Hello,
I have never done PCA analysis before and the concept is new to me. I wonder if I can get help from more experienced bioinformatians here.
I have few samples sequenced for whole exome, all originated from the same origin. From what I understand, it's a good analysis to see the similarities between samples, so I'd like to do PCA analysis for my samples.
My question is how to organize the data? Currently I have the samples VCF. I'm using Python and R. Any thoughts how to organize the data?
It's very general question, but I don't know how to begin., so any help will be appreciated.
Many thanks!
Editing my question to focus it:
I'd like to create as the below figure, only not gene expression, but of mutations of genes I have. I have a matrix of different samples and for each thousands of mutations, with the allele frequency. I'd like to create clustering based on the existing mutations and their allele frequency as the gradient.
Anyone knows which package I should use in R to do that?
@Kevin has a nice tutorial available here: PCA plot from read count matrix from RNA-Seq
While that is geared towards RNAseq you may find it generally useful.
Thanks! I'll take a look at it
The figure you show is hierarchical clustering (heatmap), not PCA... You can use
heatmap.2function from Rgplotsfor heatmaps.You are right. I meant I wanted another figure similar to the figure I uploaded. The PCA analysis I couldn't find a way to do it not for gene expression data. Do you know if there is any size limitation for
heatmap.2?I think the limit will be your hardware, using big data sets require great computing power (so good hardware with many RAM and cpu knots).
Bella_p : If your needs have changed then it may be worth creating a new post or looking through other posts on Biostars first. Now the header of your post no longer matches the requirements.
@Kevin also happens to have a tutorial for HeatMaps: How to plot a heatmap with two different distance matrices for X and Y