{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T04:06:23Z","timestamp":1773720383107,"version":"3.50.1"},"reference-count":63,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T00:00:00Z","timestamp":1617062400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000092","name":"U.S. National Library of Medicine","doi-asserted-by":"publisher","award":["F31LM013053"],"award-info":[{"award-number":["F31LM013053"]}],"id":[{"id":"10.13039\/100000092","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Stanford Data Science Program"},{"name":"BioX Undergraduate Summer Research Fellowship"},{"name":"NIH","award":["GM102365"],"award-info":[{"award-number":["GM102365"]}]},{"name":"Chan Zuckerberg Biohub"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"published-print":{"date-parts":[[2021,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Women are at more than 1.5-fold higher risk for clinically relevant adverse drug events. While this higher prevalence is partially due to gender-related effects, biological sex differences likely also impact drug response. Publicly available gene expression databases provide a unique opportunity for examining drug response at a cellular level. However, missingness and heterogeneity of metadata prevent large-scale identification of drug exposure studies and limit assessments of sex bias. To address this, we trained organism-specific models to infer sample sex from gene expression data, and used entity normalization to map metadata cell line and drug mentions to existing ontologies. Using this method, we inferred sex labels for 450,371 human and 245,107 mouse microarray and RNA-seq samples from refine.bio.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Overall, we find slight female bias (52.1%) in human samples and (62.5%) male bias in mouse samples; this corresponds to a majority of mixed sex studies in humans and single sex studies in mice, split between female-only and male-only (25.8% vs. 18.9% in human and 21.6% vs. 31.1% in mouse, respectively). In drug studies, we find limited evidence for sex-sampling bias overall; however, specific categories of drugs, including human cancer and mouse nervous system drugs, are enriched in female-only and male-only studies, respectively. We leverage our expression-based sex labels to further examine the complexity of cell line sex and assess the frequency of metadata sex label misannotations (2\u20135%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our results demonstrate limited overall sex bias, while highlighting high bias in specific subfields and underscoring the importance of including sex labels to better understand the underlying biology. We make our inferred and normalized labels, along with flags for misannotated samples, publicly available to catalyze the routine use of sex as a study variable in future analyses.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-021-04070-2","type":"journal-article","created":{"date-parts":[[2021,3,30]],"date-time":"2021-03-30T13:03:01Z","timestamp":1617109381000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Large-scale labeling and assessment of sex bias in publicly available expression data"],"prefix":"10.1186","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9703-3594","authenticated-orcid":false,"given":"Emily","family":"Flynn","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4692-6338","authenticated-orcid":false,"given":"Annie","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3859-2905","authenticated-orcid":false,"given":"Russ B.","family":"Altman","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,3,30]]},"reference":[{"issue":"10","key":"4070_CR1","doi-asserted-by":"publisher","first-page":"999","DOI":"10.1007\/s00228-008-0494-6","volume":"64","author":"Y Zopf","year":"2008","unstructured":"Zopf Y, Rabe C, Neubert A, Gassmann KG, Rascher W, Hahn EG, Brune K, Dormann H. Women encounter ADRs more often than do men. Eur J Clin Pharmacol. 