Some single cell packages (ZIFA, CIDR) make a point of addressing the zero inflation in single cell data. Since RUV is also used for single cell, I have two questions:

1) Would it be beneficial (at least, in theory) to add zero inflated models to RUVseq and RUVnormalize?

2) This is a more theoretical question. I think that bulk RNA data are also zero inflated, if less than single cell data, but still bulk RNA could have used zero inflated models successfully. However, since the sample size for bulk RNA is small, there was simply to way to fit the extra parameters. With single cell data the number of observations is much larger and that's the main reason why it became meaningful to consider the issue of zero inflation. Did I get that right?

Regards,

Nik Tuzov

I'll leave others to answer number 1, but I'll poke my head in for question 2.

Consider the cause of the zero inflation in single-cell RNA-seq data. It's due to a combination of low input RNA, low capture efficiency and strong amplification of captured transcripts to obtain enough cDNA for sequencing. This means that successful capture (and subsequent amplification) of a small number of transcripts in a few cells results in a separate distribution of large counts along with lots of zero counts corresponding to cells in which the transcripts failed to be captured. Indeed, if you get rid of the amplification effects with UMIs, you find that the zero inflation is greatly attenuated - possibly still present due to biological heterogeneity, but that's another story.

In bulk RNA-seq, these considerations are not particularly relevant as you have high input quantities of RNA. This results in high-complexity libraries, reducing the chance of sequencing multiple amplicons of the same original cDNA molecule. Bulk populations also have more stable average expression profiles than single cells, so there's less chance of getting one replicate with zero and the others with large non-zero counts. Obviously, you will always get some zeros when your mean is close to zero - this is already handled by count-based models with no need for an extra zero inflation term.

Any zero inflation would manifest as large dispersion estimates in negative binomial models. I haven't seen that in bulk RNA-seq data - or specifically, I haven't seen that in a way that is caused by an excess of zeros (as large dispersions tend to be caused by a spread of non-zero counts); or when I have seen it, it's usually caused by something biological, e.g., I forgot to block on sex and Xist is now "zero-inflated". Clearly, the better solution in the latter case would be to block on that factor, or find hidden factors... with RUVseq.

I'll try to answer the first question.

I think it is beneficial to consider zero-infated distributions to model single-cell data. In fact, we developed the zinbwave package that implements a zero-inflated negative binomial model. However, we generally use that model for another situation, in which what is unknown is the factor of interest (i.e., the "wanted" variation), and this is because in our experience most of the times the first pass in a single-cell data analysis is to find the low-dimensional signal of interest in the data (e.g., clustering or pseudo time ordering).

It is technically possible to estimate the factors of unwanted variation with the zinbwave model in a similar way to what RUVSeq does, e.g., by fitting the model only on the negative control genes: the resulting W would have the same interpretation of RUV's W.

For unsupervised problems, however, the RUVSeq model doesn't work well because it may be risky to naively remove unwanted variation from the data without controlling for the signal of interest. RUVnormalize and the CRAN package ruv implement more sophisticated variations of RUV which work well for unsupervised problems, but are harder to generalize to zero-inflated model. 

I hope this answered your question, although I realize that it is not a very satisfactory answer.