SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures.
Identifieur interne : 002D23 ( Main/Exploration ); précédent : 002D22; suivant : 002D24SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures.
Auteurs : Hong-Qiang Wang [États-Unis] ; Lindsey K. Tuominen ; Chung-Jui TsaiSource :
- Bioinformatics (Oxford, England) [ 1367-4811 ] ; 2011.
Descripteurs français
- KwdFr :
- MESH :
- génétique : Populus.
- métabolisme : Populus.
- Algorithmes, Analyse de profil d'expression de gènes, Modèles linéaires, Simulation numérique, Séquençage par oligonucléotides en batterie.
English descriptors
- KwdEn :
- MESH :
- genetics : Populus.
- metabolism : Populus.
- Algorithms, Computer Simulation, Gene Expression Profiling, Linear Models, Oligonucleotide Array Sequence Analysis.
Abstract
MOTIVATION
The pre-estimate of the proportion of null hypotheses (π(0)) plays a critical role in controlling false discovery rate (FDR) in multiple hypothesis testing. However, hidden complex dependence structures of many genomics datasets distort the distribution of p-values, rendering existing π(0) estimators less effective.
RESULTS
From the basic non-linear model of the q-value method, we developed a simple linear algorithm to probe local dependence blocks. We uncovered a non-static relationship between tests' p-values and their corresponding q-values that is influenced by data structure and π(0). Using an optimization framework, these findings were exploited to devise a Sliding Linear Model (SLIM) to more reliably estimate π(0) under dependence. When tested on a number of simulation datasets with varying data dependence structures and on microarray data, SLIM was found to be robust in estimating π(0) against dependence. The accuracy of its π(0) estimation suggests that SLIM can be used as a stand-alone tool for prediction of significant tests.
AVAILABILITY
The R code of the proposed method is available at http://aspendb.uga.edu/downloads for academic use.
DOI: 10.1093/bioinformatics/btq650
PubMed: 21098430
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Tuominen</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></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2011">2011</date><idno type="RBID">pubmed:21098430</idno><idno type="pmid">21098430</idno><idno type="doi">10.1093/bioinformatics/btq650</idno><idno type="wicri:Area/Main/Corpus">003000</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">003000</idno><idno type="wicri:Area/Main/Curation">003000</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">003000</idno><idno type="wicri:Area/Main/Exploration">003000</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures.</title><author><name sortKey="Wang, Hong Qiang" sort="Wang, Hong Qiang" uniqKey="Wang H" first="Hong-Qiang" last="Wang">Hong-Qiang Wang</name><affiliation wicri:level="2"><nlm:affiliation>Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA.</nlm:affiliation><country xml:lang="fr">États-Unis</country><wicri:regionArea>Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602</wicri:regionArea><placeName><region type="state">Géorgie (États-Unis)</region></placeName></affiliation></author><author><name sortKey="Tuominen, Lindsey K" sort="Tuominen, Lindsey K" uniqKey="Tuominen L" first="Lindsey K" last="Tuominen">Lindsey K. Tuominen</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></analytic><series><title level="j">Bioinformatics (Oxford, England)</title><idno type="eISSN">1367-4811</idno><imprint><date when="2011" type="published">2011</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Algorithms (MeSH)</term><term>Computer Simulation (MeSH)</term><term>Gene Expression Profiling (MeSH)</term><term>Linear Models (MeSH)</term><term>Oligonucleotide Array Sequence Analysis (MeSH)</term><term>Populus (genetics)</term><term>Populus (metabolism)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Algorithmes (MeSH)</term><term>Analyse de profil d'expression de gènes (MeSH)</term><term>Modèles linéaires (MeSH)</term><term>Populus (génétique)</term><term>Populus (métabolisme)</term><term>Simulation numérique (MeSH)</term><term>Séquençage par oligonucléotides en batterie (MeSH)</term></keywords><keywords scheme="MESH" qualifier="genetics" xml:lang="en"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="génétique" xml:lang="fr"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="metabolism" xml:lang="en"><term>Populus</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Populus</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Algorithms</term><term>Computer Simulation</term><term>Gene Expression Profiling</term><term>Linear Models</term><term>Oligonucleotide Array Sequence Analysis</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Algorithmes</term><term>Analyse de profil d'expression de gènes</term><term>Modèles linéaires</term><term>Simulation numérique</term><term>Séquençage par oligonucléotides en batterie</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>MOTIVATION</b></p><p>The pre-estimate of the proportion of null hypotheses (π(0)) plays a critical role in controlling false discovery rate (FDR) in multiple hypothesis testing. However, hidden complex dependence structures of many genomics datasets distort the distribution of p-values, rendering existing π(0) estimators less effective.