how to normalize data for rna-seq after limma for heatmap or cox or other downstream analysis, should I use the same way as in edger, jusr use the logCPM data here thanks a lot

dge <- DGEList(counts=mRNAdata)
dge <- calcNormFactors(dge)
**logCPM** <- cpm(dge, log=TRUE, prior.count=3)

v <- voom(dge,design,plot=TRUE, normalize="quantile")
fit <- lmFit(v, design)
cont.matrix=makeContrasts(contrasts=c('tumor-normal'),levels = design)
fit2=contrasts.fit(fit,cont.matrix)
fit2=eBayes(fit2)
tempOutput = topTable(fit2, coef='tumor-normal', n=Inf)
DEG_limma_voom = na.omit(tempOutput)

Generally, yes, you should use the expression values contained in your logCPM variable for downstream clustering (heatmap) and Cox regression. For the heatmap, you may additionally want to center and/or scale the data (to Z-scores) in order to 'improve' visualisation in relation to the heatmap colours. For Cox regression, in a survival analysis context, you may want to dichotomise the variable by median (but there are limitations to this), or categorise it into deciles, quartiles, or something else in order to generate strata for easier interpretatation.

Kevin