global environment. This is considered bad practice and should be avoided in all cases. Scoping assignments in these instances are to give users the options of exporting the tables or matrices that form the visualization. We have modified the activity of the exportTable parameter to return a data frame used if TRUE or visualization if FALSE (default). Extra files such as .DS_Store should be removed from the git repository and from the package. Use the .gitignore and .Rbuildignore files for this. Thank you for the suggestion, we have removed .DS_Store. Avoid importing code within R functions, for example `require(ggplot2)` calls. Instead, document the dependencies using roxygen2, for example `@importFrom ggplot2 ggplot`. Thank you for the suggestion, we have made this change. To access data in a Seurat object, I highly recommend using the functions defined in Seurat for this purpose rather than accessing the slots directly. For example, use obj[[]] to access metadata rather than [email protected] and Idents(obj) rather than [email protected] Thanks for the suggestion, we have changed this in the new version of the R package. In general the documentation of functions can be greatly improved. Try to include a text description of each function, a detailed description of the parameters, document the returned values, and include an executable example. Both reviewers have made this excellent suggestion, the new version of scRepertoire has more comprehensive documentation, including descriptions and working examples. It is generally not advisable to overwrite functions in base R or other packages with variable names, for example the `call` variable in `clonalDiversity` overwrites the base R `call` function. Thank you for the suggestion, we have modified this for all functions. Replace code like `class(df)[1] == “Seurat”` with `inherits(x = df, what = "Seurat”)` We have made this change and also replace all class() evaluations. In plotting functions such as clonalOverlap, consider returning the ggplot object rather than printing the object. For example, replace `suppressWarnings(print(plot))` with `return(plot)`. This will allow users to modify the plot that is generated. Excellent suggestion and this has been fixed. Some code sections are duplicated, for example L91:104 and L131:144 in seuratFunctions.R. Consider putting duplicated code into functions. Thank you for the suggestion, part of the change in the “call” to cloneCall variable in the functions also including creating a function to deal with the user selection. These lines were consistently repeated across multiple functions, but now have been reduced. We have also extensively modularized the code and added these to utils.R . Imported functions should be added to the namespace. Documenting the imports using roxygen2 (as has been done for parameters and exports) will take care of this. This has been completed. The highlightClonotypes function is a bit redundant with existing functions in Seurat, ie DimPlot function with the cells.highlight parameter. Agreed, although redundant, this was a request from an early beta tester, we plan to keep the function in. Competing Interests: None Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Drufuca L, Bonnal RJP and Pagani M. Reviewer Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.24415.r59209 ) The direct URL for this report is: https://f1000research.com/articles/9-47/v1#referee-response-59209 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Mar 2020 Lorenzo Drufuca , School of Medicine and Surgery, Università degli Studi di Milano Bicocca, Milan, Italy Raoul Jean Pierre Bonnal , National Institute of Molecular Genetics, Milan, Italy Massimiliano Pagani , National Institute of Molecular Genetics, Milan, Italy Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.24415.r59209 The paper describes a new package (scRepertoire) of functions for the analysis of clonality in single cell experiments in the R analytical environment. While it is true, as it is claimed, that 'a gap exists in the ... Continue reading READ ALL The paper describes a new package (scRepertoire) of functions for the analysis of clonality in single cell experiments in the R analytical environment. While it is true, as it is claimed, that 'a gap exists in the processing of V(D)J sequencing, descriptive statistics, clonal comparisons, and repertoire diversity with the current SCS R packages', nonetheless few packages already exist for the analysis of clonality in bulk samples (such as the inspiring package tcR). From the text it is not immediately clear why there is the need for a brand new package rather than a simpler 'wrapper' to bridge the gap between single cell based data and existing analytical packages. Although package functionalities are clearly illustrated in the paper, software documentation could be improved: behaviour of some functions is not immediately clear without studying the code directly (e.g. the way multi-chains clonotypes are treated by combineContigs()). Similar to its inspiring package (tcR) and leveraging on the vegan package, scRepertoire implements different measures for diversity. Nonetheless, it has been suggested that conventional diversity measures fail to describe properly clonotype distributions and several complementary methods have been proposed like the Recon package by Kaplinsky 1 and Arnaout 1 or Startrac package by Zhang and colleagues 2 . Interaction with such packages would greatly increase scRepertoire analytical effectiveness. Similarly, the function abundanceContig(), especially in the unscaled form, is of little use per se. Interestingly, recent methods have been proposed to for the comparative analysis of clone size distributions 3 and could be easily incorporated into scRepertoire adding considerable power to it (though assessment of required numerosity should be introduced). The package claims to be designed both for TCR and BCR analysis but definition of clonality in B cells is slightly different than in T cells due to isotype switch and somatic hypermutation phenomena following activation. Therefore clonotype identity between two cells should be defined differently between BCR and TCR analysis. Concerning overlap, beside the nice representation as a heatmap, it could be useful to have the chance to output the matrix itself rather than the plot only. 10x vdj methods occasionally fails to reconstruct complete clonotypes or it reconstructs putatively aberrant clonotypes (clonotypes with multiple beta chains). Currently scRepertoire does not allow to filter for specific chain compositions but such feature would be worth adding, together with a graphical visualization of relative frequencies of chain composition across clonotypes. Paired 10x gene expression profile and vdj scoped analysis are not guaranteed to reconstruct the information for the exact same pool of barcodes, thus the combineSeurat() function could be improved by allowing to specify whether an inner or Seurat-sided joining is to be performed and ensuring that the joining is performed correctly. Is the rationale for developing the new software tool clearly explained? Partly Is the description of the software tool technically sound? Partly Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes References 1. Kaplinsky J, Arnaout R: Robust estimates of overall immune-repertoire diversity from high-throughput measurements on samples. Nature Communications . 2016; 7 (1). Publisher Full Text 2. Zhang L, Yu X, Zheng L, Zhang Y, et al.: Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature . 564 (7735): 268-272 PubMed Abstract | Publisher Full Text 3. Koch H, Starenki D, Cooper SJ, Myers RM, et al.: powerTCR: A model-based approach to comparative analysis of the clone size distribution of the T cell receptor repertoire. PLoS Comput Biol . 14 (11): e1006571 PubMed Abstract | Publisher Full Text Competing Interests: No competing interests were disclosed. Reviewer Expertise: Integrative Biology, Cancer Immunology We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Drufuca L, Bonnal RJP and Pagani M. Reviewer Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.24415.r59209 ) The direct URL for this report is: https://f1000research.com/articles/9-47/v1#referee-response-59209 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 15 Jun 2020 Nicholas Borcherding , Cancer Biology Graduate Program, University of Iowa, Iowa City, USA 15 Jun 2020 Author Response The paper describes a new package (scRepertoire) of functions for the analysis of clonality in single cell experiments in the R analytical environment. While it is true, as it ... Continue reading The paper describes a new package (scRepertoire) of functions for the analysis of clonality in single cell experiments in the R analytical environment. While it is true, as it is claimed, that 'a gap exists in the processing of V(D)J sequencing, descriptive statistics, clonal comparisons, and repertoire diversity with the current SCS R packages', nonetheless few packages already exist for the analysis of clonality in bulk samples (such as the inspiring package tcR). From the text it is not immediately clear why there is the need for a brand new package rather than a simpler 'wrapper' to bridge the gap between single cell based data and existing analytical packages. Thank you for your input. To clarify, indeed a gap exists. The vast majority of packages available for V(D)J single-cell analysis is python-based reconstructions of TCR/BCR sequences (see, scTCRseq , TRAPeS and TraCeR). The tcR package that the reviewer indicates is in fact deprecated and only functional