User: aec

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aec30
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Posts by aec

<prev • 47 results • page 1 of 5 • next >
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question about "write.fit" function in limma-voom
... Dear all, Anybody knows how to extract the write.fit table with method="nestedF" ? the function only gives option for method="separate" or "global". Thanks, ...
limma write.fit method nestedf written 4 days ago by aec30
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Comment: C: Applying sva is compatible with limma duplicateCorrelation?
... Do you think, then, this option is invalid? y=DGEList(counts=counts) A<-rowSums(y$counts) isexpr<-A>500 y=y[isexpr,keep.lib.size=FALSE] y=calcNormFactors(y) group=factor(info_new$group) patient=factor(info_new$patient) mod <- model.matrix(~group) mod0 <- model.matrix(~1) v=voom(y,mo ...
written 12 weeks ago by aec30
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Comment: C: normalization affects drastically limma-voom + sva results
... You are right Gordon, if I add this step: logcpm <- cpm(yy, prior.count=2, log=TRUE) n.sv = num.sv(logcpm,mod,method="leek") n.sv [1] 1 I got the same results as 'voom'.  If I want to calculate n.sv using the rnaseq DESeq2 worklow, how to do it? it is not specified. Should I use the 'rlog' or ...
written 12 weeks ago by aec30
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Comment: C: normalization affects drastically limma-voom + sva results
... Ok, then option 5 is discarded. But what about the rest? option 4 used TMM normalization and option 6 uses DESeq2 normalization, they both give 57 sv. options 1,2,3 use voom and give 1 sv. option 6 follows this code https://www.bioconductor.org/help/workflows/rnaseqGene/ : dat <- counts(dds ...
written 12 weeks ago by aec30
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Comment: C: normalization affects drastically limma-voom + sva results
... Gordon, My problem is that I have been trying SVA with different normalization options as follows: y=DGEList(counts=counts) A<-rowSums(y$counts) isexpr<-A>500 y=y[isexpr,keep.lib.size=FALSE] yy=calcNormFactors(y) mod <- model.matrix(~group, data=info) mod0 <- model.matrix(~1, data= ...
written 12 weeks ago by aec30
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normalization affects drastically limma-voom + sva results
... Dear all, Depending on the normalization approach (none, quantile, TMM  or DESeq2) applied to the limma-voom function, the number of surrogate variables found by SVA and number of differentially expressed genes changes a lot. My question is, does SVA replace the normalization step? For example, if ...
normalization sva limma-voom written 3 months ago by aec30 • updated 12 weeks ago by Gordon Smyth32k
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Comment: C: Applying sva is compatible with limma duplicateCorrelation?
... Thanks for your comments sina.nassari.  1) so then what is better: mod <- model.matrix(~sex+age+city+group) mod0 <- model.matrix(~sex+age+city) ​mod1 <- model.matrix(~group+sva_obj$sv) or  mod <- model.matrix(~group) mod0 <- model.matrix(~1) mod1 <- model.matrix(~group+sva_ob ...
written 3 months ago by aec30
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Applying sva is compatible with limma duplicateCorrelation?
... Dear all, I have a complex bulk RNA-seq dataset (from a human tissue) with multiple covariates (sex, city, age), different cell type composition, repeated measures (same patient sequenced 4 times --> 4 different locations of the tissue). I am applying sva to correct for different cell type compo ...
sva duplicatecorrelation limma-voom written 3 months ago by aec30 • updated 3 months ago by sina.nassiri50
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Comment: C: How to adjust for different cell type mixtures in differential expression analys
... Ok Ryan, thanks! ...
written 3 months ago by aec30
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How to adjust for different cell type mixtures in differential expression analysis?
... Dear all,  I computed enrichment scores for 64 cell types with xCELL from my bulk RNAseq samples. Now I would like to detect differential expression across 3 groups (control, case1, case2) but adjusting for the different cell type compositions (continuous variables). I was thinking of taking only t ...
rnaseq differential expression adjustment cell types mixtures written 3 months ago by aec30 • updated 3 months ago by Ryan C. Thompson6.1k

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