Variability in donor leukocyte counts confound the use of common RNA sequencing data normalization strategies in transcriptomic biomarker studies performed with whole blood
Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used...
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| Veröffentlicht in: | Scientific reports Jg. 13; H. 1; S. 15514 - 14 |
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| Abstract | Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition. |
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| AbstractList | Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition. Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition.Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition. Abstract Gene expression data generated from whole blood via next generation sequencing is frequently used in studies aimed at identifying mRNA-based biomarker panels with utility for diagnosis or monitoring of human disease. These investigations often employ data normalization techniques more typically used for analysis of data originating from solid tissues, which largely operate under the general assumption that specimens have similar transcriptome composition. However, this assumption may be violated when working with data generated from whole blood, which is more cellularly dynamic, leading to potential confounds. In this study, we used next generation sequencing in combination with flow cytometry to assess the influence of donor leukocyte counts on the transcriptional composition of whole blood specimens sampled from a cohort of 138 human subjects, and then subsequently examined the effect of four frequently used data normalization approaches on our ability to detect inter-specimen biological variance, using the flow cytometry data to benchmark each specimens true cellular and molecular identity. Whole blood samples originating from donors with differing leukocyte counts exhibited dramatic differences in both genome-wide distributions of transcript abundance and gene-level expression patterns. Consequently, three of the normalization strategies we tested, including median ratio (MRN), trimmed mean of m-values (TMM), and quantile normalization, noticeably masked the true biological structure of the data and impaired our ability to detect true interspecimen differences in mRNA levels. The only strategy that improved our ability to detect true biological variance was simple scaling of read counts by sequencing depth, which unlike the aforementioned approaches, makes no assumptions regarding transcriptome composition. |
| ArticleNumber | 15514 |
| Author | O’Connell, Grant C. |
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| Cites_doi | 10.1586/erc.12.159 10.1073/pnas.252784499 10.1056/NEJMoa0912965 10.1016/j.jbi.2018.07.016 10.1186/gb-2010-11-3-r25 10.1186/s12864-015-2353-z 10.1186/s12864-020-07304-4 10.1186/s12883-019-1245-2 10.1093/bioinformatics/btp616 10.1186/1471-2105-9-559 10.1038/npjgenmed.2016.38 10.1093/bib/bbs046 10.1038/s41598-020-67708-w 10.1038/s41587-019-0201-4 10.1038/nature09247 10.1093/bib/bbx008 10.1186/1471-2164-11-96 10.1093/labmed/lmx035 10.1371/journal.pone.0026905 10.1126/science.aax9198 10.1093/biostatistics/kxr031 10.1038/s41598-019-56218-z 10.1186/1471-2164-7-115 10.1111/ajt.15011 10.1186/s13104-016-2335-5 10.1186/s13059-022-02648-4 10.1155/2015/621690 10.1007/s12975-018-0623-1 10.1093/nar/gkz114 10.1016/j.molmed.2007.08.003 10.1177/14604086211034008 10.1038/ni1310 10.1073/pnas.0610204104 10.1186/s13059-014-0550-8 10.1515/tnsci-2018-0024 10.1056/NEJMoa040465 10.1186/s13054-020-03374-8 10.1016/j.ajem.2019.10.023 10.1186/1471-2105-11-27 10.1186/s12859-019-3247-x 10.1093/ajcp/53.5.647 10.1038/s41598-020-59516-z 10.4161/cib.25849 10.1038/npre.2010.4282.2 10.1080/10618600.1996.10474713 |
