Questions tagged [combat]
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Methylation data: beta values are normally distributed
I am trying to incorporate Methylation data (Illumina 450K) into my pipeline. I was provided with two versions of the same data: one (dataset $a$) normalized with ComBat and the other (dataset $b$) ...
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How to use combat in order to remove batch effects?
I have RNA seq data and I need to use combat to remove the batch effects. Somehow when I run it, it isnt actually doing anything.
The code:
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How to remove batch effect from TCGA and GTEx data
I want to play some differentially expressed genes for gliomas cancer. I collected cancer tissue profiles from the TCGA database and normal tissue profiles from the GTEx database. I straightforward ...
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Error in apply(dat[, batch == batch_level], 1, function(x) { : dim(X) must have a positive length
I am new here so forgive me if I don't provide all the details in the first go. There is a very similar question (how to solve “dim(X) must have a positive length” at running ComBat function in R) but ...
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how to solve “dim(X) must have a positive length” at running ComBat function in R
I used ComBat() for batch effect correction in my expression data. basically, that function inputs are expression data, Batch covariate, and Model matrix for the ...
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ComBat batch correction: understanding the model
A collaborator of mine is using ComBat for some RNA-seq data. I would like to understand what it's doing, and I have a specific question about the structure of the model. Equation 2.1 of the paper ...
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sva for RNA-Seq data without known phenotype
I have been working on RNA-Seq data from two different cohorts, and they show very strong batch effect (~35% variance explained by 1st component in PCA). Since I am trying to do a class discovery from ...
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ComBat for batch effects removal on scRNA-seq data
Is it possible to use sva's ComBat for batch effects removal on scRNA-seq data?
Is there any difference between RNA-seq and scRNA-seq data which doesn't allow to use ComBat on single-cell data? (I am ...
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Order of batch effects removal, data imputation and library size normalization in scRNA-seq data
I am preprocessing scRNA-seq data. What is the best practice in use to run both ComBat for batch effects removal, data imputation (to mitigate dropout) and library size normalization?
I thought that ...
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NaN values after ComBat analysis on TCGA COAD RNAseq
I have FPKM-UQ data from COAD-TCGA.
I generated an expression set of this data using:
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