The Elephant in the Analysis: Post-Acquisition Matrix Adjustment and the Transparency Gap in High-Parameter Cytometry

The Elephant in the Analysis: Post-Acquisition Matrix Adjustment and the Transparency Gap in High-Parameter Cytometry | 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 The Elephant in the Analysis: Post-Acquisition Matrix Adjustment and the Transparency Gap in High-Parameter Cytometry Siu-hong Ho This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8889013/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract High-parameter and spectral flow cytometry increase sensitivity to errors in compensation and spectral unmixing. Foundational guidance has emphasized that matrices should be generated to ensure measurement accuracy rather than adjusted based on visual appearance. However, recent community discussions acknowledge that limited post-acquisition correction may be appropriate when transparently reported and its analytical impact documented. This study systematically evaluated reporting practices for post-acquisition compensation or spectral unmixing adjustment across all Optimized Multicolor Immunophenotyping Panels (OMIPs) published in Cytometry Part A (OMIP-001 to OMIP-119). The MIFlowCyt Author Checklist and associated supplementary materials were reviewed for explicit evidence of post-calculation matrix modification. Post-acquisition adjustment was reported in 23 of 119 OMIPs (~ 19%) across both conventional and spectral platforms. Adjustments were most commonly guided by NxN inspection, fluorescence-minus-one controls, and expected biological expression patterns. Reporting detail varied widely and was frequently qualitative. These findings demonstrate variability in current reporting practices and support the need for consistent documentation of matrix evaluation and any post-acquisition modification. Post-acquisition compensation Spectral flow cytometry OMIP MIFlowCyt Reporting transparency 1. Introduction Advances in high-parameter and spectral flow cytometry have expanded analytical capability while increasing sensitivity to spillover and unmixing performance. Foundational guidance emphasizes that compensation should ensure measurement accuracy rather than improve visual appearance and that manual adjustment based solely on plot aesthetics may introduce observer bias ( 1 ). In practice, these recommendations have often been interpreted as an expectation that control-derived compensation or unmixing matrices remain unchanged after calculation. At the same time, practical experience and recent community discussions suggest that residual artifacts may require evaluation and, in some cases, limited correction. Recommendations from the CYTO 2025 Conference Workshop WS08 indicate that any post-acquisition modification should be applied on a case-by-case basis and transparently reported together with its impact on data interpretation ( 2 ). Optimized Multicolor Immunophenotyping Panels (OMIPs) represent a widely used reference for methodological reporting in high-parameter cytometry and include MIFlowCyt-compliant documentation with detailed supplementary materials. These publications provide a useful resource for evaluating current reporting practices. The objective of this study was to systematically assess the prevalence, characteristics, and reporting detail of post-acquisition compensation or spectral unmixing adjustment across OMIPs published in Cytometry Part A. 2. Materials and Methods All OMIPs published in Cytometry Part A from OMIP-001 through OMIP-119 were systematically reviewed using journal archives and the official OMIP index. For each OMIP, the MIFlowCyt Author Checklist and associated supplementary materials were examined for explicit descriptions of post-acquisition compensation or spectral unmixing modification. Data were extracted into structured tables using predefined categories, including platform, presence of adjustment, basis for evaluation, magnitude of adjustment, scope of affected parameter pairs, and reported impact on downstream analysis. Only OMIPs with explicit post-calculation matrix modification were included in the adjustment analysis. 3. Results A total of 119 OMIPs were reviewed. Explicit post-acquisition compensation or spectral unmixing adjustment was reported in 23 OMIPs (approximately 19%), spanning both conventional and spectral workflows (Table 1 ). Both conventional and spectral approaches were represented, with spectral panels predominating among more recent publications. Adjustment procedures were most commonly guided by NxN plot inspection and supported by fluorescence-minus-one controls or expected biological expression patterns. Reporting detail varied substantially. Many OMIPs described adjustments qualitatively using terms such as “minor” or “minimal,” while quantitative reporting was less common. When numerical values were provided, matrix modifications were typically limited in magnitude (approximately − 3% to + 9%) and affected a small fraction of parameter combinations. Several studies explicitly reported that post-acquisition adjustments did not alter downstream biological interpretation. A summary of reporting characteristics