CorSig: a general framework for estimating statistical significance of correlation and its application to gene co-expression analysis.
Identifieur interne : 002747 ( Main/Exploration ); précédent : 002746; suivant : 002748CorSig: a general framework for estimating statistical significance of correlation and its application to gene co-expression analysis.
Auteurs : Hong-Qiang Wang [République populaire de Chine] ; Chung-Jui TsaiSource :
- PloS one [ 1932-6203 ] ; 2013.
Descripteurs français
- KwdFr :
- MESH :
- génétique : Populus.
- métabolisme : Flavonoïdes, Populus.
- méthodes : Analyse de profil d'expression de gènes, Statistiques comme sujet.
- Analyse sur microréseau, Logiciel, Modèles statistiques, Simulation numérique.
English descriptors
- KwdEn :
- MESH :
- chemical , metabolism : Flavonoids.
- genetics : Populus.
- metabolism : Populus.
- methods : Gene Expression Profiling, Statistics as Topic.
- Computer Simulation, Microarray Analysis, Models, Statistical, Software.
Abstract
UNLABELLED
With the rapid increase of omics data, correlation analysis has become an indispensable tool for inferring meaningful associations from a large number of observations. Pearson correlation coefficient (PCC) and its variants are widely used for such purposes. However, it remains challenging to test whether an observed association is reliable both statistically and biologically. We present here a new method, CorSig, for statistical inference of correlation significance. CorSig is based on a biology-informed null hypothesis, i.e., testing whether the true PCC (ρ) between two variables is statistically larger than a user-specified PCC cutoff (τ), as opposed to the simple null hypothesis of ρ = 0 in existing methods, i.e., testing whether an association can be declared without a threshold. CorSig incorporates Fisher's Z transformation of the observed PCC (r), which facilitates use of standard techniques for p-value computation and multiple testing corrections. We compared CorSig against two methods: one uses a minimum PCC cutoff while the other (Zhu's procedure) controls correlation strength and statistical significance in two discrete steps. CorSig consistently outperformed these methods in various simulation data scenarios by balancing between false positives and false negatives. When tested on real-world Populus microarray data, CorSig effectively identified co-expressed genes in the flavonoid pathway, and discriminated between closely related gene family members for their differential association with flavonoid and lignin pathways. The p-values obtained by CorSig can be used as a stand-alone parameter for stratification of co-expressed genes according to their correlation strength in lieu of an arbitrary cutoff. CorSig requires one single tunable parameter, and can be readily extended to other correlation measures. Thus, CorSig should be useful for a wide range of applications, particularly for network analysis of high-dimensional genomic data.
SOFTWARE AVAILABILITY
A web server for CorSig is provided at http://202.127.200.1:8080/probeWeb. R code for CorSig is freely available for non-commercial use at http://aspendb.uga.edu/downloads.
DOI: 10.1371/journal.pone.0077429
PubMed: 24194884
PubMed Central: PMC3806744
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">CorSig: a general framework for estimating statistical significance of correlation and its application to gene co-expression analysis.</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="3"><nlm:affiliation>Intelligent Computing Lab, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Intelligent Computing Lab, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei</wicri:regionArea><placeName><settlement type="city">Hefei</settlement><region type="province">Anhui</region></placeName></affiliation></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="2013">2013</date><idno type="RBID">pubmed:24194884</idno><idno type="pmid">24194884</idno><idno type="doi">10.1371/journal.pone.0077429</idno><idno type="pmc">PMC3806744</idno><idno type="wicri:Area/Main/Corpus">002413</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">002413</idno><idno type="wicri:Area/Main/Curation">002413</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">002413</idno><idno type="wicri:Area/Main/Exploration">002413</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">CorSig: a general framework for estimating statistical significance of correlation and its application to gene co-expression analysis.