[Comparison of statistical methods for detecting differential expression in microarray data].
Identifieur interne : 003748 ( Main/Exploration ); précédent : 003747; suivant : 003749[Comparison of statistical methods for detecting differential expression in microarray data].
Auteurs : Wen-Juan Shan [République populaire de Chine] ; Chun-Fa Tong ; Ji-Sen ShiSource :
- Yi chuan = Hereditas [ 0253-9772 ] ; 2008.
Descripteurs français
- KwdFr :
- MESH :
- génétique : Populus.
- méthodes : Analyse de profil d'expression de gènes, Séquençage par oligonucléotides en batterie.
- Modèles statistiques.
English descriptors
- KwdEn :
- MESH :
- genetics : Populus.
- methods : Gene Expression Profiling, Oligonucleotide Array Sequence Analysis.
- Models, Statistical.
Abstract
DNA microarray is a new tool in biotechnology, which allows simultaneously monitoring thousands of gene expression in cells. The goal of differential gene expression analysis is to detect genes with significant change of gene expression levels arising from experimental conditions. Although various statistical methods have been suggested to confirm differential gene expression, only a few studies compared performance of the statistical methods. This paper presented comparison of statistical methods for finding differentially expressed genes (DEGs) from the microarray data. Using simulated and real datasets (Populus cDNA microarray data), we compared eight methods of identifying differential gene expression. The simulated datasets included four differential distributions (normal distribution, uniform distribution, c2 distribution, and exponential distribution). The results of simulated datasets analysis showed that the eight methods were more preferable with the microarray data of uniform distribution than normal distribution. They were not preferable with the c2 distribution and exponential distribution. Of these eight methods, SAM (Significance Analysis of Microarrays) and Wilcoxon rank sum test performed well in most cases. The results of real cDNA microarray data of Populus showed that there was much similarity of SAM, Samroc, and regression modeling approach. Wilcoxon rank sum test was different from them. Samroc and regression modeling approach were similar in the eight methods. For both simulated and real datasets, SAM, Samroc, and regression modeling approach performed better than other methods.
DOI: 10.3724/sp.j.1005.2008.01640
PubMed: 19073583
Affiliations:
Links toward previous steps (curation, corpus...)
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The goal of differential gene expression analysis is to detect genes with significant change of gene expression levels arising from experimental conditions. Although various statistical methods have been suggested to confirm differential gene expression, only a few studies compared performance of the statistical methods. This paper presented comparison of statistical methods for finding differentially expressed genes (DEGs) from the microarray data. Using simulated and real datasets (Populus cDNA microarray data), we compared eight methods of identifying differential gene expression. The simulated datasets included four differential distributions (normal distribution, uniform distribution, c2 distribution, and exponential distribution). The results of simulated datasets analysis showed that the eight methods were more preferable with the microarray data of uniform distribution than normal distribution. They were not preferable with the c2 distribution and exponential distribution. Of these eight methods, SAM (Significance Analysis of Microarrays) and Wilcoxon rank sum test performed well in most cases. The results of real cDNA microarray data of Populus showed that there was much similarity of SAM, Samroc, and regression modeling approach. Wilcoxon rank sum test was different from them. Samroc and regression modeling approach were similar in the eight methods. For both simulated and real datasets, SAM, Samroc, and regression modeling approach performed better than other methods.</div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">19073583</PMID><DateCompleted><Year>2009</Year><Month>04</Month><Day>29</Day></DateCompleted><DateRevised><Year>2019</Year><Month>09</Month><Day>23</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Print">0253-9772</ISSN><JournalIssue CitedMedium="Print"><Volume>30</Volume><Issue>12</Issue><PubDate><Year>2008</Year><Month>Dec</Month></PubDate></JournalIssue><Title>Yi chuan = Hereditas</Title><ISOAbbreviation>Yi Chuan</ISOAbbreviation></Journal><ArticleTitle>[Comparison of statistical methods for detecting differential expression in microarray data].</ArticleTitle><Pagination><MedlinePgn>1640-6</MedlinePgn></Pagination><Abstract><AbstractText>DNA microarray is a new tool in biotechnology, which allows simultaneously monitoring thousands of gene expression in cells. The goal of differential gene expression analysis is to detect genes with significant change of gene expression levels arising from experimental conditions. Although various statistical methods have been suggested to confirm differential gene expression, only a few studies compared performance of the statistical methods. This paper presented comparison of statistical methods for finding differentially expressed genes (DEGs) from the microarray data. Using simulated and real datasets (Populus cDNA microarray data), we compared eight methods of identifying differential gene expression. The simulated datasets included four differential distributions (normal distribution, uniform distribution, c2 distribution, and exponential distribution). The results of simulated datasets analysis showed that the eight methods were more preferable with the microarray data of uniform distribution than normal distribution. They were not preferable with the c2 distribution and exponential distribution. Of these eight methods, SAM (Significance Analysis of Microarrays) and Wilcoxon rank sum test performed well in most cases. The results of real cDNA microarray data of Populus showed that there was much similarity of SAM, Samroc, and regression modeling approach. Wilcoxon rank sum test was different from them. Samroc and regression modeling approach were similar in the eight methods. For both simulated and real datasets, SAM, Samroc, and regression modeling approach performed better than other methods.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Shan</LastName><ForeName>Wen-Juan</ForeName><Initials>WJ</Initials><AffiliationInfo><Affiliation>The Key Laboratory of Forest Genetics and Gene Engineering of the State Administration and Jiangsu Province, Nanjing Forestry University, Nanjing 210037, China. fanzi03@163.com</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tong</LastName><ForeName>Chun-Fa</ForeName><Initials>CF</Initials></Author><Author ValidYN="Y"><LastName>Shi</LastName><ForeName>Ji-Sen</ForeName><Initials>JS</Initials></Author></AuthorList><Language>chi</Language><PublicationTypeList><PublicationType UI="D004740">English Abstract</PublicationType><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>China</Country><MedlineTA>Yi Chuan</MedlineTA><NlmUniqueID>9436478</NlmUniqueID><ISSNLinking>0253-9772</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D020869" MajorTopicYN="N">Gene Expression Profiling</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="Y">Models, Statistical</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D020411" MajorTopicYN="N">Oligonucleotide Array Sequence Analysis</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2008</Year><Month>12</Month><Day>17</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2008</Year><Month>12</Month><Day>17</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2009</Year><Month>4</Month><Day>30</Day><Hour>9</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">19073583</ArticleId><ArticleId IdType="pii">0253-9772(2008)12-1640-07</ArticleId><ArticleId IdType="doi">10.3724/sp.j.1005.2008.01640</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country></list><tree><noCountry><name sortKey="Shi, Ji Sen" sort="Shi, Ji Sen" uniqKey="Shi J" first="Ji-Sen" last="Shi">Ji-Sen Shi</name><name sortKey="Tong, Chun Fa" sort="Tong, Chun Fa" uniqKey="Tong C" first="Chun-Fa" last="Tong">Chun-Fa Tong</name></noCountry><country name="République populaire de Chine"><noRegion><name sortKey="Shan, Wen Juan" sort="Shan, Wen Juan" uniqKey="Shan W" first="Wen-Juan" last="Shan">Wen-Juan Shan</name></noRegion></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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