User: gavinpaulkelly

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Posts by gavinpaulkelly

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Comment: C: DEseq2 for several experiments with one condition
... In clarifying a previous question, the OP responded "I have 9 experiments that each experiment is effect of one drug on one cancer (drugs and cancers are different in each experiment )" so I think we may need further clarification on what exact question is being asked. I suspect the colData don't te ...
written 1 day ago by gavinpaulkelly80
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Comment: C: DEseq2 for several experiments with one condition
... How does this differ from your previous question? https://support.bioconductor.org/p/91023/#91066 ...
written 2 days ago by gavinpaulkelly80
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Comment: C: DESeq2 with contaminating cell types
... Sorry, I should have said design ~ A + B + C -1 so that we're not attempting to fit with rank-deficient matrix. I think you should then be able to estimate the A vs B identically to how you would if you'd had pure 0's and 1's in the matrix, so results(..., contrast=list("A", "B")) will give the est ...
written 6 days ago by gavinpaulkelly80
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Comment: C: DE by DEseq2
... OK, this is quite important information.  So when you say "all treated vs all controls samples", what are you trying to achieve: to find genes that have common magnitude of effect for every drug treatment?  That sounds an unpromising approach, and I'd recommend getting in touch with a local statisti ...
written 7 days ago by gavinpaulkelly80
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Comment: C: DE by DEseq2
... Correct - if you give a bit more detail about how one experiment differs from another, we may be able to offer better advice. ...
written 7 days ago by gavinpaulkelly80
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Answer: A: DESeq2 with contaminating cell types
... I'd be tempted to attempt correcting for contamination in the design matrix itself.  So if for each sample, you know (estimate) its A:B:C composition proportions, then the overall expression would be (proportion of A)*(expression in A) + (proportion in B)*(expression in B) + (proportion in C) * (exp ...
written 7 days ago by gavinpaulkelly80
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Answer: A: DE by DEseq2
... Yes, it looks like DESeq2 would be a good choice.  If the 'experiments' are all done under the same technology, and have been quantified using the same bioinformatic techniques, then you can simply provide DESeq2 with the 18-columned matrix.  You could normalise out any technical differences between ...
written 7 days ago by gavinpaulkelly80
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Answer: A: DESeq2 analysis with a continuous covariate
... It looks like 'nested' and 'time' (assuming treatment_time and ischemic_time are the same thing) are 100% correlated, so putting both in the model is going to lead to problems in estimation.  A correct model to test for e.g. difference between treated and untreated would be ~patient + condition and ...
written 7 days ago by gavinpaulkelly80
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Answer: A: DESeq2: Multivariate Models and Model Statistic
... The part where you say "between the UR and the DR, accounting for the differences in Sample/Condition" means that you're probably wanting dds = DESeqDataSetFromHTSeqCount(sampleTable=sampleTable, directory=directory, design=~Type+Condition) dds = DESeq(dds) res = results(dds, name="type_DR_vs_UR") ...
written 8 days ago by gavinpaulkelly80
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Answer: A: DESeq2 comparing interaction terms between multiple genotypes
... That looks correct to me; to get the third pairwise interaction, you could do results(dds, contrast=list("Genotypemutant1.TypeRibo", "Genotypemutant2.TypeRibo")) where the first element of the list is the numerator (so gets treated positively) and the second is the denominator (so gets subtracted ...
written 8 days ago by gavinpaulkelly80

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