aggregate: Sample aggregation

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

Sample aggregation

Description

Aggregation of sample features based on a grouping variable.

Usage

aggregateMean(d, cls = "class")

## S4 method for signature 'AnalysisData'
aggregateMean(d, cls = "class")

aggregateMedian(d, cls = "class")

## S4 method for signature 'AnalysisData'
aggregateMedian(d, cls = "class")

aggregateSum(d, cls = "class")

## S4 method for signature 'AnalysisData'
aggregateSum(d, cls = "class")

Arguments

d	S4 object of class AnalysisData
cls	info columns across which to aggregate the data

Details

Sample aggregation allows the electronic pooling of sample features based on a grouping variable. This is useful in situations such as the presence of technical replicates that can be aggregated to reduce the effects of pseudo replication.

Value

An S4 object of class AnalysisData containing the aggregated data.

Methods

• aggregateMean: Aggregate sample features to the group mean.

• aggregateMedian: Aggregate sample features to the group median.

• aggregateSum: Aggregate sample features to the group total.

Examples

## Each of the following examples shows the application of the aggregation method and then 
## a Principle Component Analysis is plotted to show it's effect on the data structure.

## Initial example data preparation
library(metaboData)

d <- analysisData(abr1$neg[,200:300],abr1$fact) %>% 
 occupancyMaximum(occupancy = 2/3)

d %>% 
 plotPCA(cls = 'day')
 
## Mean aggregation
d %>% 
 aggregateMean(cls = c('day','class')) %>% 
 plotPCA(cls = 'day',ellipses = FALSE)
 
## Median aggregation
d %>% 
 aggregateMedian(cls = c('day','class')) %>% 
 plotPCA(cls = 'day',ellipses = FALSE)
 
## Sum aggregation
d %>% 
 aggregateSum(cls = c('day','class')) %>% 
 plotPCA(cls = 'day',ellipses = FALSE)

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