User: nonCodingGene

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Posts by nonCodingGene

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Comment: C: edgeR coefficients for DEG analysis
... To me I think the two first ways of doing the analysis are the right ones, but I do not understand what the last two comparison mean, so this confuses my a little bit. ~1+Cell.type+Patient coef = 2 ~0+Cell.type+Patient contrast = c(-1,1,0,0,0,0,0,0) ~0+Cell.type+Patient coef = 2 ~1+Cell.typ ...
written 3 months ago by nonCodingGene10
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edgeR coefficients for DEG analysis
... I'm trying to perform a DEG analysis of two cell types extracted from 8 different patients (data comes from scRNA). I'm trying to compare the two cells while adjusting for the patients, there are two possible models: a) ~0+cell.type+patients b) ~cell.type.patients lrt <- glmWeightedF(fit, co ...
edger written 3 months ago by nonCodingGene10 • updated 3 months ago by James W. MacDonald48k
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Comment: C: weights must be finite positive value
... What can be the source of this and how can I solve it? I'm using edgeR for scRNA-seq, in order to manage droputs I use zinbwave Just in case I've checked whether one condition has a gene for which its counts are all 0, and this does not happens. This is how I calculate weights: zinb <- zinb ...
written 3 months ago by nonCodingGene10
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Comment: C: weights must be finite positive value
... sum(is.na(dge$weights)) outputs 0 ...
written 3 months ago by nonCodingGene10
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Comment: C: weights must be finite positive value
... Sorry, I forgot to show that also tested for max(dge$weights) and min(dge$weights), and values where 1 and 0. ...
written 3 months ago by nonCodingGene10
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weights must be finite positive value
... I'm using edgeR and an error for which I have not found a solution just appeared. design <- model.matrix(~Cell.type, data = colData(summ.exp_norm)) dge$weights <- zinb.weights dge <- estimateDisp(dge, design) Error in .compressWeights(y, weights) : weights must be finite positive values ...
edger estimatedispersions written 3 months ago by nonCodingGene10 • updated 3 months ago by Aaron Lun21k
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Comment: A: Limma RNAseq comparison with Cuffdiff
... Update, I've modify the classes vector such classes <- factor(c("NEG","NEG","NEG", "MDK","MDK","MDK"), as.character(c("NEG", "MDK"))) Now logFC are quite close to the ones obtained in cuffdiff but none of the top 7 significant genes from cuffdiff are significant in limma. 3157 significant gen ...
written 14 months ago by nonCodingGene10
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Limma RNAseq comparison with Cuffdiff
... I've the output of a differential expression analysis for RNA done with cuffdiff and I'm trying to check it with limma but I'm not getting the results that I would expect. w164 <- read.table("COUNTfile.txt", header = T, row.names = 1) head(w164) WM164_NEG_1 WM164_NEG_2 WM164_NEG_3 W ...
limma written 14 months ago by nonCodingGene10 • updated 14 months ago by Gordon Smyth35k
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Comment: C: sampleNames differ between assayData and phenoData
... The identical() test returns a FALSE, so thats is the problem. Thanks for the answer. ...
written 2.4 years ago by nonCodingGene10
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sampleNames differ between assayData and phenoData
... Sorry for this question, but for some reason something is happening that I'm not able to find. I'm having the next problem while creating the expression set.     pd <- data.frame(state = colnames(q@mat)) rownames(pd) <- pd[,1] pd[,1] <- 1 pheno = new("AnnotatedDataFrame" ...
expresionset written 2.4 years ago by nonCodingGene10 • updated 2.4 years ago by Martin Morgan ♦♦ 22k

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Popular Question 2.4 years ago, created a question with more than 1,000 views. For AnnotatedDataFrame is not a defined class

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