occupancyFilter: Feature occupancy filtering

In jasenfinch/metabolyseR: Methods for Pre-Treatment, Data Mining and Correlation Analyses of Metabolomics Data

Feature occupancy filtering

Description

Feature filtering based on class occupancy.

Usage

occupancyMaximum(d, cls = "class", occupancy = 2/3)

## S4 method for signature 'AnalysisData'
occupancyMaximum(d, cls = "class", occupancy = 2/3)

occupancyMinimum(d, cls = "class", occupancy = 2/3)

## S4 method for signature 'AnalysisData'
occupancyMinimum(d, cls = "class", occupancy = 2/3)

Arguments

d	S4 object of class AnalysisData
cls	sample information column name to use for class data
occupancy	feature occupancy filtering threshold, below which features will be removed

Details

Occupancy provides a useful metric by which to filter poorly represented features (features containing a majority zero or missing values). An occupancy threshold provides a means of specifying this majority with variables below the threshold excluded from further analyses. However, this can be complicated by an underlying class structure present within the data where a variable may be well represented within one class but not in another.

Value

An S4 object of class AnalysisData containing the class occupancy filtered data.

Methods

• occupancyMaximium: Maximum occupancy threshold feature filtering. Where the maximum occupancy across all classes is above the threshold. Therefore, for a feature to be retained, only a single class needs to have an occupancy above the threshold.

• occupancyMinimum: Minimum occupancy threshold feature filtering. Where the minimum occupancy across all classes is required to be above the threshold. Therefore, for a feature to be retained, all classes would need to have an occupancy above the threshold.

Examples

## Each of the following examples shows the application 
## of the feature occupancy filtering method method and 
## then a Principle Component Analysis is plotted to show 
## its effect on the data structure.

## Initial example data preparation
library(metaboData)

d <- analysisData(abr1$neg[,200:300],abr1$fact)
 
## Maximum occupancy threshold feature filtering
d %>% 
 occupancyMaximum(cls = 'day') %>% 
 plotPCA(cls = 'day')
 
## Minimum occupancy threshold feature filtering
d %>% 
 occupancyMinimum(cls = 'day') %>% 
 plotPCA(cls = 'day')

jasenfinch/metabolyseR documentation built on Sept. 18, 2023, 1:25 a.m.