A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias
We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes,...
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| Veröffentlicht in: | PLoS biology 2023-03, Vol.21 (3), p.e3002007-e3002007 |
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| creator | Päll, Taavi Luidalepp, Hannes Tenson, Tanel Maiväli, Ülo |
| description | We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of |
| doi_str_mv | 10.1371/journal.pbio.3002007 |
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We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of <100 actual effects, were extremely few. Furthermore, although many HT-seq workflows assume that most genes are not differentially expressed, 37% of experiments have π0-s of less than 0.5, as if most genes changed their expression level. Most HT-seq experiments have very small sample sizes and are expected to be underpowered. Nevertheless, the estimated π0-s do not have the expected association with N, suggesting widespread problems of experiments with controlling false discovery rate (FDR). Both the fractions of different p-value histogram types and the π0 values are strongly associated with the differential expression analysis program used by the original authors. While we could double the proportion of theoretically expected p-value distributions by removing low-count features from the analysis, this treatment did not remove the association with the analysis program. Taken together, our results indicate widespread bias in the differential expression profiling field and the unreliability of statistical methods used to analyze HT-seq data.</description><identifier>ISSN: 1545-7885</identifier><identifier>ISSN: 1544-9173</identifier><identifier>EISSN: 1545-7885</identifier><identifier>DOI: 10.1371/journal.pbio.3002007</identifier><identifier>PMID: 36862747</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Analysis ; Bias ; Biology and Life Sciences ; Computer and Information Sciences ; DNA sequencing ; Engineering and Technology ; Experiments ; Gene expression ; Gene Expression Profiling - methods ; Genes ; High-Throughput Nucleotide Sequencing ; Histograms ; Hypotheses ; Hypothesis testing ; Medical research ; Medicine and Health Sciences ; Meta ; Methods ; Next-generation sequencing ; Nucleotide sequencing ; People and Places ; Physical Sciences ; Quality assessment ; Regression analysis ; Reproducibility ; Research and Analysis Methods ; Sample Size ; Science ; Science Policy ; Sequence Analysis, RNA - methods ; Statistical analysis ; Statistical inference ; Statistical methods</subject><ispartof>PLoS biology, 2023-03, Vol.21 (3), p.e3002007-e3002007</ispartof><rights>Copyright: © 2023 Päll et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</rights><rights>COPYRIGHT 2023 Public Library of Science</rights><rights>2023 Päll et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2023 Päll et al 2023 Päll et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c696t-a9b3da88718aa2b1cbd3343d6f57521af5944454b62b717ec5ecadbb466070d43</citedby><cites>FETCH-LOGICAL-c696t-a9b3da88718aa2b1cbd3343d6f57521af5944454b62b717ec5ecadbb466070d43</cites><orcidid>0000-0002-8035-6896</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013925/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013925/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,724,777,781,861,882,2096,2915,23847,27905,27906,53772,53774,79349,79350</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36862747$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><contributor>Munafò, Marcus</contributor><creatorcontrib>Päll, Taavi</creatorcontrib><creatorcontrib>Luidalepp, Hannes</creatorcontrib><creatorcontrib>Tenson, Tanel</creatorcontrib><creatorcontrib>Maiväli, Ülo</creatorcontrib><title>A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias</title><title>PLoS biology</title><addtitle>PLoS Biol</addtitle><description>We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of <100 actual effects, were extremely few. Furthermore, although many HT-seq workflows assume that most genes are not differentially expressed, 37% of experiments have π0-s of less than 0.5, as if most genes changed their expression level. Most HT-seq experiments have very small sample sizes and are expected to be underpowered. Nevertheless, the estimated π0-s do not have the expected association with N, suggesting widespread problems of experiments with controlling false discovery rate (FDR). Both the fractions of different p-value histogram types and the π0 values are strongly associated with the differential expression analysis program used by the original authors. While we could double the proportion of theoretically expected p-value distributions by removing low-count features from the analysis, this treatment did not remove the association with the analysis program. Taken together, our results indicate widespread bias in the differential expression profiling field and the unreliability of statistical methods used to analyze HT-seq data.