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 10 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 10 months ago by riccardo40 • updated 10 months ago by Michael Love17k
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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 10 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 10 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 10 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$SV3)) Thank you ...
written 10 months ago by riccardo40
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Comment: C: deseq2 course experiment
... Sorry I think that this is more correct: rldBlindTrueRemBatch = removeBatchEffect(rldBlindTrueCounts, covariates = as.matrix(ddssva@colData$SV1, ddssva@colData$SV2, ddssva@colData$SV3)) Since my ddsva@colData is:                cond          SV1         SV2         SV3            <factor> ...
written 10 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 10 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 10 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 10 months ago by riccardo40

Latest awards to riccardo

Popular Question 13 months ago, created a question with more than 1,000 views. For DESeq2 and shrinkage of log2 fold changes
Popular Question 14 months ago, created a question with more than 1,000 views. For DESeq2 and ComBat
Popular Question 24 months ago, created a question with more than 1,000 views. For DESeq2 FPKM normalization
Popular Question 24 months ago, created a question with more than 1,000 views. For DESeq2 and ComBat

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