Clustering Gene Expression Data Based on Predicted Differential Effects of G V Interaction

在线阅读 下载PDF 导出详情
摘要 Microarrayhasbecomeapopularbiotechnologyinbiologicalandmedicalresearch.However,systematicandstochasticvariabilitiesinmicroarraydataareexpectedandunavoidable,resultingintheproblemthattherawmeasurementshaveinherent"noise"withinmicroarrayexperiments.Currently,logarithmicratiosareusuallyanalyzedbyvariousclusteringmethodsdirectly,whichmayintroducebiasinterpretationinidentifyinggroupsofgenesorsamples.Inthispaper,astatisticalmethodbasedonmixedmodelapproacheswasproposedformicroarraydataclusteranalysis.TheunderlyingrationaleofthismethodistopartitiontheobservedtotalgeneexpressionlevelintovariousvariationscausedbydifferentfactorsusinganANOVAmodel,andtopredictthedifferentialeffectsofGV(genebyvariety)interactionusingtheadjustedunbiasedprediction(AUP)method.ThepredictedGVinteractioneffectscanthenbeusedastheinputsofclusteranalysis.Weillustratedtheapplicationofourmethodwithageneexpressiondatasetandelucidatedtheutilityofourapproachusinganexternalvalidation.
机构地区 不详
出版日期 2005年01月11日(中国期刊网平台首次上网日期,不代表论文的发表时间)
  • 相关文献