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| References | Langfelder, Horvath (CR44) 2008; 9 Arora, Pattwell, Holland, Bolouri (CR10) 2020; 10 Love, Huber, Anders (CR13) 2014; 15 Evans, Hardin, Stoebel (CR16) 2018; 19 Maza, Frasse, Senin, Bouzayen, Zouine (CR32) 2013; 6 Qiu, Fitzgerald, Mitra (CR25) 2022; 24 Hemond, Glanz, Bakshi, Chitnis, Healy (CR26) 2019; 19 Pham (CR6) 2010; 362 O’Connell (CR4) 2016; 1 O’Connell, Chang (CR45) 2018; 9 Li (CR20) 2020; 24 Kim, Paggi, Park, Bennett, Salzberg (CR42) 2019; 37 CR31 Bhat (CR23) 2013; 11 Mohr, Liew (CR1) 2007; 13 Jang (CR17) 2020; 21 Rha, Kim, Yoon, Cho (CR24) 2020; 10 Zyprych-Walczak (CR34) 2015; 2015 Jeffrey (CR41) 2006; 7 Ross, Robert, Ihaka, Gentleman (CR15) 1996; 5 Lin (CR33) 2016; 17 Scherzer (CR5) 2007; 104 Li, Witten, Johnstone, Tibshirani (CR8) 2012; 13 Abrams, Johnson, Huang, Payne, Coombes (CR9) 2019; 20 Robinson, McCarthy, Smyth (CR14) 2010; 26 Whitney (CR35) 2003; 100 Dillies (CR11) 2013; 14 Li, Ge, Peng, Li, Li (CR29) 2022; 23 Friedewald (CR7) 2019; 19 Repsilber (CR28) 2010; 11 Robinson, Oshlack (CR30) 2010; 11 CR46 Uhlen (CR40) 2019; 366 Valk (CR2) 2004; 350 O’Connell (CR38) 2017; 48 Min (CR36) 2010; 11 Xu (CR37) 2011; 6 O’Connell (CR39) 2018 Berry (CR3) 2010; 466 Liao, Smyth, Shi (CR43) 2019; 47 Howard, Kanetsky, Egan (CR21) 2019; 9 Palmer, Diehn, Alizadeh, Brown (CR27) 2006; 7 Han, Men (CR12) 2018; 85 Huang, Fu, Huang, Huang (CR22) 2020; 38 Orfanakis, Ostlund, Bishop, Athens (CR18) 1970; 53 Forget (CR19) 2017; 10 CC Hemond (41443_CR26) 2019; 19 Y Li (41443_CR29) 2022; 23 E Maza (41443_CR32) 2013; 6 GC O’Connell (41443_CR39) 2018 MI Love (41443_CR13) 2014; 15 JL Min (41443_CR36) 2010; 11 Q Xu (41443_CR37) 2011; 6 P Forget (41443_CR19) 2017; 10 Y Qiu (41443_CR25) 2022; 24 J Zyprych-Walczak (41443_CR34) 2015; 2015 MD Robinson (41443_CR14) 2010; 26 MD Robinson (41443_CR30) 2010; 11 GC O’Connell (41443_CR45) 2018; 9 S Arora (41443_CR10) 2020; 10 Y Lin (41443_CR33) 2016; 17 M Uhlen (41443_CR40) 2019; 366 CR Scherzer (41443_CR5) 2007; 104 X Li (41443_CR20) 2020; 24 Y Liao (41443_CR43) 2019; 47 MX Pham (41443_CR6) 2010; 362 H Han (41443_CR12) 2018; 85 NG Orfanakis (41443_CR18) 1970; 53 D Kim (41443_CR42) 2019; 37 41443_CR46 Z Huang (41443_CR22) 2020; 38 C Palmer (41443_CR27) 2006; 7 KL Jeffrey (41443_CR41) 2006; 7 JJ Friedewald (41443_CR7) 2019; 19 JS Jang (41443_CR17) 2020; 21 GC O’Connell (41443_CR38) 2017; 48 MPR Berry (41443_CR3) 2010; 466 I Ross (41443_CR15) 1996; 5 P Langfelder (41443_CR44) 2008; 9 C Evans (41443_CR16) 2018; 19 ZB Abrams (41443_CR9) 2019; 20 M-A Dillies (41443_CR11) 2013; 14 T Bhat (41443_CR23) 2013; 11 R Howard (41443_CR21) 2019; 9 AR Whitney (41443_CR35) 2003; 100 D Repsilber (41443_CR28) 2010; 11 41443_CR31 S Mohr (41443_CR1) 2007; 13 J Li (41443_CR8) 2012; 13 M-S Rha (41443_CR24) 2020; 10 PJM Valk (41443_CR2) 2004; 350 GC O’Connell (41443_CR4) 2016; 1 |
| References_xml | – volume: 11 start-page: 55 year: 2013 end-page: 59 ident: CR23 article-title: Neutrophil to lymphocyte ratio and cardiovascular diseases: A review publication-title: Expert Rev. Cardiovasc. Ther. doi: 10.1586/erc.12.159 – volume: 100 start-page: 1896 year: 2003 end-page: 1901 ident: CR35 article-title: Individuality and variation in gene expression patterns in human blood publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.252784499 – volume: 362 start-page: 1890 year: 2010 end-page: 1900 ident: CR6 article-title: Gene-expression profiling for rejection surveillance after cardiac transplantation publication-title: N. Engl. J. Med. doi: 10.1056/NEJMoa0912965 – volume: 85 start-page: 80 year: 2018 end-page: 92 ident: CR12 article-title: How does normalization