gating: Gating strategy for mass cytometry data using spatial...
In gateR: Flow/Mass Cytometry Gating via Spatial Kernel Density Estimation

gating

R Documentation

Gating strategy for mass cytometry data using spatial relative risk functions

Description

Extracts cells within statistically significant combinations of fluorescent markers, successively, for a set of markers. Statistically significant combinations are identified using two-tailed p-values of a relative risk surface assuming asymptotic normality. This function is currently available for two-level comparisons of a single condition (e.g., case/control) or two conditions (e.g., case/control at time 1 and time 2). Provides functionality for basic visualization and multiple testing correction.

Usage

gating(
  dat,
  vars,
  n_condition = c(1, 2),
  numerator = TRUE,
  bandw = NULL,
  alpha = 0.05,
  p_correct = "none",
  nbc = NULL,
  plot_gate = FALSE,
  save_gate = FALSE,
  name_gate = NULL,
  path_gate = NULL,
  rcols = c("#FF0000", "#CCCCCC", "#0000FF"),
  lower_lrr = NULL,
  upper_lrr = NULL,
  c1n = NULL,
  c2n = NULL,
  win = NULL,
  ...,
  doplot = lifecycle::deprecated(),
  verbose = lifecycle::deprecated()
)

Arguments

`dat`	Input data frame flow cytometry data with the following features (columns): 1) ID, 2) Condition A ID, 3) Condition B ID (optional), and a set of markers.
`vars`	A vector of characters with the name of features (columns) within `dat` to use as markers for each gate. See details below.
`n_condition`	A numeric value of either 1 or 2 designating if the gating is performed with one condition or two conditions.
`numerator`	Logical. If `TRUE` (the default), cells will be extracted within all statistically significant numerator (i.e., case) clusters. If `FALSE`, cells will be extracted within all statistically significant denominator (i.e., control) clusters.
`bandw`	Optional, numeric. Fixed bandwidth for the kernel density estimation. Default is based on the internal `[sparr]{OS}` function.
`alpha`	Numeric. The two-tailed alpha level for significance threshold (default is 0.05).
`p_correct`	Optional. Character string specifying whether to apply a correction for multiple comparisons including a False Discovery Rate `p_correct = "FDR"`, a spatially dependent Sidak correction `p_correct = "correlated Sidak"`, a spatially dependent Bonferroni correction `p_correct = "correlated Bonferroni"`, an independent Sidak correction `p_correct = "uncorrelated Sidak"`, an independent Bonferroni correction `p_correct = "uncorrelated Bonferroni"`, and a correction based on Random Field Theory using an equation by Adler and Hasofer `p_correct = "Adler and Hasofer"` or an equation by Friston et al. `p_correct = "Friston"`. If `p_correct = "none"` (the default), then no correction is applied.
`nbc`	Optional. An integer for the number of bins when `p_correct = "correlated"`. Similar to `nbclass` argument in `modified.ttest`. The default is 30.
`plot_gate`	Logical. If `TRUE`, the output includes basic data visualizations.
`save_gate`	Logical. If `TRUE`, the output saves each visualization as a separate PNG file.
`name_gate`	Optional, character. The filename of the visualization(s). The default is "gate_k" where "k" is the gate number.
`path_gate`	Optional, character. The path of the visualization(s). The default is the current working directory.
`rcols`	Character string of length three (3) specifying the colors for: 1) group A (numerator), 2) neither, and 3) group B (denominator) designations. The defaults are `c("#FF0000", "#cccccc", "#0000FF")` or `c("red", "grey80", "blue")`.
`lower_lrr`	Optional, numeric. Lower cut-off value for the log relative risk value in the color key (typically a negative value). The default is no limit, and the color key will include the minimum value of the log relative risk surface.
`upper_lrr`	Optional, numeric. Upper cut-off value for the log relative risk value in the color key (typically a positive value). The default is no limit, and the color key will include the maximum value of the log relative risk surface.
`c1n`	Optional, character. The name of the level for the numerator of condition A. The default is NULL, and the first level is treated as the numerator.
`c2n`	Optional, character. The name of the level for the numerator of condition B. The default is NULL, and the first level is treated as the numerator.
`win`	Optional. Object of class `owin` for a custom two-dimensional window within which to estimate the surfaces. The default is NULL and calculates a convex hull around the data.
`...`	Arguments passed to `risk` to select resolution.
`doplot`	`doplot` is no longer supported and has been renamed `plot_gate`.
`verbose`	`verbose` is no longer supported; this function will not display verbose output from internal `risk` function.

