Here are my questions:
(1) Which kind of data is the best type for correlation or survival analysis, e.g., DESeq2 normalised count, TPM or FPKM?
(2) Which kind of normalised count data could be used for my desired analysis?
i. RSEM expected_count (DESeq2 standardized)
This kind of data could be fetched from UCSC XENA (https://xenabrowser.net/datapages/?dataset=TCGA-GTEx-TARGET-gene-exp-counts.deseq2-normalized.log2&host=https%3A%2F%2Ftoil.xenahubs.net&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443)
ii. Transformation by the following DESeq2 code
dds <- DESeqDataSetFromMatrix(countData = exprSet, colData = metadata, design = ~ group) dds <- dds[rowSums(counts(dds))>1,] vsd <- vst(dds, blind = FALSE) expr.normalised <- as.data.frame(assay(vsd)) #used as for correlation or survival analysis.