across OMIPs is provided in Table 2 . Table 1 OMIPs reporting post-acquisition compensation or spectral unmixing adjustment OMIP No. Platform Adjustment Basis Magnitude Scope Impact 30 ( 4 ) Conventional Yes Software adjustment NR NR NR 36 ( 5 ) Conventional Yes NxN inspection NR NR NR 44 ( 6 ) Conventional Yes NxN inspection NR NR NR 47 ( 7 ) Conventional Yes NxN inspection NR NR NR 57 ( 8 ) Conventional Yes NxN inspection NR NR NR 61 ( 9 ) Conventional Yes NxN + FMO 18.4→13% Few No effect 69 ( 10 ) Spectral Yes NxN + biology Max 2.8% 16/1560 No effect 70 ( 11 ) Conventional Yes NxN + FMO Minor Few No effect 80 ( 12 ) Conventional Yes NxN + FMO NR NR NR 90 ( 13 ) Conventional Yes Software adjustment NR NR NR 101 ( 14 ) Conventional Yes NxN + FMO Minor NR NR 105 ( 15 ) Spectral Yes Algorithm + NxN −7.4 to 8.4% 28/900 NR 106 ( 16 ) Conventional Yes NxN + FMO NR NR NR 108 ( 17 ) Spectral Yes NxN inspection Minimal NR NR 109 ( 18 ) Spectral Yes NxN + biology −3.3 to 1.78% 8 pairs No effect 111 ( 19 ) Spectral Yes Software adjuster NR NR NR 112 ( 20 ) Spectral Yes NxN + FMO NR 21/840 NR 116 ( 21 ) Spectral Yes Spectral reference adjustment NR NR NR 117 ( 22 ) Spectral Yes NxN + biology −3 to + 2% NR NR 119 ( 23 ) Spectral Yes Manual correction −3 to + 9% 8–11/1260 NR . 1 NR – Not reported Table 2 Summary of reporting characteristics for post-acquisition adjustment Category Observation Total OMIPs reviewed 119 OMIPs with adjustment 23 (~ 19%) Workflow types Conventional and spectral Most common evaluation method NxN inspection Additional criteria used FMO controls; expected biological expression Quantitative reporting Limited Typical magnitude (when reported) Approximately − 3% to + 9% Scope (when reported) Small fraction of parameter pairs Reported impact No change in biological interpretation when assessed * Tables may have a footer. 4. Discussion This systematic evaluation demonstrates that post-acquisition compensation or spectral unmixing adjustment is reported in a subset of OMIPs and reflects routine evaluation of residual artifacts in high-parameter datasets. Although the frequency of reported adjustment is modest, the practice spans both conventional and spectral workflows. Reporting detail varies considerably. Many studies provide qualitative descriptions without defining objective criteria, quantitative magnitude, or analytical impact. When numerical information is available, modifications are typically limited in scope and magnitude and are frequently reported not to affect biological conclusions. Before post-acquisition modification is considered, potential experimental and analytical sources of discrepancy should be evaluated, including control quality, instrument performance stability, reagent characteristics, and acquisition consistency. In many cases, recalculation or optimization of reference controls may resolve observed artifacts. Based on reporting patterns observed across OMIPs, several elements appear important for transparent documentation when post-acquisition compensation or spectral unmixing adjustment is performed. These include: ( 1 ) whether post-acquisition modification was applied; ( 2 ) the method used to evaluate matrix performance (e.g., NxN inspection, fluorescence-minus-one controls, or biological expectations); ( 3 ) the scope of affected parameter pairs; ( 4 ) the magnitude or range of adjustment when applicable; and ( 5 ) assessment of the impact on downstream analysis. Most of these elements reflect information already generated during routine analysis and would add minimal reporting burden while improving interpretability and reproducibility. 5. Conclusions Consistent documentation of matrix evaluation and any subsequent modification would improve transparency, facilitate interpretation, and help distinguish routine technical optimization from inappropriate data manipulation. References Roederer M (2001) Compensation in flow cytometry. Cytometry 45:194–205 Wallace PK et al (2026) Cyt-Geist: Reports of the CYTO 2025 workshops. Cytometry A Lee JA et al (2008) MIFlowCyt: the minimum information about a flow cytometry experiment. Cytometry A 73A:926–930 Wingender G, Kronenberg M (2015) OMIP-030: Characterization of human T cell subsets via surface markers. Cytometry A 87A:1067–1069. 10.1002/cyto.a.22788 Healy ZR, Murdoch DM (2016) OMIP-036: Co-inhibitory receptor (immune checkpoint) expression analysis in human T cell subsets. Cytometry A 89A:889–892. 10.1002/cyto.a.22938 Mair F, Prlic M (2018) OMIP-044: 28-color immunophenotyping of the human dendritic cell compartment. Cytometry A 93A:402–405. 10.1002/cyto.a.23331 Liechti T, Günthard HF, Trkola A (2018) OMIP-047: High-dimensional phenotypic characterization of B cells. Cytometry A 93A:592–596. 10.1002/cyto.a.23488 Buus TB, Jee MH, Ødum N (2019) OMIP-057: Mouse γδ T-cell development characterized by a 14 color flow cytometry panel. Cytometry A 95A:726–729. 10.1002/cyto.a.23754 DiPiazza AT, Hill JP, Graham BS, Ruckwardt TJ (2019) OMIP-061: 20-color flow cytometry panel for high-dimensional characterization of murine antigen-presenting cells. Cytometry A 95A:1226–1230. 