for bulk sequencing, there is no barcoded-based method. However, the authors of tcR have a newer R package, immunarch, that is freely available and produced by the company immunomind. This newer package offers single-cell support, but the workflow within the package does not allow for the combination of both TCR/BCR chains, and will call clonotypes with a single chain. In addition, as the reviewers keenly mention in the accompanying comments, single-cell technologies have unique pitfalls that need to be addressed as opposed to just creating wrapper functions. These concepts have been added to a newer version of the manuscript. Although package functionalities are clearly illustrated in the paper, software documentation could be improved: behaviour of some functions is not immediately clear without studying the code directly (e.g. the way multi-chains clonotypes are treated by combineContigs()). We have added extensive documentation to the package. Similar to its inspiring package (tcR) and leveraging on the vegan package, scRepertoire implements different measures for diversity. Nonetheless, it has been suggested that conventional diversity measures fail to describe properly clonotype distributions and several complementary methods have been proposed like the Recon package by Kaplinsky and Arnaout or Startrac package by Zhang and colleagues. Interaction with such packages would greatly increase scRepertoire analytical effectiveness. We have added the function StartracDiversity() to the development version of the package to allow for the migr, exp, and tans indices to be calculated from the Seurat object after the addition of the clonotype information. This function is detailed in the vignette due to limited word count of this article. Similarly, the function abundanceContig(), especially in the unscaled form, is of little use per se. Interestingly, recent methods have been proposed to for the comparative analysis of clone size distributions and could be easily incorporated into scRepertoire adding considerable power to it (though assessment of required numerosity should be introduced). We have added the function clonesizeDistribution() to allow users to examine the hierarchical clustering based on clone size distributions of samples to the scRepertoire package along with a comprehensive description and covered the usage in the vignette accompanying the package. Due to the length limitation for the manuscript, we only reference the function in the manuscript with the appropriate citation. The package claims to be designed both for TCR and BCR analysis but definition of clonality in B cells is slightly different than in T cells due to isotype switch and somatic hypermutation phenomena following activation. Therefore clonotype identity between two cells should be defined differently between BCR and TCR analysis. We agree with the need to add thresholding for clonotype assignment for the BCR in the context of somatic hypermutation. We have separated the combineContig() function into combineTCR() and combineBCR() functions, the latter organizes the data similarly, but defines nucleotide sequences by the normalized Hamming Distance, with sequences greater than >0.85 assigned to the same group. In the strict definition of the clonotypes in the combineBCR() the vgene is also added to both the light and heavy chains. We have kept the ability to visualize and analyze clonotypes at the amino acid, nucleotide sequence and gene level as well, to all users more choices. Concerning overlap, beside the nice representation as a heatmap, it could be useful to have the chance to output the matrix itself rather than the plot only. When appropriate, the visualization functions have the exportTable variable that allows a user to get the table or matrix that enables the visualization. Although this feature is already present, we will add discussion to the manuscript and accompanying vignette. 10x vdj methods occasionally fails to reconstruct complete clonotypes or it reconstructs putatively aberrant clonotypes (clonotypes with multiple beta chains). Currently scRepertoire does not allow to filter for specific chain compositions but such feature would be worth adding, together with a graphical visualization of relative frequencies of chain composition across clonotypes. If the authors understand the suggestion, the reviewers are asking for additional filtering options for combineTCR() and combineBCR(), two separate functions created from the reviewers earlier points. The function has the ability to remove barcodes with NA values or with multiple chains. More specifically to the combineTCR() function we have added removeMulti (removes all barcodes with >= 3 chains) and filterMulti (selects the 2 highest expressing chains). In terms of the chain filtering and visualizations, we are more than happy to add additional filtering and visualization tools for users. If the reviewer could clarify more on the how they envisioned the chain composition visualization, we would appreciate more insight. Paired 10x gene expression profile and vdj scoped analysis are not guaranteed to reconstruct the information for the exact same pool of barcodes, thus the combineSeurat() function could be improved by allowing to specify whether an inner or Seurat-sided joining is to be performed and ensuring that the joining is performed correctly. We have added the variable NA into the new combineBCR() and combineTCR(), which functions by removing cells without clonotype information. If we understand the reviewer correctly, the differential joining function would also need to address the seurat object as a whole instead of just merging the meta.data. The paper describes a new package (scRepertoire) of functions for the analysis of clonality in single cell experiments in the R analytical environment. While it is true, as it is claimed, that 'a gap exists in the processing of V(D)J sequencing, descriptive statistics, clonal comparisons, and repertoire diversity with the current SCS R packages', nonetheless few packages already exist for the analysis of clonality in bulk samples (such as the inspiring package tcR). From the text it is not immediately clear why there is the need for a brand new package rather than a simpler 'wrapper' to bridge the gap between single cell based data and existing analytical packages. Thank you for your input. To clarify, indeed a gap exists. The vast majority of packages available for V(D)J single-cell analysis is python-based reconstructions of TCR/BCR sequences (see, scTCRseq , TRAPeS and TraCeR). The tcR package that the reviewer indicates is in fact deprecated and only functional for bulk sequencing, there is no barcoded-based method. However, the authors of tcR have a newer R package, immunarch, that is freely available and produced by the company immunomind. This newer package offers single-cell support, but the workflow within the package does not allow for the combination of both TCR/BCR chains, and will call clonotypes with a single chain. In addition, as the reviewers keenly mention in the accompanying comments, single-cell technologies have unique pitfalls that need to be addressed as opposed to just creating wrapper functions. These concepts have been added to a newer version of the manuscript. Although package functionalities are clearly illustrated in the paper, software documentation could be improved: behaviour of some functions is not immediately clear without studying the code directly (e.g. the way multi-chains clonotypes are treated by combineContigs()). We have added extensive documentation to the package. Similar to its inspiring package (tcR) and leveraging on the vegan package, scRepertoire implements different measures for diversity. Nonetheless, it has been suggested that conventional diversity measures fail to describe properly clonotype distributions and several complementary methods have been proposed like the Recon package by Kaplinsky and Arnaout or Startrac package by Zhang and colleagues. Interaction with such packages would greatly increase scRepertoire analytical effectiveness. We have added the function StartracDiversity() to the development version of the package to allow for the migr, exp, and tans indices to be calculated from the Seurat object after the addition of the clonotype information. This function is detailed in the vignette due to limited word count of this article. Similarly, the function abundanceContig(), especially in the unscaled form, is of little use per se. Interestingly, recent methods have been proposed to for the comparative analysis of clone size distributions and could be easily incorporated into scRepertoire adding considerable power to it (though assessment of required numerosity should be introduced). We have added the function clonesizeDistribution() to allow users to examine the hierarchical clustering based on clone size distributions of samples to the scRepertoire package along with a comprehensive description and covered the usage in the vignette accompanying the package. Due to the length limitation for the manuscript, we only reference the function in the manuscript with the appropriate citation. The package claims to be designed both for TCR and BCR analysis but definition of clonality in B cells is slightly different than in T cells due to isotype switch and somatic hypermutation phenomena following activation. Therefore clonotype identity between two cells should be defined differently between BCR and TCR analysis. We agree with the need to add thresholding for clonotype assignment for the BCR in the context of somatic hypermutation. We have separated the combineContig() function into combineTCR() and combineBCR() functions, the latter organizes the data similarly, but defines nucleotide sequences by the normalized Hamming Distance, with sequences greater than >0.85 assigned to the same group. In the strict definition of the clonotypes in the combineBCR() the vgene is also added to both the light and heavy chains. We have kept the ability to visualize and analyze clonotypes at the amino acid, nucleotide sequence and gene level as well, to all users more choices. Concerning overlap, beside the nice representation as a heatmap, it could be useful to have the chance to output the matrix itself rather than the plot only. When appropriate, the visualization functions have the exportTable variable that allows a user to get the table or matrix that enables the visualization. Although this feature is already present, we will add discussion to the manuscript and accompanying vignette. 