FilterData: Filter input data by abundance values and number of missing...

In IntLIM: Integration of Omics Data Using Linear Modeling

Filter input data by abundance values and number of missing values.

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.

Usage

FilterData(
  inputData,
  analyteType1perc = 0,
  analyteType2perc = 0,
  analyteMiss = 0,
  suppressWarnings = FALSE,
  cov.cutoff = 0
)

Arguments

inputData	IntLimData object (output of ReadData()) with analylte levels and associated meta-data
analyteType1perc	percentile cutoff (0-1) for filtering analyte type 1 (e.g. remove analytes with mean values < 'analyteType1perc' percentile) (default: 0)
analyteType2perc	percentile cutoff (0-1) for filtering analyte type 2 (default: no filtering of analytes) (default:0)
analyteMiss	missing value percent cutoff (0-1) for filtering both analyte types (analytes with > 80% missing values will be removed) (default:0)
suppressWarnings	whether or not to print warnings. If TRUE, warnings will not be printed.
cov.cutoff	percentile cutoff (0-1) for the covariances of the anaytes (default: 0.30)

Value

filtData IntLimData object with input data after filtering

IntLIM documentation built on Aug. 22, 2022, 5:05 p.m.