|
df <- data.frame(lrt$fitted.values) |
Our PCA plots, while useful, always look a bit funny. I have recently had more success building PCAs from variance-stabilized transformations (the vst() function in DEseq2). We should look into the most similar approach in edgeR. Currently we use the fitted.values field of the edgeR object but I think this represents just a linear transformation of the raw counts, and may still inflate the importance of high-variance genes in the PCA.
Will require a bit of research.
https://www.dnastar.com/ArrayStar_Help/index.html#!Documents/deseq2andedger.htm
https://support.bioconductor.org/p/69770/
kwanlibr/R/core_edger_analyzer.R
Line 543 in 174209b
Our PCA plots, while useful, always look a bit funny. I have recently had more success building PCAs from variance-stabilized transformations (the
vst()function in DEseq2). We should look into the most similar approach in edgeR. Currently we use thefitted.valuesfield of the edgeR object but I think this represents just a linear transformation of the raw counts, and may still inflate the importance of high-variance genes in the PCA.Will require a bit of research.
https://www.dnastar.com/ArrayStar_Help/index.html#!Documents/deseq2andedger.htm
https://support.bioconductor.org/p/69770/