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:

library(AMR)
library(dplyr)
glimpse(example_isolates)
# Rows: 2,000
# Columns: 49
# $ date            <date> 2002-01-02, 2002-01-03, 2002-01-07, 2002-01-07, 2002-…
# $ hospital_id     <fct> D, D, B, B, B, B, D, D, B, B, D, D, D, D, D, B, B, B, …
# $ ward_icu        <lgl> FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, FALSE, FALSE, TR…
# $ ward_clinical   <lgl> TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FA…
# $ ward_outpatient <lgl> FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE…
# $ age             <dbl> 65, 65, 45, 45, 45, 45, 78, 78, 45, 79, 67, 67, 71, 71…
# $ gender          <chr> "F", "F", "F", "F", "F", "F", "M", "M", "F", "F", "M",…
# $ patient_id      <chr> "A77334", "A77334", "067927", "067927", "067927", "067…
# $ mo              <mo> "B_ESCHR_COLI", "B_ESCHR_COLI", "B_STPHY_EPDR", "B_STPH…
# $ PEN             <rsi> R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, R, …
# $ OXA             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ FLC             <rsi> NA, NA, R, R, R, R, S, S, R, S, S, S, NA, NA, NA, NA, …
# $ AMX             <rsi> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, …
# $ AMC             <rsi> I, I, NA, NA, NA, NA, S, S, NA, NA, S, S, I, I, R, I, …
# $ AMP             <rsi> NA, NA, NA, NA, NA, NA, R, R, NA, NA, NA, NA, NA, NA, …
# $ TZP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ CZO             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ FEP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ CXM             <rsi> I, I, R, R, R, R, S, S, R, S, S, S, S, S, NA, S, S, R,…
# $ FOX             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ CTX             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …
# $ CAZ             <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, S, S…
# $ CRO             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …
# $ GEN             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ TOB             <rsi> NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, S, S, NA…
# $ AMK             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ KAN             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ TMP             <rsi> R, R, S, S, R, R, R, R, S, S, NA, NA, S, S, S, S, S, R…
# $ SXT             <rsi> R, R, S, S, NA, NA, NA, NA, S, S, NA, NA, S, S, S, S, …
# $ NIT             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ FOS             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ LNZ             <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…
# $ CIP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, S, S, NA, NA, NA, NA, …
# $ MFX             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ VAN             <rsi> R, R, S, S, S, S, S, S, S, S, NA, NA, R, R, R, R, R, S…
# $ TEC             <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…
# $ TCY             <rsi> R, R, S, S, S, S, S, S, S, I, S, S, NA, NA, I, R, R, S…
# $ TGC             <rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, …
# $ DOX             <rsi> NA, NA, S, S, S, S, S, S, S, NA, S, S, NA, NA, NA, R, …
# $ ERY             <rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, …
# $ CLI             <rsi> R, R, NA, NA, NA, R, NA, NA, NA, NA, NA, NA, R, R, R, …
# $ AZM             <rsi> R, R, R, R, R, R, S, S, R, S, S, S, R, R, R, R, R, R, …
# $ IPM             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, S, S, …
# $ MEM             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ MTR             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ CHL             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ COL             <rsi> NA, NA, R, R, R, R, R, R, R, R, R, R, NA, NA, NA, R, R…
# $ MUP             <rsi> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA…
# $ RIF             <rsi> R, R, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, R, R, R,…

Now to transform this to a data set with only resistance percentages per taxonomic order and genus:

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)
# # A tibble: 6 × 10
# # Groups:   order [5]
#   order             genus          AMC   CXM   CTX   CAZ   GEN   TOB   TMP   SXT
#   <chr>             <chr>        <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
# 1 (unknown order)   (unknown ge…    NA    NA    NA    NA    NA    NA    NA    NA
# 2 Actinomycetales   Schaalia        NA    NA    NA    NA    NA    NA    NA    NA
# 3 Bacteroidales     Bacteroides     NA    NA    NA    NA    NA    NA    NA    NA
# 4 Campylobacterales Campylobact…    NA    NA    NA    NA    NA    NA    NA    NA
# 5 Caryophanales     Gemella         NA    NA    NA    NA    NA    NA    NA    NA
# 6 Caryophanales     Listeria        NA    NA    NA    NA    NA    NA    NA    NA

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:

pca_result <- pca(resistance_data)
# ℹ Columns selected for PCA: "AMC", "CAZ", "CTX", "CXM", "GEN", "SXT", "TMP"
#   and "TOB". Total observations available: 7.

The result can be reviewed with the good old summary() function:

summary(pca_result)
# Groups (n=4, named as 'order'):
# [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"
# Importance of components:
#                           PC1    PC2    PC3     PC4     PC5     PC6       PC7
# Standard deviation     2.1539 1.6807 0.6138 0.33879 0.20808 0.03140 5.121e-17
# Proportion of Variance 0.5799 0.3531 0.0471 0.01435 0.00541 0.00012 0.000e+00
# Cumulative Proportion  0.5799 0.9330 0.9801 0.99446 0.99988 1.00000 1.000e+00
# Groups (n=4, named as 'order'):
# [1] "Caryophanales"    "Enterobacterales" "Lactobacillales"  "Pseudomonadales"

Good news. The first two components explain a total of 93.3% 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

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:

ggplot_pca(pca_result)

You can also print an ellipse per group, and edit the appearance:

ggplot_pca(pca_result, ellipse = TRUE) +
  ggplot2::labs(title = "An AMR/PCA biplot!")