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Create a data.frame to plot MB curves and AUC

mb_results.Rd

Creates step curve info for magnitude breadth (MB) plots, with option to include response status and have logged transformation for AUC calculation.

Usage

mb_results(
  magnitude,
  response = NULL,
  lower_trunc = 100,
  upper_trunc = 22000,
  x_transform = c("log10", "raw")
)

Arguments

magnitude

values to create step curve and AUC for (numeric vector)

response

response status, vector of type integer (0/1) or logical (TRUE/FALSE). If NULL assumes no response information considered.

lower_trunc

the lower truncation value (numeric scalar that must be 0 or higher).

upper_trunc

the upper truncation value (numeric scalar that must be higher than lower_trunc). Set to Inf for no upper truncation

x_transform

a character vector specifying the transformation for AUC calculation, if any. Must be one of "log10" (default) or "raw".

Value

Returns a data frame with the following columns: * magnitude - magnitude values (similar to times in a survival analysis) * breadth - percent of antigens greater or equal to the magnitude value * n_remaining - number of antigens remaining greater or equal to the magnitude value * n_here - number of antigens at the exact magnitude value * aucMB - area under the magnitude breadth curve

Details

AUC is calculated from 0 (or 1 if x_transform = "log10") to the x values (after lower_trunc/upper_trunc x value truncation).

If response is given, non-responding values (response = 0 or FALSE) will have their values set to the lower_trunc for MB curves and AUC calculations.

The output can be used for plotting step function curves, with magnitude on the x axis and breadth on the y axis. Note if x_transform = 'log10' the resulting plot is best displayed on the log10 scale (ggplot2::scale_x_log10).

aucMB can be used for boxplots and group comparisons. Note aucMB is repeated for values of magnitude and breadth.

Examples



mb_results(magnitude = 96:105, response = c(rep(0,5), rep(1,5)),
           lower_trunc = 100, x_transform = 'log10')
#>   magnitude breadth n_remaining n_here    aucMB
#> 1         1     1.0          10     NA 101.4841
#> 2       100     0.5          10      5 101.4841
#> 3       101     0.4           5      1 101.4841
#> 4       102     0.3           4      1 101.4841
#> 5       103     0.2           3      1 101.4841
#> 6       104     0.1           2      1 101.4841
#> 7       105     0.0           1      1 101.4841
mb_results(magnitude = 96:105, response = c(rep(0,5), rep(1,5)),
           lower_trunc = 100, x_transform = 'raw')
#>   magnitude breadth n_remaining n_here aucMB
#> 1         0     1.0          10     NA 101.5
#> 2       100     0.5          10      5 101.5
#> 3       101     0.4           5      1 101.5
#> 4       102     0.3           4      1 101.5
#> 5       103     0.2           3      1 101.5
#> 6       104     0.1           2      1 101.5
#> 7       105     0.0           1      1 101.5

# Simple Example
library(dplyr)
dat = data.frame(magnitude = c(500,800,20,150,30000,10,1,2000,10000,900),
                 response = c(1,1,0,1,1,0,0,1,1,1),
                 ptid = c(1,1,2,2,3,3,3,3,3,3))
ind_results <-
 dat |>
  dplyr::group_by(ptid) |>
  dplyr::group_modify(~ mb_results(magnitude = .x$magnitude, response = .x$response))

overall_results <-
 mb_results(magnitude = dat$magnitude, response = dat$response)

ggplot2::ggplot(data = overall_results, ggplot2::aes(x = magnitude, y = breadth)) +
  ggplot2::geom_step(data = ind_results, ggplot2::aes(group = ptid),
          linetype = "dashed", direction = 'hv', lwd = .35, alpha = .7) +
  ggplot2::geom_step(direction = 'hv', lwd = .65) +
  ggplot2::scale_x_log10()



# BAMA Assay Example comparing MB Across Antigens

data(exampleData_BAMA)

data_here <-
 exampleData_BAMA |>
 filter(visitno == 2)

group_results <-
  data_here |>
  dplyr::group_by(group) |>
  dplyr::group_modify(~ mb_results(magnitude = .x$magnitude , response = .x$response))

ind_results <-
  data_here |>
  dplyr::group_by(group, pubID) |>
  dplyr::group_modify(~ mb_results(magnitude = .x$magnitude , response = .x$response))

ggplot2::ggplot(data = group_results,
                ggplot2::aes(x = magnitude, y = breadth, color = factor(group))) +
  ggplot2::geom_step(data = ind_results, ggplot2::aes(group = pubID),
          linetype = "dashed", direction = 'hv', lwd = .35, alpha = .7) +
  ggplot2::geom_step(direction = 'hv', lwd = .65) +
  ggplot2::scale_x_log10('Response Magnitude',
           breaks = c(100,1000,10000, 22000),
           labels = c(expression(""<=100),1000,10000, expression("">=22000))) +
  ggplot2::ylab('Magnitude Breadth (%)') +
  ggplot2::scale_color_discrete('Group') +
  ggplot2::coord_cartesian(xlim = c(95, 23000)) +
  ggplot2::theme_bw()



 # AUC-MB plot

AUC_MB <- dplyr::distinct(ind_results, group, pubID, aucMB)

ggplot2::ggplot(AUC_MB, ggplot2::aes(x = factor(group), y = aucMB,
                                     color = factor(group))) +
  ggplot2::geom_boxplot(outlier.color = NA, show.legend = FALSE) +
  ggplot2::geom_point(position = ggplot2::position_jitter(width = 0.25,
           height = 0, seed = 1), size = 1.5, show.legend = FALSE) +
  ggplot2::scale_y_log10('AUC-MB') +
  ggplot2::xlab('Group') +
  ggplot2::theme_bw()