What would you recommend for feature selection in say, single-cell RNA seq studies (Typical dataset is ~10,000 x ~30000 (cells x genes) with >90% of your table filled with 0s (which could be due to biological or technical noise)
PCA and UMAP are yes, dimensionality reduction methods, but are often seen as tools for feature selection.
PCA and UMAP are yes, dimensionality reduction methods, but are often seen as tools for feature selection.
See slide 61 Here: https://physiology.med.cornell.edu/faculty/skrabanek/lab/ang...