Hi,

I am trying to apply voom to expected counts from RSEM (I don't have raw counts). My next step is to compute a differential test using limma.

# matrix I am trying to apply voom to has 1 row and 8021 columns:
> dim(tmp)
[1]    1 8021

# first five columns
> tmp[,1:5]
                    SRR1068687 SRR1068788 SRR1068808 SRR1068832 SRR1068855
ENSG00000149294.16     2528.61     756.53         36     158.95    1652.77

# no NAs in the data
> which(is.na(tmp))
integer(0)

# this is my design. shows that I have replicates.
> rbind(head(design),tail(design))

                                     studyGTEx studyTARGET
SRR1068687                                   1           0
SRR1068788                                   1           0
SRR1068808                                   1           0
SRR1068832                                   1           0
SRR1068855                                   1           0
SRR1068880                                   1           0
f3b75630.42da.4b66.96c5.94e9a8142261         0           1
f62f350c.f8b5.4715.ace1.387f7e59cb91         0           1
f637ca92.407d.432e.ba08.2bebe05b96f4         0           1
f835cce2.1ed3.4c59.95a2.4bfef161082b         0           1
fb8c3046.eb74.43ff.ae1d.327453221555         0           1
fdee8bef.5ce2.4ca6.b6b9.3423219a1ea4         0           1

# I get an error when I try to apply voom:
> voom(tmp, design = design)
Error in approxfun(l, rule = 2) : 
  need at least two non-NA values to interpolate

I searched for the error and I found that it is usually encountered when you have no replicates. However in my case I have enough replicates - studyGTEx has about 7000 and the remaining are studyTARGET.  What could be the problem?

NOTE: I have already tried adding more rows (to make nrow(tmp)>1) and I still get the same error.

Crossposted: https://www.biostars.org/p/211073/

You say 

NOTE: I have already tried adding more rows (to make nrow(tmp)>1) and I still get the same error.

With all respect, I don't think that can be true. The error message from voom() is caused by the fact that you gave voom() data for only one gene. That's what the error message is telling you. How could voom() possibly fit a mean-variance trend line through one point? If you included data for even as few as two genes, then the error message would disappear.

However you should not be using voom() with FPKM values anyway, as already covered by Steve's answer.

Why are you trying to analyze a single gene in isolation? Is it because you are running out of computer memory?

Edit: The question has since been edited to refer to RSEM expected counts instead of FPKM. Yes, voom() can run on expected counts.

A trick to reducing voom's memory footprint would be input unit weights as part of the voom call:

w <- array(1, dim(y))
v <- voom(y, design, weights=w)

This won't change the results, but will induce voom to run the linear model for each gene separately instead of all together. I have just run voom() in this way myself on a 60000 by 8000 matrix without any problems. It took a few minutes.

Does your matrix only have one row and 8021 columns because you are only interested in the differential expression of one gene across 8021 samples? If so, you don't really want to use the data in this way. These differential expression methods (limma, edgeR, DESeq2, etc) borrow information across genes in order to do their magic, and you're not giving it a chance to do that here.

So, you'll want to build an expression matrix that has all of your genes across these samples at first, then remove genes that are very lowly expressed, then voom (or whatever) them.

If FPKM values are all you have, then instead of using voom, you'll want to do an "ordinary" limma analysis on the log2(fpkm + 0.25) of your FPKM values, and use eBayes(fit, trend=TRUE). Reference the following thread for more information:

A: Differential expression of RNA-seq data using limma and voom()

One last thing to note is that I think you should be careful about combining your "studyTARGET" data with GTEx. I'm not really sure exactly what you're doing, but if you find differential expression in studyTARGET vs GTEx, how will you convince yourself that your finding is a biological phenomenon instead of a purely technical one?