10x vdj methods occasionally fails to reconstruct complete clonotypes or it reconstructs putatively aberrant clonotypes (clonotypes with multiple beta chains). Currently scRepertoire does not allow to filter for specific chain compositions but such feature would be worth adding, together with a graphical visualization of relative frequencies of chain composition across clonotypes. If the authors understand the suggestion, the reviewers are asking for additional filtering options for combineTCR() and combineBCR(), two separate functions created from the reviewers earlier points. The function has the ability to remove barcodes with NA values or with multiple chains. More specifically to the combineTCR() function we have added removeMulti (removes all barcodes with >= 3 chains) and filterMulti (selects the 2 highest expressing chains). In terms of the chain filtering and visualizations, we are more than happy to add additional filtering and visualization tools for users. If the reviewer could clarify more on the how they envisioned the chain composition visualization, we would appreciate more insight. Paired 10x gene expression profile and vdj scoped analysis are not guaranteed to reconstruct the information for the exact same pool of barcodes, thus the combineSeurat() function could be improved by allowing to specify whether an inner or Seurat-sided joining is to be performed and ensuring that the joining is performed correctly. We have added the variable NA into the new combineBCR() and combineTCR(), which functions by removing cells without clonotype information. If we understand the reviewer correctly, the differential joining function would also need to address the seurat object as a whole instead of just merging the meta.data. Competing Interests: None Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 15 Jun 2020 Nicholas Borcherding , Cancer Biology Graduate Program, University of Iowa, Iowa City, USA 15 Jun 2020 Author Response The paper describes a new package (scRepertoire) of functions for the analysis of clonality in single cell experiments in the R analytical environment. While it is true, as it ... Continue reading The paper describes a new package (scRepertoire) of functions for the analysis of clonality in single cell experiments in the R analytical environment. While it is true, as it is claimed, that 'a gap exists in the processing of V(D)J sequencing, descriptive statistics, clonal comparisons, and repertoire diversity with the current SCS R packages', nonetheless few packages already exist for the analysis of clonality in bulk samples (such as the inspiring package tcR). From the text it is not immediately clear why there is the need for a brand new package rather than a simpler 'wrapper' to bridge the gap between single cell based data and existing analytical packages. Thank you for your input. To clarify, indeed a gap exists. The vast majority of packages available for V(D)J single-cell analysis is python-based reconstructions of TCR/BCR sequences (see, scTCRseq , TRAPeS and TraCeR). The tcR package that the reviewer indicates is in fact deprecated and only functional for bulk sequencing, there is no barcoded-based method. However, the authors of tcR have a newer R package, immunarch, that is freely available and produced by the company immunomind. This newer package offers single-cell support, but the workflow within the package does not allow for the combination of both TCR/BCR chains, and will call clonotypes with a single chain. In addition, as the reviewers keenly mention in the accompanying comments, single-cell technologies have unique pitfalls that need to be addressed as opposed to just creating wrapper functions. These concepts have been added to a newer version of the manuscript. Although package functionalities are clearly illustrated in the paper, software documentation could be improved: behaviour of some functions is not immediately clear without studying the code directly (e.g. the way multi-chains clonotypes are treated by combineContigs()). We have added extensive documentation to the package. Similar to its inspiring package (tcR) and leveraging on the vegan package, scRepertoire implements different measures for diversity. Nonetheless, it has been suggested that conventional diversity measures fail to describe properly clonotype distributions and several complementary methods have been proposed like the Recon package by Kaplinsky and Arnaout or Startrac package by Zhang and colleagues. Interaction with such packages would greatly increase scRepertoire analytical effectiveness. We have added the function StartracDiversity() to the development version of the package to allow for the migr, exp, and tans indices to be calculated from the Seurat object after the addition of the clonotype information. This function is detailed in the vignette due to limited word count of this article. Similarly, the function abundanceContig(), especially in the unscaled form, is of little use per se. Interestingly, recent methods have been proposed to for the comparative analysis of clone size distributions and could be easily incorporated into scRepertoire adding considerable power to it (though assessment of required numerosity should be introduced). We have added the function clonesizeDistribution() to allow users to examine the hierarchical clustering based on clone size distributions of samples to the scRepertoire package along with a comprehensive description and covered the usage in the vignette accompanying the package. Due to the length limitation for the manuscript, we only reference the function in the manuscript with the appropriate citation. The package claims to be designed both for TCR and BCR analysis but definition of clonality in B cells is slightly different than in T cells due to isotype switch and somatic hypermutation phenomena following activation. Therefore clonotype identity between two cells should be defined differently between BCR and TCR analysis. We agree with the need to add thresholding for clonotype assignment for the BCR in the context of somatic hypermutation. We have separated the combineContig() function into combineTCR() and combineBCR() functions, the latter organizes the data similarly, but defines nucleotide sequences by the normalized Hamming Distance, with sequences greater than >0.85 assigned to the same group. In the strict definition of the clonotypes in the combineBCR() the vgene is also added to both the light and heavy chains. We have kept the ability to visualize and analyze clonotypes at the amino acid, nucleotide sequence and gene level as well, to all users more choices. Concerning overlap, beside the nice representation as a heatmap, it could be useful to have the chance to output the matrix itself rather than the plot only. When appropriate, the visualization functions have the exportTable variable that allows a user to get the table or matrix that enables the visualization. Although this feature is already present, we will add discussion to the manuscript and accompanying vignette. 10x vdj methods occasionally fails to reconstruct complete clonotypes or it reconstructs putatively aberrant clonotypes (clonotypes with multiple beta chains). Currently scRepertoire does not allow to filter for specific chain compositions but such feature would be worth adding, together with a graphical visualization of relative frequencies of chain composition across clonotypes. If the authors understand the suggestion, the reviewers are asking for additional filtering options for combineTCR() and combineBCR(), two separate functions created from the reviewers earlier points. The function has the ability to remove barcodes with NA values or with multiple chains. More specifically to the combineTCR() function we have added removeMulti (removes all barcodes with >= 3 chains) and filterMulti (selects the 2 highest expressing chains). In terms of the chain filtering and visualizations, we are more than happy to add additional filtering and visualization tools for users. If the reviewer could clarify more on the how they envisioned the chain composition visualization, we would appreciate more insight. Paired 10x gene expression profile and vdj scoped analysis are not guaranteed to reconstruct the information for the exact same pool of barcodes, thus the combineSeurat() function could be improved by allowing to specify whether an inner or Seurat-sided joining is to be performed and ensuring that the joining is performed correctly. We have added the variable NA into the new combineBCR() and combineTCR(), which functions by removing cells without clonotype information. If we understand the