impact RNA-seq disease diagnosis? publication-title: J. Biomed. Inform. doi: 10.1016/j.jbi.2018.07.016 – volume: 11 start-page: R25 year: 2010 ident: CR30 article-title: A scaling normalization method for differential expression analysis of RNA-seq data publication-title: Genome Biol. doi: 10.1186/gb-2010-11-3-r25 – volume: 17 start-page: 28 year: 2016 ident: CR33 article-title: Comparison of normalization and differential expression analyses using RNA-Seq data from 726 individual publication-title: BMC Genomics doi: 10.1186/s12864-015-2353-z – volume: 21 start-page: 890 year: 2020 ident: CR17 article-title: Comparative evaluation for the globin gene depletion methods for mRNA sequencing using the whole blood-derived total RNAs publication-title: BMC Genomics doi: 10.1186/s12864-020-07304-4 – volume: 19 start-page: 23 year: 2019 ident: CR26 article-title: The neutrophil-to-lymphocyte and monocyte-to-lymphocyte ratios are independently associated with neurological disability and brain atrophy in multiple sclerosis publication-title: BMC Neurol. doi: 10.1186/s12883-019-1245-2 – volume: 26 start-page: 139 year: 2010 end-page: 140 ident: CR14 article-title: edgeR: A Bioconductor package for differential expression analysis of digital gene expression data publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp616 – volume: 5 start-page: 299 year: 1996 end-page: 314 ident: CR15 article-title: R: A language for data analysis and graphics publication-title: J. Comput. Graph. Stat. – volume: 9 start-page: 559 year: 2008 ident: CR44 article-title: WGCNA: An R package for weighted correlation network analysis publication-title: BMC Bioinform. doi: 10.1186/1471-2105-9-559 – volume: 1 start-page: 16038 year: 2016 end-page: 16038 ident: CR4 article-title: Machine-learning approach identifies a pattern of gene expression in peripheral blood that can accurately detect ischaemic stroke publication-title: npj Genom. Med. doi: 10.1038/npjgenmed.2016.38 – volume: 14 start-page: 671 year: 2013 end-page: 683 ident: CR11 article-title: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis publication-title: Brief. Bioinform. doi: 10.1093/bib/bbs046 – volume: 10 start-page: 10862 year: 2020 ident: CR24 article-title: Association between the neutrophil-to-lymphocyte ratio and obstructive sleep apnea: A meta-analysis publication-title: Sci. Rep. doi: 10.1038/s41598-020-67708-w – volume: 37 start-page: 907 year: 2019 end-page: 915 ident: CR42 article-title: Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype publication-title: Nat. Biotechnol. doi: 10.1038/s41587-019-0201-4 – volume: 466 start-page: 973 year: 2010 end-page: 977 ident: CR3 article-title: An interferon-inducible neutrophil-driven blood transcriptional signature in human tuberculosis publication-title: Nature doi: 10.1038/nature09247 – volume: 19 start-page: 776 year: 2018 end-page: 792 ident: CR16 article-title: Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions publication-title: Brief. Bioinform. doi: 10.1093/bib/bbx008 – volume: 11 start-page: 96 year: 2010 ident: CR36 article-title: Variability of gene expression profiles in human blood and lymphoblastoid cell lines publication-title: BMC Genomics doi: 10.1186/1471-2164-11-96 – volume: 48 start-page: 346 year: 2017 end-page: 356 ident: CR38 article-title: Leukocyte dynamics influence reference gene stability in whole blood: Data-driven qRT-PCR normalization is a robust alternative for measurement of transcriptional biomarkers publication-title: Lab. Med. doi: 10.1093/labmed/lmx035 – volume: 6 start-page: 1 year: 2011 end-page: 11 ident: CR37 article-title: Investigation of variation in gene expression profiling of human blood by extended principle component analysis