methyLImp: Linear regression model for beta-values imputation

In aprodi/methyLImp: Missing Value Estimation of DNA Methylation Data

Description

Imputes missing DNA methylation data

Usage

methyLImp(dat, min = 0, max = 1, max.sv = NULL, col.list = NULL) 

Arguments

dat	Matrix of betas or M-values
min	Minimum value of the distribution
max	Maximum value of the distribution
max.sv	Max number of singular values to be used in the psuedoinvers matrix. The parameter is used to reduce the matrix rank.
col.list	Convert col.list, if any, from names to numbers

Value

Returns imputed values for the missing values.

Author(s)

Pietro Di Lena

References

Di Lena P et al: Missing value estimation methods for DNA methylation data. submitted to Bioinformatics

Examples

## Load the methyLImp dataset, containing no missing value
data(gse64495)
summary(gse64495)

## Artificially introduce 10% missing values in the first sample
## with the gen_randNA function
set.seed(50)
samp <- 1
frac <- 0.1
gse64495.mis <- gen_randNA(gse64495,samp,frac)
summary(gse64495.mis)

## Impute the missing values with the methyLImp routine.
## Note that variables need to be on the columns and
## samples on the rows.
gse64495.imp <- methyLImp(t(gse64495.mis),min=0,max=0)
gse64495.imp <- t(gse64495.imp)

## Compare imputed and original values
miss <- is.na(gse64495.mis[,samp])
orig <- gse64495[miss,samp]
pred <- gse64495.imp[miss,samp]
gen_stat(orig,pred)

aprodi/methyLImp documentation built on May 25, 2019, 2:20 p.m.