</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="3"><nlm:affiliation>Intelligent Computing Lab, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.</nlm:affiliation><country xml:lang="fr">République populaire de Chine</country><wicri:regionArea>Intelligent Computing Lab, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei</wicri:regionArea><placeName><settlement type="city">Hefei</settlement><region type="province">Anhui</region></placeName></affiliation></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">PloS one</title><idno type="eISSN">1932-6203</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Computer Simulation (MeSH)</term><term>Flavonoids (metabolism)</term><term>Gene Expression Profiling (methods)</term><term>Microarray Analysis (MeSH)</term><term>Models, Statistical (MeSH)</term><term>Populus (genetics)</term><term>Populus (metabolism)</term><term>Software (MeSH)</term><term>Statistics as Topic (methods)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Analyse de profil d'expression de gènes (méthodes)</term><term>Analyse sur microréseau (MeSH)</term><term>Flavonoïdes (métabolisme)</term><term>Logiciel (MeSH)</term><term>Modèles statistiques (MeSH)</term><term>Populus (génétique)</term><term>Populus (métabolisme)</term><term>Simulation numérique (MeSH)</term><term>Statistiques comme sujet (méthodes)</term></keywords><keywords scheme="MESH" type="chemical" qualifier="metabolism" xml:lang="en"><term>Flavonoids</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="methods" xml:lang="en"><term>Gene Expression Profiling</term><term>Statistics as Topic</term></keywords><keywords scheme="MESH" qualifier="métabolisme" xml:lang="fr"><term>Flavonoïdes</term><term>Populus</term></keywords><keywords scheme="MESH" qualifier="méthodes" xml:lang="fr"><term>Analyse de profil d'expression de gènes</term><term>Statistiques comme sujet</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Computer Simulation</term><term>Microarray Analysis</term><term>Models, Statistical</term><term>Software</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Analyse sur microréseau</term><term>Logiciel</term><term>Modèles statistiques</term><term>Simulation numérique</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>UNLABELLED</b></p><p>With the rapid increase of omics data, correlation analysis has become an indispensable tool for inferring meaningful associations from a large number of observations. Pearson correlation coefficient (PCC) and its variants are widely used for such purposes. However, it remains challenging to test whether an observed association is reliable both statistically and biologically. We present here a new method, CorSig, for statistical inference of correlation significance. CorSig is based on a biology-informed null hypothesis, i.e., testing whether the true PCC (ρ) between two variables is statistically larger than a user-specified PCC cutoff (τ), as opposed to the simple null hypothesis of ρ = 0 in existing methods, i.e., testing whether an association can be declared without a threshold. CorSig incorporates Fisher's Z transformation of the observed PCC (r), which facilitates use of standard techniques for p-value computation and multiple testing corrections. We compared CorSig against two methods: one uses a minimum PCC cutoff while the other (Zhu's procedure) controls correlation strength and statistical significance in two discrete steps. CorSig consistently outperformed these methods in various simulation data scenarios by balancing between false positives and false negatives. When tested on real-world Populus microarray data, CorSig effectively identified co-expressed genes in the flavonoid pathway, and discriminated between closely related gene family members for their differential association with flavonoid and lignin pathways. The p-values obtained by CorSig can be used as a stand-alone parameter for stratification of co-expressed genes according to their correlation strength in lieu of an arbitrary cutoff. CorSig requires one single tunable parameter, and can be readily extended to other correlation measures. Thus, CorSig should be useful for a wide range of applications, particularly for network analysis of high-dimensional genomic data.