How to combine different data types and perform PCA in R
0
0
Entering edit mode
4.4 years ago

Hi,

I have a question about Principal Component Analysis in R. I have around 10 datasets of interest comprising of both normalized Microarray expression (Agilent, Affymetrix, and Illumina platforms) and RNA-Seq expression (Illumina) data. For each of the dataset, a normalized and corresponding sample metadata (sample grouping information) R.Data file has been saved . I would like to know how to pool and merge all the data and view as a single PCA plot to observe different groups of my interest (i.e Disease vs Control samples). Earlier I have performed PCA on single dataset a time, however, I do not know how to perform pooled PCA analysis. Note: I have both probe-level data and gene level normalized data. All 10 datasets have Disease vs Control samples.

Please assist me with this.

Thank you,

Toufiq

PCA Microarray RNA-Seq data frame R • 1.7k views
ADD COMMENT
0
Entering edit mode

I'm not sure how valid it is to ordinate using different sequencing types, because you might see separation based primarily on method and not on a biologically meaningful differences. But, you could try and see what it looks like I suppose. I would ensure the samples are normalized using the same method (TPM, RPKM, etc),, and then log transform before PCA

ADD REPLY

Login before adding your answer.

Traffic: 830 users visited in the last hour
Help About
FAQ
Access RSS
API
Stats

Use of this site constitutes acceptance of our User Agreement and Privacy Policy.

Powered by the version 2.3.6