mirror of
https://github.com/msberends/AMR.git
synced 2024-12-25 18:46:11 +01:00
90 lines
2.5 KiB
Plaintext
Executable File
90 lines
2.5 KiB
Plaintext
Executable File
---
|
|
title: "How to conduct principal component analysis (PCA) for AMR"
|
|
output:
|
|
rmarkdown::html_vignette:
|
|
toc: true
|
|
toc_depth: 3
|
|
vignette: >
|
|
%\VignetteIndexEntry{How to conduct principal component analysis (PCA) for AMR}
|
|
%\VignetteEncoding{UTF-8}
|
|
%\VignetteEngine{knitr::rmarkdown}
|
|
editor_options:
|
|
chunk_output_type: console
|
|
---
|
|
|
|
```{r setup, include = FALSE, results = 'markup'}
|
|
knitr::opts_chunk$set(
|
|
collapse = TRUE,
|
|
comment = "#",
|
|
fig.width = 7.5,
|
|
fig.height = 4.5,
|
|
dpi = 100
|
|
)
|
|
```
|
|
|
|
**NOTE: This page will be updated soon, as the pca() function is currently being developed.**
|
|
|
|
# Introduction
|
|
|
|
# Transforming
|
|
|
|
For PCA, we need to transform our AMR data first. This is what the `example_isolates` data set in this package looks like:
|
|
|
|
```{r, message = FALSE}
|
|
library(AMR)
|
|
library(dplyr)
|
|
glimpse(example_isolates)
|
|
```
|
|
|
|
Now to transform this to a data set with only resistance percentages per taxonomic order and genus:
|
|
|
|
```{r, warning = FALSE}
|
|
resistance_data <- example_isolates %>%
|
|
group_by(order = mo_order(mo), # group on anything, like order
|
|
genus = mo_genus(mo)) %>% # and genus as we do here
|
|
summarise_if(is.rsi, resistance) %>% # then get resistance of all drugs
|
|
select(order, genus, AMC, CXM, CTX,
|
|
CAZ, GEN, TOB, TMP, SXT) # and select only relevant columns
|
|
|
|
head(resistance_data)
|
|
```
|
|
|
|
# Perform principal component analysis
|
|
|
|
The new `pca()` function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:
|
|
|
|
```{r pca}
|
|
pca_result <- pca(resistance_data)
|
|
```
|
|
|
|
The result can be reviewed with the good old `summary()` function:
|
|
|
|
```{r}
|
|
summary(pca_result)
|
|
```
|
|
|
|
```{r, echo = FALSE}
|
|
proportion_of_variance <- summary(pca_result)$importance[2, ]
|
|
```
|
|
|
|
Good news. The first two components explain a total of `r cleaner::percentage(sum(proportion_of_variance[1:2]))` of the variance (see the PC1 and PC2 values of the *Proportion of Variance*. We can create a so-called biplot with the base R `biplot()` function, to see which antimicrobial resistance per drug explain the difference per microorganism.
|
|
|
|
# Plotting the results
|
|
|
|
```{r}
|
|
biplot(pca_result)
|
|
```
|
|
|
|
But we can't see the explanation of the points. Perhaps this works better with our new `ggplot_pca()` function, that automatically adds the right labels and even groups:
|
|
|
|
```{r}
|
|
ggplot_pca(pca_result)
|
|
```
|
|
|
|
You can also print an ellipse per group, and edit the appearance:
|
|
|
|
```{r}
|
|
ggplot_pca(pca_result, ellipse = TRUE) +
|
|
ggplot2::labs(title = "An AMR/PCA biplot!")
|
|
```
|