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Possibility to add DESeq2 normalization and showing it on heatmap #138

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shashj opened this issue Jul 28, 2019 · 6 comments
Open

Possibility to add DESeq2 normalization and showing it on heatmap #138

shashj opened this issue Jul 28, 2019 · 6 comments

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@shashj
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shashj commented Jul 28, 2019

@vlakam

I was just thinking of a possibility to add DESeq2 normalization (median-based) in phantasus and displaying that as a heatmap. I was going through phantasus code to do that, I found that to get a heatmap, one has to do the operations in javascript itself. Is it possible to do a matrix operation in R and then reflect it to the heatmap? I would be happy to contribute if that is the case.

Thanks

@vlakam
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vlakam commented Jul 30, 2019

Part of #44.
If you willing to contribute to Phantasus, it would be very helpful to implement wrap for Deseq2 simillar as for limma here https://github.com/ctlab/phantasus/blob/master/R/limmaAnalysis.R
And as for now (sic) there is no ETA for this tool. Sorry.

@shashj
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shashj commented Jul 30, 2019

Yeah I would do that for DESeq2, but my question is a little bit different, I wanted to a differential expression calculation as well as show the normalized (by DESeq2) heatmap. The first part is doable as per my understanding but the second part needs to be written in javascript. Is that correct?

@vlakam
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vlakam commented Jul 30, 2019

That is correct. My point was that if you run into difficulties with Javascript part (because it's kinda actually huge to understand), we would implement that part by ourself. If you share your ideas about UI among your PR it would be also a huge help.

@shashj
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shashj commented Jul 30, 2019

Got it thanks

@assaron
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assaron commented Aug 6, 2019

@shashj we also consider implementing limma+voom based pipeline, as there visualization and differential expression are coupled better, compared to DESeq2, where rlog transformation is better for visualization, and raw counts should be used for differential expression.

@assaron
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assaron commented Aug 12, 2019

On the second glance, implementing the geometric mean normalization isn't that hard, the DESeq2 implementation is pretty straightforward: https://github.com/mikelove/DESeq2/blob/master/R/core.R#L481

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