User: Biologist

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Biologist70
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Posts by Biologist

<prev • 143 results • page 1 of 15 • next >
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Comment: C: Why DESeq2 and edgeR give different results?
... Hi Michael, I have a very small cohort 28 cancer vs 3 Normal only. For this type of cohort for differential analysis do I need keep any special arguments while using deseq2? ...
written 24 days ago by Biologist70
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Comment: C: Why DESeq2 and edgeR give different results?
... Thank you very much ...
written 24 days ago by Biologist70
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Why DESeq2 and edgeR give different results?
... Dear All, I have a matrix "U2" with 44 samples (41 Disease + 3 Normal) as columns and approx. 15k genes as rows. "coldata" is where samples as rows and columns "Subtypes" and  "Type". Column "Type" is with Disease and Normal. library(DESeq2) dds <- DESeqDataSetFromMatrix(countData = U2, colDat ...
rnaseq edger deseq2 differential gene expression written 24 days ago by Biologist70 • updated 23 days ago by Gordon Smyth33k
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Comment: C: Filtering step for differential analysis in edgeR
... one last question. When I used "keep <- filterByExpr(y, design)” it retained only 5000 among 15000 genes for further analysis and when I used "keep <- rowSums(cpm(y) > 0.5) >= 1" it retained 12000 among 15000 genes. May I know why there is difference in number. I see that you mentioned ...
written 26 days ago by Biologist70
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Comment: C: Filtering step for differential analysis in edgeR
... Hello Gordon, As you said, I did all the installations again and now I can see filterByExpr function. Could you please tell how does this function works? how is it different from other step using cpm? Nothing mentioned about this "filterByExpr" function in the tutorial. ...
written 27 days ago by Biologist70
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Comment: C: Filtering step for differential analysis in edgeR
... Thanks for the reply. Sorry, I didn't know that. I'm interested in DE lncRNA genes. lncRNAs are basically with low expression value. When I used glmTreat I got only 200 DE lncRNA genes among 1000 (both Differentially expressed protein coding and lncRNAs). I expected them to be more. If not glmTreat ...
written 5 weeks ago by Biologist70
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Comment: C: Filtering step for differential analysis in edgeR
... Sorry, that was a typo in my question. it is fold change threshold of 1.2 only. Yes, with this I get only 1000 DEGs. Yes, I already checked the clustering part and everything for my samples. Everything was good. With glmQLFTest I got 1200 DEG's and with glmTreat (log2FC 1.2 and FDR <=0.05) I got ...
written 5 weeks ago by Biologist70
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Comment: C: Filtering step for differential analysis in edgeR
... I'm using glmTreat function only. It is like following: tr <- glmTreat(fit, contrast=contrast.matrix, lfc=log2(1.2)) topTags(tr) tab2 <- topTags(tr,n=Inf) keep <- tab2$table$FDR <= 0.05 DEG <- tab2$table[keep,] ...
written 5 weeks ago by Biologist70
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Filtering step for differential analysis in edgeR
... I have 35 lung cancer samples and 4 normal tissues. I'm trying to do differential analysis. With the available read counts data using edgeR for differential analysis.  For the filtering steps I'm using this which is mentioned in edgeR tutorial keep <- rowSums(cpm(y) > 0.5) >= 2 This is ...
edger R bioconductor differential gene expression filtering written 5 weeks ago by Biologist70 • updated 5 weeks ago by Gordon Smyth33k
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Comment: C: Principle component analysis plot with gene expression data
... Thank you very much Aaron. In my code above for the ggbiplot, for variable groups I gave type. Is that right? ...
written 6 weeks ago by Biologist70

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