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A01 Humanized.R
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A01 Humanized.R
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library(flowCore)
library(flowWorkspace)
library(ggcyto)
library(flowAI)
library(gridExtra)
library(tidyverse)
library(flowStats)
library(CytoML)
library(Rtsne)
library(FlowSOM)
library(ggplot2)
library(ggpubr)
library(dplyr)
library(lme4)
library(multcomp)
library(tidyr)
library(ggplot2)
library(gridExtra)
library(readxl)
library(ggpubr)
library(Rmisc)
library(tidyverse)
library(plyr)
library(GGally)
library(ggplot2)
library(tidyverse)
library(gapminder)
########### 1. Generate Counts From Manual Gating ###########
###########
data_dir <- "C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/FCS/"
study_dir <- ""
ws <- open_flowjo_xml(paste0(data_dir,"19-331-123 Full Dataset.wsp"))
#ws <- open_flowjo_xml(paste0(data_dir,"19-331-122 Full Dataset WSP 220414.wsp"))
ws
#fj_ws_get_samples(ws, group_id = c(1))$name
#myfiles <- list.files(path="C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate", pattern = "Samples", ignore.case = TRUE)
#fs <- read.flowSet(myfiles, path="C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/TD033", truncate_max_range = FALSE)
#CS <- load_cytoset_from_fcs(files = myfiles, path="C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/TD033")
###THE ISSUE IS ALL THE NEW FILES IN THE COMBING WS HAVE THE NO. OF CELLS AT THE END .fsc_xxxx????###
gs <- flowjo_to_gatingset(ws, name = 6, path=data_dir)
# channel.ignore.case = F,
# skip_faulty_gate = T,
# include_empty_tree = F,
# cytoset = CS) #,compensation = compensation_matrix)
gs
gs_get_pop_paths(gs)[4]
recompute(gs)
plot(gs)
######### % Human
######### % Human
######### % Human
human_counts <- gs_pop_get_count_fast(gs, format = "long", subpopulations = gs_get_pop_paths(gs)[4])
human_counts
human_counts <- human_counts %>% pivot_wider(id_cols = name,
names_from = Population,
values_from = c("Count", "ParentCount"))
write.csv(human_counts, "C:/Users/edmondsonef/Desktop/Humanized.count.csv")
######### STACKED BAR CHART
######### STACKED BAR CHART
######### STACKED BAR CHART
gs_get_pop_paths(gs)[c(11,15,18,22,23,27,30,31,32,35)]
#counts_table <-gs_pop_get_count_fast(gs[1])
counts_table <- gs_pop_get_count_fast(gs, format = "long", subpopulations = gs_get_pop_paths(gs)[c(11,15,18,22,23,27,30,31,32,35)])
counts_table
counts_table <- counts_table %>% pivot_wider(id_cols = name,
names_from = Population,
values_from = c("Count", "ParentCount"))
write.csv(counts_table, "C:/Users/edmondsonef/Desktop/counts_table.csv")
counts_table <- read.csv("C:/Users/edmondsonef/Desktop/counts_table.csv")
counts_table_t <- counts_table[-1] %>% t() %>% as.data.frame() %>% setNames(counts_table[,1])
props_table <- t(t(counts_table_t) / colSums(counts_table_t[])) * 100
props_table_t <- props_table[] %>% t() %>% as.data.frame() %>% setNames(row.names(props_table))
counts <- as.data.frame.matrix(counts_table_t)
props <- as.data.frame.matrix(props_table)
write.csv(props, "C:/Users/edmondsonef/Desktop/props.csv")
props <- read.csv("C:/Users/edmondsonef/Desktop/props.csv", header = T, stringsAsFactors = F)
props <- data.frame(props[,-1], row.names = props[,1])
ggdf <- reshape2::melt(data.frame(cluster = rownames(props), props),
