Integration of Bulk RNA-seq Pipeline Metrics for Assessing Low-Quality Samples

Integration of Bulk RNA-seq Pipeline Metrics for Assessing Low-Quality Samples | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (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;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Integration of Bulk RNA-seq Pipeline Metrics for Assessing Low-Quality Samples Samuel Hamilton, Gaurav Gadhvi, Tyler Therron, Deborah R. Winter This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6976695/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 27 Jan, 2026 Read the published version in BMC Bioinformatics → Version 1 posted 10 You are reading this latest preprint version Abstract Background With the rise of RNA-seq as an essential and ubiquitous tool for biomedical research, the need for guidelines on quality control (QC) is pressing. Specifically, there remains limited data as to which technical metrics are most informative in identifying low-quality samples. Results Here, we addressed this issue by developing the Quality Control Diagnostic Renderer (QC-DR), software designed to simultaneously visualize a comprehensive panel of QC metrics generated by an RNA-seq pipeline and flag samples with aberrant values when compared to a reference dataset. As an example, we applied QC-DR to the Successful Clinical Response in Pneumonia Therapy (SCRIPT) dataset, a large clinical RNA-seq dataset of sequenced alveolar macrophages (n = 252). Next, we used this dataset to assess relationships between a variety of QC metrics and sample quality. Among the most highly correlated pipeline QC metrics were % and # Uniquely Aligned Reads , % rRNA reads , # Detected Genes , and our newly developed metric of Area Under the Gene Body Coverage Curve (AUC-GBC ), while experimental QC metrics derived from the lab were not significantly correlated. We then trained a set of machine learning models on the SCRIPT dataset to evaluate the relative contribution of QC metrics to sample quality prediction. Our model performs well when tested on an independent dataset despite differences in the distribution of QC metrics. Conclusions Our results support the conclusion that any individual QC metric is limited in its predictive value and suggests approaches based on the integration of multiple metrics with QC thresholds. In summary, our work provides new insights, practical guidance, and novel QC software which can be used to improve the methodological rigor of RNA-seq studies. Quality Control RNA-seq open-source software machine learning technical bias Full Text Additional Declarations No competing interests reported. Supplementary Files ExtendedData1.pdf ADDITIONAL FILES: Extended Data 1: QC-DR Example (SCRIPT). QC-DR Applied to an example batch of the SCRIPT Dataset with Default Settings. SupplementaryTable1.csv Supplementary Table 1. Example Input File of QC Metrics for QC-DR Using SCRIPT Dataset SupplementaryTable2.xlsx Supplementary Table 2. Example Gene Expression Distribution Input File for QC-DR Using SCRIPT Dataset SupplementaryTable3.csv Supplementary Table 3. Example Gene Base Coverage Input File for QC-DR Using SCRIPT Dataset SupplementaryTable4.xlsx Supplementary Table 4. Example QC Cutoffs Input/Output File for QC-DR Using SCRIPT Dataset SupplementaryTable5.csv Supplementary Table 5. Example QC Flags Output File for QC-DR Using SCRIPT Dataset SupplementaryTable6.csv Supplementary Table 6. Experimental QC, Pipeline QC, and Endpoint Metrics for SCRIPT Dataset SupplementaryTable7.xlsx Supplementary Table 7. List and Source of Macrophage Marker Genes and Non-Macrophage Contaminating Genes SupplementaryTable8.csv Supplementary Table 8. Random Forest Model Quality Score and Predictions for SCRIPT Dataset SupplementaryTable9.csv Supplementary Table 9. Pipeline QC and Endpoint Metrics for Lung Transplant Dataset SupplementaryTable10.csv Supplementary Table 10. Random Forest Model Quality Score and Predictions for Lung Transplant Dataset SupplementaryTable11.xlsx Supplementary Table 11. Example Custom QC Cutoffs for QC-DR Using Lung Transplant Dataset ExtendedData2.pdf Extended Data 2: QC-DR Example (Lung Transplant). Example of QC-DR Applied to Lung Transplant Dataset Using Custom Cutoffs. Cite Share Download PDF Status: Published Journal Publication published 27 Jan, 2026 Read the published version in BMC Bioinformatics → Version 1 posted Editorial decision: Revision requested 28 Jul, 2025 Reviews received at journal 24 Jul, 2025 Reviews received at journal 21 Jul, 2025 Reviewers agreed at journal 02 Jul, 2025 Reviewers agreed at journal 01 Jul, 2025 Reviewers invited by journal 01 Jul, 2025 Editor assigned by journal 01 Jul, 2025 Editor invited by journal 30 Jun, 2025 Submission checks completed at journal 27 Jun, 2025 First submitted to journal 27 Jun, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6976695","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":479833584,"identity":"7f88deba-2415-4118-a355-70b38780bdbd","order_by":0,"name":"Samuel