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94 lines
2.5 KiB
Plaintext
Executable File
94 lines
2.5 KiB
Plaintext
Executable File
---
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title: "How to conduct principal component analysis (PCA) for AMR"
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output:
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rmarkdown::html_vignette:
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toc: true
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toc_depth: 3
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vignette: >
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%\VignetteIndexEntry{How to conduct principal component analysis (PCA) for AMR}
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%\VignetteEncoding{UTF-8}
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%\VignetteEngine{knitr::rmarkdown}
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editor_options:
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chunk_output_type: console
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---
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```{r setup, include = FALSE, results = 'markup'}
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knitr::opts_chunk$set(
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collapse = TRUE,
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comment = "#",
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fig.width = 7.5,
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fig.height = 4.5,
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dpi = 100
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)
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```
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**NOTE: This page will be updated soon, as the pca() function is currently being developed.**
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# Introduction
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# Transforming
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For PCA, we need to transform our AMR data first. This is what the `example_isolates` data set in this package looks like:
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```{r, message = FALSE}
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library(AMR)
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library(dplyr)
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glimpse(example_isolates)
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```
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Now to transform this to a data set with only resistance percentages per taxonomic order and genus:
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```{r, warning = FALSE}
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resistance_data <- example_isolates %>%
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group_by(
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order = mo_order(mo), # group on anything, like order
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genus = mo_genus(mo)
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) %>% # and genus as we do here
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summarise_if(is.sir, resistance) %>% # then get resistance of all drugs
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select(
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order, genus, AMC, CXM, CTX,
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CAZ, GEN, TOB, TMP, SXT
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) # and select only relevant columns
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head(resistance_data)
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```
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# Perform principal component analysis
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The new `pca()` function will automatically filter on rows that contain numeric values in all selected variables, so we now only need to do:
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```{r pca}
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pca_result <- pca(resistance_data)
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```
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The result can be reviewed with the good old `summary()` function:
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```{r}
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summary(pca_result)
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```
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```{r, echo = FALSE}
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proportion_of_variance <- summary(pca_result)$importance[2, ]
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```
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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.
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# Plotting the results
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```{r}
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biplot(pca_result)
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```
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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:
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```{r}
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ggplot_pca(pca_result)
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```
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You can also print an ellipse per group, and edit the appearance:
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```{r}
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ggplot_pca(pca_result, ellipse = TRUE) +
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ggplot2::labs(title = "An AMR/PCA biplot!")
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```
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