reviewer correctly, the differential joining function would also need to address the seurat object as a whole instead of just merging the meta.data. The paper describes a new package (scRepertoire) of functions for the analysis of clonality in single cell experiments in the R analytical environment. While it is true, as it is claimed, that 'a gap exists in the processing of V(D)J sequencing, descriptive statistics, clonal comparisons, and repertoire diversity with the current SCS R packages', nonetheless few packages already exist for the analysis of clonality in bulk samples (such as the inspiring package tcR). From the text it is not immediately clear why there is the need for a brand new package rather than a simpler 'wrapper' to bridge the gap between single cell based data and existing analytical packages. Thank you for your input. To clarify, indeed a gap exists. The vast majority of packages available for V(D)J single-cell analysis is python-based reconstructions of TCR/BCR sequences (see, scTCRseq , TRAPeS and TraCeR). The tcR package that the reviewer indicates is in fact deprecated and only functional for bulk sequencing, there is no barcoded-based method. However, the authors of tcR have a newer R package, immunarch, that is freely available and produced by the company immunomind. This newer package offers single-cell support, but the workflow within the package does not allow for the combination of both TCR/BCR chains, and will call clonotypes with a single chain. In addition, as the reviewers keenly mention in the accompanying comments, single-cell technologies have unique pitfalls that need to be addressed as opposed to just creating wrapper functions. These concepts have been added to a newer version of the manuscript. Although package functionalities are clearly illustrated in the paper, software documentation could be improved: behaviour of some functions is not immediately clear without studying the code directly (e.g. the way multi-chains clonotypes are treated by combineContigs()). We have added extensive documentation to the package. Similar to its inspiring package (tcR) and leveraging on the vegan package, scRepertoire implements different measures for diversity. Nonetheless, it has been suggested that conventional diversity measures fail to describe properly clonotype distributions and several complementary methods have been proposed like the Recon package by Kaplinsky and Arnaout or Startrac package by Zhang and colleagues. Interaction with such packages would greatly increase scRepertoire analytical effectiveness. We have added the function StartracDiversity() to the development version of the package to allow for the migr, exp, and tans indices to be calculated from the Seurat object after the addition of the clonotype information. This function is detailed in the vignette due to limited word count of this article. Similarly, the function abundanceContig(), especially in the unscaled form, is of little use per se. Interestingly, recent methods have been proposed to for the comparative analysis of clone size distributions and could be easily incorporated into scRepertoire adding considerable power to it (though assessment of required numerosity should be introduced). We have added the function clonesizeDistribution() to allow users to examine the hierarchical clustering based on clone size distributions of samples to the scRepertoire package along with a comprehensive description and covered the usage in the vignette accompanying the package. Due to the length limitation for the manuscript, we only reference the function in the manuscript with the appropriate citation. The package claims to be designed both for TCR and BCR analysis but definition of clonality in B cells is slightly different than in T cells due to isotype switch and somatic hypermutation phenomena following activation. Therefore clonotype identity between two cells should be defined differently between BCR and TCR analysis. We agree with the need to add thresholding for clonotype assignment for the BCR in the context of somatic hypermutation. We have separated the combineContig() function into combineTCR() and combineBCR() functions, the latter organizes the data similarly, but defines nucleotide sequences by the normalized Hamming Distance, with sequences greater than >0.85 assigned to the same group. In the strict definition of the clonotypes in the combineBCR() the vgene is also added to both the light and heavy chains. We have kept the ability to visualize and analyze clonotypes at the amino acid, nucleotide sequence and gene level as well, to all users more choices. Concerning overlap, beside the nice representation as a heatmap, it could be useful to have the chance to output the matrix itself rather than the plot only. When appropriate, the visualization functions have the exportTable variable that allows a user to get the table or matrix that enables the visualization. Although this feature is already present, we will add discussion to the manuscript and accompanying vignette. 10x vdj methods occasionally fails to reconstruct complete clonotypes or it reconstructs putatively aberrant clonotypes (clonotypes with multiple beta chains). Currently scRepertoire does not allow to filter for specific chain compositions but such feature would be worth adding, together with a graphical visualization of relative frequencies of chain composition across clonotypes. If the authors understand the suggestion, the reviewers are asking for additional filtering options for combineTCR() and combineBCR(), two separate functions created from the reviewers earlier points. The function has the ability to remove barcodes with NA values or with multiple chains. More specifically to the combineTCR() function we have added removeMulti (removes all barcodes with >= 3 chains) and filterMulti (selects the 2 highest expressing chains). In terms of the chain filtering and visualizations, we are more than happy to add additional filtering and visualization tools for users. If the reviewer could clarify more on the how they envisioned the chain composition visualization, we would appreciate more insight. Paired 10x gene expression profile and vdj scoped analysis are not guaranteed to reconstruct the information for the exact same pool of barcodes, thus the combineSeurat() function could be improved by allowing to specify whether an inner or Seurat-sided joining is to be performed and ensuring that the joining is performed correctly. We have added the variable NA into the new combineBCR() and combineTCR(), which functions by removing cells without clonotype information. If we understand the reviewer correctly, the differential joining function would also need to address the seurat object as a whole instead of just merging the meta.data. Competing Interests: None Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 2 VERSION 2 PUBLISHED 27 Jan 2020 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 Version 2 (revision) 15 Jun 20 read read Version 1 27 Jan 20 read read Lorenzo Drufuca , Università degli Studi di Milano Bicocca, Milan, Italy Raoul Jean Pierre Bonnal , National Institute of Molecular Genetics, Milan, Italy Massimiliano Pagani , National Institute of Molecular Genetics, Milan, Italy Tim Stuart , New York Genome Center, New York City, USA Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2020 Pagani M et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Aug 2020 | for Version 2 Raoul Jean Pierre Bonnal , National Institute of Molecular Genetics, Milan, Italy Lorenzo Drufuca , School of Medicine and Surgery, Università degli Studi di Milano Bicocca, Milan, Italy Massimiliano Pagani , National Institute of Molecular Genetics, Milan, Italy 0 Views copyright © 2020 Pagani M et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions I can confirm that the authors addressed all the issues and the paper is now suitable for indexing. Competing Interests No competing interests were disclosed. We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Bonnal RJP, Drufuca L and Pagani M. Peer Review Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.27282.r64820) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/9-47/v2#referee-response-64820 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2020 Stuart T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Jul 2020 | for Version 2 Tim Stuart , New York Genome Center, New York City, NY, USA 0 Views copyright © 2020 Stuart T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The authors have now addressed all my original comments. Competing Interests No competing interests were disclosed. Reviewer Expertise Single-cell genomics, bioinformatics, epigenomics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Stuart T. Peer Review Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.27282.r64821) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/9-47/v2#referee-response-64821 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2020 Stuart T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Mar 2020 | for Version 1 Tim Stuart , New York Genome Center, New York City, NY, USA 0 Views copyright © 2020 Stuart T. