TS.Lasso: Two-Step Lasso for missing peaks imputation

In GMSimpute: Generalized Mass Spectrum Missing Peaks Abundance Imputation

Description

TS.Lasso recovers the abundance of various types of missing peaks.

Usage

TS.Lasso(input_data, alpha = 1, nfolds = 10, log.scale = TRUE)

Arguments

input_data	Raw abundance matrix with missing value, with features in rows and samples in columns.
alpha	Weights for L1 penalty in Elastic Net. The default and suggested value is alpha=1, which is for Lasso.
nfolds	The number of folds used in parameter (lambda) tuning.
log.scale	Whether the input_data needs log scale transform.The default is log.scale=T, assuming input_data is the raw abundance matrix. If input_data is log abundance matrix, set log.scale=F.

Value

imputed.final

The imputed abundance matrix at the scale of input_data.

Examples

data('tcga.bc')
# tcga.bc contains mass specturm abundance of 150 metabolites for 30 breast cancer
# tumor and normal tissue samples with missing values.

imputed=TS.Lasso(tcga.bc,log.scale=TRUE)
# Impute raw abundance matrix tcga.bc

GMSimpute documentation built on May 1, 2019, 10:13 p.m.