Hi everyone!
Currently, I am working with microarray raw data. After background correction, normalization and summarization I realized that my data had a huge batch effect when I used a principal component analysis (PCA).
I know how to remove batch effects when they are known, however, in this case, I have no idea what effect is.
I have tried to use packages in R such as ruv or sva, but they don't return the corrected matrix. My problem is that I need this corrected matrix for downstream analysis.
What can I do?
1 answer
The removeBatchEffect function in limma allows you to regress-out continuous covariates (it can also "protect" your design/comparison on the way). Also look at other posts like this one, this one, etc. (remember that for testing it is best to include the covariates in the model with the uncorrected data).
Log in to answer this question.
-
Batch Effect Correction in RNA-seq Data Using SVA and DESeq2
written by Nelo •
-
Batch correction for RNA-seq using ComBat-seq for same group and batch
written by botloggy •
-
Combining Microarray and RNA-Seq datasets visualization and comparison
written by mohammedtoufiq91
-
Differential Gene Expression after batch correction with DESC?
written by skjw1029 •
-
Is it correct to merge two raw CEL datasets and then perform quality control, background correction…
written by Sib •
-
which value from ruvadj in missMethyl can present methylation status
written by RC •
-
microarray data handling
written by vincent.bioscience •
-
sea corrected expression matrix
written by ATCG
-
Correct for batch-effects which are not visible in pca-plot of raw-data (RNA-Seq)
written by Tobias.Wohland
-
Extreme values for first PC - genomic data
written by Paula Sanchez •
what kind of array is it ?
It's a microarray data, concretely, an Agilent microarray data.