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This paper presents a new R package for the analysis of T-cell and B-cell clonotypes inferred from V(D)J recombination events. The package is designed for the analysis of data generated using the 10x Genomics V(D)J kit that allows clonotypes and transcriptomes to be co-assayed in the same individual cells. Overall the package appears to fill an unmet need in the analysis of single-cell V(D)J data, and works nicely with the existing Seurat package for single-cell analysis. While the focus of the paper is on the software, a more detailed explanation of the biology of V(D)J recombination and how this information is detected in single cells would improve the clarity of the manuscript. A description of the major existing challenges and goals of immune clonotype analysis could also be added. I do not have extensive expertise in the immunology and so my review focuses on the software implementation. I leave discussion of the value and impact of the software to other reviewers. However I will reiterate comments from other reviewers in saying that there should be a distinction made between T and B cell clonotypes, due to the Ig class switching that occurs in B cells. The paper also states that “The expression data for the 12,911 cells built into the package”. I believe the clonotype information is included in the package in the data directory, but I don’t think the expression data is included. Including large gene expression matrix within the R package is not advisable anyway, and the authors should make it available through other means (NCBI GEO, for example). Code review I would encourage the authors to submit the package to CRAN or another R repository. This assist in the distribution of the package, and the process of getting the package accepted on CRAN will likely greatly improve the code quality. At a minimum, the authors should try to get the package to pass R CMD check with no notes, warnings, or errors. Currently the package includes all rendered figures in the vignettes folder. I would recommend removing these files, and also removing the `ggsave` function calls within the vignette to prevent these files being written while compiling the vignette. This will reduce the size of the package. Several functions in the package use scoping assignment to assign variables in the global environment. This is considered bad practice and should be avoided in all cases. Extra files such as .DS_Store should be removed from the git repository and from the package. Use the .gitignore and .Rbuildignore files for this. Avoid importing code within R functions, for example `require(ggplot2)` calls. Instead, document the dependencies using roxygen2, for example `@importFrom ggplot2 ggplot`. To access data in a Seurat object, I highly recommend using the functions defined in Seurat for this purpose rather than accessing the slots directly. For example, use obj[[]] to access metadata rather than [email protected] and Idents(obj) rather than [email protected] In general the documentation of functions can be greatly improved. Try to include a text description of each function, a detailed description of the parameters, document the returned values, and include an executable example. It is generally not advisable to overwrite functions in base R or other packages with variable names, for example the `call` variable in `clonalDiversity` overwrites the base R `call` function. Replace code like `class(df)[1] == “Seurat”` with `inherits(x = df, what = "Seurat”)` In plotting functions such as clonalOverlap, consider returning the ggplot object rather than printing the object. For example, replace `suppressWarnings(print(plot))` with `return(plot)`. This will allow users to modify the plot that is generated. Some code sections are duplicated, for example L91:104 and L131:144 in seuratFunctions.R. Consider putting duplicated code into functions. Imported functions should be added to the namespace. Documenting the imports using roxygen2 (as has been done for parameters and exports) will take care of this. The highlightClonotypes function is a bit redundant with existing functions in Seurat, ie DimPlot function with the cells.highlight parameter. Is the rationale for developing the new software tool clearly explained? Partly Is the description of the software tool technically sound? Yes Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Single-cell genomics, bioinformatics, epigenomics I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 15 Jun 2020 Nicholas Borcherding, Cancer Biology Graduate Program, University of Iowa, Iowa City, USA This paper presents a new R package for the analysis of T-cell and B-cell clonotypes inferred from V(D)J recombination events. The package is designed for the analysis of data generated using the 10x Genomics V(D)J kit that allows clonotypes and transcriptomes to be co-assayed in the same individual cells. Overall the package appears to fill an unmet need in the analysis of single-cell V(D)J data, and works nicely with the existing Seurat package for single-cell analysis. While the focus of the paper is on the software, a more detailed explanation of the biology of V(D)J recombination and how this information is detected in single cells would improve the clarity of the manuscript. A description of the major existing challenges and goals of immune clonotype analysis could also be added. The authors agree that a more extensive explanation of VDJ biology would be a benefit, however, due to the limitations of less than 1000 words and 10 citations, we are limited by the structure of the F1000Research. We have added citations to the readme file to help users. I do not have extensive expertise in the immunology and so my review focuses on the software implementation. I leave discussion of the value and impact of the software to other reviewers. However I will reiterate comments from other reviewers in saying that there should be a distinction made between T and B cell clonotypes, due to the Ig class switching that occurs in B cells. The paper also states that “The expression data for the 12,911 cells built into the package”. I believe the clonotype information is included in the package in the data directory, but I don’t think the expression data is included. Including large gene expression matrix within the R package is not advisable anyway, and the authors should make it available through other means (NCBI GEO, for example). The seurat object with the package is available in the zenodo repository and linked in the github repository. The raw data has been deposited in GEO and more information can be found in the two preprints cited. Code review I would encourage the authors to submit the package to CRAN or another R repository. This assist in the distribution of the package, and the process of getting the package accepted on CRAN will likely greatly improve the code quality. At a minimum, the authors should try to get the package to pass R CMD check with no notes, warnings, or errors. Thank you for the suggestions, the authors believe this point helped the structure of the package immensely. We have used the devtools check function and as of the version in the github repository and zenodo at resubmission, there is no warnings, errors, or notes. We have begun the process of submitting the package to Bioconductor and have passed the build phase of the process. Currently the package includes all rendered figures in the vignettes folder. I would recommend removing these files, and also removing the `ggsave` function calls within the vignette to prevent these files being written while compiling the vignette. This will reduce the size of the package. Thank you for the suggestion, this has been completed. Several functions in the package use scoping assignment to assign variables in the global environment. This is considered bad practice and should be avoided in all cases. Scoping assignments in these instances are to give users the options of exporting the tables or matrices that form the visualization. We have modified the activity of the exportTable parameter to return a data frame used if TRUE or visualization if FALSE (default). Extra files such as .DS_Store should be removed from the git repository and from the package. Use the .gitignore and .Rbuildignore files for this. Thank you for the suggestion, we have removed .DS_Store. Avoid importing code within R functions, for example `require(ggplot2)` calls. Instead, document the dependencies using roxygen2, for example `@importFrom ggplot2 ggplot`. Thank you for the suggestion, we have made this change. To access data in a Seurat object, I highly recommend using the functions defined in Seurat for this purpose rather than accessing the slots directly. For example, use obj[[]] to access metadata rather than [email protected] and Idents(obj) rather than [email protected] Thanks for the suggestion, we have changed this in the new version of the R package. In general the documentation of functions can be greatly improved. Try to include a text description of each function, a detailed description of the parameters, document the returned values, and include an executable example. Both reviewers have made this excellent suggestion, the new version of scRepertoire has more comprehensive documentation, including descriptions and working examples. It is generally not advisable to overwrite functions in base R or other packages with variable names, for example the `call` variable in `clonalDiversity` overwrites the base R `call` function. Thank you for the suggestion, we have modified this for all functions. Replace code like `class(df)[1] == “Seurat”` with `inherits(x = df, what = "Seurat”)` We have made this change and also replace all class() evaluations. In plotting functions such as clonalOverlap, consider returning the ggplot object rather than printing the object. For example, replace `suppressWarnings(print(plot))` with `return(plot)`. This will allow users to modify the plot that is generated. Excellent suggestion and this has been