I am re-analyzing a data set from many years ago and since then voom has come along and I would like to address its applicability to a typical proteomics label-free analysis.

The data is a proteomics data set which is spectral count information for ~200 proteins (I am using spectral counts not emPAI, NSAF, etc). This is across 11 samples, 8 disease 3 control. Also to note is that spectral count data in proteomics can be zero, and therefore the data is quite sparse at times. For instance, approximately 20% of the proteins have zero values for at least 6 of the samples. The counts themselves can run from 0 to ~150 (this can range depending on the mass spec and experimental workflow). Lastly, it is accepted in proteomics that zeros can be replaced with a half-minimum observed value prior to statistical analysis.

A conservative approach to this type of data is to log transform and analyze with a equal two sample t-test. Another is to not transform and use a rank sum approach. Other approaches are proteomics specific like PLGEM, or count specific like EdgeR, DESeq, baySeq, etc, which I somewhat described here.

I have used a voom-limma approach on this data. I used the data as is, data with replaced zeros (with 0.5), and data removing proteins with 6 or more zero measurements. Each of these produces slightly different results based on how voom converts the counts. This line in the manual concerns me with how to best prune my data before running voom limma: 

The limma-voom method assumes that rows with zero or very low counts have been removed.

I am not sure (1) how many zeros can be allowed, and (2) what "low" means. I have run across some other discussions which imply that voom-limma might be better at dealing with the low counts we see in proteomics as opposed to other count based methods like DESeq.

Any guidance would be appreciated, and if this isn't applicable, that is fine. Proteomics is often driving downstream experiments and this data has already had secondary confirmation by westerns and IHC. In other words, if voom-limma isn't applicable, a t-test works just fine at telling the story we have experimentally confirmed.

Thanks ahead of time for any help.

We have experimented with a few of different statistical approaches to proteomics data and spectral counts. We are still learning, but spectral counts seem to behave somewhat differently to RNA-seq counts, showing much more technical variability, so I don't think that I would recommend voom or classic edgeR or DESeq.

Rather than voom, a slightly simpler and very robust approach would be to tranform to y = log2(count+1), quantile normalize, then run a limma pipeline with eBayes(trend=TRUE, robust=TRUE). I feel confident that this would be better than using ordinary t-tests.

Alternatively, if there are a lot of zeros in your data, then a quasi-negative-binomial approach may well be preferable. Have a look at Section 4.7 "Profiles of Unrelated Nigerian Individuals" of the edgeR User's Guide for how a quasi-likelihood pipeline might go.

Whatever pipeline you use, you should filter out low count proteins, as Aaron and Bernd have already discussed. How much filtering to do? The mean-variance plots will guide you. For voom, have a look at the plot produced by voom(plot=TRUE). Ideally the trend line should be monotonically decreasing. If it goes sharply up before trending down, then you haven't filtered enough. For limma and log-counts, look at plotSA(fit) where fit is output from lmFit and eBayes. The trend line should be smooth. If it increases sharply at the low count end, then you haven't filtered enough. For edgeR quasi-likelihood, look at plotBCV and plotQLDisp. Again the trends should be smooth.

Counts in spectral count data are usually very low, therefore, I expect that the normal approximation from voom will not be the best solution, but it depends on your data. I would prefer one of edgeR, DESeq, DESeq2, or similar. 

Filtering proteins with a low number of counts or a low number of non-zero values is a good idea, for both voom / limma or edgeR, DESeq, because it is very unlikely that you will obtain significant hits for proteins with a very low spectral count. See "Independent filtering increases detection power for high-throughput experiments., Bourgon, et al., PNAS, 2010".

I'll flip your questions around a bit, such that we keep proteins where there are at least N libraries with a count above X. The code might look a bit like this, for a matrix of counts:

keep <- rowSums(counts > X) >= N
counts <- counts[keep,]

Now, our problem involves choosing appropriate values for N and X. For N, the value can be determined from your experimental design. In your case, you've got 8 samples in one group and 3 samples in the other. This suggests that N=3 is most appropriate. Otherwise, you might filter out significant proteins that are present in the smaller group and near-absent in the larger group.

The value of X is more arbitrary. From a biological viewpoint, how low must expression be in order to be considered negligble? That's hard to answer, but from a statistical perspective, we want to get rid of those low, discrete counts that will mess up voom's approximations. A value of 5 - 10 might be appropriate for your data. Alternatively, you can filter on CPM values above 1 - 2, as is done in the case studies in the edgeR user's guide.

Of course, as Bernd has suggested, count-based models might be more appropriate if many of your counts are low.

P.S. voom already adds 0.5 to the counts during its internal log-transformation, so there's no need to do it manually. Also, if you remove proteins with 6 or more zero measurements, then you could be throwing out significant proteins that are totally absent in the larger group but present in the smaller group.