User: hughes.drew

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hughes.drew80
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Posts by hughes.drew

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Comment: C: DESeq2 with contaminating cell types
... Thanks, Michael. This is very clear and I think explains why trying to implement this with DESeq2 didn't perform as well as I'd hoped in practice. I've tracked down a handful of tools that attempt to estimate cell type composition from heterogeneous RNA-seq data (mostly for tumor samples), but I'm s ...
written 10 months ago by hughes.drew80
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Comment: C: DESeq2 with contaminating cell types
... Thanks, again (sorry for the slow response). I went ahead and tried this. Conceptually, I think it's exactly what I was asking for. In practice, I don't do as good of a job as I would like/need (among genes that I strongly believe are due to differential contamination, I drop some from my differenti ...
written 10 months ago by hughes.drew80
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Comment: C: DESeq2 with contaminating cell types
... Awesome, thanks for your suggestion. My colData matrix now looks something like this: sample A B C A_1 0 0.999161006 0.000838994 A_2 0 0.999457391 0.000 ...
written 10 months ago by hughes.drew80
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DESeq2 with contaminating cell types
... I have two cell types (A and B) that I purified from bulk tissue using FACS and profiled by RNA-seq, and I'd like to perform differential expression using DESeq2. I estimate that my contamination rates are generally low for my sorted cells (1-2%). Unfortunately, I believe my contamination rates are ...
deseq2 rna-seq written 10 months ago by hughes.drew80 • updated 10 months ago by Michael Love15k
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Comment: C: DEseq2 extended to open chromatin anlaysis: normalization, dispersion fit, and "
... OK--thanks again. Those plots actually include all 50,000 regions, so I think the initial size factor estimation worked adequately well. I very much appreciate all of your help. ...
written 3.2 years ago by hughes.drew80
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Comment: C: DEseq2 extended to open chromatin anlaysis: normalization, dispersion fit, and "
... Will this be different from the first MA plot (which you noted has points on the right side that generally fall below the x-axis)? Generating the plots you've described--log(cell type mean(normalized read counts)) vs. sample mean(log(normalized read counts))--for all three pairs of cell types, I see ...
written 3.2 years ago by hughes.drew80
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Comment: C: DEseq2 extended to open chromatin anlaysis: normalization, dispersion fit, and "
... It looks like estimating size factors over the intersection doesn't change much vs. the original list of regions (union): Original size factors (sample 1,2,...,6): 50,000 regions round(sizeFactors(dds),3) 1.173 0.595 1.254 1.529 0.660 1.280 Size factors from intersection (sample 1,2,...,6): 679 ...
written 3.2 years ago by hughes.drew80
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Comment: C: DEseq2 extended to open chromatin anlaysis: normalization, dispersion fit, and "
... Awesome--thanks for your response. 1) "It doesn't necessarily assume similar distributions, but that the median ratio will capture the size relationship." This is very encouraging (not nearly as strong as "most genes don't change.") It's much more in line with what I would have expected from the m ...
written 3.2 years ago by hughes.drew80
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DEseq2 extended to open chromatin anlaysis: normalization, dispersion fit, and "too many" differences
... I'm hoping to use DEseq2 to look for regions of open chromatin that differ between cell types. It seems like the general statistical framework should be highly analogous between RNA-seq and DNase-seq (or, in my case, ATAC-seq). However, I wanted to check that some of the assumptions underlying DEseq ...
dnaseq deseq2 written 3.2 years ago by hughes.drew80 • updated 3.2 years ago by Michael Love15k

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