2008;64(10):999\u20131004.","journal-title":"Eur J Clin Pharmacol"},{"issue":"7","key":"4070_CR2","doi-asserted-by":"publisher","first-page":"e196700","DOI":"10.1001\/jamanetworkopen.2019.6700","volume":"2","author":"S Feldman","year":"2019","unstructured":"Feldman S, Ammar W, Lo K, Trepman E, van Zuylen M, Etzioni O. Quantifying sex bias in clinical studies at scale with automated data extraction. JAMA Netw Open. 2019;2(7):e196700.","journal-title":"JAMA Netw Open"},{"issue":"7299","key":"4070_CR3","doi-asserted-by":"publisher","first-page":"688","DOI":"10.1038\/465688a","volume":"465","author":"AM Kim","year":"2010","unstructured":"Kim AM, Tingen CM, Woodruff TK. Sex bias in trials and treatment must end. Nature. 2010;465(7299):688\u20139.","journal-title":"Nature"},{"issue":"11","key":"4070_CR4","doi-asserted-by":"publisher","first-page":"1342","DOI":"10.1089\/jwh.2017.6873","volume":"27","author":"VS Prakash","year":"2018","unstructured":"Prakash VS, Mansukhani NA, Helenowski IB, Woodruff TK, Kibbe MR. Sex bias in interventional clinical trials. J Women\u2019s Health. 2018;27(11):1342\u20138.","journal-title":"J Women\u2019s Health"},{"issue":"July","key":"4070_CR5","doi-asserted-by":"publisher","first-page":"83","DOI":"10.1016\/j.phrs.2017.04.027","volume":"121","author":"C Tannenbaum","year":"2017","unstructured":"Tannenbaum C, Day D, Alliance M. Age and sex in drug development and testing for adults. Pharmacol Res. 2017;121(July):83\u201393.","journal-title":"Pharmacol Res"},{"issue":"3","key":"4070_CR6","doi-asserted-by":"publisher","first-page":"565","DOI":"10.1016\/j.neubiorev.2010.07.002","volume":"35","author":"AK Beery","year":"2011","unstructured":"Beery AK, Zucker I. Sex bias in neuroscience and biomedical research. Neurosci Biobehav Rev. 2011;35(3):565\u201372.","journal-title":"Neurosci Biobehav Rev"},{"issue":"17","key":"4070_CR7","doi-asserted-by":"publisher","first-page":"5257","DOI":"10.1073\/pnas.1502843112","volume":"112","author":"SL Klein","year":"2015","unstructured":"Klein SL, Schiebinger L, Stefanick ML, Cahill L, Danska J, de Vries GJ, Kibbe MR, et al. Opinion: sex inclusion in basic research drives discovery. Proc Natl Acad Sci USA. 2015;112(17):5257\u20138.","journal-title":"Proc Natl Acad Sci USA"},{"issue":"1","key":"4070_CR8","doi-asserted-by":"publisher","first-page":"C3","DOI":"10.1152\/ajpcell.00281.2013","volume":"306","author":"K Shah","year":"2014","unstructured":"Shah K, McCormack CE, Bradbury NA. Do you know the sex of your cells? Am J Physiol Cell Physiol. 2014;306(1):C3-18.","journal-title":"Am J Physiol Cell Physiol"},{"issue":"7500","key":"4070_CR9","doi-asserted-by":"publisher","first-page":"282","DOI":"10.1038\/509282a","volume":"509","author":"JA Clayton","year":"2014","unstructured":"Clayton JA, Collins FS. Policy: NIH to balance sex in cell and animal studies. Nature. 2014;509(7500):282\u20133.","journal-title":"Nature"},{"key":"4070_CR10","doi-asserted-by":"publisher","first-page":"e56344","DOI":"10.7554\/eLife.56344","volume":"9","author":"NC Woitowich","year":"2020","unstructured":"Woitowich NC, Beery A, Woodruff T. Meta-research: a 10-year follow-up study of sex inclusion in the biological sciences. eLife. 2020;9:e56344.","journal-title":"eLife"},{"issue":"4","key":"4070_CR11","doi-asserted-by":"publisher","first-page":"262","DOI":"10.2174\/138920207781386942","volume":"8","author":"MV Chengalvala","year":"2007","unstructured":"Chengalvala MV, Chennathukuzhi VM, Johnston DS, Stevis PE, Kopf GS. Gene expression profiling and its practice in drug development. Curr Genomics. 2007;8(4):262\u201370.","journal-title":"Curr Genomics"},{"issue":"1","key":"4070_CR12","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1038\/JID.2015.298","volume":"136","author":"BY Kong","year":"2016","unstructured":"Kong BY, Haugh IM, Schlosser BJ, Getsios S, Paller AS. Mind the gap: sex bias in basic skin research. J Invest Dermatol. 