GMS.Lasso: Generalized Mass Spectrum missing peaks imputation with...

In GMSimpute: Generalized Mass Spectrum Missing Peaks Abundance Imputation

Description

GMS.Lasso recovers the abundance of missing peaks via either TS.Lasso or the minimum abundance per compound.

Usage

GMS.Lasso(input_data, alpha = 1, nfolds = 10, log.scale = TRUE,
  TS.Lasso = 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, log.scale=F.
TS.Lasso	Whether to use TS.Lasso or the minimum per compound for imputation.

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.compound.min=GMS.Lasso(tcga.bc,log.scale=TRUE,TS.Lasso=FALSE)
# Impute raw abundance matrix tcga.bc with compound minimum

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

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