An efficient method to identify differentially expressed genes in microarray experiments.
Identifieur interne : 003995 ( Main/Exploration ); précédent : 003994; suivant : 003996An efficient method to identify differentially expressed genes in microarray experiments.
Auteurs : Huaizhen Qin [États-Unis] ; Tao Feng ; Scott A. Harding ; Chung-Jui Tsai ; Shuanglin ZhangSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2008.
Descripteurs français
- KwdFr :
- Algorithmes (MeSH), Alignement de séquences (MeSH), Analyse de profil d'expression de gènes (MeSH), Analyse de regroupements (MeSH), Analyse en composantes principales (MeSH), Biologie informatique (méthodes), Gènes de plante (MeSH), Humains (MeSH), Langages de programmation (MeSH), Logiciel (MeSH), Modèles théoriques (MeSH), Reconnaissance automatique des formes (MeSH), Régulation de l'expression des gènes tumoraux (MeSH), Séquençage par oligonucléotides en batterie (méthodes), Tumeurs (métabolisme).
- MESH :
- métabolisme : Tumeurs.
- méthodes : Biologie informatique, Séquençage par oligonucléotides en batterie.
- Algorithmes, Alignement de séquences, Analyse de profil d'expression de gènes, Analyse de regroupements, Analyse en composantes principales, Gènes de plante, Humains, Langages de programmation, Logiciel, Modèles théoriques, Reconnaissance automatique des formes, Régulation de l'expression des gènes tumoraux.
English descriptors
- KwdEn :
- Algorithms (MeSH), Cluster Analysis (MeSH), Computational Biology (methods), Gene Expression Profiling (MeSH), Gene Expression Regulation, Neoplastic (MeSH), Genes, Plant (MeSH), Humans (MeSH), Models, Theoretical (MeSH), Neoplasms (metabolism), Oligonucleotide Array Sequence Analysis (methods), Pattern Recognition, Automated (MeSH), Principal Component Analysis (MeSH), Programming Languages (MeSH), Sequence Alignment (MeSH), Software (MeSH).
- MESH :
- metabolism : Neoplasms.
- methods : Computational Biology, Oligonucleotide Array Sequence Analysis.
- Algorithms, Cluster Analysis, Gene Expression Profiling, Gene Expression Regulation, Neoplastic, Genes, Plant, Humans, Models, Theoretical, Pattern Recognition, Automated, Principal Component Analysis, Programming Languages, Sequence Alignment, Software.
Abstract
MOTIVATION
Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss.
RESULTS
We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes.
AVAILABILITY
The C++ code to implement the proposed method is available upon request for academic use.
DOI: 10.1093/bioinformatics/btn215
PubMed: 18453554
PubMed Central: PMC3607310
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Harding</name></author><author><name sortKey="Tsai, Chung Jui" sort="Tsai, Chung Jui" uniqKey="Tsai C" first="Chung-Jui" last="Tsai">Chung-Jui Tsai</name></author><author><name sortKey="Zhang, Shuanglin" sort="Zhang, Shuanglin" uniqKey="Zhang S" first="Shuanglin" last="Zhang">Shuanglin Zhang</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2008">2008</date><idno type="RBID">pubmed:18453554</idno><idno type="pmid">18453554</idno><idno type="doi">10.1093/bioinformatics/btn215</idno><idno type="pmc">PMC3607310</idno><idno type="wicri:Area/Main/Corpus">003899</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">003899</idno><idno type="wicri:Area/Main/Curation">003899</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">003899</idno><idno type="wicri:Area/Main/Exploration">003899</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">An efficient method to identify differentially expressed genes in microarray experiments.</title><author><name sortKey="Qin, Huaizhen" sort="Qin, Huaizhen" uniqKey="Qin H" first="Huaizhen" last="Qin">Huaizhen Qin</name><affiliation wicri:level="2"><nlm:affiliation>Department of Mathematical Sciences, Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Department of Mathematical Sciences, Biotechnology Research Center, School of Forest Resources and Environmental Science, Michigan Technological University, Houghton, MI 49931</wicri:regionArea><placeName><region type="state">Michigan</region></placeName></affiliation></author><author><name sortKey="Feng, Tao" sort="Feng, Tao" uniqKey="Feng T" first="Tao" last="Feng">Tao Feng</name></author><author><name sortKey="Harding, Scott A" sort="Harding, Scott A" uniqKey="Harding S" first="Scott A" last="Harding">Scott A. Harding</name></author><author><name sortKey="Tsai, Chung Jui" sort="Tsai, Chung Jui" uniqKey="Tsai C" first="Chung-Jui" last="Tsai">Chung-Jui Tsai</name></author><author><name sortKey="Zhang, Shuanglin" sort="Zhang, Shuanglin" uniqKey="Zhang S" first="Shuanglin" last="Zhang">Shuanglin Zhang</name></author></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</idno><imprint><date when="2008" type="published">2008</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Cluster Analysis (MeSH)</term><term>Computational Biology (methods)</term><term>Gene Expression Profiling (MeSH)</term><term>Gene Expression Regulation, Neoplastic (MeSH)</term><term>Genes, Plant (MeSH)</term><term>Humans (MeSH)</term><term>Models, Theoretical (MeSH)</term><term>Neoplasms (metabolism)</term><term>Oligonucleotide Array Sequence Analysis (methods)</term><term>Pattern Recognition, Automated (MeSH)</term><term>Principal Component Analysis (MeSH)</term><term>Programming Languages (MeSH)</term><term>Sequence Alignment (MeSH)</term><term>Software (MeSH)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Alignement de séquences (MeSH)</term><term>Analyse de profil d'expression de gènes (MeSH)</term><term>Analyse de regroupements (MeSH)</term><term>Analyse en composantes principales (MeSH)</term><term>Biologie informatique (méthodes)</term><term>Gènes de plante (MeSH)</term><term>Humains (MeSH)</term><term>Langages de programmation (MeSH)</term><term>Logiciel (MeSH)</term><term>Modèles théoriques (MeSH)</term><term>Reconnaissance automatique des formes (MeSH)</term><term>Régulation de l'expression des gènes tumoraux (MeSH)</term><term>Séquençage par oligonucléotides en batterie (méthodes)</term><term>Tumeurs (métabolisme)</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Neoplasms</term></keywords><keywords scheme="MESH" qualifier="methods" xml:lang="en"><term>Computational Biology</term><term>Oligonucleotide Array Sequence Analysis</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Tumeurs</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Biologie informatique</term><term>Séquençage par oligonucléotides en batterie</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Cluster Analysis</term><term>Gene Expression Profiling</term><term>Gene Expression Regulation, Neoplastic</term><term>Genes, Plant</term><term>Humans</term><term>Models, Theoretical</term><term>Pattern Recognition, Automated</term><term>Principal Component Analysis</term><term>Programming Languages</term><term>Sequence Alignment</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Alignement de séquences</term><term>Analyse de profil d'expression de gènes</term><term>Analyse de regroupements</term><term>Analyse en composantes principales</term><term>Gènes de plante</term><term>Humains</term><term>Langages de programmation</term><term>Logiciel</term><term>Modèles théoriques</term><term>Reconnaissance automatique des formes</term><term>Régulation de l'expression des gènes tumoraux</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>MOTIVATION</b></p><p>Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. In all three analyses, our method proved more robust than the popular alternatives for identification of differentially expressed genes.</p></div><div type="abstract" xml:lang="en"><p><b>AVAILABILITY</b></p><p>The C++ code to implement the proposed method is available upon request for academic use.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">18453554</PMID><DateCompleted><Year>2008</Year><Month>09</Month><Day>09</Day></DateCompleted><DateRevised><Year>2018</Year><Month>11</Month><Day>13</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>24</Volume><Issue>14</Issue><PubDate><Year>2008</Year><Month>Jul</Month><Day>15</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>An efficient method to identify differentially expressed genes in microarray experiments.</ArticleTitle><Pagination><MedlinePgn>1583-9</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/btn215</ELocationID><Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">Microarray experiments typically analyze thousands to tens of thousands of genes from small numbers of biological replicates. The fact that genes are normally expressed in functionally relevant patterns suggests that gene-expression data can be stratified and clustered into relatively homogenous groups. Cluster-wise dimensionality reduction should make it feasible to improve screening power while minimizing information loss.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">We propose a powerful and computationally simple method for finding differentially expressed genes in small microarray experiments. The method incorporates a novel stratification-based tight clustering algorithm, principal component analysis and information pooling. Comprehensive simulations show that our method is substantially more powerful than the popular SAM and eBayes approaches. We applied the method to three real microarray datasets: one from a Populus nitrogen stress experiment with 3 biological replicates; and two from public microarray datasets of human cancers with 10 to 40 biological replicates. 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Harding</name><name sortKey="Tsai, Chung Jui" sort="Tsai, Chung Jui" uniqKey="Tsai C" first="Chung-Jui" last="Tsai">Chung-Jui Tsai</name><name sortKey="Zhang, Shuanglin" sort="Zhang, Shuanglin" uniqKey="Zhang S" first="Shuanglin" last="Zhang">Shuanglin Zhang</name></noCountry><country name="États-Unis"><region name="Michigan"><name sortKey="Qin, Huaizhen" sort="Qin, Huaizhen" uniqKey="Qin H" first="Huaizhen" last="Qin">Huaizhen Qin</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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