fixed. Some code sections are duplicated, for example L91:104 and L131:144 in seuratFunctions.R. Consider putting duplicated code into functions. Thank you for the suggestion, part of the change in the “call” to cloneCall variable in the functions also including creating a function to deal with the user selection. These lines were consistently repeated across multiple functions, but now have been reduced. We have also extensively modularized the code and added these to utils.R . Imported functions should be added to the namespace. Documenting the imports using roxygen2 (as has been done for parameters and exports) will take care of this. This has been completed. The highlightClonotypes function is a bit redundant with existing functions in Seurat, ie DimPlot function with the cells.highlight parameter. Agreed, although redundant, this was a request from an early beta tester, we plan to keep the function in. View more View less Competing Interests None reply Respond Report a concern Stuart T. Peer Review Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.24415.r60873) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/9-47/v1#referee-response-60873 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2020 Pagani M et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Mar 2020 | for Version 1 Lorenzo Drufuca , School of Medicine and Surgery, Università degli Studi di Milano Bicocca, Milan, Italy Raoul Jean Pierre Bonnal , National Institute of Molecular Genetics, Milan, Italy Massimiliano Pagani , National Institute of Molecular Genetics, Milan, Italy 0 Views copyright © 2020 Pagani M et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions The paper describes a new package (scRepertoire) of functions for the analysis of clonality in single cell experiments in the R analytical environment. While it is true, as it is claimed, that 'a gap exists in the processing of V(D)J sequencing, descriptive statistics, clonal comparisons, and repertoire diversity with the current SCS R packages', nonetheless few packages already exist for the analysis of clonality in bulk samples (such as the inspiring package tcR). From the text it is not immediately clear why there is the need for a brand new package rather than a simpler 'wrapper' to bridge the gap between single cell based data and existing analytical packages. Although package functionalities are clearly illustrated in the paper, software documentation could be improved: behaviour of some functions is not immediately clear without studying the code directly (e.g. the way multi-chains clonotypes are treated by combineContigs()). Similar to its inspiring package (tcR) and leveraging on the vegan package, scRepertoire implements different measures for diversity. Nonetheless, it has been suggested that conventional diversity measures fail to describe properly clonotype distributions and several complementary methods have been proposed like the Recon package by Kaplinsky 1 and Arnaout 1 or Startrac package by Zhang and colleagues 2 . Interaction with such packages would greatly increase scRepertoire analytical effectiveness. Similarly, the function abundanceContig(), especially in the unscaled form, is of little use per se. Interestingly, recent methods have been proposed to for the comparative analysis of clone size distributions 3 and could be easily incorporated into scRepertoire adding considerable power to it (though assessment of required numerosity should be introduced). The package claims to be designed both for TCR and BCR analysis but definition of clonality in B cells is slightly different than in T cells due to isotype switch and somatic hypermutation phenomena following activation. Therefore clonotype identity between two cells should be defined differently between BCR and TCR analysis. Concerning overlap, beside the nice representation as a heatmap, it could be useful to have the chance to output the matrix itself rather than the plot only. 10x vdj methods occasionally fails to reconstruct complete clonotypes or it reconstructs putatively aberrant clonotypes (clonotypes with multiple beta chains). Currently scRepertoire does not allow to filter for specific chain compositions but such feature would be worth adding, together with a graphical visualization of relative frequencies of chain composition across clonotypes. Paired 10x gene expression profile and vdj scoped analysis are not guaranteed to reconstruct the information for the exact same pool of barcodes, thus the combineSeurat() function could be improved by allowing to specify whether an inner or Seurat-sided joining is to be performed and ensuring that the joining is performed correctly. Is the rationale for developing the new software tool clearly explained? Partly Is the description of the software tool technically sound? Partly Are sufficient details of the code, methods and analysis (if applicable) provided to allow replication of the software development and its use by others? Yes Is sufficient information provided to allow interpretation of the expected output datasets and any results generated using the tool? Yes Are the conclusions about the tool and its performance adequately supported by the findings presented in the article? Yes References 1. Kaplinsky J, Arnaout R: Robust estimates of overall immune-repertoire diversity from high-throughput measurements on samples. Nature Communications . 2016; 7 (1). Publisher Full Text 2. Zhang L, Yu X, Zheng L, Zhang Y, et al.: Lineage tracking reveals dynamic relationships of T cells in colorectal cancer. Nature . 564 (7735): 268-272 PubMed Abstract | Publisher Full Text 3. Koch H, Starenki D, Cooper SJ, Myers RM, et al.: powerTCR: A model-based approach to comparative analysis of the clone size distribution of the T cell receptor repertoire. PLoS Comput Biol . 14 (11): e1006571 PubMed Abstract | Publisher Full Text Competing Interests No competing interests were disclosed. Reviewer Expertise Integrative Biology, Cancer Immunology We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 15 Jun 2020 Nicholas Borcherding, Cancer Biology Graduate Program, University of Iowa, Iowa City, USA The paper describes a new package (scRepertoire) of functions for the analysis of clonality in single cell experiments in the R analytical environment. While it is true, as it is claimed, that 'a gap exists in the processing of V(D)J sequencing, descriptive statistics, clonal comparisons, and repertoire diversity with the current SCS R packages', nonetheless few packages already exist for the analysis of clonality in bulk samples (such as the inspiring package tcR). From the text it is not immediately clear why there is the need for a brand new package rather than a simpler 'wrapper' to bridge the gap between single cell based data and existing analytical packages. Thank you for your input. To clarify, indeed a gap exists. The vast majority of packages available for V(D)J single-cell analysis is python-based reconstructions of TCR/BCR sequences (see, scTCRseq , TRAPeS and TraCeR). The tcR package that the reviewer indicates is in fact deprecated and only functional for bulk sequencing, there is no barcoded-based method. However, the authors of tcR have a newer R package, immunarch, that is freely available and produced by the company immunomind. This newer package offers single-cell support, but the workflow within the package does not allow for the combination of both TCR/BCR chains, and will call clonotypes with a single chain. In addition, as the reviewers keenly mention in the accompanying comments, single-cell technologies have unique pitfalls that need to be addressed as opposed to just creating wrapper functions. These concepts have been added to a newer version of the manuscript. Although package functionalities are clearly illustrated in the paper, software documentation could be improved: behaviour of some functions is not immediately clear without studying the code directly (e.g. the way multi-chains clonotypes are treated by combineContigs()). We have added extensive documentation to the package. Similar to its inspiring package (tcR) and leveraging on the vegan package, scRepertoire implements different measures for diversity. Nonetheless, it has been suggested that conventional diversity measures fail to describe properly clonotype distributions and several complementary methods have been proposed like the Recon package by Kaplinsky and Arnaout or Startrac package by Zhang and colleagues. Interaction with such packages would greatly increase scRepertoire analytical effectiveness. We have added the function StartracDiversity() to the development version of the package to allow for the migr, exp, and tans indices to be calculated from the Seurat object after the addition of the clonotype information. This function is detailed in the vignette due to limited word count of this article. Similarly, the function abundanceContig(), especially in the unscaled form, is of little use per se. Interestingly, recent methods have been proposed to for the comparative analysis of clone size distributions and could be easily incorporated into scRepertoire adding considerable power to it (though assessment of required numerosity should be introduced). We have added the function clonesizeDistribution() to allow users to examine the hierarchical clustering based on clone size distributions of samples to the scRepertoire package along with a comprehensive description and covered the usage in the vignette accompanying the package. Due to the length limitation for the manuscript, we only reference the function in the manuscript with the appropriate citation. The package claims to be designed both for TCR and BCR analysis but definition of clonality in B cells is slightly different than in T cells due to isotype switch and somatic hypermutation phenomena following activation. Therefore clonotype identity between two cells should be defined differently between BCR and TCR analysis. We agree with the need to add thresholding for clonotype assignment for the BCR in the context of somatic hypermutation. We have separated the combineContig() function into combineTCR() and combineBCR() functions, the latter organizes the data similarly, but defines nucleotide sequences by the normalized Hamming Distance, with sequences greater than >0.85 assigned to the same group. In the strict definition of the clonotypes in the combineBCR() the vgene is also added to both the light and heavy chains. We have kept the ability to visualize and analyze clonotypes at the amino acid, nucleotide sequence and gene level as well, to all users more choices. Concerning overlap, beside the nice representation as a heatmap, it could be useful to have the chance to output the matrix itself rather than the plot only. When appropriate, the visualization functions have the exportTable variable that allows a user to get the table or matrix that enables the visualization. Although this feature is already present, we will add discussion to the manuscript and accompanying vignette. 