</p></div><div type="abstract" xml:lang="en"><p><b>SOFTWARE AVAILABILITY</b></p><p>A web server for CorSig is provided at http://202.127.200.1:8080/probeWeb. R code for CorSig is freely available for non-commercial use at http://aspendb.uga.edu/downloads.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">24194884</PMID><DateCompleted><Year>2015</Year><Month>02</Month><Day>24</Day></DateCompleted><DateRevised><Year>2019</Year><Month>12</Month><Day>10</Day></DateRevised><Article PubModel="Electronic-eCollection"><Journal><ISSN IssnType="Electronic">1932-6203</ISSN><JournalIssue CitedMedium="Internet"><Volume>8</Volume><Issue>10</Issue><PubDate><Year>2013</Year></PubDate></JournalIssue><Title>PloS one</Title><ISOAbbreviation>PLoS One</ISOAbbreviation></Journal><ArticleTitle>CorSig: a general framework for estimating statistical significance of correlation and its application to gene co-expression analysis.</ArticleTitle><Pagination><MedlinePgn>e77429</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1371/journal.pone.0077429</ELocationID><Abstract><AbstractText Label="UNLABELLED">With the rapid increase of omics data, correlation analysis has become an indispensable tool for inferring meaningful associations from a large number of observations. Pearson correlation coefficient (PCC) and its variants are widely used for such purposes. However, it remains challenging to test whether an observed association is reliable both statistically and biologically. We present here a new method, CorSig, for statistical inference of correlation significance. CorSig is based on a biology-informed null hypothesis, i.e., testing whether the true PCC (ρ) between two variables is statistically larger than a user-specified PCC cutoff (τ), as opposed to the simple null hypothesis of ρ = 0 in existing methods, i.e., testing whether an association can be declared without a threshold. CorSig incorporates Fisher's Z transformation of the observed PCC (r), which facilitates use of standard techniques for p-value computation and multiple testing corrections. We compared CorSig against two methods: one uses a minimum PCC cutoff while the other (Zhu's procedure) controls correlation strength and statistical significance in two discrete steps. CorSig consistently outperformed these methods in various simulation data scenarios by balancing between false positives and false negatives. When tested on real-world Populus microarray data, CorSig effectively identified co-expressed genes in the flavonoid pathway, and discriminated between closely related gene family members for their differential association with flavonoid and lignin pathways. The p-values obtained by CorSig can be used as a stand-alone parameter for stratification of co-expressed genes according to their correlation strength in lieu of an arbitrary cutoff. CorSig requires one single tunable parameter, and can be readily extended to other correlation measures. Thus, CorSig should be useful for a wide range of applications, particularly for network analysis of high-dimensional genomic data.</AbstractText><AbstractText Label="SOFTWARE AVAILABILITY" NlmCategory="UNASSIGNED">A web server for CorSig is provided at http://202.127.200.1:8080/probeWeb. R code for CorSig is freely available for non-commercial use at http://aspendb.uga.edu/downloads.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Wang</LastName><ForeName>Hong-Qiang</ForeName><Initials>HQ</Initials><AffiliationInfo><Affiliation>Intelligent Computing Lab, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, China.</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Tsai</LastName><ForeName>Chung-Jui</ForeName><Initials>CJ</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D023362">Evaluation Study</PublicationType><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>2013</Year><Month>10</Month><Day>23</Day></ArticleDate></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>PLoS One</MedlineTA><NlmUniqueID>101285081</NlmUniqueID><ISSNLinking>1932-6203</ISSNLinking></MedlineJournalInfo><ChemicalList><Chemical><RegistryNumber>0</RegistryNumber><NameOfSubstance UI="D005419">Flavonoids</NameOfSubstance></Chemical></ChemicalList><CitationSubset>IM</CitationSubset><MeshHeadingList><MeshHeading><DescriptorName