2016;136(1):12\u20134.","journal-title":"J Invest Dermatol"},{"issue":"April","key":"4070_CR13","doi-asserted-by":"publisher","first-page":"100835","DOI":"10.1016\/j.yfrne.2020.100835","volume":"57","author":"GM Mamlouk","year":"2020","unstructured":"Mamlouk GM, Dorris DM, Barrett LR, Meitzen J. Sex bias and omission in neuroscience research is influenced by research model and journal, but not reported NIH funding. Front Neuroendocrinol. 2020;57(April):100835.","journal-title":"Front Neuroendocrinol"},{"issue":"1\u20132","key":"4070_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.pain.2005.06.020","volume":"117","author":"JS Mogil","year":"2005","unstructured":"Mogil JS, Chanda ML. The case for the inclusion of female subjects in basic science studies of pain. Pain. 2005;117(1\u20132):1\u20135.","journal-title":"Pain"},{"issue":"1","key":"4070_CR15","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1093\/nar\/30.1.207","volume":"30","author":"R Edgar","year":"2002","unstructured":"Edgar R, Domrachev M, Lash AE. Gene expression omnibus: NCBI gene expression and hybridization array data repository. Nucl Acids Res. 2002;30(1):207\u201310.","journal-title":"Nucl Acids Res"},{"issue":"Suppl_1","key":"4070_CR16","first-page":"D19","volume":"39","author":"R Leinonen","year":"2010","unstructured":"Leinonen R, Sugawara H, Shumway M, International Nucleotide Sequence Database Collaboration. The sequence read archive. Nucl Acids Res. 2010;39(Suppl_1):D19-21.","journal-title":"Nucl Acids Res"},{"issue":"1","key":"4070_CR17","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1093\/nar\/gkg091","volume":"31","author":"A Brazma","year":"2003","unstructured":"Brazma A, Parkinson H, Sarkans U, Shojatalab M, Vilo J, Abeygunawardena N, Holloway E, et al. Arrayexpress\u2014a public repository for microarray gene expression data at the EBI. Nucl Acids Res. 2003;31(1):68\u201371.","journal-title":"Nucl Acids Res"},{"issue":"4","key":"4070_CR18","doi-asserted-by":"publisher","first-page":"365","DOI":"10.1038\/ng1201-365","volume":"29","author":"A Brazma","year":"2001","unstructured":"Brazma A, Hingamp P, Quackenbush J, Sherlock G, Spellman P, Stoeckert C, Aach J, et al. Minimum information about a microarray experiment (MIAME)\u2014toward standards for microarray data. Nat Genet. 2001;29(4):365\u201371.","journal-title":"Nat Genet"},{"issue":"12","key":"4070_CR19","doi-asserted-by":"publisher","first-page":"2265","DOI":"10.1007\/s00204-015-1632-4","volume":"89","author":"M Lohr","year":"2015","unstructured":"Lohr M, Hellwig B, Edlund K, Mattsson JSM, Botling J, Schmidt M, Hengstler JG, Micke P, Rahnenf\u00fchrer J. Identification of sample annotation errors in gene expression datasets. Arch Toxicol. 2015;89(12):2265\u201372.","journal-title":"Arch Toxicol"},{"issue":"August","key":"4070_CR20","doi-asserted-by":"publisher","first-page":"2103","DOI":"10.12688\/f1000research.9471.1","volume":"5","author":"L Toker","year":"2016","unstructured":"Toker L, Feng M, Pavlidis P. Whose sample is it anyway? Widespread misannotation of samples in transcriptomics studies. F1000Research. 2016;5(August):2103.","journal-title":"F1000Research"},{"issue":"18","key":"4070_CR21","doi-asserted-by":"publisher","first-page":"2914","DOI":"10.1093\/bioinformatics\/btx334","volume":"33","author":"MN Bernstein","year":"2017","unstructured":"Bernstein MN, Doan A, Dewey CN. MetaSRA: normalized human sample-specific metadata for the sequence read archive. Bioinformatics. 2017;33(18):2914\u201323.","journal-title":"Bioinformatics"},{"issue":"4","key":"4070_CR22","doi-asserted-by":"publisher","first-page":"319","DOI":"10.1038\/nbt.3838","volume":"35","author":"L Collado-Torres","year":"2017","unstructured":"Collado-Torres L, Nellore A, Kammers K, Ellis SE, Taub MA, Hansen KD, Jaffe AE, Langmead B, Leek JT. Reproducible RNA-seq analysis using recount2. Nat Biotechnol. 