Question: Should I remove samples after normalization of miRNA seq read counts ?
0
gravatar for Björn
3.0 years ago by
Björn50
Björn50 wrote:

I followed https://www.bioconductor.org/help/workflows/RNAseq123/ for my rnaseq analysis. As my read counts were around 2.5 million, I had to use higher CPM. Hope this should be fine for downstream analysis.

After following command:

par(mfrow=c(1,2))                                  
lcpm <- cpm(y2, log=TRUE)                                
boxplot(lcpm, las=2, col=group$Sample, main="")            
title(main="A. Example: Unnormalised data at CPM-2",ylab="Log-cpm") 
y2 <- calcNormFactors(y2)  
y2$samples$norm.factors
lcpm <- cpm(y, log=TRUE)
boxplot(lcpm, las=2, col=group$Sample, main="")
title(main="B. Example: Normalised data at CPM-2",ylab="Log-cpm")

I got following boxplot graph. ![enter image description here][1] [1]: https://ibb.co/gWW2r6

Based on normalized data, which samples should I remove from analysis ?

mirnas edger rna-seq • 741 views
ADD COMMENTlink modified 2.3 years ago by Biostar ♦♦ 20 • written 3.0 years ago by Björn50
0
gravatar for Kevin Blighe
3.0 years ago by
Kevin Blighe69k
Republic of Ireland
Kevin Blighe69k wrote:

Some of the samples look different from the others, in terms of their data distribution via the box-and-whiskers plot; however, I would reserve judgement on outliers without seeing, in addition, a PCA bi-plot and violin plot.

Kevin

ADD COMMENTlink modified 2.3 years ago • written 3.0 years ago by Kevin Blighe69k
Please log in to add an answer.

Help
Access

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.
Powered by Biostar version 2.3.0
Traffic: 1992 users visited in the last hour
_