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Fold change and p-value cutoffs significantly alter microarray interpretations
As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of
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| Published in: | BMC bioinformatics 2012-03, Vol.13 Suppl 2 (Suppl 2), p.S11-S11, Article S11 |
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| creator | Dalman, Mark R Deeter, Anthony Nimishakavi, Gayathri Duan, Zhong-Hui |
| description | As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of |
| doi_str_mv | 10.1186/1471-2105-13-s2-s11 |
| format | article |
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The data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient.
Our analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.</description><identifier>ISSN: 1471-2105</identifier><identifier>EISSN: 1471-2105</identifier><identifier>DOI: 10.1186/1471-2105-13-s2-s11</identifier><identifier>PMID: 22536862</identifier><language>eng</language><publisher>England: BioMed Central</publisher><subject>Angiogenesis ; Bioinformatics ; Conferences ; Danio rerio ; Data collections ; Data Interpretation, Statistical ; Data processing ; Development ; DNA microarrays ; Gene expression ; Gene Expression Profiling - methods ; Gene Expression Regulation ; Genomes ; Heart ; Hormones ; Hypoxia ; Leptin ; Oligonucleotide Array Sequence Analysis - methods ; Organisms ; Proceedings ; Statistics ; Studies</subject><ispartof>BMC bioinformatics, 2012-03, Vol.13 Suppl 2 (Suppl 2), p.S11-S11, Article S11</ispartof><rights>2012 Dalman et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</rights><rights>Copyright ©2012 Dalman et al.; licensee BioMed Central Ltd. 2012 Dalman et al.; licensee BioMed Central Ltd.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-b693t-66ee5b885b00f39e6ba2d93413911a7c0a20b5400af3c7a30b88c354d00f32553</citedby><cites>FETCH-LOGICAL-b693t-66ee5b885b00f39e6ba2d93413911a7c0a20b5400af3c7a30b88c354d00f32553</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3305783/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/927706908?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,25753,27924,27925,37012,37013,44590,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/22536862$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Dalman, Mark R</creatorcontrib><creatorcontrib>Deeter, Anthony</creatorcontrib><creatorcontrib>Nimishakavi, Gayathri</creatorcontrib><creatorcontrib>Duan, Zhong-Hui</creatorcontrib><title>Fold change and p-value cutoffs significantly alter microarray interpretations</title><title>BMC bioinformatics</title><addtitle>BMC Bioinformatics</addtitle><description>As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but yet still leaving large voids in the number of genes assayed, such as leptin a pleiotropic hormone directly related to hypoxia-induced angiogenesis.
The data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient.
Our analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.</description><subject>Angiogenesis</subject><subject>Bioinformatics</subject><subject>Conferences</subject><subject>Danio rerio</subject><subject>Data collections</subject><subject>Data Interpretation, Statistical</subject><subject>Data processing</subject><subject>Development</subject><subject>DNA microarrays</subject><subject>Gene expression</subject><subject>Gene Expression Profiling - methods</subject><subject>Gene Expression Regulation</subject><subject>Genomes</subject><subject>Heart</subject><subject>Hormones</subject><subject>Hypoxia</subject><subject>Leptin</subject><subject>Oligonucleotide Array Sequence Analysis - methods</subject><subject>Organisms</subject><subject>Proceedings</subject><subject>Statistics</subject><subject>Studies</subject><issn>1471-2105</issn><issn>1471-2105</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNqNkkFr3DAQhU1padK0v6BQTE-9uJ2RLFm-FEropoHQHtKexViWNlpsayvZgf33sbPpki0EcpJ48_QxozdZ9h7hM6KSX7CssGAIokBeJFYkxBfZ6UF9-eh-kr1JaQOAlQLxOjthTHCpJDvNfq5C1-bmhoa1zWlo821xS91kczONwbmUJ78evPOGhrHb5dSNNua9NzFQjLTL_TAL22hHGn0Y0tvslaMu2XcP51n2Z_X99_mP4urXxeX5t6uikTUfCymtFY1SogFwvLayIdbWvEReI1JlgBg0ogQgx01FHGav4aJsFzsTgp9ll3tuG2ijt9H3FHc6kNf3QohrTXH0prPaImCpOCudhbIiVBWH2llkzirVWjezvu5Z26npbWvsMEbqjqDHlcHf6HW41ZyDqBSfAas9oPHhCcBxxYReL9noJRuNXF8zfY04gz49dBLD38mmUfc-Gdt1NNgwJY3ASimRq_oZVgTJhZTqGVZQqixZtVA__mfdhCkOc5K6ZlUFsoaFx_emeQlSitYdJkXQy2Y-MduHx798ePNvFfkdku_eTQ</recordid><startdate>20120313</startdate><enddate>20120313</enddate><creator>Dalman, Mark R</creator><creator>Deeter, Anthony</creator><creator>Nimishakavi, Gayathri</creator><creator>Duan, Zhong-Hui</creator><general>BioMed Central</general><general>BioMed Central Ltd</general><general>BMC</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>3V.</scope><scope>7QO</scope><scope>7SC</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>K9.