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
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).