ASI Masterclass - Workshop Session 3
Thomas Ashhurst, Givanna Putri, Felix Marsh-Wakefield, Jennifer Habel, Wuji Zhang
26/10/2021
SESSION 3 - respiratory (3:00 pm)
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Session #3: High-dimensional analysis of immune responses to COVID-19 in the respiratory tract Tue 26-Oct, 3:00 pm – 4:30 pm Lead instructors: Givanna Putri, Wuji Zhang In this session we will be investigating the immune response to COVID-19 in the respiratory tract, from samples acquired via an endotracheal tube. In particular we will explore a variety of myeloid cells that infiltrate the respiratory tract via the blood, including macrophages and neutrophils, and how they compare to similar cells in the blood.
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Dataset
We will be using R & RStudio to analyse cells from the blood or respiratory tract of COVID-19 patients, measured by flow cytometry. We will be using the R package ‘Spectre’ to do this. The instructors will guide attendees through the analysis process in R, stopping regularly to discuss what has been done, and what we can observe about the COVID-19 immune response. The COVID-19 datasets used in this workshop originate from these two studies from the Kedzierska Laboratory: Koutsakos et al 2021. Cell Reports Medicine and Zhang et al 2021. medRxiv. To prepare for the workshop we selectively chose representative samples and performed some initial cleanup and batch alignment, which we will discuss during the workshop.
Open R script
1. Load packages
#######################################################################################################
#### 1. Load packages, and set working directory
####################################################################################################### ### Load libraries
library(Spectre)
Spectre::package.check() # Check that all required packages are installed
Spectre::package.load() # Load required packages ### Set PrimaryDirectory
dirname(rstudioapi::getActiveDocumentContext()$path) # Finds the directory where this script is located
setwd(dirname(rstudioapi::getActiveDocumentContext()$path)) # Sets the working directory to where the script is located
getwd()
PrimaryDirectory <- getwd()
PrimaryDirectory ### Set 'input' directory
setwd(PrimaryDirectory)
setwd("data/")
InputDirectory <- getwd()
setwd(PrimaryDirectory) ### Set 'metadata' directory
setwd(PrimaryDirectory)
setwd("metadata/")
MetaDirectory <- getwd()
setwd(PrimaryDirectory) ### Create output directory
dir.create("Output_Spectre", showWarnings = FALSE)
setwd("Output_Spectre")
OutputDirectory <- getwd()
setwd(PrimaryDirectory)2. Import data
#######################################################################################################
#### 2. Import and prep data
####################################################################################################### ### Import data
setwd(InputDirectory)
list.files(InputDirectory, ".csv")
data.list <- Spectre::read.files(file.loc = InputDirectory,
file.type = ".csv",
do.embed.file.names = TRUE) ### Check the data
check <- do.list.summary(data.list)
check$name.table # Review column names and their subsequent values
check$ncol.check # Review number of columns (features, markers) in each sample
check$nrow.check # Review number of rows (cells) in each sample
data.list[[1]] ### Merge data
cell.dat <- Spectre::do.merge.files(dat = data.list)
cell.dat ### Read in metadata
setwd(MetaDirectory)
meta.dat <- fread("sample.details.csv")
meta.dat3. Data transformation
#######################################################################################################
#### 3. Data transformation
####################################################################################################### setwd(OutputDirectory)
dir.create("Output 1 - transformed plots")
setwd("Output 1 - transformed plots") ### Arcsinh transformation
as.matrix(names(cell.dat))
low.asinh <- names(cell.dat)[c(3:19)]
low.asinh
high.cofactor <- 5000
low.cofactor <- 1000 plot.against <- "CD66b_asinh"
cell.dat <- do.asinh(cell.dat, low.asinh, cofactor = low.cofactor)
transformed.cols <- c(paste0(low.asinh, "_asinh"))
transformed.cols
sub <- do.subsample(cell.dat, 20000) for(i in transformed.cols){
make.colour.plot(sub, i, plot.against)
} ### Re-scale FSC SSC
cell.dat <- do.rescale(cell.dat, c('FSC-A', 'SSC-A'), new.min = 0, new.max = 4)
cell.dat4. Add metadata
#######################################################################################################
#### 4. Add metadata and set some preferences
####################################################################################################### ### Add metadata to data.table
meta.dat
sample.info <- meta.dat[,c(1:3)]
sample.info
cell.dat <- do.add.cols(cell.dat, "FileName", sample.info, "FileName", rmv.ext = TRUE)
cell.dat ### Columns
as.matrix(names(cell.dat))
cellular.cols <- names(cell.dat)[c(22:40)]
as.matrix(cellular.cols)
as.matrix(names(cell.dat))
cluster.cols <- names(cell.dat)[c(22:40)]
as.matrix(cluster.cols)
exp.name <- "COVID19 respiratory"
sample.col <- "Sample"
group.col <- "Group" ### Re-order data
as.matrix(unique(cell.dat[[group.col]]))
cell.dat <- do.reorder(cell.dat, sample.col, c('Respiratory_1', 'Respiratory_2', 'Respiratory_3'))
cell.dat5. Clustering
#######################################################################################################
#### 5. Clustering and dimensionality reduction
####################################################################################################### setwd(OutputDirectory)
dir.create("Output 2 - clustering")
setwd("Output 2 - clustering") ### Clustering
cell.dat <- run.flowsom(cell.dat, cluster.cols, meta.k = 40)
cell.dat ### Dimensionality reduction
cell.sub <- do.subsample(cell.dat, c(2000, 10000, 20000), sample.col)
cell.sub
cell.sub <- run.fitsne(cell.sub, cluster.cols, perplexity = 200)
cell.sub ### DR plots
make.colour.plot(cell.sub, "FItSNE_X", "FItSNE_Y", "FlowSOM_metacluster", col.type = 'factor', add.label = TRUE)
make.multi.plot(cell.sub, "FItSNE_X", "FItSNE_Y", cellular.cols, figure.title = 'Cellular cols')make.multi.plot(cell.sub, "FItSNE_X", "FItSNE_Y", cluster.cols, figure.title = 'Cluster cols')