Effect of high variation in transcript expression on identifying differentially expressed genes in RNA‐seq analysis
Summary Great efforts have been made on the algorithms that deal with RNA‐seq data to enhance the accuracy and efficiency of differential expression (DE) analysis. However, no consensus has been reached on the proper threshold values of fold change and adjusted p‐value for filtering differentially e...
Gespeichert in:
Veröffentlicht in: | Annals of human genetics 2021-11, Vol.85 (6), p.235-244 |
---|---|
Hauptverfasser: | , , , , , , |
Format: | Artikel |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
container_end_page | 244 |
---|---|
container_issue | 6 |
container_start_page | 235 |
container_title | Annals of human genetics |
container_volume | 85 |
creator | Cui, Weitong Xue, Huaru Geng, Yifan Zhang, Jing Liang, Yajun Tian, Xuewen Wang, Qinglu |
description | Summary
Great efforts have been made on the algorithms that deal with RNA‐seq data to enhance the accuracy and efficiency of differential expression (DE) analysis. However, no consensus has been reached on the proper threshold values of fold change and adjusted p‐value for filtering differentially expressed genes (DEGs). It is generally believed that the more stringent the filtering threshold, the more reliable the result of a DE analysis. Nevertheless, by analyzing the impact of both adjusted p‐value and fold change thresholds on DE analyses, with RNA‐seq data obtained for three different cancer types from the Cancer Genome Atlas (TCGA) database, we found that, for a given sample size, the reproducibility of DE results became poorer when more stringent thresholds were applied. No matter which threshold level was applied, the overlap rates of DEGs were generally lower for small sample sizes than for large sample sizes. The raw read count analysis demonstrated that the transcript expression of the same gene in different samples, whether in tumor groups or in normal groups, showed high variations, which resulted in a drastic fluctuation in fold change values and adjustedp‐values when different sets of samples were used. Overall, more stringent thresholds did not yield more reliable DEGs due to high variations in transcript expression; the reliability of DEGs obtained with small sample sizes was more susceptible to these variations. Therefore, less stringent thresholds are recommended for screening DEGs. Moreover, large sample sizes should be considered in RNA‐seq experimental designs to reduce the interfering effect of variations in transcript expression on DEG identification. |
doi_str_mv | 10.1111/ahg.12441 |
format | Article |
fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2557534314</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2581701361</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3531-fbca98ed911752d53e9765a3e3325d2a3c3a8d721e88294a65f678d786fbdc8d3</originalsourceid><addsrcrecordid>eNp1kc9q3DAQh0Vp6W6THPoCRZBLevBGY0m2fFxC_kFoICRno7VGu1q89q5kt_Etj9Bn7JNEziY5BDoXMZqPj2F-hHwHNoNYp3q1nEEqBHwiUxBZkYBixWcyZYzxRCjGJuRbCGvGIFWCfyUTLriAQmVT0p9bi1VHW0tXbrmiv7V3unNtQ11DO6-bUHm37Sg-bj2GMA7GmcGmc3ZwzZIaFw1-7HVdD28gGrrEBsOoufs1__f0N-CO6kbXQ3DhkHyxug549PoekIeL8_uzq-Tm9vL6bH6TVFxySOyi0oVCUwDkMjWSY5FnUnPkPJUm1bziWpk8BVQqLYTOpM3y-KEyuzCVMvyAnOy9W9_uegxduXGhwrrWDbZ9KFMpcxmPASKixx_Qddv7uO9IKcgZ8Awi9XNPVb4NwaMtt95ttB9KYOWYRRmzKF-yiOyPV2O_2KB5J9-OH4HTPfDH1Tj831TOry73ymeHt5S3</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2581701361</pqid></control><display><type>article</type><title>Effect of high variation in transcript expression on identifying differentially expressed genes in RNA‐seq analysis</title><source>MEDLINE</source><source>Wiley Online Library Free Content</source><source>Access via Wiley Online Library</source><creator>Cui, Weitong ; Xue, Huaru ; Geng, Yifan ; Zhang, Jing ; Liang, Yajun ; Tian, Xuewen ; Wang, Qinglu</creator><creatorcontrib>Cui, Weitong ; Xue, Huaru ; Geng, Yifan ; Zhang, Jing ; Liang, Yajun ; Tian, Xuewen ; Wang, Qinglu</creatorcontrib><description>Summary