</description><subject>Analysis</subject><subject>Bias</subject><subject>Biology and Life Sciences</subject><subject>Computer and Information Sciences</subject><subject>DNA sequencing</subject><subject>Engineering and Technology</subject><subject>Experiments</subject><subject>Gene expression</subject><subject>Gene Expression Profiling - methods</subject><subject>Genes</subject><subject>High-Throughput Nucleotide Sequencing</subject><subject>Histograms</subject><subject>Hypotheses</subject><subject>Hypothesis testing</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Meta</subject><subject>Methods</subject><subject>Next-generation sequencing</subject><subject>Nucleotide sequencing</subject><subject>People and Places</subject><subject>Physical Sciences</subject><subject>Quality assessment</subject><subject>Regression analysis</subject><subject>Reproducibility</subject><subject>Research and Analysis Methods</subject><subject>Sample Size</subject><subject>Science</subject><subject>Science Policy</subject><subject>Sequence Analysis, RNA - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><collection>PLoS Biology</collection><jtitle>PLoS biology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Päll, Taavi</au><au>Luidalepp, Hannes</au><au>Tenson, Tanel</au><au>Maiväli, Ülo</au><au>Munafò, Marcus</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias</atitle><jtitle>PLoS biology</jtitle><addtitle>PLoS Biol</addtitle><date>2023-03-02</date><risdate>2023</risdate><volume>21</volume><issue>3</issue><spage>e3002007</spage><epage>e3002007</epage><pages>e3002007-e3002007</pages><issn>1545-7885</issn><issn>1544-9173</issn><eissn>1545-7885</eissn><abstract>We assess inferential quality in the field of differential expression profiling by high-throughput sequencing (HT-seq) based on analysis of datasets submitted from 2008 to 2020 to the NCBI GEO data repository. We take advantage of the parallel differential expression testing over thousands of genes, whereby each experiment leads to a large set of p-values, the distribution of which can indicate the validity of assumptions behind the test. From a well-behaved p-value set π0, the fraction of genes that are not differentially expressed can be estimated. We found that only 25% of experiments resulted in theoretically expected p-value histogram shapes, although there is a marked improvement over time. Uniform p-value histogram shapes, indicative of <100 actual effects, were extremely few. Furthermore, although many HT-seq workflows assume that most genes are not differentially expressed, 37% of experiments have π0-s of less than 0.5, as if most genes changed their expression level. Most HT-seq experiments have very small sample sizes and are expected to be underpowered. Nevertheless, the estimated π0-s do not have the expected association with N, suggesting widespread problems of experiments with controlling false discovery rate (FDR). Both the fractions of different p-value histogram types and the π0 values are strongly associated with the differential expression analysis program used by the original authors. While we could double the proportion of theoretically expected p-value distributions by removing low-count features from the analysis, this treatment did not remove the association with the analysis program. Taken together, our results indicate widespread bias in the differential expression profiling field and the unreliability of statistical methods used to analyze HT-seq data.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>36862747</pmid><doi>10.1371/journal.pbio.3002007</doi><orcidid>https://orcid.org/0000-0002-8035-6896</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Analysis Bias Biology and Life Sciences Computer and Information Sciences DNA sequencing Engineering and Technology Experiments Gene expression Gene Expression Profiling - methods Genes High-Throughput Nucleotide Sequencing Histograms Hypotheses Hypothesis testing Medical research Medicine and Health Sciences Meta Methods Next-generation sequencing Nucleotide sequencing People and Places Physical Sciences Quality assessment Regression analysis Reproducibility Research and Analysis Methods Sample Size Science Science Policy Sequence Analysis, RNA - methods Statistical analysis Statistical inference Statistical methods |
| title | A field-wide assessment of differential expression profiling by high-throughput sequencing reveals widespread bias |
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