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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(collapse = TRUE,
comment = "#>",
fig.align = 'center',
fig.path = "man/figures/README-",
message = FALSE,
warning = FALSE)
```
# metabolyseR
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[](https://lifecycle.r-lib.org/articles/stages.html#stable)
[](https://github.com/jasenfinch/metabolyseR/actions/workflows/R-CMD-check.yaml)
[](https://codecov.io/gh/jasenfinch/metabolyseR/branch/master)
[](https://github.com/jasenfinch/metabolyseR/blob/master/DESCRIPTION)
[](https://zenodo.org/badge/latestdoi/88983134)
[](https://GitHub.com/jasenfinch/metabolyseR/releases/)
<!-- badges: end -->
> **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:
```r
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.
```r
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](https://jasenfinch.github.io/metabolyseR/articles/metabolyseR.html) 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](https://reprex.tidyverse.org)) at
<https://github.com/jasenfinch/metabolyseR/issues>.
## Quick start example analysis
This example analysis will use the `abr1` data set from the [metaboData](https://aberhrml.github.io/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](https://magrittr.tidyverse.org/) package.
First load the necessary packages.
```{r setup}
library(metabolyseR)
library(metaboData)
```
For this example we will use only the negative acquisition mode data (`abr1$neg`) and sample meta-information (`abr1$fact`).
Create an `AnalysisData` class object using the following:
```{r analysis_data}
d <- analysisData(abr1$neg,abr1$fact)
```
The data includes `r nSamples(d)` samples and `r nFeatures(d)` mass spectral features as shown below.
```{r print_analysis_data}
d
```
The `clsAvailable()` function can be used to identify the columns available in our meta-information table.
```{r}
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:
```{r}
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.
```{r pre_treat}
d <- d %>%
occupancyMaximum(cls = 'day', occupancy = 2/3) %>%
transformTICnorm()
```
```{r pre_treat_result}
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:
```{r pca}
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.
```{r supervised_RF}
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.
```{r anova}
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:
```{r explanatoty_features_extract}
explan_feat <- explanatoryFeatures(anova_results,threshold = 0.05)
```
```{r,explanatory_features}
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.
```{r rf_heatmap,fig.height=10,fig.width=5}
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.
```{r feature_plot}
plotFeature(anova_results,feature = 'N341',cls = 'day')
```