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1、AlgorithmtoIdentifyFrequentCoupledModulesfromTwo-LayeredNetworkSeriesPresentation:YangLiBackground:Background:?Currentnetworkanalysismethodsallfocusononeormultiplenetworksofthesametype.?Cellsareorganizedbymulti-layernetworks.?Differentkindsofnetworksinfluenceeachother.?RNA-se
2、qhasprovidedabundantmulti-levelprofilingdata------expressiondata&splicingdata.?Splicingcoupleswithtranscriptionwiththefollowingthreesplicingreactions------capping,splicing,cleavageandpolyadenylation.CouplednetworkCouplednetworkEssentialsEssentials:?RNA-seqdatasetsselection,pr
3、ocessingandnetworkconstruction?Non-uniformsamplingforfastcomputation?Formulatetheproblemasatenser-based0-1nonlinearintegerprogrammingmodel?Relaxthe0-1nonlinearintegerprogrammingintoacontinuouscomputationalproblem?OptimizationalgorithmandpatternextractionRNA-seqdatasetsselecti
4、on:RNA-seqdatasetsselection:?Database:NCBI’sSequenceReadArchive(SRA)?Criterions:allhumanRNA-seqdatasets,eachofwhichcontainsatleastsixsamples(38datasets)---------Theminimumvalueforrobustcorrelationestimationis6.Dataprocessing:Dataprocessing:①Foreachdataset,weusedtheTophattoolt
5、omapshortreadstothehg18referencegenome(reportonlytheoptimalalignmentanddiscardthosereadsthatmapequallywelltomultiplepositions).②WeappliedthetranscriptassemblytoolCufflinkstoestimateexpressionsforalltranscriptswithknownUCSCtranscriptionannotations.③Calculatedtheinclusionrateof
6、eachexonineverysample.Networkconstruction:Networkconstruction:?ForeachRNA-seqdatasetaweightedgeneco-expressionnetworkcanbeconstructed(edgesweightsarecorrelationsbetweentheexpressionprofilesoftwogenes).?Thesameprocedureisappliedtobuildaweightedexonco-splicingnetworkforthesameR
7、NA-seqdataset(edgeweightsrepresentcorrelationsbetweentheinclusionratesoftwoexons).Weightscomputation:Weightscomputation:①Computeleave-one-outPearsoncorrelationefficientr;②ComputeFisher'stransformationscoren?3?1+r?;z=ln??2?1?r?①Standardizethez-scorestoenforcezeromeanandunitvar
8、iance;②Setvirtualsamplesizen'=10,andcompute?2?exp?z??1?n'?3?.r'=?2?e
