Hi everyone, 

I am wondering about the edgeR normalization step (calcNormFactors) for CRISPR screens. The edgeR user guide says that TMM normalization is recommended for RNA-Seq data because "the highly expressed genes can consume a substantial proportion of the total library size, causing the remaining genes to be under-sampled in that sample. Unless this RNA composition effect is adjusted for, the remaining genes may falsely appear to be down-regulated in that sample." -- the same applies for CRISPR enrichment screens, where in many cases individual guides can take over large fractions of the library (in comparison to the starting point). However, in most of the case studies I have seen for CRISPR screens, no normalization is applied. What is the reason for this?

A related question concerns enrichment screens with a substantial bottleneck. For example, what would be the best way to deal with FACS screens where a small number of cells has been sorted from a complex library? Most guides will be well-represented in the baseline sample, but will have zero counts in the sorted sample, where only a few guides enrich massively. What kind of normalization (if any) should/could be applied? What is the best way to set up the analysis in edgeR in such situations?

Any thoughts would be much appreciated!

Thank you. 

Kamila

In lieu of someone more qualified, I'll have a stab at answering your questions.

For your first question: TMM normalization in calcNormFactors relies on the assumption that most genes (or guides, or whatever features you're giving it) are not DE between samples. This may not be true for small screens with low numbers of guides, especially if those guides were chosen to target genes that are relevant to the phenotype of interest - and thus are more likely to be DE. In such cases, it may be better to perform library size normalization, given that TMM normalization will not be accurate when its assumption is violated. This will correct for sequencing depth at least. However, the use of library size normalization requires you to accept that you are now testing for differential proportions rather than differential abundance of the guides.

For your second question: again, this depends on whether you can assume that the majority of guides are non-DE in the sorted cells. If not, you cannot use TMM normalization. Library size normalization also sounds problematic if a few guides are dominating the library, and even more so if those guides are DE. Presumably the sorted cells undergo separate library preparation and sequencing from the baseline samples, so you can't use information from the latter to help normalize the former. I also assume that you don't have dedicated "house-keeping" guides that are expected to be present and non-DE in the sorted cells and that you can use for normalization. The only solution I can think of is to sort more cells and sequence more deeply to obtain non-zero counts for more guides.

Of course, there is the other issue of the number of sorted cells. If the size of the sorted population decreases over the course of the screen, how should that be handled in the normalization procedure? You'd probably need to assume that most guides do not affect the viability of the sorted subset, which would allow you to assume that changes in the size of the sorted subset are uninteresting and can be normalized out. To do that, you would enough guides, hence the suggestion for increased sequencing.

Thanks a lot Aaron! I totally agree with you, for small screens TMM may not be suitable, but I think it is a good choice for genome-wide screens. The case studies we discussed (which are also in the edgeR user guide) are genome-wide screens and they don't do it, but I think I will go ahead and do TMM. I think it makes a lot of sense, particularly if you have a few very potently enriching guides in your screen.

Just to make sure I understand you correctly: what do you mean by "performing" library normalization? This is something edgeR does automatically and in every case, correct? So in the classic edgeR pipeline, there is no choice between library normalization and no library normalization?

Thanks also for grappling with my second question. Unfortunately, I don't have the option of sequencing more cells. I already have to sort for many hours to get the few cells that I've got. :) Actually just using "regular edgeR" (with automatic library normalization) and gene set analysis with roast/camera gives pretty good results I have to say. It is true that the abundances of guides that did make it into the sorted samples is overblown with library normalization, but it still works on a gene set level because it means something if all guides belonging to a gene have high abundances. The other thing one could do (and I am curious what you think of this) would be to do repeated random sampling from the counts of the starting population. Draw guides x times (with x corresponding to the number of sorted cells) and calculate how often one gets the observed enrichment by chance. This would circumvent the need for any direct comparison of libraries.