<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>

CC BY-SA 3.0
2017-09-20T03:37:46.317Z
I agree it is still an active area of research. I will check out Phenix, looks like it could be promising. Thanks.
Reilstein
CC BY-SA 3.0
2017-12-21T10:17:50.383Z
@Reilstein: can you say anything about your experience with Phenix?
winni2k
CC BY-SA 3.0
2017-12-21T19:10:22.077Z
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.
Reilstein
CC BY-SA 3.0
2018-01-13T15:41:38.210Z
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.
winni2k
CC BY-SA 3.0
2018-01-15T04:20:16.483Z
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.
Reilstein
CC BY-SA 3.0
2017-09-18T10:24:31.850Z
2017-09-18T10:24:31.850Z
winni2k
<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>

<methylation><sequence-analysis><imputation>
Recommendations for missing value imputation - DNA methylation data
# 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

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## 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**: @Reilstein: 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.  

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