FilterData: Filter input data by abundance values (gene and metabolite...

In Mathelab/IntLIM: Integration of Omics Data Using Linear Modeling

Filter input data by abundance values (gene and metabolite data) and number of missing values (metabolite data only).

Description

Filter data by abundance (with user-input percentile cutoff) of missing values (with user-input percent cutoff). Missing values are commonly found in metabolomics data so the parameter currently only applies to metabolomics data.

Usage

FilterData(inputData, geneperc = 0, metabperc = 0, metabmiss = 0)

Arguments

inputData	MultiDataSet object (output of ReadData()) with gene expression, metabolite abundances, and associated meta-data
geneperc	percentile cutoff (0-1) for filtering genes (e.g. remove genes with mean values < 'geneperc' percentile) (default: 0)
metabperc	percentile cutoff (0-1) for filtering metabolites (default: no filtering of metabolites) (default:0)
metabmiss	missing value percent cutoff (0-1) for filtering metabolites (metabolites with > 80% missing values will be removed) (default:0)

Value

filtData MultiDataSet object with input data after filtering

Examples

dir <- system.file("extdata", package="IntLIM", mustWork=TRUE)
csvfile <- file.path(dir, "NCItestinput.csv")
inputData <- ReadData(csvfile,metabid='id',geneid='id')
inputDatafilt <- FilterData(inputData,geneperc=0.5)

Mathelab/IntLIM documentation built on July 9, 2022, 5:10 p.m.