2017;35(4):319\u201321.","journal-title":"Nat Biotechnol"},{"issue":"9","key":"4070_CR23","doi-asserted-by":"publisher","first-page":"e54","DOI":"10.1093\/nar\/gky102","volume":"46","author":"SE Ellis","year":"2018","unstructured":"Ellis SE, Collado-Torres L, Jaffe A, Leek JT. Improving the value of public RNA-seq expression data by phenotype prediction. Nucl Acids Res. 2018;46(9):e54.","journal-title":"Nucl Acids Res"},{"key":"4070_CR24","unstructured":"Sam B, Bent SJ, Bianco-Miotto T, Roberts CT. massiR: Array Datasets. 2014. http:\/\/www.academia.edu\/download\/41619451\/massiR_a_method_for_predicting_the_sex_o20160127-31079-18mcqr1.pdf."},{"issue":"Suppl 14","key":"4070_CR25","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1186\/s12859-017-1888-1","volume":"18","author":"CB Giles","year":"2017","unstructured":"Giles CB, Brown CA, Ripperger M, Dennis Z, Roopnarinesingh X, Porter H, Perz A, Wren JD. ALE: automated label extraction from GEO metadata. BMC Bioinformatics. 2017;18(Suppl 14):509.","journal-title":"BMC Bioinformatics"},{"issue":"9","key":"4070_CR26","doi-asserted-by":"publisher","first-page":"e184","DOI":"10.1371\/journal.pmed.0050184","volume":"5","author":"A Ramasamy","year":"2008","unstructured":"Ramasamy A, Mondry A, Holmes CC, Altman DG. Key issues in conducting a meta-analysis of gene expression microarray datasets. PLoS Med. 2008;5(9):e184.","journal-title":"PLoS Med"},{"issue":"1","key":"4070_CR27","doi-asserted-by":"publisher","first-page":"1366","DOI":"10.1038\/s41467-018-03751-6","volume":"9","author":"A Lachmann","year":"2018","unstructured":"Lachmann A, Torre D, Keenan AB, Jagodnik KM, Lee HJ, Wang L, Silverstein MC, Ma\u2019ayan A. Massive mining of publicly available RNA-seq data from human and mouse. Nat Commun. 2018;9(1):1366.","journal-title":"Nat Commun"},{"key":"4070_CR28","unstructured":"Greene CS, Hu D, Jones RWW, Liu S, Mejia DS, Patro R, Piccolo SR, Romero AR, Sarkar H, Savonen CL, Taroni JN, Vauclain WE, Prasad DV, Wheeler KG. refine.bio: a resource of uniformly processed publicly available gene expression datasets. https:\/\/www.refine.bio."},{"issue":"7675","key":"4070_CR29","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1038\/nature24265","volume":"550","author":"T Tukiainen","year":"2017","unstructured":"Tukiainen T, Villani A-C, Yen A, Rivas MA, Marshall JL, Satija R, Aguirre M, et al. Landscape of X chromosome inactivation across human tissues. Nature. 2017;550(7675):244\u20138.","journal-title":"Nature"},{"key":"4070_CR30","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.33312","author":"A Molaro","year":"2017","unstructured":"Molaro A, Malik HS. Culture shock. eLife. 2017. https:\/\/doi.org\/10.7554\/eLife.33312.","journal-title":"eLife"},{"issue":"7391","key":"4070_CR31","doi-asserted-by":"publisher","first-page":"603","DOI":"10.1038\/nature11003","volume":"483","author":"J Barretina","year":"2012","unstructured":"Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, Wilson CJ, et al. The cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483(7391):603\u20137.","journal-title":"Nature"},{"issue":"September","key":"4070_CR32","doi-asserted-by":"publisher","first-page":"12846","DOI":"10.1038\/ncomms12846","volume":"7","author":"Z Wang","year":"2016","unstructured":"Wang Z, Monteiro CD, Jagodnik KM, Fernandez NF, Gundersen GW, Rouillard AD, Jenkins SL, et al. Extraction and analysis of signatures from the gene expression omnibus by the crowd. Nat Commun. 