UI="D003198" MajorTopicYN="N">Computer Simulation</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D005419" MajorTopicYN="N">Flavonoids</DescriptorName><QualifierName UI="Q000378" MajorTopicYN="N">metabolism</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D020869" MajorTopicYN="N">Gene Expression Profiling</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D046228" MajorTopicYN="N">Microarray Analysis</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D015233" MajorTopicYN="Y">Models, Statistical</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><MeshHeading><DescriptorName UI="D012984" MajorTopicYN="Y">Software</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D013223" MajorTopicYN="N">Statistics as Topic</DescriptorName><QualifierName UI="Q000379" MajorTopicYN="Y">methods</QualifierName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="received"><Year>2013</Year><Month>05</Month><Day>18</Day></PubMedPubDate><PubMedPubDate PubStatus="accepted"><Year>2013</Year><Month>09</Month><Day>02</Day></PubMedPubDate><PubMedPubDate PubStatus="entrez"><Year>2013</Year><Month>11</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2013</Year><Month>11</Month><Day>7</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2015</Year><Month>2</Month><Day>25</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>epublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">24194884</ArticleId><ArticleId IdType="doi">10.1371/journal.pone.0077429</ArticleId><ArticleId IdType="pii">PONE-D-13-23057</ArticleId><ArticleId IdType="pmc">PMC3806744</ArticleId></ArticleIdList><ReferenceList><Reference><Citation>New Phytol. 2006;172(1):47-62</Citation><ArticleIdList><ArticleId IdType="pubmed">16945088</ArticleId></ArticleIdList></Reference><Reference><Citation>Trends Biotechnol. 2002 Nov;20(11):467-72</Citation><ArticleIdList><ArticleId IdType="pubmed">12413821</ArticleId></ArticleIdList></Reference><Reference><Citation>J Exp Biol. 2007 May;210(Pt 9):1548-58</Citation><ArticleIdList><ArticleId IdType="pubmed">17449819</ArticleId></ArticleIdList></Reference><Reference><Citation>Nature. 2003 Apr 24;422(6934):835-47</Citation><ArticleIdList><ArticleId IdType="pubmed">12695777</ArticleId></ArticleIdList></Reference><Reference><Citation>Plant Cell Physiol. 2010 Jan;51(1):144-63</Citation><ArticleIdList><ArticleId IdType="pubmed">19996151</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 1999 Jun 8;96(12):6745-50</Citation><ArticleIdList><ArticleId IdType="pubmed">10359783</ArticleId></ArticleIdList></Reference><Reference><Citation>BMC Genomics. 2006;7:40</Citation><ArticleIdList><ArticleId IdType="pubmed">16515682</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 2005 Jun 21;102(25):8961-5</Citation><ArticleIdList><ArticleId IdType="pubmed">15951424</ArticleId></ArticleIdList></Reference><Reference><Citation>Bioinformatics. 2008 Nov 1;24(21):2491-7</Citation><ArticleIdList><ArticleId IdType="pubmed">18784117</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2011 Dec 16;334(6062):1518-24</Citation><ArticleIdList><ArticleId IdType="pubmed">22174245</ArticleId></ArticleIdList></Reference><Reference><Citation>Bioinformatics. 2001 Feb;17(2):126-36</Citation><ArticleIdList><ArticleId IdType="pubmed">11238068</ArticleId></ArticleIdList></Reference><Reference><Citation>DNA Res. 2009 Oct;16(5):249-60</Citation><ArticleIdList><ArticleId IdType="pubmed">19767600</ArticleId></ArticleIdList></Reference><Reference><Citation>J Comput Biol. 2005 Sep;12(7):1029-45</Citation><ArticleIdList><ArticleId IdType="pubmed">16201920</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 1998 Apr 28;95(9):5407-12</Citation><ArticleIdList><ArticleId IdType="pubmed">9560289</ArticleId></ArticleIdList></Reference><Reference><Citation>Nat Genet. 2005 Apr;37(4):382-90</Citation><ArticleIdList><ArticleId IdType="pubmed">15778709</ArticleId></ArticleIdList></Reference><Reference><Citation>Science. 2007 Nov 16;318(5853):1108-13</Citation><ArticleIdList><ArticleId IdType="pubmed">17932254</ArticleId></ArticleIdList></Reference><Reference><Citation>BMC Bioinformatics. 2007;8:220</Citation><ArticleIdList><ArticleId IdType="pubmed">17592643</ArticleId></ArticleIdList></Reference><Reference><Citation>Comput Biol Chem. 2005 Feb;29(1):37-46</Citation><ArticleIdList><ArticleId IdType="pubmed">15680584</ArticleId></ArticleIdList></Reference><Reference><Citation>Front Genet. 