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Question: Adaptation of DESeq/edgeR model for CRISPR drop-out screen
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gravatar for emanuelvgoncalves
14 days ago by
emanuelvgoncalves0 wrote:

Hi,

Currently I use DESeq2 to analyse large scale CRIPSR loss-of-function screens (gene KO), where cell viability is measured with targeted sequencing, raw counts, for control and KO conditions.

All great so far although CRISPR has a couple of other artefacts that need to be taken in consideration. This is possible at the moment by applying a correction step after the fold-changes but I feel like a more integrated solution is possible. I think that DESeq/edgeR can be adapted to integrate a gene-wise correction method.

I know this is currently being addressed my multiple people but there isn't really an integrated approach yet. I would be very much interested to hear other's experience/opinions/suggestions about this.

Thanks,

ADD COMMENTlink modified 14 days ago by Aaron Lun17k • written 14 days ago by emanuelvgoncalves0
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gravatar for Aaron Lun
14 days ago by
Aaron Lun17k
Cambridge, United Kingdom
Aaron Lun17k wrote:

Have you read https://dx.doi.org/10.12688/f1000research.3928.2? This uses edgeR to analyze data from high-throughput screens like the ones that you are describing.

ADD COMMENTlink modified 14 days ago • written 14 days ago by Aaron Lun17k

Hi,

Yes I'm familiar with that pipeline and I'm doing something similar to that but with DESeq2.

The problem is that there are other potential artefacts of CRISPR data, e.g. copy-number, that need to be taken into account. A couple of methods account for this, one using a linear model with genes copy-number as covariate ( http://rdcu.be/xUQv ).

This seems to be working well, although it feels a more integrated analysis could be possible, hence my post. 

Thanks,

ADD REPLYlink written 14 days ago by emanuelvgoncalves0

Any kind of correction per sample and per gene (or more generally, per row) can go into normalization factors for DESeq2. You can use estimateSizeFactors() with normMatrix having positive, multiplicative normalization factors, and then DESeq2 will correct for sequencing depth on top of those factors. Try it with some small simulated data to see how it works.

ADD REPLYlink modified 14 days ago • written 14 days ago by Michael Love14k
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