2016;7(September):12846.","journal-title":"Nat Commun"},{"issue":"2","key":"4070_CR33","doi-asserted-by":"publisher","first-page":"25","DOI":"10.7171\/jbt.18-2902-002","volume":"29","author":"A Bairoch","year":"2018","unstructured":"Bairoch A. The cellosaurus, a cell-line knowledge resource. J Biomol Tech JBT. 2018;29(2):25\u201338.","journal-title":"J Biomol Tech JBT"},{"issue":"Database issue","key":"4070_CR34","doi-asserted-by":"publisher","first-page":"D668","DOI":"10.1093\/nar\/gkj067","volume":"34","author":"DS Wishart","year":"2006","unstructured":"Wishart DS, Knox C, Guo AC, Shrivastava S, Hassanali M, Stothard P, Chang Z, Woolsey J. DrugBank: a comprehensive resource for in silico drug discovery and exploration. Nucl Acids Res. 2006;34(Database issue):D668\u201372.","journal-title":"Nucl Acids Res"},{"issue":"6443","key":"4070_CR35","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1126\/science.aaw7570","volume":"364","author":"RM Shansky","year":"2019","unstructured":"Shansky RM. Are hormones a \u201cfemale problem\u201d for animal research? Science. 2019;364(6443):825\u20136.","journal-title":"Science"},{"key":"4070_CR36","doi-asserted-by":"publisher","first-page":"143","DOI":"10.1016\/j.cobeha.2018.06.016","volume":"23","author":"AK Beery","year":"2018","unstructured":"Beery AK. Inclusion of females does not increase variability in rodent research studies. Curr Opin Behav Sci. 2018;23:143\u20139.","journal-title":"Curr Opin Behav Sci."},{"issue":"4","key":"4070_CR37","doi-asserted-by":"publisher","first-page":"e0122786","DOI":"10.1371\/journal.pone.0122786","volume":"10","author":"M Mennecozzi","year":"2015","unstructured":"Mennecozzi M, Landesmann B, Palosaari T, Harris G, Whelan M. Sex differences in liver toxicity\u2014Do female and male human primary hepatocytes react differently to toxicants in vitro? PLoS ONE. 2015;10(4):e0122786.","journal-title":"PLoS ONE"},{"key":"4070_CR38","doi-asserted-by":"publisher","DOI":"10.7554\/eLife.28070","author":"J Xu","year":"2017","unstructured":"Xu J, Peng X, Chen Y, Zhang Y, Ma Q, Liang L, Carter AC, Lu X, Wu C-I. Free-living human cells reconfigure their chromosomes in the evolution back to uni-cellularity. eLife. 2017. https:\/\/doi.org\/10.7554\/eLife.28070.","journal-title":"eLife"},{"issue":"4","key":"4070_CR39","first-page":"636","volume":"15","author":"KM Sullivan","year":"1993","unstructured":"Sullivan KM, Mannucci A, Kimpton CP, Gill P. A rapid and quantitative DNA sex test: fluorescence-based PCR analysis of X\u2013Y homologous gene amelogenin. Biotechniques. 1993;15(4):636\u20138 640\u201341.","journal-title":"Biotechniques"},{"issue":"1","key":"4070_CR40","doi-asserted-by":"publisher","first-page":"11226","DOI":"10.1038\/s41598-018-29506-3","volume":"8","author":"E Fasterius","year":"2018","unstructured":"Fasterius E, Szigyarto C-K. Analysis of public RNA-sequencing data reveals biological consequences of genetic heterogeneity in cell line populations. Sci Rep. 2018;8(1):11226.","journal-title":"Sci Rep"},{"issue":"2","key":"4070_CR41","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1086\/688181","volume":"42","author":"SA Ritz","year":"2017","unstructured":"Ritz SA. Complexities of addressing sex in cell culture research. Signs J Women Cult Soc. 2017;42(2):307\u201327.","journal-title":"Signs J Women Cult Soc"},{"issue":"1","key":"4070_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/ijc.25242","volume":"127","author":"A Capes-Davis","year":"2010","unstructured":"Capes-Davis A, Theodosopoulos G, Atkin I, Drexler HG, Kohara A, MacLeod RAF, Masters JR, et al. Check your cultures! A list of cross-contaminated or misidentified cell lines. Int J Cancer. 2010;127(1):1\u20138.","journal-title":"Int J Cancer"},{"issue":"4","key":"4070_CR43","doi-asserted-by":"publisher","first-page":"435","DOI":"10.14573\/altex.1806151","volume":"35","author":"R De Souza Santos","year":"2018","unstructured":"De Souza Santos R, Frank AP, Palmer BF, Clegg DJ. Sex and media: considerations for cell culture studies. Altex. 