Dear mixOmics team,
Thank you for your hard work in creating this package.
I’m trying to integrate two -omics datasets and was attempting to use the performance function of the PLS-DA model to determine the number of components. I was rewarded with the “Error in solve.default(Sr) : system is computationally singular” message.
I saw previous posts related to this matter and I’m confident that there isn’t an issue with zeroes/missing values in the data.
Utilizing the “loo” validation method, rather than “Mfold,” still gave me the same error.
I suspect it may be related to my extremely low sample size (12 subjects with 3 class features, so basically n=4) versus the number of variables (~2000 genes). I saw in the FAQ it was noted:
“With a small n you can adopt an exploratory approach that does not require a performance assessment.”
In a scenario like this, is there any suggestions about a workflow to still derive some sort of meaningful statement about the dataset and to choose appropriate/optimal variable and component numbers?
Thank you so much for your assistance and thank you again for all the work that has been done.