UtilsCytoRSV

The goal of UtilsCytoRSV is to facilitate common data processing and visualisation tasks regarding cytometry data (with CyTOF and flow in mind).

Installation

You can install UtilsCytoRSV from GitHub with:

if (!requireNamespace("remotes", quietly = TRUE)) install.packages("remotes")
remotes::install_github("SATVILab/UtilsCytoRSV")

Examples

library(UtilsCytoRSV)

Calculation.

Subtract background.

.data_test <- data.frame(
pid = rep(c("a", "b"), each = 3),
stim = c("mtb", "ebv", "uns") |>
  c("uns", "ebv", "mtb"),
resp1 = 1:6,
resp2 = 17:12 * 2
)
data_out <- subtract_background(
 .data = .data_test,
 grp = "pid",
 stim = "stim",
 uns = "uns",
 resp = c("resp1", "resp2"),
 remove_uns = FALSE
)
#> [1] "resp1"
#> [1] "resp2"

Sum over marker(s).

data("data_count")
data_test <- data_count |>
  calc_prop(den = "count_pop_den",
            num = "count_pop_num") |>
  dplyr::select(-c(count_pop_den, count_pop_num)) |>
  dplyr::arrange(SubjectID, VisitType, stim, cyt_combn)

data_out <- sum_over_markers(
  .data = data_test,
  grp = c("SubjectID", "VisitType", "stim"),
  cmbn = "cyt_combn",
  markers_to_sum = c("IFNg", "IL2", "IL17"),
  levels = c("-", "+"),
resp = "prop"
)

Plotting

It provides a 2D hex plot with useful defaults.

if (!requireNamespace("flowCore", quietly = TRUE)) {
  if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
  }
  BiocManager::install("flowCore")
}
library(UtilsCytoRSV)
suppressWarnings(data("GvHD", package = "flowCore"))
ex_tbl <- flowCore::exprs(GvHD[[1]]) |>
  tibble::as_tibble()
marker <- c("FL2-H", "FL3-H")
plot_cyto(
  data = ex_tbl,
  marker = marker
)

The ranges can be made equal between the x- and y-axes.

plot_cyto(
  data = ex_tbl,
  marker = marker,
  limits_equal = TRUE
)

Each axis can be forced to include particular values (especially useful if viewing gated data, which may have only positive-expressing cells and you then want to show that there are no negative-expressing cells).

plot_cyto(
  data = ex_tbl,
  marker = marker,
  limits_expand = list(y = -5e3)
)

Utilities

You can get a vector to label channels based on the FCS file using chnl_lab, and then supply this to plot_cyto to have better axis labels. Note that the inverse function, marker_lab, is also available to convert from markers to channels.

chnl_lab <- chnl_lab(GvHD)
plot_cyto(
  data = ex_tbl,
  marker = marker,
  lab = chnl_lab,
  limits_equal = TRUE
)