2018;35(4):435\u201340.","journal-title":"Altex"},{"issue":"10","key":"4070_CR44","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1038\/laban.1105","volume":"45","author":"S Deeney","year":"2016","unstructured":"Deeney S, Powers KN, Crombleholme TM. A comparison of sexing methods in fetal mice. Lab Anim. 2016;45(10):380\u20134.","journal-title":"Lab Anim"},{"issue":"2","key":"4070_CR45","doi-asserted-by":"publisher","first-page":"495","DOI":"10.5705\/ss.2011.023a","volume":"21","author":"B Zhou","year":"2011","unstructured":"Zhou B, Wong WH. A bootstrap-based non-parametric ANOVA method with applications to factorial microarray data. Stat Sin. 2011;21(2):495\u2013514.","journal-title":"Stat Sin"},{"issue":"2","key":"4070_CR46","doi-asserted-by":"publisher","first-page":"R29","DOI":"10.1186\/gb-2014-15-2-r29","volume":"15","author":"CW Law","year":"2014","unstructured":"Law CW, Chen Y, Shi W, Smyth GK. Voom: precision weights unlock linear model analysis tools for RNA-seq read counts. Genome Biol. 2014;15(2):R29.","journal-title":"Genome Biol."},{"key":"4070_CR47","doi-asserted-by":"publisher","DOI":"10.1093\/gigascience\/giz074","author":"TH Webster","year":"2019","unstructured":"Webster TH, Couse M, Grande BM, Karlins E, Phung TN, Richmond PA, Whitford W, Wilson MA. Identifying, understanding, and correcting technical artifacts on the sex chromosomes in next-generation sequencing data. GigaScience. 2019. https:\/\/doi.org\/10.1093\/gigascience\/giz074.","journal-title":"GigaScience"},{"issue":"1","key":"4070_CR48","doi-asserted-by":"publisher","first-page":"7","DOI":"10.1186\/s12915-017-0352-z","volume":"15","author":"M Gershoni","year":"2017","unstructured":"Gershoni M, Pietrokovski S. The landscape of sex-differential transcriptome and its consequent selection in human adults. BMC Biol. 2017;15(1):7.","journal-title":"BMC Biol"},{"issue":"7","key":"4070_CR49","doi-asserted-by":"publisher","first-page":"1961","DOI":"10.1016\/j.celrep.2019.10.019","volume":"29","author":"E Bongen","year":"2019","unstructured":"Bongen E, Lucian H, Khatri A, Fragiadakis GK, Bjornson ZB, Nolan GP, Utz PJ, Khatri P. Sex differences in the blood transcriptome identify robust changes in immune cell proportions with aging and influenza infection. Cell Reports. 2019;29(7):1961-73.e4.","journal-title":"Cell Reports"},{"key":"4070_CR50","doi-asserted-by":"crossref","unstructured":"Perry, P. O. 2009. Bcv: cross-validation for the SVD (bi-cross-validation). R package version.","DOI":"10.32614\/CRAN.package.bcv"},{"issue":"8","key":"4070_CR51","doi-asserted-by":"publisher","first-page":"1427","DOI":"10.1093\/bioinformatics\/bty784","volume":"35","author":"OM Enache","year":"2019","unstructured":"Enache OM, Lahr DL, Natoli TE, Litichevskiy L, Wadden D, Flynn C, Gould J, Asiedu JK, Narayan R, Subramanian A. The GCTx format and cmap{Py, R, M, J} packages: resources for optimized storage and integrated traversal of annotated dense matrices. Bioinformatics. 2019;35(8):1427\u20139.","journal-title":"Bioinformatics"},{"issue":"4","key":"4070_CR52","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1038\/nmeth.4197","volume":"14","author":"R Patro","year":"2017","unstructured":"Patro R, Duggal G, Love MI, Irizarry RA, Kingsford C. Salmon provides fast and bias-aware quantification of transcript expression. Nat Methods. 2017;14(4):417\u20139.","journal-title":"Nat Methods"},{"key":"4070_CR53","doi-asserted-by":"publisher","DOI":"10.1080\/02664763.2019.1630372","author":"RA Peterson","year":"2019","unstructured":"Peterson RA, Cavanaugh JE. Ordered quantile normalization: a semiparametric transformation built for the cross-validation era. J Appl Stat. 2019. https:\/\/doi.org\/10.1080\/02664763.2019.1630372.","journal-title":"J Appl Stat"},{"issue":"23","key":"4070_CR54","doi-asserted-by":"publisher","first-page":"2798","DOI":"10.1093\/bioinformatics\/btn520","volume":"24","author":"Y Zhu","year":"2008","unstructured":"Zhu Y, Davis S, Stephens R, Meltzer PS, Chen Y. GEOmetadb: powerful alternative search engine for the gene expression omnibus. Bioinformatics. 