I have successfully used RUVSeq to correct samples from a "classical" control vs treatment experiment for batch effects using RUVr, RUVg, RUVs and svaseq and all gave similar results, which were satisfactory.

Now I want to use RUVSeq in a clustering problem and I understand I can only use RUVs.

I obtained public RNA-seq from various tissues with replicates and after running RUVs, the resulting PCA doesn't separate samples by tissue, while the rlog'ed uncorrected counts and svaseq corrected counts resulted in the expected clustering by tissue.

My question is: how to use RUVs in clustering problems? My code:

counts_norm = round(counts_deseq, digits=0)
differences <- makeGroups(groups)
batch_ruv_reps <- RUVs(counts_norm, rownames(counts_norm), k=3, differences)
counts_ruvseq = batch_ruv_reps$normalizedCounts # plot PCA using this matrix

groups is a vector of tissue names, counts_deseq is a matrix of counts normalized using DESeq2's rlog function

differences is:

     [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
[1,]    1    2    3   -1   -1   -1   -1   -1
[2,]    4    5   -1   -1   -1   -1   -1   -1
[3,]    6    7    8    9   -1   -1   -1   -1
[4,]   23   24   25   26   27   28   29   30
[5,]   10   11   12   -1   -1   -1   -1   -1
[6,]   15   16   17   18   19   20   21   22
[7,]   13   14   -1   -1   -1   -1   -1   -1

I changed k and didn't get better results. Is there anything else I should be doing?

Thank you.

I'm not sure if this is the only problem, but you should not use RUVs on the rlog transformed values from DESeq2. RUVs assumes that your input matrix contains counts, and rounded transformed data from DESeq2 is not the the expected input for RUVs (which will internally take the log of the counts).

A better pipeline for what you are trying to do is to use the transformed data from DESeq2 (without rounding) and use the RUVnormalize package that uses a linear model version of RUV. The `naiveReplicateRUV` function will be the analog of RUVs, or you can have a look at the `iterativeRUV` function to estimate the factors of unwanted variation and the clusters iteratively.