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Generate a Series of Plots for AMR Gene and Combination Analysis

Source: R/amr_upset.R
amr_upset.Rd

This function generates a set of visualizations to analyze AMR gene combinations, MIC values, and gene prevalence from a given binary matrix. It creates several plots, including MIC distributions, a bar plot for the percentage of strains per combination, a dot plot for gene combinations in strains, and a plot for gene prevalence. It also outputs a table summarizing the MIC distribution (median, lower, upper) and number resistant, for each marker combination.

Usage

amr_upset(
  binary_matrix,
  min_set_size = 2,
  order = "",
  plot_set_size = FALSE,
  plot_category = TRUE,
  print_category_counts = FALSE,
  print_set_size = FALSE,
  boxplot_colour = "grey",
  assay = "mic"
)

Arguments

binary_matrix

A data frame containing the original binary matrix output from the get_binary_matrix function. Expected columns are an identifier (column 1, any name), pheno (class sir, with S/I/R categories to colour points), mic (class mic, with MIC values to plot), and other columns representing gene presence/absence (binary coded, i.e., 1 = present, 0 = absent).

min_set_size

An integer specifying the minimum size for a gene set to be included in the analysis and plots. Default is 2. Only genes with at least this number of occurrences are included in the plots.

order

A character string indicating the order of the combinations on the x-axis. Options are:

  • "" (default): decreasing frequency of combinations

  • "genes": order by the number of genes in each combination

  • "value": order by the median assay value (MIC or disk zone) for each combination.

plot_set_size

Logical indicating whether to include a bar plot showing the set size (i.e., number of times each combination of markers is observed). Default is FALSE.

plot_category

Logical indicating whether to include a stacked bar plot showing, for each marker combination, the proportion of samples with each phenotype classification (specified by the pheno column in the input file). Default is TRUE.

print_category_counts

Logical indicating whether, if plot_category is TRUE, to print the number of strains in each resistance category for each marker combination in the plot. Default is FALSE.

print_set_size

Logical indicating whether, if plot_set_size is TRUE, to print the number of strains with each marker combination on the plot. Default is FALSE.

boxplot_colour

Colour for lines of the box plots summarising the MIC distribution for each marker combination. Default is "grey".

assay

A character string indicating whether to plot MIC or disk diffusion data. Must be one of:

  • "mic": plot MIC data stored in column mic

  • "disk": plot disk diffusion data stored in column disk

Value

A list containing the following elements:

  • plot: A grid of plots displaying: (i) grid showing the marker combinations observed, MIC distribution per marker combination, frequency per marker and (optionally) phenotype classification and/or number of samples for each marker combination.

  • summary: A data frame summarizing each marker combination observed, including median MIC (and interquartile range), number of resistant isolates, and positive predictive value for resistance.

Examples

ecoli_geno <- import_amrfp(ecoli_geno_raw, "Name")
binary_matrix <- get_binary_matrix(
  geno_table = ecoli_geno,
  pheno_table = ecoli_ast,
  antibiotic = "Ciprofloxacin",
  drug_class_list = c("Quinolones"),
  sir_col = "pheno_clsi",
  keep_assay_values = TRUE,
  keep_assay_values_from = "mic"
)
#>  Defining NWT in binary matrix using ecoff column provided: ecoff 

amr_upset(binary_matrix, min_set_size = 3, order = "value", assay = "mic")

#> $plot

#> 
#> $summary
#> # A tibble: 103 × 14
#>    marker_list             marker_count     n   R.ppv     R NWT.ppv   NWT
#>    <chr>                          <dbl> <int>   <dbl> <dbl>   <dbl> <dbl>
#>  1 ""                                 0  2590 0.00386    10  0.0197    51
#>  2 "qnrB"                             1     1 1           1  1          1
#>  3 "parE_E460K, gyrA_S83W"            2     1 1           1  1          1
#>  4 "parE_D475E"                       1    61 0           0  0          0
#>  5 "qnrA1"                            1     2 0           0  1          2
#>  6 "gyrA_S83A"                        1     3 0           0  0.667      2
#>  7 "qnrB4"                            1     2 1           2  1          2
#>  8 "parE_I355T"                       1    24 0           0  0          0
#>  9 "marR_S3N"                         1    38 0.105       4  0.263     10
#> 10 "marR_S3N, parE_D475E"             2     4 0           0  0.25       1
#> # ℹ 93 more rows
#> # ℹ 7 more variables: median_excludeRangeValues <dbl>,
#> #   q25_excludeRangeValues <dbl>, q75_excludeRangeValues <dbl>,
#> #   n_excludeRangeValues <int>, median_ignoreRanges <dbl>,
#> #   q25_ignoreRanges <dbl>, q75_ignoreRanges <dbl>
#>