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>From the basic non-linear model of the q-value method, we developed a simple linear algorithm to probe local dependence blocks. We uncovered a non-static relationship between tests' p-values and their corresponding q-values that is influenced by data structure and π(0). Using an optimization framework, these findings were exploited to devise a Sliding Linear Model (SLIM) to more reliably estimate π(0) under dependence. When tested on a number of simulation datasets with varying data dependence structures and on microarray data, SLIM was found to be robust in estimating π(0) against dependence. The accuracy of its π(0) estimation suggests that SLIM can be used as a stand-alone tool for prediction of significant tests.</p></div><div type="abstract" xml:lang="en"><p><b>AVAILABILITY</b></p><p>The R code of the proposed method is available at http://aspendb.uga.edu/downloads for academic use.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" IndexingMethod="Curated" Owner="NLM"><PMID Version="1">21098430</PMID><DateCompleted><Year>2011</Year><Month>04</Month><Day>06</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>01</Day></DateRevised><Article PubModel="Print-Electronic"><Journal><ISSN IssnType="Electronic">1367-4811</ISSN><JournalIssue CitedMedium="Internet"><Volume>27</Volume><Issue>2</Issue><PubDate><Year>2011</Year><Month>Jan</Month><Day>15</Day></PubDate></JournalIssue><Title>Bioinformatics (Oxford, England)</Title><ISOAbbreviation>Bioinformatics</ISOAbbreviation></Journal><ArticleTitle>SLIM: a sliding linear model for estimating the proportion of true null hypotheses in datasets with dependence structures.</ArticleTitle><Pagination><MedlinePgn>225-31</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1093/bioinformatics/btq650</ELocationID><Abstract><AbstractText Label="MOTIVATION" NlmCategory="BACKGROUND">The pre-estimate of the proportion of null hypotheses (π(0)) plays a critical role in controlling false discovery rate (FDR) in multiple hypothesis testing. However, hidden complex dependence structures of many genomics datasets distort the distribution of p-values, rendering existing π(0) estimators less effective.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">From the basic non-linear model of the q-value method, we developed a simple linear algorithm to probe local dependence blocks. We uncovered a non-static relationship between tests' p-values and their corresponding q-values that is influenced by data structure and π(0). Using an optimization framework, these findings were exploited to devise a Sliding Linear Model (SLIM) to more reliably estimate π(0) under dependence. When tested on a number of simulation datasets with varying data dependence structures and on microarray data, SLIM was found to be robust in estimating π(0) against dependence. The accuracy of its π(0) estimation suggests that SLIM can be used as a stand-alone tool for prediction of significant tests.</AbstractText><AbstractText Label="AVAILABILITY" NlmCategory="BACKGROUND">The R code of the proposed method is available at http://aspendb.uga.edu/downloads for academic use.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Hong-Qiang</ForeName><Initials>HQ</Initials><AffiliationInfo><Affiliation>Warnell School of Forestry and Natural Resources, University of Georgia, Athens, GA 30602, USA.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tuominen</LastName><ForeName>Lindsey K</ForeName><Initials>LK</Initials></Author><Author ValidYN="Y"><LastName>Tsai</LastName><ForeName>Chung-Jui</ForeName><Initials>CJ</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType><PublicationType UI="D013485">Research Support, Non-U.S. Gov't</PublicationType><PublicationType UI="D013486">Research Support, U.S. Gov't, Non-P.H.S.</PublicationType></PublicationTypeList><ArticleDate DateType="Electronic"><Year>2010</Year><Month>11</Month><Day>18</Day></ArticleDate></Article><MedlineJournalInfo><Country>England</Country><MedlineTA>Bioinformatics</MedlineTA><NlmUniqueID>9808944</NlmUniqueID><ISSNLinking>1367-4803</ISSNLinking></MedlineJournalInfo><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D000465" MajorTopicYN="Y">Algorithms</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D020869" MajorTopicYN="Y">Gene Expression Profiling</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D016014" MajorTopicYN="N">Linear Models</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D020411" MajorTopicYN="N">Oligonucleotide Array Sequence Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D032107" MajorTopicYN="N">Populus</DescriptorName><QualifierName UI="Q000235" MajorTopicYN="N">genetics</QualifierName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2010</Year><Month>11</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2010</Year><Month>11</Month><Day>26</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2011</Year><Month>4</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">21098430</ArticleId><ArticleId IdType="pii">btq650</ArticleId><ArticleId IdType="doi">10.1093/bioinformatics/btq650</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>États-Unis</li></country><region><li>Géorgie (États-Unis)</li></region></list><tree><noCountry><name sortKey="Tsai, Chung Jui" sort="Tsai, Chung Jui" uniqKey="Tsai C" first="Chung-Jui" last="Tsai">Chung-Jui Tsai</name><name sortKey="Tuominen, Lindsey K" sort="Tuominen, Lindsey K" uniqKey="Tuominen L" first="Lindsey K" last="Tuominen">Lindsey K. Tuominen</name></noCountry><country name="États-Unis"><region name="Géorgie (États-Unis)"><name sortKey="Wang, Hong Qiang" sort="Wang, Hong Qiang" uniqKey="Wang H" first="Hong-Qiang" last="Wang">Hong-Qiang Wang</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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