Great efforts have been made on the algorithms that deal with RNA‐seq data to enhance the accuracy and efficiency of differential expression (DE) analysis. However, no consensus has been reached on the proper threshold values of fold change and adjusted p‐value for filtering differentially expressed genes (DEGs). It is generally believed that the more stringent the filtering threshold, the more reliable the result of a DE analysis. Nevertheless, by analyzing the impact of both adjusted p‐value and fold change thresholds on DE analyses, with RNA‐seq data obtained for three different cancer types from the Cancer Genome Atlas (TCGA) database, we found that, for a given sample size, the reproducibility of DE results became poorer when more stringent thresholds were applied. No matter which threshold level was applied, the overlap rates of DEGs were generally lower for small sample sizes than for large sample sizes. The raw read count analysis demonstrated that the transcript expression of the same gene in different samples, whether in tumor groups or in normal groups, showed high variations, which resulted in a drastic fluctuation in fold change values and adjustedp‐values when different sets of samples were used. Overall, more stringent thresholds did not yield more reliable DEGs due to high variations in transcript expression; the reliability of DEGs obtained with small sample sizes was more susceptible to these variations. Therefore, less stringent thresholds are recommended for screening DEGs. Moreover, large sample sizes should be considered in RNA‐seq experimental designs to reduce the interfering effect of variations in transcript expression on DEG identification.</description><identifier>ISSN: 0003-4800</identifier><identifier>EISSN: 1469-1809</identifier><identifier>DOI: 10.1111/ahg.12441</identifier><identifier>PMID: 34341986</identifier><language>eng</language><publisher>England: Wiley Subscription Services, Inc</publisher><subject>Algorithms ; Differential expression ; false discovery rate ; fold change ; Gene Expression ; Genomes ; Humans ; Neoplasms - genetics ; Ribonucleic acid ; RNA ; RNA, Messenger - genetics ; RNA-Seq ; sample size ; threshold ; Transcription ; Variation</subject><ispartof>Annals of human genetics, 2021-11, Vol.85 (6), p.235-244</ispartof><rights>2021 John Wiley & Sons Ltd/University College London</rights><rights>2021 John Wiley & Sons Ltd/University College London.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3531-fbca98ed911752d53e9765a3e3325d2a3c3a8d721e88294a65f678d786fbdc8d3</citedby><cites>FETCH-LOGICAL-c3531-fbca98ed911752d53e9765a3e3325d2a3c3a8d721e88294a65f678d786fbdc8d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fahg.12441$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fahg.12441$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1417,1433,27924,27925,45574,45575,46409,46833</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/34341986$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Cui, Weitong</creatorcontrib><creatorcontrib>Xue, Huaru</creatorcontrib><creatorcontrib>Geng, Yifan</creatorcontrib><creatorcontrib>Zhang, Jing</creatorcontrib><creatorcontrib>Liang, Yajun</creatorcontrib><creatorcontrib>Tian, Xuewen</creatorcontrib><creatorcontrib>Wang, Qinglu</creatorcontrib><title>Effect of high variation in transcript expression on identifying differentially expressed genes in RNA‐seq analysis</title><title>Annals of human genetics</title><addtitle>Ann Hum Genet</addtitle><description>Summary
