removing outliers from RNA-seq data
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Entering edit mode
6.0 years ago
jfertaj ▴ 100

Hi all,

I have a data.frame from a rna-seq experiment, and I would like to remove some outliers. The  data is huge with 350 samples and 32291 genes. The data are log2 RPKM values (I did the log2 because I am planning to do WGCNA analysis and the authors recommend to make a log2 transformation of the data).

I am using the PcaHubert function from rrcov package to find outliers, here is the code I am using:

    df <- read.table("/path/to/file/rpkm.txt")
    dim(df) #32291   352
    df <- df[,-c(1,2)] # first 2 columns have accessory data
    
    library(rrcov)
    pcaHub <- PcaHubert(t(df))
    outliers <- which(pcaHub@flag=='FALSE')

The outliers would be those samples with the flag `FALSE` after doing the RobustPCA, do you think it is appropriate to remove outliers using this method?

Any comments would be greatly appreciated

Thanks

RNA-Seq R WGCNA outliers • 5.8k views
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3
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5.9 years ago
Deepak Tanwar ★ 4.1k

If you are going to use WGCNA package for network analysis, than you would be having the option to remove the outliers(samples). Follow the WGCNA Tutorials.

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5.9 years ago
Manvendra Singh ★ 2.1k

Yes, I think PCA is also a good choice to remove outliers.

you can also hierarchically cluster the samples on spearman's correlation of gene expression. then it would be easy to detect and remove outliers from dendrogram.

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Hello There is this parameter "crit.pca.distances" in function PcaHubert what should be the value for this other than default value. And what is this parameter?

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