10.1002/cyto.a.23880 Park LM, Lannigan J, Jaimes MC (2020) OMIP-069: Forty-color full spectrum flow cytometry panel for deep immunophenotyping of major cell subsets in human peripheral blood. Cytometry A 97A:1044–1051. 10.1002/cyto.a.24213 Frutoso M, Mair F, Prlic M (2020) OMIP-070: NKp46-based 27-color phenotyping to define natural killer cells isolated from human tumor tissues. Cytometry A 97A:1052–1056. 10.1002/cyto.a.24230 Vanikova S, Koladiya A, Musil J (2022) OMIP-080: 29-color flow cytometry panel for comprehensive evaluation of NK and T cells reconstitution after hematopoietic stem cell transplantation. Cytometry A 101A:21–26. 10.1002/cyto.a.24510 Stroukov W, Mastronicolas D, Albany CJ, Catak Z, Lombardi G, Scotta C (2023) OMIP-090: A 20-parameter flow cytometry panel for rapid analysis of cell diversity and homing capacity in human conventional and regulatory T cells. Cytometry A 103A:362–367. 10.1002/cyto.a.24720 Imbratta C, Reid TD, Toefy A, Scriba TJ, Nemes E (2024) OMIP-101: 27-color flow cytometry panel for immunophenotyping of major leukocyte populations in fixed whole blood. Cytometry A 105A:165–170. 10.1002/cyto.a.24827 DeNiro G, Que K, Fujimoto T, Koo SM, Schneider B, Mukhopadhyay A, Kim J, Sawant A, Nguyen TA (2024) OMIP-105: A 30-color full-spectrum flow cytometry panel to characterize the immune cell landscape in spleen and tumor within a syngeneic MC-38 murine colon carcinoma model. Cytometry A 105A:659–665. 10.1002/cyto.a.24886 Musil J, Ptacek A, Vanikova S (2024) OMIP-106: A 30-color panel for analysis of checkpoint inhibitory networks in the bone marrow of acute myeloid leukemia patients. Cytometry A 105A:729–736. 10.1002/cyto.a.24892 Gunes ME, Wolbrom DH, Nygaard ED, Manell E, Jordache P, Qudus S, Cadelina A, Weiner J, Nowak G (2024) OMIP-108: 22-color flow cytometry panel for detection and monitoring of chimerism and immune reconstitution in porcine-to-baboon models of operational xenotransplant tolerance studies. Cytometry A 105A:800–806. 10.1002/cyto.a.24899 Park LM, Lannigan J, Low Q, Jaimes MC, Bonilla DL (2024) OMIP-109: 45-color full spectrum flow cytometry panel for deep immunophenotyping of the major lineages present in human peripheral blood mononuclear cells with emphasis on the T cell memory compartment. Cytometry A 105A:807–815. 10.1002/cyto.a.24900 Barman S, Kelly A, Dong D, Patel A, Buonopane MJ, Gonzales J, Janoschek B, Draghi A 2nd, Dowling DJ (2025) OMIP-111: Immune-profiling of T helper 1 (Th1), Th2, and Th17 signatures in murine splenocytes by targeting intracellular cytokines. Cytometry A 107A:221–225. 10.1002/cyto.a.24926 Waaijer LA, van Cranenbroek B, Koenen HJPM (2025) OMIP-112: 42-parameter (40-color) spectral flow cytometry panel for comprehensive immunophenotyping of human peripheral blood leukocytes. Cytometry A 107A:226–232. 10.1002/cyto.a.24927 Balin S, Marzano P, Manganaro D, Villa A, Zucali PA, Mavilio D, Della Bella S (2025) OMIP-116: A 39-color full spectrum flow cytometric panel to deeply characterize human thymopoiesis. Cytometry A 107A:501–507. 10.1002/cyto.a.24951 Venglar O, Radova E, Broskevicova L, Hajek R, Jelinek T (2025) OMIP-117: 40-parameter/37-color spectral cytometry panel for robust immunoprofiling of human lymphoid subsets in cancer patients. Cytometry A 107A:641–648. 10.1002/cyto.a.24962 Harris RJ, Arkle B, Evans E, Smith CHV, Teevan R, Bath N (2025) OMIP-119: A 36-color full-spectrum flow cytometry panel for deep immunophenotyping of peripheral blood and ex vivo expanded human T cells. Cytometry A 107A:787–792. 10.1002/cyto.a.70001 Additional Declarations The authors declare no competing interests. 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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-8889013","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591799989,"identity":"d8118480-5673-4366-b728-87e188f9e2a3","order_by":0,"name":"Siu-hong Ho","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYDACZjACAgkGxgdQMQOitTDDlBLQwoDQwiZBlBb5dh4D5oKKO3YN0s3Hqm78qYtmYG/eJoFPi8FhoJYZZ54lN8gcS7ud23Y4t4HnWBl+Lcw85r952w4nM0jkmN3ObTiQ2wBk4NUi3wy0BaalOOdPXW6D/Bv8WhgOQ7TYgbQw57AxA23hwa/F4DBbATPPmcMJbBJpydIgv7TxpBVb4HVY/+ENzDwVh+35JZIPfgY5rJ/98MYbeB0GBYltMBYbMcpBwJ5YhaNgFIyCUTACAQB8RD+myKgaFAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0009-0009-8737-5633","institution":"Icahn School of Medicine at Mount Sinai","correspondingAuthor":true,"prefix":"","firstName":"Siu-hong","middleName":"","lastName":"Ho","suffix":""}],"badges":[],"createdAt":"2026-02-16 01:44:36","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8889013/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8889013/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eThe Elephant in the Analysis: Post-Acquisition Matrix Adjustment and the Transparency Gap in High-Parameter Cytometry\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAdvances in high-parameter and spectral flow cytometry have expanded analytical capability while increasing sensitivity to spillover and unmixing performance. Foundational guidance emphasizes that compensation should ensure measurement accuracy rather than improve visual appearance and that manual adjustment based solely on plot aesthetics may introduce observer bias (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). In practice, these recommendations have often been interpreted as an expectation that control-derived compensation or unmixing matrices remain unchanged after calculation.