publication-title: PLoS ONE doi: 10.1371/journal.pone.0026905 – volume: 366 start-page: eaax9198 year: 2019 ident: CR40 article-title: A genome-wide transcriptomic analysis of protein-coding genes in human blood cells publication-title: Science doi: 10.1126/science.aax9198 – volume: 13 start-page: 523 year: 2012 end-page: 538 ident: CR8 article-title: Normalization, testing, and false discovery rate estimation for RNA-sequencing data publication-title: Biostatistics doi: 10.1093/biostatistics/kxr031 – volume: 9 start-page: 19673 year: 2019 ident: CR21 article-title: Exploring the prognostic value of the neutrophil-to-lymphocyte ratio in cancer publication-title: Sci. Rep. doi: 10.1038/s41598-019-56218-z – volume: 7 start-page: 115 year: 2006 ident: CR27 article-title: Cell-type specific gene expression profiles of leukocytes in human peripheral blood publication-title: BMC Genomics doi: 10.1186/1471-2164-7-115 – volume: 19 start-page: 98 year: 2019 end-page: 109 ident: CR7 article-title: Development and clinical validity of a novel blood-based molecular biomarker for subclinical acute rejection following kidney transplant publication-title: Am. J. Transplant. doi: 10.1111/ajt.15011 – ident: CR46 – volume: 10 start-page: 12 year: 2017 ident: CR19 article-title: What is the normal value of the neutrophil-to-lymphocyte ratio? publication-title: BMC Res. Notes doi: 10.1186/s13104-016-2335-5 – volume: 23 start-page: 79 year: 2022 ident: CR29 article-title: Exaggerated false positives by popular differential expression methods when analyzing human population samples publication-title: Genome Biol. doi: 10.1186/s13059-022-02648-4 – volume: 2015 start-page: 1 year: 2015 end-page: 10 ident: CR34 article-title: The impact of normalization methods on RNA-Seq data analysis publication-title: Biomed. Res. Int. doi: 10.1155/2015/621690 – year: 2018 ident: CR39 article-title: Shifts in leukocyte counts drive the differential expression of transcriptional stroke biomarkers in whole blood publication-title: Transl. Stroke Res. doi: 10.1007/s12975-018-0623-1 – volume: 47 start-page: e47 year: 2019 end-page: e47 ident: CR43 article-title: The R package Rsubread is easier, faster, cheaper and better for alignment and quantification of RNA sequencing reads publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkz114 – volume: 13 start-page: 422 year: 2007 end-page: 432 ident: CR1 article-title: The peripheral-blood transcriptome: New insights into disease and risk assessment publication-title: Trends Mol. Med. doi: 10.1016/j.molmed.2007.08.003 – volume: 24 start-page: 195 year: 2022 end-page: 203 ident: CR25 article-title: Association of the neutrophil–lymphocyte ratio to patient outcomes after trauma: A systematic review publication-title: Trauma doi: 10.1177/14604086211034008 – volume: 7 start-page: 274 year: 2006 end-page: 283 ident: CR41 article-title: Positive regulation of immune cell function and inflammatory responses by phosphatase PAC-1 publication-title: Nat. Immunol. doi: 10.1038/ni1310 – volume: 104 start-page: 955 year: 2007 end-page: 960 ident: CR5 article-title: Molecular markers of early Parkinson’s disease based on gene expression in blood publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.0610204104 – volume: 15 start-page: 550 year: 2014 ident: CR13 article-title: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 publication-title: Genome Biol doi: 10.1186/s13059-014-0550-8 – ident: CR31 – volume: 9 start-page: 161 year: 2018 end-page: 166 ident: CR45 article-title: Analysis of early stroke-induced changes in circulating leukocyte counts using transcriptomic deconvolution publication-title: Transl. Neurosci. doi: 10.1515/tnsci-2018-0024 – volume: 350 start-page: 