Flow/Mass Cytometry Gating via Spatial Kernel Density Estimation.

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

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 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.

Examples of a single condition with two groups:

1. Disease case vs. Healthy control
2. Time 2 vs. Time 1 (baseline)

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:

1. Estimate a relative risk surface for:
   1. Condition 2B vs. Condition 2A
   2. Condition 1B vs. Condition 1A
2. Estimate the relative risk surface for the ratio:

$$\frac{ \big(\frac{Condition2B}{Condition2A}\big)}{\big(\frac{Condition1B}{Condition1A}\big)}$$

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("lance-waller-lab/gateR")

Available functions

The repository also includes the code and resources to create the project hexagon sticker.

Available sample data sets

Authors

• Ian D. Buller - Social & Scientific Systems, Inc., a division of DLH Corporation, Silver Spring, Maryland (current) - Occupational and Environmental Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, Maryland (former) - Environmental Health Sciences, James T. Laney School of Graduate Studies, Emory University, Atlanta, Georgia. (original) - GitHub - ORCID

See also the list of contributors who participated in this project. Main contributors include:

• Elena Hsieh - Immunology & Microbiology and Pediatrics, University of Colorado Anschutz School of Medicine - GitHub - ORCID
• Debashis Ghosh - Biostatistics & Informatics, Colorado School of Public Health, Aurora, Colorado - GitHub - ORCID
• Lance A. Waller - Biostatistics and Bioinformatics, Emory University, Atlanta, Georgia - GitHub - ORCID

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])

# ------------------------------------------ #
# 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")

Funding

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.

Acknowledgments

When citing this package for publication, please follow:

citation("gateR")

Questions? Feedback?

For questions about the package, please contact the maintainer Dr. Ian D. Buller or submit a new issue.