remat: Reformulated data matrix after modification of outliers and...

In rMisbeta: A Robust Missing Imputation Method for Gene Expression Data

Description

remat() function returns reformulated data matrix by modifying the outliers and missing value using the robust mean produced by RobMeanVar(). This function also produces the weights of each feature. The lower weights indicate that the corresponding feature is corrupted by the outliers.

Usage

remat(Datao,cl)

Arguments

Datao	Datao denotes a vector of data matrix with missing values and outliers.
cl	Binary class level. Usually 1 and 2.

Value

This function returns the following two components

remat	reformulated data matrix after modification of outliers and imputed the missing values
betawt	The weights of each feature. The lower weights indicate that the corresponding feature is corrupted by the outliers

Author(s)

Md.Shahjaman; [e-mail]

References

Shahjaman M, Mollah MHM, Rahman MR, Islam SSM and Mollah NHM. Robust identification of differentially expressed genes from RNA-seq data. Genomics 2020; 112(2): 2000:2010.

Examples

nG=1000
n1=n2=5
Simdat=Sim2Group(nG,n1,n2,var0=0.1,pde=0.1) 
xx=Simdat$outmat
Datao=xx
MisOutdat<-OutMisDat(xx,pctOut=0.1,pctMis=0.1)
cl=rep(c(1,2),each=n1)
res=remat(MisOutdat,cl)
up_mat<-res$remat

rMisbeta documentation built on Jan. 13, 2021, 6:28 a.m.