Date repository last updated: January 23, 2024
Overview
The gateR
package is a suite of R
functions to identify significant spatial clustering of flow and mass cytometry data used in immunological investigations. For a twogroup comparison, we detect clusters using the kernelbased spatial relative risk function estimated using the sparr package. The tests are conducted in a twodimensional space comprised of two fluorescent markers.
Examples of a single condition with two groups:
For a twogroup comparison of two conditions, we estimate two relative risk surfaces for one condition and then a ratio of the relative risks. For example:
\[\frac{ \big(\frac{Condition2B}{Condition2A}\big)}{\big(\frac{Condition1B}{Condition1A}\big)}\]
Within areas where the relative risk exceeds an asymptotic normal assumption, the gateR
package has the functionality to examine the features of these cells. Basic visualization is also supported.
Installation
To install the release version from CRAN:
install.packages("gateR")
To install the development version from GitHub:
devtools::install_github("lancewallerlab/gateR")
Available functions
Function  Description 

gating

Main function. Conduct a gating strategy for flow and mass cytometry data. 
rrs

Called within gating , one condition comparison.

lotrrs

Called within gating , two condition comparison.

pval_correct

Called within rrs and lotrrs , calculates various multiple testing corrections for the alpha level. Five methods account for (spatially) dependent, multiple testing.

lrr_plot

Called within rrs and lotrrs , provides functionality for basic visualization of a log relative risk surface.

pval_plot

Called within rrs and lotrrs , provides functionality for basic visualization of a significant pvalue surface.

The repository also includes the code and resources to create the project hexagon sticker.
Available sample data sets
Data  Description 

randCyto

A sample dataset containing information about flow cytometry data with two binary conditions and four markers. The data are a random subset of the ‘extdata’ data in the flowWorkspaceData package found on Bioconductor and formatted for gateR input.

Authors
See also the list of contributors who participated in this project. Main contributors include:
set.seed(1234) # for reproducibility
#  #
# Necessary packages #
#  #
library(gateR)
library(dplyr)
library(flowWorkspaceData)
library(ncdfFlow)
library(stats)
#  #
# Data preparation #
#  #
# Use 'extdata' from the {flowWorkspaceData} package
flowDataPath < system.file("extdata", package = "flowWorkspaceData")
fcsFiles < list.files(pattern = "CytoTrol", flowDataPath, full = TRUE)
ncfs < ncdfFlow::read.ncdfFlowSet(fcsFiles)
fr1 < ncfs[[1]]
fr2 < ncfs[[2]]
## Comparison of two samples (single condition) "g1"
## Two gates (four markers) "CD4", "CD38", "CD8", and "CD3"
## Arcsinh Transformation for all markers
## Remove cells with NA and Inf values
# First sample
obs_dat1 < data.frame("id" = seq(1, nrow(fr1@exprs), 1),
"g1" = rep(1, nrow(fr1@exprs)),
"arcsinh_CD4" = asinh(fr1@exprs[ , 5]),
"arcsinh_CD38" = asinh(fr1@exprs[ , 6]),
"arcsinh_CD8" = asinh(fr1@exprs[ , 7]),
"arcsinh_CD3" = asinh(fr1@exprs[ , 8]))
# Second sample
obs_dat2 < data.frame("id" = seq(1, nrow(fr2@exprs), 1),
"g1" = rep(2, nrow(fr2@exprs)),
"arcsinh_CD4" = asinh(fr2@exprs[ , 5]),
"arcsinh_CD38" = asinh(fr2@exprs[ , 6]),
"arcsinh_CD8" = asinh(fr2@exprs[ , 7]),
"arcsinh_CD3" = asinh(fr2@exprs[ , 8]))
# Full set
obs_dat < rbind(obs_dat1, obs_dat2)
obs_dat < obs_dat[complete.cases(obs_dat), ] # remove NAs
obs_dat < obs_dat[is.finite(rowSums(obs_dat)), ] # remove Infs
obs_dat$g1 < as.factor(obs_dat$g1) # set "g1" as binary factor
## Create a second condition (randomly split the data)
## In practice, use data with a measured second condition
g2 < stats::rbinom(nrow(obs_dat), 1, 0.5)
obs_dat$g2 < as.factor(g2)
obs_dat < obs_dat[ , c(1:2,7,3:6)]
# Export 'randCyto' data for CRAN examples
randCyto < dplyr::sample_frac(obs_dat, size = 0.1) # random subsample
#  #
# Run gateR with one condition #
#  #
# Single condition
## A pvalue uncorrected for multiple testing
test_gating < gateR::gating(dat = obs_dat,
vars = c("arcsinh_CD4", "arcsinh_CD38",
"arcsinh_CD8", "arcsinh_CD3"),
n_condition = 1,
plot_gate = TRUE,
upper_lrr = 1,
lower_lrr = 1)
#  #
# Postgate assessment #
#  #
# Density of arcsinhtransformed CD4 postgating
graphics::plot(stats::density(test_gating$obs[test_gating$obs$g1 == 1, 4]),
main = "arcsinh CD4",
lty = 2)
graphics::lines(stats::density(test_gating$obs[test_gating$obs$g1 == 2, 4]),
lty = 3)
graphics::legend("topright",
legend = c("Sample 1", "Sample 2"),
lty = c(2, 3),
bty = "n")
#  #
# Run gateR with two conditions #
#  #
## A pvalue uncorrected for multiple testing
test_gating2 < gateR::gating(dat = obs_dat,
vars = c("arcsinh_CD4", "arcsinh_CD38",
"arcsinh_CD8", "arcsinh_CD3"),
n_condition = 2)
#  #
# Perform a single gate without data extraction #
#  #
# Single condition
## A pvalue uncorrected for multiple testing
## For "arcsinh_CD4" and "arcsinh_CD38"
test_rrs < gateR::rrs(dat = obs_dat[ , 7:6])
# Two conditions
## A pvalue uncorrected for multiple testing
## For "arcsinh_CD8" and "arcsinh_CD3"
test_lotrrs < gateR::lotrrs(dat = obs_dat[ , 5:4])
#  #
# Run gateR with multiple testing correction #
#  #
## False Discovery Rate
test_gating_fdr < gateR::gating(dat = obs_dat,
vars = c("arcsinh_CD4", "arcsinh_CD38",
"arcsinh_CD8", "arcsinh_CD3"),
n_condition = 1,
p_correct = "FDR")
This package was developed while the author was originally a doctoral student at in the Environmental Health Sciences doctoral program at Emory University and later as a postdoctoral fellow supported by the Cancer Prevention Fellowship Program at the National Cancer Institute. Any modifications since December 05, 2022 were made while the author was an employee of Social & Scientific Systems, Inc., a division of DLH Corporation.
When citing this package for publication, please follow:
citation("gateR")
For questions about the package, please contact the maintainer Dr. Ian D. Buller or submit a new issue.