When using PCA it is possible to plot what is the percentage of variance which is captured by Principle component 1 (x axis) and principle component 2 (y- axis).
Is it possible to describe how much of the variance is captured by the first and second dimension in plotMDS (limma package)?
Thank you,
Arik
plotMDS does not return the % variance explained by each dimension. The % variance explained is not generally defined for MDS.
Update June 2021
The internal mathematics of plotMDS has been rewritten so that it does now return % variance explained. In the new approach, any dimensions with negative eigenvalues (rare but possible) are dropped and the % returned for the other dimensions is relative to the sum of all positive eigenvalues. The plots produced by plotMDS are unchanged.
If `gene.selection="common" then the MDS is equivalent to PCA and plotMDS could compute % var explained. But it doesn't. The help page help("plotMDS") explains what the function does and what information it returns.
The purpose of plotMDS is to see how the samples group. The purpose is not to perform dimension reduction, so I don't see how % var explained would be of much help. Hence I didn't implement it.
Instead I made sure that distances on the plot have a concrete interpretation in terms of fold-changes between the samples (leading logFC). That seems to me to be more important and useful.
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version 2.3.6
Duplicated question https://support.bioconductor.org/p/131603/
The duplicate post was removed from support.bioconductor.org.
See related post: C: Is plotMDS (from limma package) a PCA (Principal Component Analysis) or a PCoA (