Great efforts have been made on the algorithms that deal with RNA‐seq data to enhance the accuracy and efficiency of differential expression (DE) analysis. However, no consensus has been reached on the proper threshold values of fold change and adjusted p‐value for filtering differentially expressed genes (DEGs). It is generally believed that the more stringent the filtering threshold, the more reliable the result of a DE analysis. Nevertheless, by analyzing the impact of both adjusted p‐value and fold change thresholds on DE analyses, with RNA‐seq data obtained for three different cancer types from the Cancer Genome Atlas (TCGA) database, we found that, for a given sample size, the reproducibility of DE results became poorer when more stringent thresholds were applied. No matter which threshold level was applied, the overlap rates of DEGs were generally lower for small sample sizes than for large sample sizes. The raw read count analysis demonstrated that the transcript expression of the same gene in different samples, whether in tumor groups or in normal groups, showed high variations, which resulted in a drastic fluctuation in fold change values and adjustedp‐values when different sets of samples were used. Overall, more stringent thresholds did not yield more reliable DEGs due to high variations in transcript expression; the reliability of DEGs obtained with small sample sizes was more susceptible to these variations. Therefore, less stringent thresholds are recommended for screening DEGs. Moreover, large sample sizes should be considered in RNA‐seq experimental designs to reduce the interfering effect of variations in transcript expression on DEG identification.</description><subject>Algorithms</subject><subject>Differential expression</subject><subject>false discovery rate</subject><subject>fold change</subject><subject>Gene Expression</subject><subject>Genomes</subject><subject>Humans</subject><subject>Neoplasms - genetics</subject><subject>Ribonucleic acid</subject><subject>RNA</subject><subject>RNA, Messenger - genetics</subject><subject>RNA-Seq</subject><subject>sample size</subject><subject>threshold</subject><subject>Transcription</subject><subject>Variation</subject><issn>0003-4800</issn><issn>1469-1809</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kc9q3DAQh0Vp6W6THPoCRZBLevBGY0m2fFxC_kFoICRno7VGu1q89q5kt_Etj9Bn7JNEziY5BDoXMZqPj2F-hHwHNoNYp3q1nEEqBHwiUxBZkYBixWcyZYzxRCjGJuRbCGvGIFWCfyUTLriAQmVT0p9bi1VHW0tXbrmiv7V3unNtQ11DO6-bUHm37Sg-bj2GMA7GmcGmc3ZwzZIaFw1-7HVdD28gGrrEBsOoufs1__f0N-CO6kbXQ3DhkHyxug549PoekIeL8_uzq-Tm9vL6bH6TVFxySOyi0oVCUwDkMjWSY5FnUnPkPJUm1bziWpk8BVQqLYTOpM3y-KEyuzCVMvyAnOy9W9_uegxduXGhwrrWDbZ9KFMpcxmPASKixx_Qddv7uO9IKcgZ8Awi9XNPVb4NwaMtt95ttB9KYOWYRRmzKF-yiOyPV2O_2KB5J9-OH4HTPfDH1Tj831TOry73ymeHt5S3</recordid><startdate>202111</startdate><enddate>202111</enddate><creator>Cui, Weitong</creator><creator>Xue, Huaru</creator><creator>Geng, Yifan</creator><creator>Zhang, Jing</creator><creator>Liang, Yajun</creator><creator>Tian, Xuewen</creator><creator>Wang, Qinglu</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>8FD</scope><scope>FR3</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>202111</creationdate><title>Effect of high variation in transcript expression on identifying differentially expressed genes in RNA‐seq analysis</title><author>Cui, Weitong ; Xue, Huaru ; Geng, Yifan ; Zhang, Jing ; Liang, Yajun ; Tian, Xuewen ; Wang, Qinglu</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3531-fbca98ed911752d53e9765a3e3325d2a3c3a8d721e88294a65f678d786fbdc8d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Algorithms</topic><topic>Differential expression</topic><topic>false discovery rate</topic><topic>fold change</topic><topic>Gene Expression</topic><topic>Genomes</topic><topic>Humans</topic><topic>Neoplasms - genetics</topic><topic>Ribonucleic acid</topic><topic>RNA</topic><topic>RNA, Messenger - genetics</topic><topic>RNA-Seq</topic><topic>sample size</topic><topic>threshold</topic><topic>Transcription</topic><topic>Variation</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Cui, Weitong</creatorcontrib><creatorcontrib>Xue, Huaru</creatorcontrib><creatorcontrib>Geng, Yifan</creatorcontrib><creatorcontrib>Zhang, Jing</creatorcontrib><creatorcontrib>Liang, Yajun</creatorcontrib><creatorcontrib>Tian, Xuewen</creatorcontrib><creatorcontrib>Wang, Qinglu</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Annals