\u003c/p\u003e \u003cp\u003eAt the same time, practical experience and recent community discussions suggest that residual artifacts may require evaluation and, in some cases, limited correction. Recommendations from the CYTO 2025 Conference Workshop WS08 indicate that any post-acquisition modification should be applied on a case-by-case basis and transparently reported together with its impact on data interpretation (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOptimized Multicolor Immunophenotyping Panels (OMIPs) represent a widely used reference for methodological reporting in high-parameter cytometry and include MIFlowCyt-compliant documentation with detailed supplementary materials. These publications provide a useful resource for evaluating current reporting practices. The objective of this study was to systematically assess the prevalence, characteristics, and reporting detail of post-acquisition compensation or spectral unmixing adjustment across OMIPs published in Cytometry Part A.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eAll OMIPs published in Cytometry Part A from OMIP-001 through OMIP-119 were systematically reviewed using journal archives and the official OMIP index.\u003c/p\u003e \u003cp\u003eFor each OMIP, the MIFlowCyt Author Checklist and associated supplementary materials were examined for explicit descriptions of post-acquisition compensation or spectral unmixing modification. Data were extracted into structured tables using predefined categories, including platform, presence of adjustment, basis for evaluation, magnitude of adjustment, scope of affected parameter pairs, and reported impact on downstream analysis.\u003c/p\u003e \u003cp\u003eOnly OMIPs with explicit post-calculation matrix modification were included in the adjustment analysis.\u003c/p\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 119 OMIPs were reviewed. Explicit post-acquisition compensation or spectral unmixing adjustment was reported in 23 OMIPs (approximately 19%), spanning both conventional and spectral workflows (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth conventional and spectral approaches were represented, with spectral panels predominating among more recent publications. Adjustment procedures were most commonly guided by NxN plot inspection and supported by fluorescence-minus-one controls or expected biological expression patterns.\u003c/p\u003e \u003cp\u003eReporting detail varied substantially. Many OMIPs described adjustments qualitatively using terms such as \u0026ldquo;minor\u0026rdquo; or \u0026ldquo;minimal,\u0026rdquo; while quantitative reporting was less common. When numerical values were provided, matrix modifications were typically limited in magnitude (approximately\u0026thinsp;\u0026minus;\u0026thinsp;3% to +\u0026thinsp;9%) and affected a small fraction of parameter combinations. Several studies explicitly reported that post-acquisition adjustments did not alter downstream biological interpretation. A summary of reporting characteristics across OMIPs is provided in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eOMIPs reporting post-acquisition compensation or spectral unmixing adjustment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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colname=\"c2\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN inspection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e44 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN inspection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e47 (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN inspection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e57 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN inspection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e61 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN\u0026thinsp;+\u0026thinsp;FMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18.4\u0026rarr;13%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e69 (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN\u0026thinsp;+\u0026thinsp;biology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMax 2.8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16/1560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70 (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN\u0026thinsp;+\u0026thinsp;FMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFew\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e80 (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN\u0026thinsp;+\u0026thinsp;FMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e90 (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoftware adjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e101 (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN\u0026thinsp;+\u0026thinsp;FMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e105 (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAlgorithm\u0026thinsp;+\u0026thinsp;NxN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;7.4 