## User: nonCodingGene

nonCodingGene •

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**10**- Status:
- New User
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- 1 year, 2 months ago
- Joined:
- 4 years, 11 months ago
- Email:
- k**********@gmail.com

#### Posts by nonCodingGene

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... 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 15 months ago by
nonCodingGene •

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... 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 ...

written 15 months ago by
nonCodingGene •

**10**• updated 15 months ago by James W. MacDonald ♦**51k**0

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... 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 15 months ago by
nonCodingGene •

**10**0

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...
sum(is.na(dge$weights))
outputs 0
...

written 15 months ago by
nonCodingGene •

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... Sorry, I forgot to show that also tested for max(dge$weights) and min(dge$weights), and values where 1 and 0.
...

written 15 months ago by
nonCodingGene •

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... 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 ...

written 15 months ago by
nonCodingGene •

**10**• updated 15 months ago by Aaron Lun •**25k**0

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... 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 2.1 years ago by
nonCodingGene •

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... 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 ...

written 2.1 years ago by
nonCodingGene •

**10**• updated 2.1 years ago by Gordon Smyth ♦**39k**0

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... The identical() test returns a FALSE, so thats is the problem.
Thanks for the answer.
...

written 3.3 years ago by
nonCodingGene •

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... 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" ...

written 3.3 years ago by
nonCodingGene •

**10**• updated 3.3 years ago by Martin Morgan ♦♦**23k**#### Latest awards to nonCodingGene

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