2013 Jan 08;3:313</Citation><ArticleIdList><ArticleId IdType="pubmed">23316213</ArticleId></ArticleIdList></Reference><Reference><Citation>BMC Bioinformatics. 2005;6:233</Citation><ArticleIdList><ArticleId IdType="pubmed">16176585</ArticleId></ArticleIdList></Reference><Reference><Citation>IEEE/ACM Trans Comput Biol Bioinform. 2006 Jul-Sep;3(3):232-8</Citation><ArticleIdList><ArticleId IdType="pubmed">17048461</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Am Thorac Soc. 2011 May;8(2):196-8</Citation><ArticleIdList><ArticleId IdType="pubmed">21543801</ArticleId></ArticleIdList></Reference><Reference><Citation>Biometrika. 1951 Jun;38(1-2):219-47</Citation><ArticleIdList><ArticleId IdType="pubmed">14848124</ArticleId></ArticleIdList></Reference><Reference><Citation>Bioinformatics. 2007 Sep 1;23(17):2298-305</Citation><ArticleIdList><ArticleId IdType="pubmed">17586543</ArticleId></ArticleIdList></Reference><Reference><Citation>Cell. 2012 Mar 16;148(6):1293-307</Citation><ArticleIdList><ArticleId IdType="pubmed">22424236</ArticleId></ArticleIdList></Reference><Reference><Citation>Annu Rev Biochem. 2005;74:53-82</Citation><ArticleIdList><ArticleId IdType="pubmed">15952881</ArticleId></ArticleIdList></Reference><Reference><Citation>Bioinformatics. 2006 Oct 15;22(20):2523-31</Citation><ArticleIdList><ArticleId IdType="pubmed">16844710</ArticleId></ArticleIdList></Reference><Reference><Citation>BMC Bioinformatics. 2004 Feb 25;5:18</Citation><ArticleIdList><ArticleId IdType="pubmed">15053845</ArticleId></ArticleIdList></Reference><Reference><Citation>Nucleic Acids Res. 2010 Jul;38(Web Server issue):W214-20</Citation><ArticleIdList><ArticleId IdType="pubmed">20576703</ArticleId></ArticleIdList></Reference><Reference><Citation>BMC Bioinformatics. 2008;9:461</Citation><ArticleIdList><ArticleId IdType="pubmed">18959772</ArticleId></ArticleIdList></Reference><Reference><Citation>Bioinformatics. 2004 Sep 22;20(14):2242-50</Citation><ArticleIdList><ArticleId IdType="pubmed">15130938</ArticleId></ArticleIdList></Reference><Reference><Citation>Bioinformatics. 2008 Jul 1;24(13):1510-5</Citation><ArticleIdList><ArticleId IdType="pubmed">18467346</ArticleId></ArticleIdList></Reference><Reference><Citation>Plant Physiol. 2002 Feb;128(2):428-38</Citation><ArticleIdList><ArticleId IdType="pubmed">11842147</ArticleId></ArticleIdList></Reference><Reference><Citation>Proc Natl Acad Sci U S A. 2009 Dec 22;106(51):22020-5</Citation><ArticleIdList><ArticleId IdType="pubmed">19996170</ArticleId></ArticleIdList></Reference><Reference><Citation>Nat Rev Mol Cell Biol. 2008 Oct;9(10):770-80</Citation><ArticleIdList><ArticleId IdType="pubmed">18797474</ArticleId></ArticleIdList></Reference><Reference><Citation>Bioinformatics. 2005 Jul 15;21(14):3131-7</Citation><ArticleIdList><ArticleId IdType="pubmed">15879452</ArticleId></ArticleIdList></Reference><Reference><Citation>New Phytol. 2005 Jan;165(1):9-28</Citation><ArticleIdList><ArticleId IdType="pubmed">15720617</ArticleId></ArticleIdList></Reference></ReferenceList></PubmedData></pubmed><affiliations><list><country><li>République populaire de Chine</li></country><region><li>Anhui</li></region><settlement><li>Hefei</li></settlement></list><tree><noCountry><name sortKey="Tsai, Chung Jui" sort="Tsai, Chung Jui" uniqKey="Tsai C" first="Chung-Jui" last="Tsai">Chung-Jui Tsai</name></noCountry><country name="République populaire de Chine"><region name="Anhui"><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)
EXPLOR_STEP=$WICRI_ROOT/Bois/explor/PoplarV1/Data/Main/Exploration
HfdSelect -h $EXPLOR_STEP/biblio.hfd -nk 002747 | SxmlIndent | more
Ou
HfdSelect -h $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd -nk 002747 | SxmlIndent | more
Pour mettre un lien sur cette page dans le réseau Wicri
{{Explor lien
|wiki= Bois
|area= PoplarV1
|flux= Main
|étape= Exploration
|type= RBID
|clé= pubmed:24194884
|texte= CorSig: a general framework for estimating statistical significance of correlation and its application to gene co-expression analysis.
}}
Pour générer des pages wiki
HfdIndexSelect -h $EXPLOR_AREA/Data/Main/Exploration/RBID.i -Sk "pubmed:24194884" \
| HfdSelect -Kh $EXPLOR_AREA/Data/Main/Exploration/biblio.hfd \
| NlmPubMed2Wicri -a PoplarV1
| This area was generated with Dilib version V0.6.37. |  |