1617 year: 2004 end-page: 1628 ident: CR2 article-title: Prognostically useful gene-expression profiles in acute myeloid leukemia publication-title: N. Engl. J. Med. doi: 10.1056/NEJMoa040465 – volume: 24 start-page: 647 year: 2020 ident: CR20 article-title: Predictive values of neutrophil-to-lymphocyte ratio on disease severity and mortality in COVID-19 patients: A systematic review and meta-analysis publication-title: Crit. Care doi: 10.1186/s13054-020-03374-8 – volume: 38 start-page: 641 year: 2020 end-page: 647 ident: CR22 article-title: Prognostic value of neutrophil-to-lymphocyte ratio in sepsis: A meta-analysis publication-title: Am. J. Emerg. Med. doi: 10.1016/j.ajem.2019.10.023 – volume: 11 start-page: 27 year: 2010 ident: CR28 article-title: Biomarker discovery in heterogeneous tissue samples-taking the in-silico deconfounding approach publication-title: BMC Bioinform. doi: 10.1186/1471-2105-11-27 – volume: 20 start-page: 679 year: 2019 ident: CR9 article-title: A protocol to evaluate RNA sequencing normalization methods publication-title: BMC Bioinform. doi: 10.1186/s12859-019-3247-x – volume: 53 start-page: 647 year: 1970 end-page: 651 ident: CR18 article-title: Normal blood leukocyte concentration values publication-title: Am. J. Clin. Pathol. doi: 10.1093/ajcp/53.5.647 – volume: 10 start-page: 2734 year: 2020 ident: CR10 article-title: Variability in estimated gene expression among commonly used RNA-seq pipelines publication-title: Sci. Rep. doi: 10.1038/s41598-020-59516-z – volume: 6 start-page: e25849 year: 2013 ident: CR32 article-title: Comparison of normalization methods for differential gene expression analysis in RNA-Seq experiments: A matter of relative size of studied transcriptomes publication-title: Commun. Integr. Biol. doi: 10.4161/cib.25849 – ident: 41443_CR46 – volume: 362 start-page: 1890 year: 2010 ident: 41443_CR6 publication-title: N. Engl. J. Med. doi: 10.1056/NEJMoa0912965 – volume: 10 start-page: 10862 year: 2020 ident: 41443_CR24 publication-title: Sci. Rep. doi: 10.1038/s41598-020-67708-w – volume: 17 start-page: 28 year: 2016 ident: 41443_CR33 publication-title: BMC Genomics doi: 10.1186/s12864-015-2353-z – volume: 14 start-page: 671 year: 2013 ident: 41443_CR11 publication-title: Brief. Bioinform. doi: 10.1093/bib/bbs046 – volume: 21 start-page: 890 year: 2020 ident: 41443_CR17 publication-title: BMC Genomics doi: 10.1186/s12864-020-07304-4 – volume: 6 start-page: e25849 year: 2013 ident: 41443_CR32 publication-title: Commun. Integr. Biol. doi: 10.4161/cib.25849 – volume: 104 start-page: 955 year: 2007 ident: 41443_CR5 publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.0610204104 – volume: 53 start-page: 647 year: 1970 ident: 41443_CR18 publication-title: Am. J. Clin. Pathol. doi: 10.1093/ajcp/53.5.647 – volume: 24 start-page: 647 year: 2020 ident: 41443_CR20 publication-title: Crit. Care doi: 10.1186/s13054-020-03374-8 – year: 2018 ident: 41443_CR39 publication-title: Transl. Stroke Res. doi: 10.1007/s12975-018-0623-1 – volume: 466 start-page: 973 year: 2010 ident: 41443_CR3 publication-title: Nature doi: 10.1038/nature09247 – volume: 24 start-page: 195 year: 2022 ident: 41443_CR25 publication-title: Trauma doi: 10.1177/14604086211034008 – volume: 350 start-page: 1617 year: 2004 ident: 41443_CR2 publication-title: N. Engl. J. Med. doi: 10.1056/NEJMoa040465 – volume: 1 start-page: 16038 year: 2016 ident: 41443_CR4 publication-title: npj Genom. Med. doi: 10.1038/npjgenmed.2016.38 – volume: 15 start-page: 550 year: 2014 ident: 41443_CR13 publication-title: Genome Biol doi: 10.1186/s13059-014-0550-8 – volume: 6 start-page: 1 year: 2011 ident: 41443_CR37 publication-title: PLoS ONE doi: 10.1371/journal.pone.0026905 – volume: 26 