I'm looking for some options for imputation for a high-dimensional dataset of DNA methylation (bisulfite sequencing) data. Dimensions on the order of 50-100 samples x ~500,000 CpG loci/features.
I've used K-nearest neighbors, but It seems that this method is not very accurate. As far as I can tell, it limits the minimum number of "genes/features" to impute at one time to something like 1500. which is kind of a lot.
I've also used missForest to impute smaller datasets with greater accuracy, but it seems computationally infeasible to do it on the full 50x500,000 dataset.
I need to impute values because I'm using the data for some statistical modeling which require complete cases.
Anyone know of better alternatives than K-nearest neighbor for large scale imputation?