**270**wrote:

I have download the beta values for methylation data form GEO on different sample. Values form different probes are missing . But instead of delete those probes form my data, i want to impute those values.

## impute package and problem

I tried to use **impute** package though it was developed for microarray data. While using impute package , the computational time is long and facing some problem regarding infinite recursion.

**Details about the following problems :**

**1. Problem :**

I got an error while i am running data. Error has explained by the following sample data and code

```
## Load data
mdata <- as.matrix(read.table('https://gubox.box.com/shared/static/qh4spcxe2ba5ymzjs0ynh8n8s08af7m0.txt', header = TRUE, check.names = FALSE, sep = '\t'))
## Install and load library
source("https://bioconductor.org/biocLite.R")
biocLite("impute")
library(impute)
## sets a limit on the number of nested expressions
options(expressions = 500000)
## Apply k-nearest neighbors for missing value imputation
res <-impute.knn(mdata)
Error: protect(): protection stack overflow
```

**2. Problem:**

Data : https://gubox.box.com/shared/static/kynad5ajjpqelncdn6djaw7ga35lkvd6.rdata [Note : Big file, 190MB]

```
library(impute)
if(exists(".Random.seed")) rm(.Random.seed)
imputedData <- impute.knn(as.matrix(exp_data))
Error: evaluation nested too deeply: infinite recursion / options(expressions=)?
```

I will appreciate

- if anybody can help regarding problem of impute package.
- Suggest any better way (package/methods) to impute beta values (computationally faster).

Thanks!!