id.vars = "cluster", value.name = "proportion", variable.name = "sample_id")
write.csv(ggdf, "C:/Users/edmondsonef/Desktop/ggdf.csv")
#ggdf <- read.csv("C:/Users/edmondsonef/Desktop/ggdf.csv", header = T, stringsAsFactors = F)
data <- read_excel("C:/Users/edmondsonef/Desktop/agg.xlsx", sheet = "Final.blood2")
ggdf <- data
color_clusters <- c("#7BAFDE", "#7570B3", "#882E72", "#B17BA6", "#DC050C",
"#33A02C", "#B2DF8A","#FF7F00", "#FB8072", "#FDB462", #"#E7298A", "#E78AC3",
"#55A1B1", "#8DD3C7", "#A6761D",
"#E6AB02", "#1965B0", "#BEAED4", "#666666", "#999999",
"#aa8282", "#d4b7b7", "#8600bf", "#ba5ce3", "#808000",
"#aeae5c", "#1e90ff", "#00bfff", "#56ff0d", "#ffff00")
# color_clusters <- c("#DC050C","#FB8072","#FDB462","#B17BA6","#7BAFDE",
# "#7570B3", "#882E72", "#B2DF8A","#FF7F00", "#33A02C")
plot <- ggplot(ggdf, aes(x = `Idx`, y = proportion, fill = clusters)) +
geom_bar(stat = "identity") +
facet_wrap(~ Group, scales = "free_x", ncol=6) +
theme_bw() +
theme(axis.title.x=element_blank(), text = element_text(size = 18))+
labs(title="Blood") +
theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
scale_fill_manual(values = color_clusters)
plot
setwd("C:/Users/edmondsonef/Desktop/R-plots/")
tiff("Blood 123 plots.tiff", units="in", width=15, height=7, res=300)
plot
dev.off()
######### STACKED BAR CHART
######### STACKED BAR CHART
######### STACKED BAR CHART
data <- read_excel("C:/Users/edmondsonef/Desktop/agg.xlsx", sheet = "counts.1")
spleen <- data[ which(data$Tissue=='Spleen'), ]
ggplot(data, aes(x = data$'% Human', y = data$'CD45 Cells')) +
geom_point(aes(color = data$Group), size = 5)+
facet_wrap(~ Tissue, scales = "free_x", ncol=3)+
scale_x_continuous(name = "% huCD45+") +
scale_y_continuous(name = "Number CD45 Cells") +
theme_bw(base_size = 18)+
stat_smooth(method = "lm",
col = "#C42126",
se = F,
size = 1)
########### 2. Preprocessing and QC ###########
########### ###########
# 2. Define the general and preprocessing variables
data_dir <- "C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/"
file_pattern <- "\\d.fcs"
reference_file <- read.FCS(paste0(data_dir,study_dir,'Samples 24Mar2022_Tube_020 Animal 112 BMC_047.fcs'), truncate_max_range = FALSE)
reference_marker <- "PE-A" # Scatter values will be scaled to have the same range
markers_of_interest <- c("BB515-A",
"BB700-P-A",
"APC-A",
"APC-Cy7-A",
"BV421-A",
"BV786-A",
"BUV395-A",
"BUV805-A",
"PE-A",
"PE-CF594-A",
"PE-Cy7-A")
live_gate <- flowCore::polygonGate(filterId = "Live",
.gate = matrix(data = c(60000, 100000, 150000,
250000, 250000, 60000,
60000, 1.6, 1.9, 2.5,
2.5, -0.3, -0.3, 1.6),
ncol = 2,
dimnames = list(c(),
c("FSC-A",
"APC-Cy7-A"))))
# 3. Define and create the directories
dir_prepr <- paste0(data_dir,study_dir,"1-Preprocessed/") #where the preprocessed data will be stored
dir_QC <- paste0(data_dir,study_dir,"2-QC/") #where the data QC results will be stored
dir_RDS <- paste0(data_dir,study_dir,"3-RDS/") #where the R objects will be stored
dir_results <- paste0(data_dir,study_dir,"4-Results/") #where the results will be stored
dir_raw <- paste0(data_dir,study_dir) #where the raw data is located
path_comp <- "C:/Users/edmondsonef/Desktop/0-Autospill/6-10Mar2022/autospill_compensation.csv" #where comp matrix is located
for (path in c(dir_prepr, dir_QC, dir_RDS, dir_results)){
dir.create(path)
