Filtering step for read counts data I have around 1200 samples as columns and 60,000 genes with Htseq-Counts data. Before normalization with voom function I want to do filtering step. 

I want to remove genes whose expression is == 0 in at least 10 samples.

Can I do this with read counts itself or should I transform counts to cpm?

If filtering with counts data is fine May I know how to do this.
If you have your counts in a data.frame called counts, something like this might work:

filtered.counts &lt;- counts[rowSums(counts==0)&lt;10, ]


For example lets assume the following data frame.

&gt; A &lt;- c(0,0,0,0,0)
&gt; B &lt;- c(0,1,0,0,0)
&gt; C &lt;- c(0,2,0,2,0)
&gt; D &lt;- c(0,5,1,1,2)
&gt; 
&gt; counts &lt;- data.frame(A=A, B=B, C=C, D=D)
&gt; counts
  A B C D
1 0 0 0 0
2 0 1 2 5
3 0 0 0 1
4 0 0 2 1
5 0 0 0 2


And now you would want to remove the genes with at least 3 zeroes or more:

&gt; filtered.counts &lt;- counts[rowSums(counts==0)&lt;3, ]
&gt; 
&gt; filtered.counts
  A B C D
2 0 1 2 5
4 0 0 2 1


Every gene with 3 or more zeroes is now removed.