start-page: 139 year: 2010 ident: 41443_CR14 publication-title: Bioinformatics doi: 10.1093/bioinformatics/btp616 – volume: 7 start-page: 115 year: 2006 ident: 41443_CR27 publication-title: BMC Genomics doi: 10.1186/1471-2164-7-115 – volume: 13 start-page: 523 year: 2012 ident: 41443_CR8 publication-title: Biostatistics doi: 10.1093/biostatistics/kxr031 – volume: 23 start-page: 79 year: 2022 ident: 41443_CR29 publication-title: Genome Biol. doi: 10.1186/s13059-022-02648-4 – volume: 11 start-page: 27 year: 2010 ident: 41443_CR28 publication-title: BMC Bioinform. doi: 10.1186/1471-2105-11-27 – volume: 2015 start-page: 1 year: 2015 ident: 41443_CR34 publication-title: Biomed. Res. Int. doi: 10.1155/2015/621690 – volume: 11 start-page: 96 year: 2010 ident: 41443_CR36 publication-title: BMC Genomics doi: 10.1186/1471-2164-11-96 – volume: 19 start-page: 776 year: 2018 ident: 41443_CR16 publication-title: Brief. Bioinform. doi: 10.1093/bib/bbx008 – ident: 41443_CR31 doi: 10.1038/npre.2010.4282.2 – volume: 9 start-page: 161 year: 2018 ident: 41443_CR45 publication-title: Transl. Neurosci. doi: 10.1515/tnsci-2018-0024 – volume: 13 start-page: 422 year: 2007 ident: 41443_CR1 publication-title: Trends Mol. Med. doi: 10.1016/j.molmed.2007.08.003 – volume: 5 start-page: 299 year: 1996 ident: 41443_CR15 publication-title: J. Comput. Graph. Stat. doi: 10.1080/10618600.1996.10474713 – volume: 20 start-page: 679 year: 2019 ident: 41443_CR9 publication-title: BMC Bioinform. doi: 10.1186/s12859-019-3247-x – volume: 38 start-page: 641 year: 2020 ident: 41443_CR22 publication-title: Am. J. Emerg. Med. doi: 10.1016/j.ajem.2019.10.023 – volume: 11 start-page: 55 year: 2013 ident: 41443_CR23 publication-title: Expert Rev. Cardiovasc. Ther. doi: 10.1586/erc.12.159 – volume: 366 start-page: eaax9198 year: 2019 ident: 41443_CR40 publication-title: Science doi: 10.1126/science.aax9198 – volume: 48 start-page: 346 year: 2017 ident: 41443_CR38 publication-title: Lab. Med. doi: 10.1093/labmed/lmx035 – volume: 9 start-page: 559 year: 2008 ident: 41443_CR44 publication-title: BMC Bioinform. doi: 10.1186/1471-2105-9-559 – volume: 47 start-page: e47 year: 2019 ident: 41443_CR43 publication-title: Nucleic Acids Res. doi: 10.1093/nar/gkz114 – volume: 100 start-page: 1896 year: 2003 ident: 41443_CR35 publication-title: Proc. Natl. Acad. Sci. USA doi: 10.1073/pnas.252784499 – volume: 37 start-page: 907 year: 2019 ident: 41443_CR42 publication-title: Nat. Biotechnol. doi: 10.1038/s41587-019-0201-4 – volume: 11 start-page: R25 year: 2010 ident: 41443_CR30 publication-title: Genome Biol. doi: 10.1186/gb-2010-11-3-r25 – volume: 19 start-page: 98 year: 2019 ident: 41443_CR7 publication-title: Am. J. Transplant. doi: 10.1111/ajt.15011 – volume: 19 start-page: 23 year: 2019 ident: 41443_CR26 publication-title: BMC Neurol. doi: 10.1186/s12883-019-1245-2 – volume: 10 start-page: 2734 year: 2020 ident: 41443_CR10 publication-title: Sci. Rep. doi: 10.1038/s41598-020-59516-z – volume: 9 start-page: 19673 year: 2019 ident: 41443_CR21 publication-title: Sci. Rep. doi: 10.1038/s41598-019-56218-z – volume: 7 start-page: 274 year: 2006 ident: 41443_CR41 publication-title: Nat. Immunol. doi: 10.1038/ni1310 – volume: 85 start-page: 80 year: 2018 ident: 41443_CR12 publication-title: J. Biomed. Inform. doi: 10.1016/j.jbi.2018.07.016 – volume: 10 start-page: 12 year: 2017 ident: 41443_CR19 publication-title: BMC Res. Notes doi: 10.1186/s13104-016-2335-5 |
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| Title | Variability in donor leukocyte counts confound the use of common RNA sequencing data normalization strategies in transcriptomic biomarker studies performed with whole blood |
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