User: hmgeiger

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Posts by hmgeiger

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Comment: C: Looking at interaction term in DESeq2 while also controlling for individual vari
... Update: Also tried a super high number of iterations (maxit=10000) with only a few additional rows converging. ...
written 7 days ago by hmgeiger0
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Comment: C: Looking at interaction term in DESeq2 while also controlling for individual vari
... There are a few genes that are DE if you do not remove these rows, and for these the counts are often high in quite a few samples. For example, here are normalized counts for one gene that did not converge, for group1 and group3. Note, in the real data there are 4 individuals for group 1 and 9 indi ...
written 8 days ago by hmgeiger0
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Comment: A: Looking at interaction term in DESeq2 while also controlling for individual vari
... Finally got results that make sense. This is my code. Only issue left is I am still getting 206 rows that do not converge in beta even after strict expression filtering and increasing maxit (following the tips here: https://support.bioconductor.org/p/65091/). Any other tips on how to get them all t ...
written 8 days ago by hmgeiger0
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Comment: A: Looking at interaction term in DESeq2 while also controlling for individual vari
... Update: Tried running the following code afterwards, but get the same p-values no matter which two groups are being compared. Also there are way too many DEGs given the expected strength of the signal. Don't think the above design is correct, but not sure how to fix it. ddsClean <- dds[which(mc ...
written 9 days ago by hmgeiger0
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Comment: A: Looking at interaction term in DESeq2 while also controlling for individual vari
... That makes sense, although for the last part ("now this matrix m1 can be provided to the full argument of DESeq") I am still unclear on the syntax. Can you take a look at my code and let me know? First, I add the patient indices to the design table. patient_indices <- rep("Index",times=nrow(de ...
written 9 days ago by hmgeiger0
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Looking at interaction term in DESeq2 while also controlling for individual variable
... I am running an experiment where we have samples from a number of individuals, pre and post treatment. These individuals were then also grouped according to features found in the DNA. So, overall the design looks like this. I named pre treatment "before" so that DESeq2 will automatically take it as ...
deseq2 written 9 days ago by hmgeiger0
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Comment: C: DESeq2 only runs interaction factor x continuous variable with old not new versi
... Got it! Thanks. This is how I ended up setting up my script. A is normal, B and C are my two test conditions. dds <- DESeq(DESeqDataSetFromMatrix(countData=featureCounts[,paste(1:24,"D",sep="")], colData=design,design=~Age + Diagnosis + Diagnosis:Age)) B_vs_control_plus_interaction_age <- ...
written 18 months ago by hmgeiger0 • updated 18 months ago by Michael Love13k
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Answer: A: DESeq2 only runs interaction factor x continuous variable with old not new versi
... The investigator wants to look at the interaction between age and diagnosis. They wouldn't be opposed to grouping age into factors as appropriate I don't think, though I also wanted to just see if it could be done keeping age as a continuous variable. ...
written 18 months ago by hmgeiger0
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Answer: A: DESeq2 only runs interaction factor x continuous variable with old not new versi
... Oh, sorry, I was previously using aliases for some of the variables. Continuing with the aliases, this should read: [1] "Intercept"               "Diagnosis_B_vs_A" [3] "Diagnosis_C_vs_A"    "Age"                     [5] "DiagnosisB.Age"     "DiagnosisC.Age"   ...
written 18 months ago by hmgeiger0
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Answer: A: DESeq2 only runs interaction factor x continuous variable with old not new versi
... I tried this, but still get the same error at the results step.  resultsNames(dds) now returns the following: [1] "Intercept"               "Diagnosis_Normal_vs_MDD" [3] "Diagnosis_SCZ_vs_MDD"    "Age"                     [5] "DiagnosisNormal.Age"     "DiagnosisSCZ.Age"   ...
written 18 months ago by hmgeiger0

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