of human genetics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Cui, Weitong</au><au>Xue, Huaru</au><au>Geng, Yifan</au><au>Zhang, Jing</au><au>Liang, Yajun</au><au>Tian, Xuewen</au><au>Wang, Qinglu</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Effect of high variation in transcript expression on identifying differentially expressed genes in RNA‐seq analysis</atitle><jtitle>Annals of human genetics</jtitle><addtitle>Ann Hum Genet</addtitle><date>2021-11</date><risdate>2021</risdate><volume>85</volume><issue>6</issue><spage>235</spage><epage>244</epage><pages>235-244</pages><issn>0003-4800</issn><eissn>1469-1809</eissn><abstract>Summary
Great efforts have been made on the algorithms that deal with RNA‐seq data to enhance the accuracy and efficiency of differential expression (DE) analysis. However, no consensus has been reached on the proper threshold values of fold change and adjusted p‐value for filtering differentially expressed genes (DEGs). It is generally believed that the more stringent the filtering threshold, the more reliable the result of a DE analysis. Nevertheless, by analyzing the impact of both adjusted p‐value and fold change thresholds on DE analyses, with RNA‐seq data obtained for three different cancer types from the Cancer Genome Atlas (TCGA) database, we found that, for a given sample size, the reproducibility of DE results became poorer when more stringent thresholds were applied. No matter which threshold level was applied, the overlap rates of DEGs were generally lower for small sample sizes than for large sample sizes. The raw read count analysis demonstrated that the transcript expression of the same gene in different samples, whether in tumor groups or in normal groups, showed high variations, which resulted in a drastic fluctuation in fold change values and adjustedp‐values when different sets of samples were used. Overall, more stringent thresholds did not yield more reliable DEGs due to high variations in transcript expression; the reliability of DEGs obtained with small sample sizes was more susceptible to these variations. Therefore, less stringent thresholds are recommended for screening DEGs. Moreover, large sample sizes should be considered in RNA‐seq experimental designs to reduce the interfering effect of variations in transcript expression on DEG identification.</abstract><cop>England</cop><pub>Wiley Subscription Services, Inc</pub><pmid>34341986</pmid><doi>10.1111/ahg.12441</doi><tpages>10</tpages></addata></record> |
fulltext | fulltext |
identifier | ISSN: 0003-4800 |
ispartof | Annals of human genetics, 2021-11, Vol.85 (6), p.235-244 |
issn | 0003-4800 1469-1809 |
language | eng |
recordid | cdi_proquest_miscellaneous_2557534314 |
source | MEDLINE; Wiley Online Library Free Content; Access via Wiley Online Library |
subjects | Algorithms Differential expression false discovery rate fold change Gene Expression Genomes Humans Neoplasms - genetics Ribonucleic acid RNA RNA, Messenger - genetics RNA-Seq sample size threshold Transcription Variation |
title | Effect of high variation in transcript expression on identifying differentially expressed genes in RNA‐seq analysis |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2024-12-24T18%3A24%3A38IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Effect%20of%20high%20variation%20in%20transcript%20expression%20on%20identifying%20differentially%20expressed%20genes%20in%20RNA%E2%80%90seq%20analysis&rft.jtitle=Annals%20of%20human%20genetics&rft.au=Cui,%20Weitong&rft.date=2021-11&rft.volume=85&rft.issue=6&rft.spage=235&rft.epage=244&rft.pages=235-244&rft.issn=0003-4800&rft.eissn=1469-1809&rft_id=info:doi/10.1111/ahg.12441&rft_dat=%3Cproquest_cross%3E2581701361%3C/proquest_cross%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2581701361&rft_id=info:pmid/34341986&rfr_iscdi=true |