to 8.4%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28/900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e106 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN\u0026thinsp;+\u0026thinsp;FMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e108 (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN inspection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMinimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e109 (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN\u0026thinsp;+\u0026thinsp;biology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;3.3 to 1.78%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8 pairs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNo effect\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e111 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSoftware adjuster\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e112 (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN\u0026thinsp;+\u0026thinsp;FMO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e21/840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e116 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpectral reference adjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e117 (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNxN\u0026thinsp;+\u0026thinsp;biology\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;3 to +\u0026thinsp;2%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e119 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSpectral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManual correction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;3 to +\u0026thinsp;9%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u0026ndash;11/1260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e.\u003csup\u003e1\u003c/sup\u003e NR \u0026ndash; Not reported\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of reporting characteristics for post-acquisition adjustment\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObservation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal OMIPs reviewed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOMIPs with adjustment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (~\u0026thinsp;19%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorkflow types\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConventional and spectral\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMost common evaluation method\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNxN inspection\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdditional criteria used\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFMO controls; expected biological expression\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuantitative reporting\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLimited\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTypical magnitude (when reported)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApproximately\u0026thinsp;\u0026minus;\u0026thinsp;3% to +\u0026thinsp;9%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScope (when reported)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall fraction of parameter pairs\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReported impact\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo change in biological interpretation when assessed\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e* Tables may have a footer.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis systematic evaluation demonstrates that post-acquisition compensation or spectral unmixing adjustment is reported in a subset of OMIPs and reflects routine evaluation of residual artifacts in high-parameter datasets. Although the frequency of reported adjustment is modest, the practice spans both conventional and spectral workflows.\u003c/p\u003e \u003cp\u003eReporting detail varies considerably. Many studies provide qualitative descriptions without defining objective criteria, quantitative magnitude, or analytical impact. When numerical information is available, modifications are typically limited in scope and magnitude and are frequently reported not to affect biological conclusions.\u003c/p\u003e \u003cp\u003eBefore post-acquisition modification is considered, potential experimental and analytical sources of discrepancy should be evaluated, including control quality, instrument performance stability, reagent characteristics, and acquisition consistency. In many cases, recalculation or optimization of reference controls may resolve observed artifacts.\u003c/p\u003e \u003cp\u003eBased on reporting patterns observed across OMIPs, several elements appear important for transparent documentation when post-acquisition compensation or spectral unmixing adjustment is performed. These include: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) whether post-acquisition modification was applied; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) the method used to evaluate matrix performance (e.g., NxN inspection, fluorescence-minus-one controls, or biological expectations); (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) the scope of affected parameter pairs; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) the magnitude or range of adjustment when applicable; and (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) assessment of the impact on downstream analysis. Most of these elements reflect information already generated during routine analysis and would add minimal reporting burden while improving interpretability and reproducibility.