I have a single-cell RNASeq sample, in which I'd like to identify latent variables (e.g. response to stress) that I think might be affecting the clustering.
The approach I was planning to use is to reduce the dimensionality of the dataset using one algorithm among:
Then identify the most important biological processes for every component (e.g. in each Principal Component), and then finally regress out the processes that might not be relevant to the analysis or that might affect the downstream clustering.
I have a few questions:
- Are there issues in this approach?
- Is there a collection of gene sets (similar to MSigDB) that can be used to identify noise latent variables?
- Which algorithm (PCA, ICA, NMF, or others) is more suited to this kind of analysis?