</scope><scope>L7M</scope><scope>LK8</scope><scope>L~C</scope><scope>L~D</scope><scope>M0N</scope><scope>M0S</scope><scope>M1P</scope><scope>M7P</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>Q9U</scope><scope>7X8</scope><scope>5PM</scope><scope>DOA</scope></search><sort><creationdate>20120313</creationdate><title>Fold change and p-value cutoffs significantly alter microarray interpretations</title><author>Dalman, Mark R ; Deeter, Anthony ; Nimishakavi, Gayathri ; Duan, Zhong-Hui</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-b693t-66ee5b885b00f39e6ba2d93413911a7c0a20b5400af3c7a30b88c354d00f32553</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Angiogenesis</topic><topic>Bioinformatics</topic><topic>Conferences</topic><topic>Danio rerio</topic><topic>Data collections</topic><topic>Data Interpretation, Statistical</topic><topic>Data processing</topic><topic>Development</topic><topic>DNA microarrays</topic><topic>Gene expression</topic><topic>Gene Expression Profiling - methods</topic><topic>Gene Expression Regulation</topic><topic>Genomes</topic><topic>Heart</topic><topic>Hormones</topic><topic>Hypoxia</topic><topic>Leptin</topic><topic>Oligonucleotide Array Sequence Analysis - methods</topic><topic>Organisms</topic><topic>Proceedings</topic><topic>Statistics</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Dalman, Mark R</creatorcontrib><creatorcontrib>Deeter, Anthony</creatorcontrib><creatorcontrib>Nimishakavi, Gayathri</creatorcontrib><creatorcontrib>Duan, Zhong-Hui</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>ProQuest Health and Medical</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Biological Sciences</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Computing Database</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>PML(ProQuest Medical Library)</collection><collection>Biological Science Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central Basic</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>Directory of Open Access Journals</collection><jtitle>BMC bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Dalman, Mark R</au><au>Deeter, Anthony</au><au>Nimishakavi, Gayathri</au><au>Duan, Zhong-Hui</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Fold change and p-value cutoffs significantly alter microarray interpretations</atitle><jtitle>BMC bioinformatics</jtitle><addtitle>BMC Bioinformatics</addtitle><date>2012-03-13</date><risdate>2012</risdate><volume>13 Suppl 2</volume><issue>Suppl 2</issue><spage>S11</spage><epage>S11</epage><pages>S11-S11</pages><artnum>S11</artnum><issn>1471-2105</issn><eissn>1471-2105</eissn><abstract>As context is important to gene expression, so is the preprocessing of microarray to transcriptomics. Microarray data suffers from several normalization and significance problems. Arbitrary fold change (FC) cut-offs of >2 and significance p-values of <0.02 lead data collection to look only at genes which vary wildly amongst other genes. Therefore, questions arise as to whether the biology or the statistical cutoff are more important within the interpretation. In this paper, we reanalyzed a zebrafish (D. rerio) microarray data set using GeneSpring and different differential gene expression cut-offs and found the data interpretation was drastically different. Furthermore, despite the advances in microarray technology, the array captures a large portion of genes known but yet still leaving large voids in the number of genes assayed, such as leptin a pleiotropic hormone directly related to hypoxia-induced angiogenesis.
The data strongly suggests that the number of differentially expressed genes is more up-regulated than down-regulated, with many genes indicating conserved signalling to previously known functions. Recapitulated data from Marques et al. (2008) was similar but surprisingly different with some genes showing unexpected signalling which may be a product of tissue (heart) or that the intended response was transient.
Our analyses suggest that based on the chosen statistical or fold change cut-off; microarray analysis can provide essentially more than one answer, implying data interpretation as more of an art than a science, with follow up gene expression studies a must. Furthermore, gene chip annotation and development needs to maintain pace with not only new genomes being sequenced but also novel genes that are crucial to the overall gene chips interpretation.</abstract><cop>England</cop><pub>BioMed Central</pub><pmid>22536862</pmid><doi>10.1186/1471-2105-13-s2-s11</doi><oa>free_for_read</oa></addata></record> |
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| subjects | Angiogenesis Bioinformatics Conferences Danio rerio Data collections Data Interpretation, Statistical Data processing Development DNA microarrays Gene expression Gene Expression Profiling - methods Gene Expression Regulation Genomes Heart Hormones Hypoxia Leptin Oligonucleotide Array Sequence Analysis - methods Organisms Proceedings Statistics Studies |
| title | Fold change and p-value cutoffs significantly alter microarray interpretations |
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