10x vdj methods occasionally fails to reconstruct complete clonotypes or it reconstructs putatively aberrant clonotypes (clonotypes with multiple beta chains). Currently scRepertoire does not allow to filter for specific chain compositions but such feature would be worth adding, together with a graphical visualization of relative frequencies of chain composition across clonotypes. If the authors understand the suggestion, the reviewers are asking for additional filtering options for combineTCR() and combineBCR(), two separate functions created from the reviewers earlier points. The function has the ability to remove barcodes with NA values or with multiple chains. More specifically to the combineTCR() function we have added removeMulti (removes all barcodes with >= 3 chains) and filterMulti (selects the 2 highest expressing chains). In terms of the chain filtering and visualizations, we are more than happy to add additional filtering and visualization tools for users. If the reviewer could clarify more on the how they envisioned the chain composition visualization, we would appreciate more insight. Paired 10x gene expression profile and vdj scoped analysis are not guaranteed to reconstruct the information for the exact same pool of barcodes, thus the combineSeurat() function could be improved by allowing to specify whether an inner or Seurat-sided joining is to be performed and ensuring that the joining is performed correctly. We have added the variable NA into the new combineBCR() and combineTCR(), which functions by removing cells without clonotype information. If we understand the reviewer correctly, the differential joining function would also need to address the seurat object as a whole instead of just merging the meta.data. View more View less Competing Interests None reply Respond Report a concern Drufuca L, Bonnal RJP and Pagani M. Peer Review Report For: scRepertoire: An R-based toolkit for single-cell immune receptor analysis [version 2; peer review: 2 approved] . F1000Research 2020, 9 :47 ( https://doi.org/10.5256/f1000research.24415.r59209) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/9-47/v1#referee-response-59209 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions Adjust parameters to alter display View on desktop for interactive features Includes Interactive Elements View on desktop for interactive features Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Stay Updated Sign up for content alerts and receive a weekly or monthly email with all newly published articles Register with F1000Research Already registered? Sign in Not now, thanks close PLEASE NOTE If you are an AUTHOR of this article, please check that you signed in with the account associated with this article otherwise we cannot automatically identify your role as an author and your comment will be labelled as a “User Comment”. If you are a REVIEWER of this article, please check that you have signed in with the account associated with this article and then go to your account to submit your report, please do not post your review here. If you do not have access to your original account, please contact us . All commenters must hold a formal affiliation as per our Policies . The information that you give us will be displayed next to your comment. User comments must be in English, comprehensible and relevant to the article under discussion. We reserve the right to remove any comments that we consider to be inappropriate, offensive or otherwise in breach of the User Comment Terms and Conditions . Commenters must not use a comment for personal attacks. When criticisms of the article are based on unpublished data, the data should be made available. I accept the User Comment Terms and Conditions Please confirm that you accept the User Comment Terms and Conditions. Affiliation ✕ refresh Please enter your institution. Note: To add your institution or organisation, start typing the name and then select the correct name from the list. Where applicable, the name will appear in both the original language and in English. Do not paste in the name. If the name does not appear in the drop-down list, we will display the information you have entered. ✕ refresh Country/Region * USA UK Canada China France Germany Afghanistan Aland Islands Albania Algeria American Samoa Andorra Angola Anguilla Antarctica Antigua and Barbuda Argentina Armenia Aruba Australia Austria Azerbaijan Bahamas Bahrain Bangladesh Barbados Belarus Belgium Belize Benin Bermuda Bhutan Bolivia Bosnia and Herzegovina Botswana Bouvet Island Brazil British Indian Ocean Territory British Virgin Islands Brunei Bulgaria Burkina Faso Burundi Cambodia Cameroon Canada Cape Verde Cayman Islands Central African Republic Chad Chile China Christmas Island Cocos (Keeling) Islands Colombia Comoros Congo Cook Islands Costa Rica Cote d'Ivoire Croatia Cuba Cyprus Czech Republic Democratic Republic of the Congo Denmark Djibouti Dominica Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Estonia Ethiopia Falkland Islands Faroe Islands Federated States of Micronesia Fiji Finland France French Guiana French Polynesia French Southern Territories Gabon Georgia Germany Ghana Gibraltar Greece Greenland Grenada Guadeloupe Guam Guatemala Guernsey Guinea Guinea-Bissau Guyana Haiti Heard Island and Mcdonald Islands Holy See (Vatican City State) Honduras Hong Kong Hungary Iceland India Indonesia Iran Iraq Ireland Israel Italy Jamaica Japan Jersey Jordan Kazakhstan Kenya Kiribati Kosovo (Serbia and Montenegro) Kuwait Kyrgyzstan Lao People's Democratic Republic Latvia Lebanon Lesotho Liberia Libya Liechtenstein Lithuania Luxembourg Macao Madagascar Malawi Malaysia Maldives Mali Malta Marshall Islands Martinique Mauritania Mauritius Mayotte Mexico Minor Outlying Islands of the United States Moldova Monaco Mongolia Montenegro Montserrat Morocco Mozambique Myanmar Namibia Nauru Nepal Netherlands Antilles New Caledonia New Zealand Nicaragua Niger Nigeria Niue Norfolk Island North Korea North Macedonia Northern Mariana Islands Norway Oman Pakistan Palau Palestinian Territory Panama Papua New Guinea Paraguay Peru Philippines Pitcairn Poland Portugal Puerto Rico Qatar Reunion Romania Russian Federation Rwanda Saint Helena Saint Kitts and Nevis Saint Lucia Saint Pierre and Miquelon Saint Vincent and the Grenadines Samoa San Marino Sao Tome and Principe Saudi Arabia Senegal Serbia Seychelles Sierra Leone Singapore Slovakia Slovenia Solomon Islands Somalia South Africa South Georgia and the South Sandwich Is South Korea South Sudan Spain Sri Lanka Sudan Suriname Svalbard and Jan Mayen Swaziland Sweden Switzerland Syria Taiwan Tajikistan Tanzania Thailand The Gambia The Netherlands Timor-Leste Togo Tokelau Tonga Trinidad and Tobago Tunisia Turkey Turkmenistan Turks and Caicos Islands Tuvalu UK USA Uganda Ukraine United Arab Emirates United States Virgin Islands Uruguay Uzbekistan Vanuatu Venezuela Vietnam Wallis and Futuna West Bank and Gaza Strip Western Sahara Yemen Zambia Zimbabwe Please select your country/region. You must enter a comment. Competing Interests Please disclose any competing interests that might be construed to influence your judgment of the article's or peer review report's validity or importance. Competing Interests Policy Provide sufficient details of any financial or non-financial competing interests to enable users to assess whether your comments might lead a reasonable person to question your impartiality. Consider the following examples, but note that this is not an exhaustive list: Examples of 'Non-Financial Competing Interests' Within the past 4 years, you have held joint grants, published or collaborated with any of the authors of the selected paper. You have a close personal relationship (e.g. parent, spouse, sibling, or domestic partner) with any of the authors. You are a close professional associate of any of the authors (e.g. scientific mentor, recent student). You work at the same institute as any of the authors. You hope/expect to benefit (e.g. favour or employment) as a result of your submission. You are an Editor for the journal in which the article is published. Examples of 'Financial Competing Interests' You expect to receive, or in the past 4 years have received, any of the following from any commercial organisation that may gain financially from your submission: a salary, fees, funding, reimbursements. You expect to receive, or in the past 4 years have received, shared grant support or other funding with any of the authors. You hold, or are currently applying for, any patents or significant stocks/shares relating to the subject matter of the paper you are commenting on. Please state your competing interests The comment has been saved. An error has occurred. Please try again. Cancel Post var lTitle = "scRepertoire: An R-based toolkit for single-cell...".replace("'", ''); var linkedInUrl = "http://www.linkedin.com/shareArticle?url=https://f1000research.com/articles/9-47/v2" + "&title=" + encodeURIComponent(lTitle) + "&summary=" + encodeURIComponent('Read the article by '); var deliciousUrl = "https://del.icio.us/post?url=https://f1000research.com/articles/9-47/v2&title=" + encodeURIComponent(lTitle); var redditUrl = "http://reddit.com/submit?url=https://f1000research.com/articles/9-47/v2" + "&title=" + encodeURIComponent(lTitle); linkedInUrl += encodeURIComponent('Borcherding N et al.'); var offsetTop = /chrome/i.test( navigator.userAgent ) ? 