}
# 4. Prepare some additional information for preprocessing the files
# given the variable choices of step 2.
files <- list.files(path = dir_raw, pattern = "Samples")
files
channels_of_interest <- GetChannels(object = reference_file,
markers = markers_of_interest,
exact = FALSE)
compensation_matrix <- read.csv(path_comp,
check.names = FALSE, row.names = 1)
colnames(compensation_matrix) <- sub(" :: .*", "",
colnames(compensation_matrix))
# Compute transformation list
ff_m <- PeacoQC::RemoveMargins(reference_file, channels_of_interest)
names(ff_m)
exprs(ff_m)
each_col(ff_m, median)
ff_c <- flowCore::compensate(ff_m, compensation_matrix)
translist <- estimateLogicle(ff_c, colnames(compensation_matrix))
ff_t <- flowCore::transform(ff_c, translist)
q5_goal <- quantile(exprs(ff_t)[,reference_marker], 0.05)
q95_goal <- quantile(exprs(ff_t)[,reference_marker], 0.95)
q5_SSCA <- quantile(exprs(ff_t)[,"SSC-A"], 0.05)
q95_SSCA <- quantile(exprs(ff_t)[,"SSC-A"], 0.95)
SSCA_a <- (q95_goal - q5_goal) / (q95_SSCA - q5_SSCA)
SSCA_b <- q5_goal - q5_SSCA * (q95_goal - q5_goal) / (q95_SSCA - q5_SSCA)
translist <- c(translist,
transformList("SSC-A", flowCore::linearTransform(a = SSCA_a,
b = SSCA_b)))
translist
# 5. Read the first fcs file into a flowframe
ff <- read.FCS(paste0(dir_raw, files[7]), truncate_max_range = FALSE)
# 6. Remove margin events
ff_m <- PeacoQC::RemoveMargins(ff, channels_of_interest)
# 7. Compensate
ff_c <- flowCore::compensate(ff_m, compensation_matrix)
# 8. Transform, logicle for marker channels, linear for scatter channel
ff_t <- flowCore::transform(ff_c, translist)
# 9. Remove doublets and filter live cells
ff_s <- PeacoQC::RemoveDoublets(ff_t)
selected_live <- flowCore::filter(ff_s, live_gate)
ff_l <- ff_s[selected_live@subSet, ]
# 10. QC with PeacoQC
PQC <- PeacoQC::PeacoQC(ff = ff_s,
channels = channels_of_interest,
plot = TRUE, save_fcs = FALSE,
output_directory = dir_QC)
# 11. Save the preprocessed data
write.FCS(PQC$FinalFF,
file = paste0(dir_prepr, files[1]))
# 12. Visualize the preprocessing
filter_plot <- function(ff_pre, ff_post, title, channel_x, channel_y){
df <- data.frame(x = exprs(ff_pre)[,channel_x],
y = exprs(ff_pre)[,channel_y])
i <- sample(nrow(df), 10000)
if (!"Original_ID" %in% colnames(exprs(ff_pre))) {
ff_pre@exprs <- cbind(ff_pre@exprs,
Original_ID = seq_len(nrow(ff_pre@exprs)))
}
p <- ggplot(df[i,], aes(x = x, y = y)) +
geom_point(size = 0.5,
color = ifelse(exprs(ff_pre)[i,"Original_ID"] %in%
exprs(ff_post)[,"Original_ID"], 'blue', 'red')) +
xlab(GetMarkers(ff_pre, channel_x)) +
ylab(GetMarkers(ff_pre, channel_y)) +
theme_minimal() + theme(legend.position = "none") +
ggtitle(title)
return(p)
}
to_plot <- list(list(ff_pre = ff,
ff_post = ff_m,
title = "Removed margin events",
channel_x = "BUV395-A",
channel_y = "BUV805-A"),
list(ff_pre = ff_t,
ff_post = ff_s,
title = "Removed doublets",
channel_x = "FSC-A",
channel_y = "FSC-H"),
list(ff_pre = ff_s,
ff_post = ff_l,
title = "Removed debris and dead cells",
channel_x = "FSC-A",
channel_y = "BUV395-A"),
list(ff_pre = ff_l,
ff_post = PQC$FinalFF,
title = "Removed low quality events",
channel_x = "Time",
channel_y = "BUV395-A"))
plot_list <- list()
for (plot in to_plot) {
plot_list[[length(plot_list) + 1]] <- filter_plot(ff_pre = plot$ff_pre,
ff_post = plot$ff_post,
title = plot$title,
channel_x = plot$channel_x,
channel_y = plot$channel_y)
}
png(paste0(dir_QC, sub("fcs", "png", files[1])), width = 1920)
print(ggpubr::ggarrange(plotlist = plot_list, nrow = 1))
dev.off()
# 13. Run the preprocessing pipeline for all the files
for (file in files){
ff <- read.FCS(paste0(dir_raw, file), truncate_max_range = FALSE)
ff_m <- PeacoQC::RemoveMargins(ff, channels_of_interest)
ff_c <- flowCore::compensate(ff_m, compensation_matrix)
ff_t <- flowCore::transform(ff_c, translist)
ff_s <- PeacoQC::RemoveDoublets(ff_t)
selected_live <- flowCore::filter(ff_s, live_gate)
ff_l <- ff_s[selected_live@subSet, ]
PQC <- PeacoQC::PeacoQC(ff = ff_l,
