README

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 two-group comparison, we detect clusters using the kernel-based spatial relative risk function estimated using the sparr package. The tests are conducted in a two-dimensional space comprised of two fluorescent markers.

For a two-group 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

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 p-value surface.

Available sample data sets

Function	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:

Usage

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 p-value 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)

# -------------------- #
# Post-gate assessment #
# -------------------- #

# Density of arcsinh-transformed CD4 post-gating
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 p-value 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 p-value uncorrected for multiple testing
## For "arcsinh_CD4" and "arcsinh_CD38"
test_rrs <- gateR::rrs(dat = obs_dat[ , -7:-6])

# Two conditions
## A p-value uncorrected for multiple testing
## For "arcsinh_CD8" and "arcsinh_CD3"
test_lotrrs <- gateR::lotrrs(dat = obs_dat[ , -5:-4])

gateR: Flow/Mass Cytometry Gating via Spatial Kernel Density Estimation

Overview

Installation

Available functions

Available sample data sets

Authors

Usage

Funding

Acknowledgments

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