## User: glmazzo

glmazzo0
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#### Posts by glmazzo

<prev • 7 results • page 1 of 1 • next >
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... Great! Thank you so much for your help. If I understood correctly, you suggest to perform the DE analysis using cqn to correct for between samples (GC content and length) bias. I know that the gene length and GC content bias within sample does not compromise DE results but that they affect the fun ...
written 18 months ago by glmazzo0
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... Dear Mike, Noiseq generates a plot for each sample for the GC content (plot showed in the post) and a plot for each sample for the length bias (plot not showed in my post) The plots look similar across the sample (both for GC content and length bias). I attached here the plots for other samples ( ...
written 18 months ago by glmazzo0
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... Hi everybody, I am working with RNASeq data.  I am using DESeq2 and I would like to correct for the GC content and length bias using cqn. When I tested the normalized counts with NOISeq to test if the biases were corrected, I noticed that when I divided out the normalization factors by the geom ...
written 18 months ago by glmazzo0 • updated 18 months ago by Michael Love20k
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... Dear Mike,  I attached three plots made with NOISeq to test for GC contenct bias, that I used to test the effect of the cqn correction. From NOISeq manual: “The GC content is divided in intervals (bins) containing 200 features. The middle point of each bin is depicted in X axis. For each bin, the ...
written 18 months ago by glmazzo0
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... Dear Mike, thank you for your help. I run the code as you suggested. cqnOffset <- cqnObject$glm.offset cqnNormFactors <- exp(cqnOffset) normFactors<- cqnNormFactors / exp(rowMeans(log(cqnNormFactors))) normalizationFactors(dds)=normFactors Anyway when I tested the normalized count ... written 18 months ago by glmazzo0 2 answers 679 views 2 answers ... Dear Mike and Robin, as Robin pointed out, I am still a bit confused about how to include the normalizationFactors matrix from cqn into DESeq2. I believe that the right way (this is the only way I see an improvement in the GC and length bias) should be: cqnOffset <- cqnObject$glm.offset  cqnN ...
written 18 months ago by glmazzo0
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... I am working with RNA seq data. I would like to compute the residuals with DESeq2 to remove confounding effect in the data before running WGCNA.   We computed the residuals by using  DESeq2 on our RNA Seq count matrix with this code: fitted.common.scale = t(t(assays(dds)[["mu"]])/sizeFactors(dds) ...
written 19 months ago by glmazzo0 • updated 19 months ago by Steve Lianoglou12k

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