4 : -10; var addthis_config = { ui_offset_top: offsetTop, services_compact : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_expanded : "facebook,twitter,www.linkedin.com,www.mendeley.com,reddit.com", services_custom : [ { name: "LinkedIn", url: linkedInUrl, icon:"/img/icon/at_linkedin.svg" }, { name: "Mendeley", url: "http://www.mendeley.com/import/?url=https://f1000research.com/articles/9-47/v2/mendeley", icon:"/img/icon/at_mendeley.svg" }, { name: "Reddit", url: redditUrl, icon:"/img/icon/at_reddit.svg" }, ] }; var addthis_share = { url: "https://f1000research.com/articles/9-47", templates : { twitter : "scRepertoire: An R-based toolkit for single-cell immune receptor.... Borcherding N et al., published by " + "@F1000Research" + ", https://f1000research.com/articles/9-47/v2" } }; if (typeof(addthis) != "undefined"){ addthis.addEventListener('addthis.ready', checkCount); addthis.addEventListener('addthis.menu.share', checkCount); } $(".f1r-shares-twitter").attr("href", "https://twitter.com/intent/tweet?text=" + addthis_share.templates.twitter); $(".f1r-shares-facebook").attr("href", "https://www.facebook.com/sharer/sharer.php?u=" + addthis_share.url); $(".f1r-shares-linkedin").attr("href", addthis_config.services_custom[0].url); $(".f1r-shares-reddit").attr("href", addthis_config.services_custom[2].url); $(".f1r-shares-mendelay").attr("href", addthis_config.services_custom[1].url); function checkCount(){ setTimeout(function(){ $(".addthis_button_expanded").each(function(){ var count = $(this).text(); if (count !== "" && count != "0") $(this).removeClass("is-hidden"); else $(this).addClass("is-hidden"); }); }, 1000); } close How to cite this report {{reportCitation}} Cancel Copy Citation Details $(function(){R.ui.buttonDropdowns('.dropdown-for-downloads');}); $(function(){R.ui.toolbarDropdowns('.toolbar-dropdown-for-downloads');}); $.get("/articles/acj/22139/27282") new F1000.Clipboard(); new F1000.ThesaurusTermsDisplay("articles", "article", "27282"); $(document).ready(function() { $( "#frame1" ).on('load', function() { var mydiv = $(this).contents().find("div"); var h = mydiv.height(); console.log(h) }); var tooltipLivingFigure = jQuery(".interactive-living-figure-label .icon-more-info"), titleLivingFigure = tooltipLivingFigure.attr("title"); tooltipLivingFigure.simpletip({ fixed: true, position: ["-115", "30"], baseClass: 'small-tooltip', content:titleLivingFigure + " " }); tooltipLivingFigure.removeAttr("title"); $("body").on("click", ".cite-living-figure", function(e) { e.preventDefault(); var ref = $(this).attr("data-ref"); $(this).closest(".living-figure-list-container").find("#" + ref).fadeIn(200); }); $("body").on("click", ".close-cite-living-figure", function(e) { e.preventDefault(); $(this).closest(".popup-window-wrapper").fadeOut(200); }); $(document).on("mouseup", function(e) { var metricsContainer = $(".article-metrics-popover-wrapper"); if (!metricsContainer.is(e.target) && metricsContainer.has(e.target).length === 0) { $(".article-metrics-close-button").click(); } }); var articleId = $('#articleId').val(); if($("#main-article-count-box").attachArticleMetrics) { $("#main-article-count-box").attachArticleMetrics(articleId, { articleMetricsView: true }); } }); var figshareWidget = $(".new_figshare_widget"); if (figshareWidget.length > 0) { window.figshare.load("f1000", function(Widget) { // Select a tag/tags defined in your page. In this tag we will place the widget. _.map(figshareWidget, function(el){ var widget = new Widget({ articleId: $(el).attr("figshare_articleId") //height:300 // this is the height of the viewer part. [Default: 550] }); widget.initialize(); // initialize the widget widget.mount(el); // mount it in a tag that's on your page // this will save the widget on the global scope for later use from // your JS scripts. This line is optional. //window.widget = widget; }); }); } close Error Close Add Reset F1000.MICROSERVICES.AFFILIATION = ''; $(document).ready(function () { $('.js-affiliations-form').each((index, form) => { new AffiliationForm({ formId: form.id, institutionErrorSelector: '.comment-enter-institution', departmentErrorSelector: '.comment-enter-department', placeSelector: '.js-add-comment-place', stateSelector: '.js-add-comment-state', zipCodeSelector: '.js-add-comment-zipcode', countrySelector: '.js-add-comment-country', countryErrorSelector: '.comment-enter-country', }); }); }); $(document).ready(function () { var reportIds = { "64820": 34, "64821": 30, "59208": 0, "59209": 70, "60873": 69, "59210": 0, }; $(".referee-response-container,.js-referee-report").each(function(index, el) { var reportId = $(el).attr("data-reportid"), reportCount = reportIds[reportId] || 0; $(el).find(".comments-count-container,.js-referee-report-views").html(reportCount); }); var uuidInput = $("#article_uuid"), oldUUId = uuidInput.val(), newUUId = "050d586b-8e65-428f-956a-bea37a8f002c"; uuidInput.val(newUUId); $("a[href*='article_uuid=']").each(function(index, el) { var newHref = $(el).attr("href").replace(oldUUId, newUUId); $(el).attr("href", newHref); }); }); An innovative open access publishing platform offering rapid publication and open peer review, whilst supporting data deposition and sharing. Browse Gateways Collections How it Works Contact For Developers Cookie Notice Privacy Notice RSS Submit Your Research Follow us © 2012-2026 F1000 Research Ltd. ISSN 2046-1402 | Legal | Partner of Research4Life • CrossRef • ORCID • FAIRSharing R.templateTests.simpleTemplate = R.template(' $text $text $text $text $text '); R.templateTests.runTests(); var F1000platform = new F1000.Platform({ name: "f1000research", displayName: "F1000Research", hostName: "f1000research.com", id: "1", editorialEmail: "[email protected]", infoEmail: "[email protected]", usePmcStats: true }); $(function(){R.ui.dropdowns('.dropdown-for-authors, .dropdown-for-about, .dropdown-for-myresearch');}); // $(function(){R.ui.dropdowns('.dropdown-for-referees');}); $(document).ready(function () { if ($(".cookie-warning").is(":visible")) { $(".sticky").css("margin-bottom", "35px"); $(".devices").addClass("devices-and-cookie-warning"); } $(".cookie-warning .close-button").click(function (e) { $(".devices").removeClass("devices-and-cookie-warning"); $(".sticky").css("margin-bottom", "0"); }); $("#tweeter-feed .tweet-message").each(function (i, message) { var self = $(message); self.html(linkify(self.html())); }); $(".partner").on("mouseenter mouseleave", function() { $(this).find(".gray-scale, .colour").toggleClass("is-hidden"); }); }); Sign In Remember me Forgotten your password? Sign In Cancel Email or password not correct. Please try again Please wait... $(function(){ // Note: All the setup needs to run against a name attribute and *not* the id due the clonish // nature of facebox... $("a[id=googleSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("GOOGLE"); $("form[id=oAuthForm]").submit(); }); $("a[id=facebookSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("FACEBOOK"); $("form[id=oAuthForm]").submit(); }); $("a[id=orcidSignInButton]").click(function(event){ event.preventDefault(); $("input[id=oAuthSystem]").val("ORCID"); $("form[id=oAuthForm]").submit(); }); }); If you've forgotten your password, please enter your email address below and we'll send you instructions on how to reset your password. The email address should be the one you originally registered with F1000. Email address not valid, please try again You registered with F1000 via Google, so we cannot reset your password. To sign in, please click here . If you still need help with your Google account password, please click here . You registered with F1000 via Facebook, so we cannot reset your password. To sign in, please click here . If you still need help with your Facebook account password, please click here . Code not correct, please try again Reset password Cancel Email us for further assistance. Server error, please try again. If your email address is registered with us, we will email you instructions to reset your password. If you think you should have received this email but it has not arrived, please check your spam filters and/or contact for further assistance. Please wait... Register $(document).ready(function () { signIn.createSignInAsRow($("#sign-in-form-gfb-popup")); $(".target-field").each(function () { var uris = $(this).val().split("/"); if (uris.pop() === "login") { $(this).val(uris.toString().replace(",","/")); } }); });