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eConsistent documentation of matrix evaluation and any subsequent modification would improve transparency, facilitate interpretation, and help distinguish routine technical optimization from inappropriate data manipulation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoederer M (2001) Compensation in flow cytometry. Cytometry 45:194\u0026ndash;205\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWallace PK et al (2026) Cyt-Geist: Reports of the CYTO 2025 workshops. Cytometry A\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee JA et al (2008) MIFlowCyt: the minimum information about a flow cytometry experiment. Cytometry A 73A:926\u0026ndash;930\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWingender G, Kronenberg M (2015) OMIP-030: Characterization of human T cell subsets via surface markers. Cytometry A 87A:1067\u0026ndash;1069. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.22788\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.22788\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHealy ZR, Murdoch DM (2016) OMIP-036: Co-inhibitory receptor (immune checkpoint) expression analysis in human T cell subsets. Cytometry A 89A:889\u0026ndash;892. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.22938\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.22938\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMair F, Prlic M (2018) OMIP-044: 28-color immunophenotyping of the human dendritic cell compartment. 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Cytometry A 95A:1226\u0026ndash;1230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.23880\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.23880\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark LM, Lannigan J, Jaimes MC (2020) OMIP-069: Forty-color full spectrum flow cytometry panel for deep immunophenotyping of major cell subsets in human peripheral blood. Cytometry A 97A:1044\u0026ndash;1051. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24213\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24213\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrutoso M, Mair F, Prlic M (2020) OMIP-070: NKp46-based 27-color phenotyping to define natural killer cells isolated from human tumor tissues. Cytometry A 97A:1052\u0026ndash;1056. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24230\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24230\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVanikova S, Koladiya A, Musil J (2022) OMIP-080: 29-color flow cytometry panel for comprehensive evaluation of NK and T cells reconstitution after hematopoietic stem cell transplantation. Cytometry A 101A:21\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24510\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24510\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStroukov W, Mastronicolas D, Albany CJ, Catak Z, Lombardi G, Scotta C (2023) OMIP-090: A 20-parameter flow cytometry panel for rapid analysis of cell diversity and homing capacity in human conventional and regulatory T cells. Cytometry A 103A:362\u0026ndash;367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24720\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24720\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eImbratta C, Reid TD, Toefy A, Scriba TJ, Nemes E (2024) OMIP-101: 27-color flow cytometry panel for immunophenotyping of major leukocyte populations in fixed whole blood. Cytometry A 105A:165\u0026ndash;170. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24827\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24827\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDeNiro G, Que K, Fujimoto T, Koo SM, Schneider B, Mukhopadhyay A, Kim J, Sawant A, Nguyen TA (2024) OMIP-105: A 30-color full-spectrum flow cytometry panel to characterize the immune cell landscape in spleen and tumor within a syngeneic MC-38 murine colon carcinoma model. Cytometry A 105A:659\u0026ndash;665. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24886\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24886\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMusil J, Ptacek A, Vanikova S (2024) OMIP-106: A 30-color panel for analysis of checkpoint inhibitory networks in the bone marrow of acute myeloid leukemia patients. Cytometry A 105A:729\u0026ndash;736. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24892\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24892\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGunes ME, Wolbrom DH, Nygaard ED, Manell E, Jordache P, Qudus S, Cadelina A, Weiner J, Nowak G (2024) OMIP-108: 22-color flow cytometry panel for detection and monitoring of chimerism and immune reconstitution in porcine-to-baboon models of operational xenotransplant tolerance studies. Cytometry A 105A:800\u0026ndash;806. