How to combine different data types and perform PCA in R
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16 months ago


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,


PCA Microarray RNA-Seq data frame R • 562 views
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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


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