Hamilton","email":"","orcid":"","institution":"Northwestern University","correspondingAuthor":false,"prefix":"","firstName":"Samuel","middleName":"","lastName":"Hamilton","suffix":""},{"id":479833585,"identity":"0397a35b-1234-419a-b842-b0548eaa86be","order_by":1,"name":"Gaurav Gadhvi","email":"","orcid":"","institution":"Northwestern University","correspondingAuthor":false,"prefix":"","firstName":"Gaurav","middleName":"","lastName":"Gadhvi","suffix":""},{"id":479833586,"identity":"e334dc35-7948-490a-b6ee-db7318176b9a","order_by":2,"name":"Tyler Therron","email":"","orcid":"","institution":"Northwestern University","correspondingAuthor":false,"prefix":"","firstName":"Tyler","middleName":"","lastName":"Therron","suffix":""},{"id":479833587,"identity":"415f7202-c26a-439c-ab42-8266bfa40884","order_by":3,"name":"Deborah R. 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Specifically, there remains limited data as to which technical metrics are most informative in identifying low-quality samples.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eHere, we addressed this issue by developing the Quality Control Diagnostic Renderer (QC-DR), software designed to simultaneously visualize a comprehensive panel of QC metrics generated by an RNA-seq pipeline and flag samples with aberrant values when compared to a reference dataset. As an example, we applied QC-DR to the Successful Clinical Response in Pneumonia Therapy (SCRIPT) dataset, a large clinical RNA-seq dataset of sequenced alveolar macrophages (n\u0026thinsp;=\u0026thinsp;252). Next, we used this dataset to assess relationships between a variety of QC metrics and sample quality. Among the most highly correlated pipeline QC metrics were \u003cem\u003e%\u003c/em\u003e and \u003cem\u003e# Uniquely Aligned Reads\u003c/em\u003e, \u003cem\u003e% rRNA reads\u003c/em\u003e, \u003cem\u003e# Detected Genes\u003c/em\u003e, and our newly developed metric of \u003cem\u003eArea Under the Gene Body Coverage Curve (AUC-GBC\u003c/em\u003e), while experimental QC metrics derived from the lab were not significantly correlated. We then trained a set of machine learning models on the SCRIPT dataset to evaluate the relative contribution of QC metrics to sample quality prediction. Our model performs well when tested on an independent dataset despite differences in the distribution of QC metrics.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results support the conclusion that any individual QC metric is limited in its predictive value and suggests approaches based on the integration of multiple metrics with QC thresholds. In summary, our work provides new insights, practical guidance, and novel QC software which can be used to improve the methodological rigor of RNA-seq studies.\u003c/p\u003e","manuscriptTitle":"Integration of Bulk RNA-seq Pipeline Metrics for Assessing Low-Quality Samples","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-03 12:43:37","doi":"10.21203/rs.3.rs-6976695/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-07-28T11:35:04+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-24T20:48:59+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-21T12:15:42+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"270973582030327318754771702723333050100","date":"2025-07-02T21:48:16+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157276180728936098235808632739265883717","date":"2025-07-01T13:35:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-07-01T08:19:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-01T08:13:07+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-06-30T14:59:19+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-06-27T14:39:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Bioinformatics","date":"2025-06-27T14:34:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-bioinformatics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"binf","sideBox":"Learn more about [BMC Bioinformatics](http://bmcbioinformatics.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/binf","title":"BMC Bioinformatics","twitterHandle":"@BMC_Bioinformatics","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"65ccba56-e367-4803-8f1e-ab8d2df482c3","owner":[],"postedDate":"July 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-02-02T16:02:21+00:00","versionOfRecord":{"articleIdentity":"rs-6976695","link":"https://doi.org/10.1186/s12859-025-06298-8","journal":{"identity":"bmc-bioinformatics","isVorOnly":false,"title":"BMC Bioinformatics"},"publishedOn":"2026-01-27 15:58:06","publishedOnDateReadable":"January 27th, 2026"},"versionCreatedAt":"2025-07-03 12:43:37","video":"","vorDoi":"10.1186/s12859-025-06298-8","vorDoiUrl":"https://doi.org/10.1186/s12859-025-06298-8","workflowStages":[]},"version":"v1","identity":"rs-6976695","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6976695","identity":"rs-6976695","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}