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

knitr::opts_chunk$set(collapse = TRUE, 
                      comment = "#>",
                      fig.align = 'center',
                      fig.path = "man/figures/README-",
                      message = FALSE,
                      warning = FALSE)

metabolyseR

A tool kit for pre-treatment, modelling, feature selection and correlation analyses of metabolomics data.

Overview

This package provides a tool kit of methods for metabolomics analyses that includes:

• data pre-treatment
• multivariate and univariate modelling/data mining techniques
• correlation analysis

Installation

The metabolyseR package can be installed from GitHub using the following:

remotes::install_github('jasenfinch/metabolyseR')

The package documentation can be browsed online at https://jasenfinch.github.io/metabolyseR/; however, if users want to compile the vignettes locally, the following can be used.

remotes::install_github('jasenfinch/metabolyseR',build_vignettes = TRUE,dependencies = TRUE)

Learn more

The package documentation can be browsed online at https://jasenfinch.github.io/metabolyseR/.

If this is your first time using metabolyseR see the Introduction vignette or the quick start analysis below for information on how to get started.

If you believe you've found a bug in metabolyseR, please file a bug (and, if possible, a reproducible example) at https://github.com/jasenfinch/metabolyseR/issues.

Quick start example analysis

This example analysis will use the abr1 data set from the metaboData package. It is nominal mass flow-injection mass spectrometry (FI-MS) fingerprinting data from a plant-pathogen infection time course experiment. The analysis will also include use of the pipe %>% from the magrittr package. First load the necessary packages.

library(metabolyseR)
library(metaboData)

For this example we will use only the negative acquisition mode data (abr1$neg) and sample meta-information (abr1$fact). Create an AnalysisData class object using the following:

d <- analysisData(abr1$neg,abr1$fact)

The data includes r nSamples(d) samples and r nFeatures(d) mass spectral features as shown below.

d

The clsAvailable() function can be used to identify the columns available in our meta-information table.

clsAvailable(d)

For this analysis, we will be using the infection time course class information contained in the day column. This can be extracted and the class frequencies tabulated using the following:

d %>%
  clsExtract(cls = 'day') %>%
  table()

As can be seen above, the experiment is made up of six infection time point classes that includes a healthy control class (H) and five day infection time points (1-5), each with 20 replicates.

For data pre-treatment prior to statistical analysis, a two-thirds maximum class occupancy filter can be applied. Features where the maximum proportion of non-missing data per class is above two-thirds are retained. A total ion count normalisation will also be applied.

d <- d %>%
  occupancyMaximum(cls = 'day', occupancy = 2/3) %>%
  transformTICnorm()

d

This has reduced the data set to r nFeatures(d) relevant features.

The structure of the data can be visualised using both unsupervised and supervised methods. For instance, the first two principle components from a principle component analysis (PCA) of the data with the sample points coloured by infection class can be plotted using:

plotPCA(d,cls = 'day',xAxis = 'PC1',yAxis = 'PC2')

And similarly, multidimensional scaling (MDS) of sample proximity values from a supervised random forest classification model along with receiver operator characteristic (ROC) curves.

plotSupervisedRF(d,cls = 'day')

A progression can clearly be seen from the earliest to latest infected time points.

For feature selection, one-way analysis of variance (ANOVA) can be performed for each feature to identify features significantly explanatory for the infection time point.

anova_results <- d %>%
  anova(cls = 'day')

A table of the significantly explanatory features can be extracted with a bonferroni correction adjusted p value < 0.05 using:

explan_feat <- explanatoryFeatures(anova_results,threshold = 0.05)

explan_feat

The ANOVA has identified r nrow(explan_feat) features significantly explanatory over the infection time course. A heat map of the mean relative intensity for each class of these explanatory features can be plotted to visualise their trends between the infection time point classes.

plotExplanatoryHeatmap(anova_results,
                       threshold = 0.05,
                       featureNames = FALSE)

Many of the explanatory features can be seen to be most highly abundant in the final infection time point 5.

Finally, box plots of the trends of individual features can be plotted, such as the N341 feature below.

plotFeature(anova_results,feature = 'N341',cls = 'day')

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