User: riccardo

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riccardo40
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Posts by riccardo

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Comment: C: find fold change in DESeq2 results
... Thank you to all for the information. ...
written 7 months ago by riccardo40
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find fold change in DESeq2 results
... Hi, maybe I am doing something wrong but I cannot find a log2FoldChange inside the results of DESeq2: res = results(dds, contrast=c("cond", test, control)) dRes = as.data.frame(res) dRes$log2FoldChange[1] [1] 12.62795 dRes[dRes$log2FoldChange == 12.62795,] [1] baseMean log2FoldChange lfc ...
deseq2 written 7 months ago by riccardo40 • updated 7 months ago by Michael Love15k
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Comment: A: deseq2 course experiment
... Hi Michael, thank you for your help. My WT2 and WT5 are my controls at different times but what if later I will be only interested in the comparison of, for example, A2 vs B2 and A5 vs B5? Should I perform again the analysis excluding WT2 and WT5 from the table of counts used by DESeq2? Or is it be ...
written 7 months ago by riccardo40
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Comment: C: deseq2 course experiment
... Hi Michael, I think that I found the problem, removeBatchEffect wants the log counts so using the log2 of the counts normalized by DESeq2 it seems work, do you agree?   ...
written 7 months ago by riccardo40
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Comment: C: deseq2 course experiment
... Hi I think that there is a problem because some values are negative, what could be the problem?  I have also another question regarding this code: dat <- counts(dds, normalized = TRUE) idx <- rowMeans(dat) > 1 dat <- dat[idx, ] mod <- model.matrix(~cond, colData(dds)) mod0 < ...
written 7 months ago by riccardo40
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Comment: C: deseq2 course experiment
... Thank you! If I wanted the read counts normalized without batch effects could I use this command? removeBatchEffect(counts(dds, normalized = TRUE), covariates = as.matrix(ddssva@colData$SV1, ddssva@colData$SV2, ddssva@colData$SV3)) Thank you ...
written 7 months ago by riccardo40
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Comment: C: deseq2 course experiment
... Sorry I think that this is more correct: rldBlindTrueRemBatch = removeBatchEffect(rldBlindTrueCounts, covariates = as.matrix(ddssva@colData$SV1, ddssva@colData$SV2, ddssva@colData$SV3)) Since my ddsva@colData is:                cond          SV1         SV2         SV3            <factor> ...
written 7 months ago by riccardo40
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Comment: C: deseq2 course experiment
... Thanks, is this the correct way to use the information from DESeq2 and SVA in limma? rldBlindTrueCounts = assay(rldBlindTrue) rldBlindTrueRemBatch = removeBatchEffect(rldBlindTrueCounts, covariates = as.matrix(ddssva@colData)) Thanks ...
written 7 months ago by riccardo40
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Comment: C: deseq2 course experiment
... Hi Michael, I would like to generate a PCA after to have used sva, I used this code: dds = DESeqDataSetFromTximport(txi, cond, ~cond) dds = dds[rowSums(counts(dds)) > 0, ] dds = estimateSizeFactors(dds) dat <- counts(dds, normalized = TRUE) idx <- rowMeans(dat) > 1 dat <- dat[id ...
written 7 months ago by riccardo40
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Comment: C: deseq2 course experiment
... I used the condition merging the treatment and the time as above, for example control2, control5 ecc. In this case sva could remove batch effect that I am not aware? Thank you. ...
written 7 months ago by riccardo40

Latest awards to riccardo

Popular Question 9 months ago, created a question with more than 1,000 views. For DESeq2 and shrinkage of log2 fold changes
Popular Question 11 months ago, created a question with more than 1,000 views. For DESeq2 and ComBat
Popular Question 21 months ago, created a question with more than 1,000 views. For DESeq2 FPKM normalization
Popular Question 21 months ago, created a question with more than 1,000 views. For DESeq2 and ComBat

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