text: user39: 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?  
  
  user492: I think this is still an active field of research.  I have heard of Phenix, which might be appropriate.
  
original_text: 39: <p>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.</p>
  
  <p>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.  </p>
  
  <p>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.</p>
  
  <p>I need to impute values because I'm using the data for some statistical modeling which require complete cases.  </p>
  
  <p>Anyone know of better alternatives than K-nearest neighbor for large scale imputation?  </p>
   492: <p>I think this is still an active field of research.  I have heard of <a href="https://mathgen.stats.ox.ac.uk/genetics_software/phenix/phenix.html" rel="nofollow noreferrer">Phenix</a>, which might be appropriate.</p>
  