2008;24(23):2798\u2013800.","journal-title":"Bioinformatics"},{"issue":"Database issue","key":"4070_CR55","doi-asserted-by":"publisher","first-page":"D28","DOI":"10.1093\/nar\/gkq967","volume":"39","author":"R Leinonen","year":"2011","unstructured":"Leinonen R, Akhtar R, Birney E, Bower L, Cerdeno-T\u00e1rraga A, Cheng Y, Cleland L, et al. The European nucleotide archive. Nucl Acids Res. 2011;39(Database issue):D28-31.","journal-title":"Nucl Acids Res"},{"key":"4070_CR56","doi-asserted-by":"publisher","DOI":"10.1038\/nprot.2009.97","author":"S Durinck","year":"2009","unstructured":"Durinck S, Spellman PT, Birney E, Huber W. Mapping identifiers for the integration of genomic datasets with the R\/Bioconductor Package biomaRt. Nat Protoc. 2009. https:\/\/doi.org\/10.1038\/nprot.2009.97.","journal-title":"Nat Protoc"},{"key":"4070_CR57","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgen.1005079","author":"JB Berletch","year":"2015","unstructured":"Berletch JB, Ma W, Yang F, Shendure J, Noble WS, Disteche CM, Deng X. Escape from X inactivation varies in mouse tissues. PLoS Genet. 2015. https:\/\/doi.org\/10.1371\/journal.pgen.1005079.","journal-title":"PLoS Genet"},{"issue":"5","key":"4070_CR58","doi-asserted-by":"publisher","first-page":"614","DOI":"10.1101\/gr.103200.109","volume":"20","author":"F Yang","year":"2010","unstructured":"Yang F, Babak T, Shendure J, Disteche CM. Global survey of escape from X inactivation by RNA-sequencing in mouse. Genome Res. 2010;20(5):614\u201322.","journal-title":"Genome Res"},{"key":"4070_CR59","first-page":"2825","volume":"12","author":"F Pedregosa","year":"2011","unstructured":"Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, Blondel M, et al. Scikit-learn: machine learning in python. J Mach Learn Res. 2011;12:2825\u201330.","journal-title":"J Mach Learn Res."},{"key":"4070_CR60","unstructured":"Friedman JH, Hastie TJ, Tibshirani RJ. Glmnet: lasso and elastic-net regularized generalized linear models. 2010b. http:\/\/CRAN.R-Project.Org\/package=Glmnet.RPackageVersion, 1\u20131."},{"issue":"February","key":"4070_CR61","doi-asserted-by":"publisher","first-page":"198","DOI":"10.1016\/j.csda.2013.09.008","volume":"70","author":"S Roberts","year":"2014","unstructured":"Roberts S, Nowak G. Stabilizing the lasso against cross-validation variability. Comput Stat Data Anal. 2014;70(February):198\u2013211.","journal-title":"Comput Stat Data Anal"},{"issue":"1","key":"4070_CR62","doi-asserted-by":"publisher","first-page":"289","DOI":"10.32614\/RJ-2016-021","volume":"8","author":"L Scrucca","year":"2016","unstructured":"Scrucca L, Michael Fop T, Murphy B, Raftery AE. Mclust 5: clustering, classification and density estimation using gaussian finite mixture models. R J. 2016;8(1):289.","journal-title":"R J"},{"issue":"D1","key":"4070_CR63","doi-asserted-by":"publisher","first-page":"D1074","DOI":"10.1093\/nar\/gkx1037","volume":"46","author":"DS Wishart","year":"2018","unstructured":"Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, et al. DrugBank 5.0: a major update to the drugbank database for 2018. Nucl Acids Res. 2018;46(D1):D1074\u201382.","journal-title":"Nucl Acids Res"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04070-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12859-021-04070-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-021-04070-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:31:31Z","timestamp":1724718691000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-021-04070-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,30]]},"references-count":63,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["4070"],"URL":"https:\/\/doi.org\/10.1186\/s12859-021-04070-2","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2020.10.26.356287","asserted-by":"object"}]},"ISSN":["1471-2105"],"issn-type":[{"value":"1471-2105","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,30]]},"assertion":[{"value":"1 December 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable, our manuscript relies exclusively on publicly available data.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"168"}}