Answers.Body: <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>
  
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Answers.Text: I agree it is still an active area of research. I will check out Phenix, looks like it could be promising. Thanks.
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Answers.Text: @Reilstein: can you say anything about your experience with Phenix?
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Answers.Text: I never ended up using Phenix because it appears to be tailored for samples/individuals with a degree of relatedness. My particular study doesn't have samples from related individuals. I opted for K-nearest neighbor imputation while keeping the total missing values allowed below 5% prior to imputation.
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Answers.Text: The description says "arbitrary level of relatedness".  From my discussions with the author, I would guess that the level of appropriate relatedness  includes the level of relatedness of "unrelated" human individuals.
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Answers.Text: ah okay, well I didn't look into it in enough depth apparently.  Thanks for following through with the author.  If I decide KNN Imputation isn't accurate enough for my purposes I will revisit this. Thanks.
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Body: <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>
  
Tags: <methylation><sequence-analysis><imputation>
Title: Recommendations for missing value imputation - DNA methylation data
ThreadText: # Recommendations for missing value imputation - DNA methylation data
  
  (Asked by: username_0 on 2017-09-17)
  
  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?
  
  **Tags:** methylation, sequence-analysis, imputation
  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2017-09-18. Score: 2
  
  I think this is still an active field of research. I have heard of [Phenix](https://mathgen.stats.ox.ac.uk/genetics_software/phenix/phenix.html), which might be appropriate.
  
  > **Comments:**
  > **username_0**: I agree it is still an active area of research. I will check out Phenix, looks like it could be promising. Thanks.  
  > **username_1**: @username_0: can you say anything about your experience with Phenix?  
  > **username_0**: I never ended up using Phenix because it appears to be tailored for samples/individuals with a degree of relatedness. My particular study doesn't have samples from related individuals. I opted for K-nearest neighbor imputation while keeping the total missing values allowed below 5% prior to imputation.  
  > **username_1**: The description says "arbitrary level of relatedness".  From my discussions with the author, I would guess that the level of appropriate relatedness  includes the level of relatedness of "unrelated" human individuals.  
  > **username_0**: ah okay, well I didn't look into it in enough depth apparently.  Thanks for following through with the author.  If I decide KNN Imputation isn't accurate enough for my purposes I will revisit this. Thanks.  
  
  ---
  
  