## User: Akula, Nirmala NIH/NIMH [C]

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#### Posts by Akula, Nirmala NIH/NIMH [C]

<prev • 32 results • page 1 of 4 • next >
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... Sorry Aaron for calling you Steve. Sincerely appreciate your response. Thanks, Nirmala ...
written 5 days ago by Akula, Nirmala NIH/NIMH [C]180
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... Thanks Steve for your response. The fact that the log fold changes are so different between different programs makes it harder to believe which one is correct.  I tried different prior.count and sometimes there is change in the LFC and sometimes not. What is the best way to chose the prior.count fo ...
written 6 days ago by Akula, Nirmala NIH/NIMH [C]180
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...   Hi, I did my analysis using EdgeR and with DESeq2 with and without lfcShrink. At FDR<10% we have the following: EdgeR - 17 genes  DESeq2 without LFC shrink - 37 DESeq2 with LFC shrink - 54 Only 10 genes overlap between all the 3 methods at FDR<10%. The fold change for EdgeR and DESeq2 ...
written 7 days ago by Akula, Nirmala NIH/NIMH [C]180 • updated 6 days ago by Aaron Lun21k
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... Thanks for your help Mike. In a new R session they are same. Interesting!  ...
written 13 days ago by Akula, Nirmala NIH/NIMH [C]180
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... Hi Mike,  When I ran your example it is TRUE. Not sure why I am getting different answers on my data. Any suggestions will be helpful. Thanks, Nirmala ...
written 13 days ago by Akula, Nirmala NIH/NIMH [C]180
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... Hi, Can someone explain why the number of differentially expressed genes at FDR<10% changes when the order of variables changes in the design? Option1: Condition given at the end of the design matrix -  54 genes at FDR<10% >dds=DESeqDataSetFromMatrix(countData=countData,colData=coldata,d ...
written 13 days ago by Akula, Nirmala NIH/NIMH [C]180 • updated 13 days ago by Michael Love19k
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Comment: C: CQN offset and edgeR
... Hi Gordon and Steve, As you suggested the results are slightly different (only in the decimals). Does this mean CQN normalization is not important in this particular analysis? The code with offset: > design <- model.matrix(~coldata$condition + race + rin + gc) > y <- DGEList(counts= ... written 13 days ago by Akula, Nirmala NIH/NIMH [C]180 2 answers 131 views 2 answers Comment: C: CQN offset and edgeR ... Hi Steve, Whether I use the cqn offset (y$offset <- mycqn$glm.offset) or not I get the same results As CQN manual mentions that sometimes standard analysis gives the same results as when using CQN or whether the offset is working or not? Is there a way to test this? Thanks, Nirmala ... written 14 days ago by Akula, Nirmala NIH/NIMH [C]180 2 answers 131 views 2 answers Comment: C: CQN offset and edgeR ... Hi Gordon, Thanks for your response. I get the following error when I use offset command in glmFit > mydataGlmFit=glmFit(y,design,offset=mycqn$glm.offset) Error in glmFit.default(y = y$counts, design = design, dispersion = dispersion, : formal argument "offset" matched by multiple actu ... written 18 days ago by Akula, Nirmala NIH/NIMH [C]180 2 answers 131 views 2 answers ... Hi, I am trying to use cqn offset in EdgeR and getting very low p-values (FDR < 10e-100). Is the offset working correctly? Or I am missing some steps in the code? > mycqn=cqn(countData,lengths=uCovar$GeneLength,x=uCovar$GCcontent, + sizeFactors=sizeFactors$V2,verbose=TRUE) > offset=mycq ...
written 18 days ago by Akula, Nirmala NIH/NIMH [C]180 • updated 18 days ago by Steve Lianoglou12k

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Popular Question 18 months ago, created a question with more than 1,000 views. For Correcting for known and surrogate variables in DESeq2

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