select.sig.gene {AffyExpress} | R Documentation |
select differentially expressed genes based on p value and/or fold change
select.sig.gene(top.table, p.value =0.05, m.value =0)
top.table |
an data frame returned from the regress function |
p.value |
p value, the default value is 0.05 |
m.value |
fold change cut-off value, default value is 0 |
A dataframe which is the similar to the one returned from regress function. An additional column 'significant' is added to the table from the "regress" function. If p value < p.value and absolute of fold change value >=m.value then signiicant = TRUE, otherwise, significant = FALSE.
Xiwei Wu xwu@coh.org, Xuejun Arthur Li xueli@coh.org
data(testData) normaldata<-pre.process("rma",testData) ## Create design matrix design<-make.design(pData(normaldata), "group") ## Create contrast matrix - Compare group "A" vs. "C" contrast<-make.contrast(design, "A", "C") ## Identify differentially expressed gene by using LIMMA method result<-regress(normaldata, design, contrast, "L") ## Select differentially expressed gene based on p <0.05 and ## fold change >=log2(1.5) select<-select.sig.gene(result, p.value=0.05, m.value=log2(1.5))