Hi!

I am using package ConsensusClusterPlus in R to discover the optimal number of gene expression clusters. Following the steps [note: the code is pseudocode, just to help the understanding]:

1 Get the RNA SEQ data (rows: genes, cols: samples/patients)

2 Keep only the top 30% Most Variable Genes by MAD :

row_sds <-  apply(data, MARGIN = 1, mad) 
row_sds <- row_sds[order(row_sds, decreasing = TRUE)]
top_percentage <- 0.3
num_rows_to_keep <- ceiling(top_percentage * length(row_sds))
row_sds <- row_sds[1:num_rows_to_keep]
data <- data[names(row_sds), ]

3 Normalize expression per gene: sweep(data,1, apply(data,1,median,na.rm=T))

4 Apply method:

ConsensusClusterPlus(data.matrix(data),
  maxK=6,
  reps=50,
  pItem=0.8,
  pFeature=1,
  title=title,
  clusterAlg="hc",
  distance="pearson",
  seed=1262118388.71279,
  plot="png")

My question is on point 1 I have a normalized RNASEQ counts using Voom Limma pipeline (similar do DESEQ2) - normalizes data across samples enabling comparisons between samples. Should I pass to ConsensusPlus the RNASEQ counts or the normalized counts?

Best Regards and Thank you,
Manuel