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Question: DESEQ2 Grouping vs interaction and dds
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6 months ago by
aangajala0 wrote:

Question #1:

I tried to look the documentation for interaction.But, Unable to understand difference between groups and interaction.So difference between following codes, Is it basically same, different ways of doing it?

dds$group <- factor(paste0(dds$race, dds$sampletype)) design(dds) <- ~ group Vs design(dds) <- ~ genotype + condition + genotype:condition Question #2: I have this coldata, miRNA expression for ( sampletype(normal and cancer), ER status, her2 status). I want to get one table of results for ER status (positive vs negative ) compared to sampletype and another table by Her2 status compared to sample type. So my question here is, do I have to have dds twice? of I can produce one dds and then compare later? basically i have three variables, all examples are based on two. ADD COMMENTlink modified 6 months ago by Michael Love19k • written 6 months ago by aangajala0 It’s hard to follow your description. Can you post example colData? ADD REPLYlink written 6 months ago by Michael Love19k > head(coldata) sampletype race androgen_receptor_statu TCGA.3C.AAAU.01A.11R.A41G.13 Primary Tumor white positive TCGA.3C.AALI.01A.11R.A41G.13 Primary Tumor black or african american negative TCGA.3C.AALJ.01A.31R.A41G.13 Primary Tumor black or african american positive TCGA.3C.AALK.01A.11R.A41G.13 Primary Tumor black or african american positive TCGA.4H.AAAK.01A.12R.A41G.13 Primary Tumor white positive TCGA.5L.AAT0.01A.12R.A41G.13 Primary Tumor white positive estrogen_receptor_status progesterone_receptor_status TCGA.3C.AAAU.01A.11R.A41G.13 positive positive TCGA.3C.AALI.01A.11R.A41G.13 positive positive TCGA.3C.AALJ.01A.31R.A41G.13 positive positive TCGA.3C.AALK.01A.11R.A41G.13 positive positive TCGA.4H.AAAK.01A.12R.A41G.13 positive positive TCGA.5L.AAT0.01A.12R.A41G.13 positive positive her2_neu_immunohistochemistry_receptor_status TNBCstatus TCGA.3C.AAAU.01A.11R.A41G.13 negative TCGA.3C.AALI.01A.11R.A41G.13 positive TCGA.3C.AALJ.01A.31R.A41G.13 indeterminate TCGA.3C.AALK.01A.11R.A41G.13 positive TCGA.4H.AAAK.01A.12R.A41G.13 equivocal TCGA.5L.AAT0.01A.12R.A41G.13 negative ADD REPLYlink written 6 months ago by aangajala0 Here is my Coldata, I am trying to verify what I am doing is correct? ADD REPLYlink written 6 months ago by aangajala0 0 6 months ago by Michael Love19k United States Michael Love19k wrote: Do you mean to compare the cancer vs normal difference across ER+ and ER-? If so, then yes an interaction is appropriate and you should use a design ~type + ER + ER:type. And yes, you would rerun DESeq() with different designs if you then wanted to switch to HER2, etc. design(dds) <- ... dds <- DESeq(dds) res1 <- results(dds) You can then do: dds <- removeResults(dds) design(dds) <- ... And so on. ADD COMMENTlink written 6 months ago by Michael Love19k Thank you. What will happen if I do not remove. Then it will just overwrite the dds, so basically it is new dds right? or does it append the dds , I mean mix with the previous one? ADD REPLYlink written 6 months ago by aangajala0 It makes no difference, it will just print a message saying that it removed the results... ADD REPLYlink written 6 months ago by Michael Love19k You just made my day saying so :) I did a lot of work yesterday with out removing. So, I was worried. ADD REPLYlink written 6 months ago by aangajala0 What is the difference between , using group and interaction? Is it going to give same results? dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ type + ER) dds$group <- factor(paste0(dds$type, dds$ER))
design(dds) <- ~ group

For more details on how different designs differ, you'll need to work with a statistician. The support forum is mostly for developers to help users with software questions, but at some point I have to limit the amount of statistical consulting I do here, or else I wouldn't have any time left.