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articles/PCA.md
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# Conduct principal component analysis (PCA) for AMR
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**NOTE: This page will be updated soon, as the pca() function is
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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
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`example_isolates` data set in this package looks like:
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``` r
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library(AMR)
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library(dplyr)
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glimpse(example_isolates)
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#> Rows: 2,000
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#> Columns: 46
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#> $ date <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-01-13, 2…
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#> $ patient <chr> "A77334", "A77334", "067927", "067927", "067927", "067927", "4…
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#> $ age <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71, 75, 50…
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#> $ gender <chr> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M", "M", "M…
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#> $ ward <chr> "Clinical", "Clinical", "ICU", "ICU", "ICU", "ICU", "Clinical"…
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#> $ mo <mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPHY_EPDR",…
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#> $ PEN <sir> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, S,…
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#> $ OXA <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ FLC <sir> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, NA, R, R…
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#> $ AMX <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
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#> $ AMC <sir> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, I, NA, N…
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#> $ AMP <sir> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, R, NA, N…
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#> $ TZP <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ CZO <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
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#> $ FEP <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ CXM <sir> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R, R, S, S…
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#> $ FOX <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, NA,…
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#> $ CTX <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
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#> $ CAZ <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S, R, R, …
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#> $ CRO <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
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#> $ GEN <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ TOB <sir> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA, NA, NA…
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#> $ AMK <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ KAN <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ TMP <sir> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R, R, R, …
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#> $ SXT <sir> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, S, NA, N…
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#> $ NIT <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R,…
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#> $ FOS <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ LNZ <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
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#> $ CIP <sir> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, NA, S, S…
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#> $ MFX <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ VAN <sir> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S, S, S, …
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#> $ TEC <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
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#> $ TCY <sir> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S, I, R, …
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#> $ TGC <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
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#> $ DOX <sir> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, R, S, NA…
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#> $ ERY <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
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#> $ CLI <sir> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, R, R, NA…
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#> $ AZM <sir> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, R, R, S,…
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#> $ IPM <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, S, S…
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#> $ MEM <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ MTR <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ CHL <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ COL <sir> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R, R, R, …
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#> $ MUP <sir> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
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#> $ RIF <sir> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R, R, R, N…
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```
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Now to transform this to a data set with only resistance percentages per
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taxonomic order and genus:
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``` r
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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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#> # A tibble: 6 × 10
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#> # Groups: order [5]
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#> order genus AMC CXM CTX CAZ GEN TOB TMP SXT
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#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
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#> 1 (unknown order) (unknown ge… NA NA NA NA NA NA NA NA
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#> 2 Actinomycetales Schaalia NA NA NA NA NA NA NA NA
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#> 3 Bacteroidales Bacteroides NA NA NA NA NA NA NA NA
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#> 4 Campylobacterales Campylobact… NA NA NA NA NA NA NA NA
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#> 5 Caryophanales Gemella NA NA NA NA NA NA NA NA
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#> 6 Caryophanales Listeria NA NA NA NA NA NA NA NA
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```
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## Perform principal component analysis
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The new [`pca()`](https://amr-for-r.org/reference/pca.md) function will
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automatically filter on rows that contain numeric values in all selected
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variables, so we now only need to do:
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``` r
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pca_result <- pca(resistance_data)
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#> ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT",
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#> "TMP", and "TOB". Total observations available: 7.
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```
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The result can be reviewed with the good old
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[`summary()`](https://rdrr.io/r/base/summary.html) function:
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``` r
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summary(pca_result)
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#> Groups (n=4, named as 'order'):
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#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"
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#> Importance of components:
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#> PC1 PC2 PC3 PC4 PC5 PC6 PC7
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#> Standard deviation 2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 1.232e-16
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#> Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00
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#> Cumulative Proportion 0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
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```
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#> Groups (n=4, named as 'order'):
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#> [1] "Caryophanales" "Enterobacterales" "Lactobacillales" "Pseudomonadales"
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Good news. The first two components explain a total of 93.3% of the
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variance (see the PC1 and PC2 values of the *Proportion of Variance*. We
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can create a so-called biplot with the base R
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[`biplot()`](https://rdrr.io/r/stats/biplot.html) function, to see which
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antimicrobial resistance per drug explain the difference per
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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
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better with our new
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[`ggplot_pca()`](https://amr-for-r.org/reference/ggplot_pca.md)
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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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