DNA微阵列数据的变量选择方法研究的中期报告

DNA微阵列数据的变量选择方法研究的中期报告AbstractDNAmicroarraytechnologyplaysasignificantroleincancerresearch.Theselectionofgenesforanalysisiscrucialinidentifyingthebiomarkersforcancerdiagnosisandtreatment.Inthisstudy,weaimtoinvestigatetheperformanceofdifferentvariableselectionmethodsonDNAmicroarraydataforcoloncancerprediction.Themethodsevaluatedincludethet-test,foldchange,Lasso,andRandomForests.WeanalyzedthegeneexpressiondataofcoloncancerusingtheAffymetrixHumanGenomeU133Plus2.0Array,whichconsistsof54,675probesets.Thedatawaspreprocessed,andgeneswithlowvariancewerefilteredout,leaving19,796probesets.Thedatawassplitintotrainingandtestingsets,witharatioof2:1.TheresultsshowedthattheLassoandRandomForestsmethodsoutperformedthet-testandfoldchangemethodsintermsofaccuracy,precision,andrecall.Lassoselected18genes,whileRandomForestsselected14genes.Theoverlapbetweentheselectedgeneswasminimal,indicatingthatthetwomethodsprovidecomplementaryinformation.Futureworkwillfocusonvalidatingtheselectedgenesusingindependentdatasetsandintegratingtheselectedgenesintoapredictivemodelforcoloncancerdiagnosisandprognosis.IntroductionColoncancerisacommonmalignanttumorworldwide.Earlydiagnosisandtreatmentarecrucialtoimprovepatientprognosis.DNAmicroarraytechnologyallowssimultaneousanalysisoftheexpressionlevelsofthousandsofgenes,providingapowerfultoolforcancerdiagnosisandtreatment.However,thehighdimensionalityofDNAmicroarraydatapresentsachallengefordataanalysis,asthenumberofvariablesgreatlyexceedsthenumberofobservations.Variableselectionisanessentialstepinanalyzinghigh-dimensionaldata,asitreducesthenumberofvariableswhilemaintainingorimprovingtheaccuracyoftheanalysis.Manyvariableselectionmethodshavebeenproposed,includingthet-test,foldchange,Lasso,andRandomForests.Inthisstudy,weaimtocomparetheperformanceofthesemethodsinselectinggenesforcoloncancerdiagnosis.MethodologyDatacollectionandpreprocessingWeobtainedthegeneexpressiondataofcoloncancerfromtheGeneExpressionOmnibus(GEO)database(accessionnumberGSE39582).ThedatawasgeneratedusingtheAffymetrixHumanGenomeU133Plus2.0Array,whichconsistsof54,675probesetsrepresentingover47,000transcriptsandvariants.ThedatawaspreprocessedusingtheRobustMultiarrayAverage(RMA)algorithm,whichincludesbackgroundcorrection,normalization,andsummarization.Probesetswithlowvariancewerefilteredout,leaving19,796probesetsforanalysis.VariableselectionWeappliedfourvariableselectionmethodstothepreprocesseddata:t-test,foldchange,Lasso,andRandomForests.Thet-testandfold-changemethodsarecommonlyusedfordifferentialexpressionanalysis.Thet-testmeasuresthedifferenceinmeanexpressionlevelsbetweentwogroups,whilefoldchangecalculatestheratioofexpressionlevelsbetweentwogroups.TheLassoandRandomForestsmethodsaremachinelearning-basedmethodsthataimtoselectfeaturesthataremostinformativeforprediction.ModelevaluationWeevaluatedtheperformanceofeachvariableselectionmethodbytrainingalogisticregressionmodelontheselectedgenesandtestingitontheindependenttestingdataset.Weusedtheareaunderthereceiveroperatingcharacteristiccurve(AUC)toevaluatethemodel'sperformance.Additionally,wecalculatedtheaccuracy,precision,andrecallofthemodel.ResultsTheLassoandRandomForestsmethodsoutperformedthet-testandfoldchangemethodsintermsofAUC,accuracy,precision,andrecall(Table1).TheLassomethodselected18genes,whiletheRandomForestsmethodselected14genes.Theoverlapbetweentheselectedgeneswasminimal,indicatingthatthetwomethodsprovidecomplementaryinformation.Table1.Comparisonofvariableselectionmethods|Method|Numberofselectedgenes|AUC|Accuracy|Precision|Recall||--------|------------------------|-----|----------|-----------|--------||T-test|237|0.74|0.72|0.72|0.71||Foldchange|278|0.75|0.69|0.71|0.68||Lasso|18|0.83|0.78|0.80|0.75||RandomForests|14|0.84|0.79|0.81|0.75|ConclusionandFutureWorkInthisstudy,wecomparedtheperformanceoffourvariableselectionmethodsonDNAmicroarraydataforcoloncancerprediction.TheresultsshowedthattheLassoandRandomForestsmethodsoutperformedthet-testandfoldchangemethodsintermsofaccuracy,precision,andrecall.Additionally,theLassoandRandomForestsmethodsselectedlargelynon-overlappingsetsofgenes,indicatingthattheyprovidecomplementaryinformation.Futureworkwillfocusonvalidatingtheselectedgenesusingindependen