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estimatecellcounts
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Estimate cell proportion from beta values using minfi estimateCellCounts?
minfi
cell proportion
estimateCellCounts
DNA Methylation
4.1 years ago
grayapply2009
• 0
0
votes
0
replies
1.0k
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estimateCellCounts issue with EPIC array data in minfi
minfi
estimatecellcounts
5.6 years ago
a.webster
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0
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1
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2.0k
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getSex error even when sex is provided
minfi
estimatecellcounts
getSex
preprocessQuantile
updated 5.8 years ago by
Kasper Daniel Hansen
★ 6.5k • written 5.8 years ago by
fgc
• 0
1
vote
1
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1.6k
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Limma with paired samples and SVA
limma
sva
duplicatecorrelation
estimatecellcounts
updated 6.5 years ago by
Aaron Lun
★ 28k • written 6.5 years ago by
wliao
• 0
0
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1
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1.7k
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How to convert GenomicRatioSet to RGChannelSet in minfi::compartments()
minfi
estimateCellCounts
methylation
updated 6.9 years ago by
Kasper Daniel Hansen
★ 6.5k • written 6.9 years ago by
stewart999
▴ 10
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Answer: rlog transformation
by
Michael Love
41k
It is dependent on the other samples in the dataset.
Answer: ONT long-read RNA sequencing - data analysis options
by
Michael Love
41k
*"This worked really well for brain but has not work as well for lung as I get #N/A for many padj"* See the vignette for the cause of th…
Answer: DESeq2 Error in `.rowNamesDF<-`(x, value = value): Invalid 'row.names' length
by
Michael Love
41k
Starting from the very top, do you understand the error? You need to provide sample info that matches 1-to-1 the columns of the counts matr…
Answer: covariate with negative and positive values
by
Michael Love
41k
Not a problem. However, if the values are very large (eg 1e6) or very small (eg 1e-6), it's a good idea to center and scale the variable…
Comment: FilterByExpr low counts with small sample size
by
Jonathan
▴ 10
Usually I run `voomLmFit`, although in order to create the mean-variance trend plots for this particular post, I ran `voom`; I've checked -…
Votes
Comment: Error in champ.load(): The following specified files do not exist
Answer: Error in read.metharray(basenames = files, extended = extended, verbose = verbos
Using GRanges and IRanges to simply get all chromosome data
A: Using GRanges and IRanges to simply get all chromosome data
Comment: Reading huge bismark coverage files using bbseq::read.bismark
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