lexi_cv: Cross validation function for imputation accuracy

In aritronath/LEXI: LEXI: lncRNA expression imputation

Description

Function to obtain accuracy parameters: correlation coefficient, P-value and RMSE of imputation model

Usage

lexi_cv(train_pcg, train_lnc, gene_index, num = 100, folds = 5)

Arguments

train_pcg	training protein coding dataset. a numeric matrix with row names indicating samples, and column names indicating protein coding gene IDs.
train_lnc	training lncRNA expression dataset. a numeric matrix with row names indicating samples, and column names indicating lncRNA IDs
gene_index	either gene name (character) or index (column number) of lncRNA to be imputed.
num	number of informative protein coding genes to be used in constructing imputation model. Default is 100 genes.
folds	number specifying folds of cross validation to obtain imputation accuracu. Default is 5.

Value

a matrix with three values corresponding to Pearson's correlation coefficient, P-value of fit and root mean square error

Examples

lexi_cv(train_pcg, train_lnc, gene_index="ENSG00000184441", num=100)
lexi_cv(train_pcg, train_lnc, gene_index=25, num=100)

aritronath/LEXI documentation built on May 10, 2019, 1:27 p.m.