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24899\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24899\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePark LM, Lannigan J, Low Q, Jaimes MC, Bonilla DL (2024) OMIP-109: 45-color full spectrum flow cytometry panel for deep immunophenotyping of the major lineages present in human peripheral blood mononuclear cells with emphasis on the T cell memory compartment. Cytometry A 105A:807\u0026ndash;815. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24900\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24900\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarman S, Kelly A, Dong D, Patel A, Buonopane MJ, Gonzales J, Janoschek B, Draghi A 2nd, Dowling DJ (2025) OMIP-111: Immune-profiling of T helper 1 (Th1), Th2, and Th17 signatures in murine splenocytes by targeting intracellular cytokines. Cytometry A 107A:221\u0026ndash;225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24926\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24926\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWaaijer LA, van Cranenbroek B, Koenen HJPM (2025) OMIP-112: 42-parameter (40-color) spectral flow cytometry panel for comprehensive immunophenotyping of human peripheral blood leukocytes. Cytometry A 107A:226\u0026ndash;232. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24927\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24927\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalin S, Marzano P, Manganaro D, Villa A, Zucali PA, Mavilio D, Della Bella S (2025) OMIP-116: A 39-color full spectrum flow cytometric panel to deeply characterize human thymopoiesis. Cytometry A 107A:501\u0026ndash;507. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24951\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24951\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenglar O, Radova E, Broskevicova L, Hajek R, Jelinek T (2025) OMIP-117: 40-parameter/37-color spectral cytometry panel for robust immunoprofiling of human lymphoid subsets in cancer patients. Cytometry A 107A:641\u0026ndash;648. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.24962\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.24962\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarris RJ, Arkle B, Evans E, Smith CHV, Teevan R, Bath N (2025) OMIP-119: A 36-color full-spectrum flow cytometry panel for deep immunophenotyping of peripheral blood and ex vivo expanded human T cells. Cytometry A 107A:787\u0026ndash;792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/cyto.a.70001\u003c/span\u003e\u003cspan address=\"10.1002/cyto.a.70001\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Icahn School of Medicine at Mount Sinai","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Post-acquisition compensation, Spectral flow cytometry, OMIP, MIFlowCyt, Reporting transparency","lastPublishedDoi":"10.21203/rs.3.rs-8889013/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8889013/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHigh-parameter and spectral flow cytometry increase sensitivity to errors in compensation and spectral unmixing. Foundational guidance has emphasized that matrices should be generated to ensure measurement accuracy rather than adjusted based on visual appearance. However, recent community discussions acknowledge that limited post-acquisition correction may be appropriate when transparently reported and its analytical impact documented. This study systematically evaluated reporting practices for post-acquisition compensation or spectral unmixing adjustment across all Optimized Multicolor Immunophenotyping Panels (OMIPs) published in \u003cem\u003eCytometry Part A\u003c/em\u003e (OMIP-001 to OMIP-119). The MIFlowCyt Author Checklist and associated supplementary materials were reviewed for explicit evidence of post-calculation matrix modification. Post-acquisition adjustment was reported in 23 of 119 OMIPs (~\u0026thinsp;19%) across both conventional and spectral platforms. Adjustments were most commonly guided by NxN inspection, fluorescence-minus-one controls, and expected biological expression patterns. Reporting detail varied widely and was frequently qualitative. These findings demonstrate variability in current reporting practices and support the need for consistent documentation of matrix evaluation and any post-acquisition modification.\u003c/p\u003e","manuscriptTitle":"The Elephant in the Analysis: Post-Acquisition Matrix Adjustment and the Transparency Gap in High-Parameter Cytometry","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 07:25:49","doi":"10.21203/rs.3.rs-8889013/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b7934177-0c10-470d-a797-607f843b6aaa","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-18T07:25:49+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 07:25:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8889013","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8889013","identity":"rs-8889013","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}