Details

This function performs a sequential gating strategy for mass cytometry data comparing two levels with one or two conditions. Gates are typically two-dimensional space comprised of two fluorescent markers. The two-level comparison allows for the estimation of a spatial relative risk function and the computation of p-value based on an assumption of asymptotic normality. Cells within statistically significant areas are extracted and used in the next gate. This function relies heavily upon the risk function. Basic visualization is available if plot_gate = TRUE.

The vars argument must be a vector with an even-numbered length where the odd-numbered elements are the markers used on the x-axis of a gate, and the even-numbered elements are the markers used on the y-axis of a gate. For example, if vars = c("V1", "V2", "V3", and "V4") then the first gate is "V1" on the x-axis and "V2" on the y-axis and then the second gate is V3" on the x-axis and "V4" on the y-axis. Makers can be repeated in successive gates.

The n_condition argument specifies if the gating strategy is performed for one condition or two conditions. If n_condition = 1, then the function performs a one condition gating strategy using the internal rrs function, which computes the statistically significant areas (clusters) of a relative risk surface at each gate and selects the cells within the clusters specified by the numerator argument. If n_condition = 2, then the function performs a two conditions gating strategy using the internal lotrrs function, which computes the statistically significant areas (clusters) of a ratio of relative risk surfaces at each gate and selects the cells within the clusters specified by the numerator argument. The condition variable(s) within dat must be of class 'factor' with two levels. The first level is considered the numerator (i.e., "case") value, and the second level is considered the denominator (i.e., "control") value. The levels can also be specified using the c1n and c2n parameters. See the documentation for the internal rrs and lotrrs functions for more details.

The p-value surface of the ratio of relative risk surfaces is estimated assuming asymptotic normality of the ratio value at each gridded knot. The bandwidth is fixed across all layers.

Provides functionality for a correction for multiple testing. If p_correct = "FDR", calculates a False Discovery Rate by Benjamini and Hochberg. If p_correct = "uncorrelated Sidak", calculates an independent Sidak correction. If p_correct = "uncorrelated Bonferroni", calculates an independent Bonferroni correction. If p_correct = "correlated Sidak" or if p_correct = "correlated Bonferroni", then the corrections take into account the into account the spatial correlation of the surface. (NOTE: If p_correct = "correlated Sidak" or if p_correct = "correlated Bonferroni", it may take a considerable amount of computation resources and time to calculate). If p_correct = "Adler and Hasofer" or if p_correct = "Friston", then calculates a correction based on Random Field Theory. If p_correct = "none" (the default), then the function does not account for multiple testing and uses the uncorrected alpha level. See the internal pval_correct function documentation for more details.

Value

An object of class list. This is a named list with the following components:

obs: An object of class 'tibble' of the same features as dat that includes the information for the cells extracted with significant clusters in the final gate.
n: An object of class 'list' of the sample size of cells at each gate. The length is equal to the number of successful gates plus the final result.
gate: An object of class 'list' of 'rrs' objects from each gate. The length is equal to the number of successful gates.
note: An object of class 'character' of the gating diagnostic message.

The objects of class 'rrs' is similar to the output of the risk function with two additional components:

rr: An object of class 'im' with the relative risk surface.
f: An object of class 'im' with the spatial density of the numerator.
g: An object of class 'im' with the spatial density of the denominator.
P: An object of class 'im' with the asymptotic p-value surface.
lrr: An object of class 'im' with the log relative risk surface.
alpha: A numeric value for the alpha level used within the gate.

Examples

if (interactive()) {
## Single condition, no multiple testing correction
  test_gate <- gating(dat = randCyto,
                      vars = c("arcsinh_CD4", "arcsinh_CD38",
                               "arcsinh_CD8", "arcsinh_CD3"),
                      n_condition = 1)
}

gateR documentation built on Feb. 16, 2023, 5:24 p.m.