mb_results: Create a data.frame to plot MB curves and AUC

In FredHutch/VISCfunctions: VISC STP/SRA functions

Create a data.frame to plot MB curves and AUC

Description

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".

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.

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

Examples

mb_results(magnitude = 96:105, response = c(rep(0,5), rep(1,5)),
           lower_trunc = 100, x_transform = 'log10')
mb_results(magnitude = 96:105, response = c(rep(0,5), rep(1,5)),
           lower_trunc = 100, x_transform = 'raw')

# 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()

FredHutch/VISCfunctions documentation built on Oct. 14, 2024, 11:33 p.m.