channels = channels_of_interest,
plot = TRUE, save_fcs = FALSE,
output_directory = dir_QC)
write.FCS(PQC$FinalFF,
file = paste0(dir_prepr, file))
# to_plot <- list(list(ff_pre = ff,
# ff_post = ff_m,
# title = "Removed margin events",
# channel_x = "BUV395-A",
### channel_y = "BUV805-A"),
# list(ff_pre = ff_t,
### ff_post = ff_s,
# title = "Removed doublets",
# channel_x = "FSC-A",
# channel_y = "FSC-H"),
# list(ff_pre = ff_s,
# ff_post = ff_l,
# title = "Removed debris and dead cells",
# channel_x = "FSC-A",
# channel_y = "BUV395-A"),
# list(ff_pre = ff_l,
### ff_post = PQC$FinalFF,
# title = "Removed low quality events",
# channel_x = "Time",
# channel_y = "BUV395-A"))
# plot_list <- list()
# for (plot in to_plot) {
# plot_list[[length(plot_list) + 1]] <- filter_plot(ff_pre = plot$ff_pre,
# ff_post = plot$ff_post,
# title = plot$title,
# channel_x = plot$channel_x,
# channel_y = plot$channel_y)
# }
png(paste0(dir_QC, sub("fcs", "png", file)), width = 1920)
print(ggpubr::ggarrange(plotlist = plot_list, nrow = 1))
dev.off()
}
########### 3. Additional QC ###########
########### ###########
# 14. Perform quality control between all files
# 14.(A) Plot the signal per channel and per file
# 14.(A)(i) Define the variables
#file_names <- sub(".*15_(.*).fcs", "\\1", files)
write.csv(files, paste0(dir_raw,"names.csv"))
library(readxl)
data <- read_excel("C:/Users/edmondsonef/Desktop/Humanized/Flow/Group.Names.xlsx",
sheet = "7-24Mar")
file_groups <- data$Group
file_groups
# 14.(A)(ii) Make the overview plot
PlotFileScatters(input = paste0(dir_prepr, files),
channels = channels_of_interest,
#names = file_groups,
legend = T,
groups = file_groups, nrow = 4,
plotFile = paste0(dir_QC, "file_scatters.png"))
# 14.(B) Perform principal commponent analysis (PCA)
# 14.(B)(i) Retrieve the median marker expression values per file
medians <- matrix(data = NA,
nrow = length(files), ncol = length(channels_of_interest),
dimnames = list(files, channels_of_interest))
for (file in files){
ff <- read.FCS(paste0(dir_prepr, file))
medians[file,] <- apply(exprs(ff)[,channels_of_interest], 2, median)
}
# 14.(B)(ii) Calculate the PCs
pc <- prcomp(medians, scale. = TRUE)
# 14.(B)(iii) Visualize the PCs
ggplot(data.frame(pc$x[,1:2], file_groups)) +
geom_point(aes(x= PC1, y = PC2, col = file_groups)) +
theme_minimal()
ggsave(paste0(dir_QC, "file_PCA.png"), width = 10)
########### 4. Create Aggegregate Files ###########
########### ###########
data_dir <- "C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/"
study_dir <- ""
dir_prepr <- paste0(data_dir,study_dir,"1-Preprocessed/") #where the preprocessed data will be stored
dir_QC <- paste0(data_dir,study_dir,"2-QC/") #where the data QC results will be stored
dir_RDS <- paste0(data_dir,study_dir,"3-RDS/") #where the R objects will be stored
dir_results <- paste0(data_dir,study_dir,"4-Results/") #where the results will be stored
dir_raw <- paste0(data_dir,study_dir) #where the raw data is located
# 15. Choose the number of cells to include in the aggregate file
n <- 10000000
# 16. Make an aggregate file
set.seed(2020)
files <- list.files(path = dir_prepr, pattern = "Samples")
files
paste0(dir_prepr, files)
agg <- AggregateFlowFrames(paste0(dir_prepr, files),
cTotal = n,
writeOutput = TRUE,
outputFile = paste0(dir_results, "aggregate.fcs"))
########### 5. Train FlowSOM model ###########
########### ###########
agg <- read.FCS(paste0(dir_results,'aggregate.fcs'), truncate_max_range = FALSE)
#Level 1 - create model to separate human cells from mouse
#SOM_x <- 10
#SOM_y <- 10
n_meta <- 15
seed <- 2020
scaling <- FALSE
#scaling <- TRUE
###All...###
###All...###
###All...###
fsom.all <- FlowSOM(input = agg,
scale = F,
transform = T,
toTransform = c(4,7:17),
colsToUse = c(4, 7:17),
seed = seed,
nClus = 15)
PlotStars(fsom = fsom.all,
backgroundValues = fsom.all$metaclustering)
#tsne <- Rtsne::Rtsne(fsom.all$map$codes, perplexity = 6)
#PlotStars(fsom = fsom.all, view = tsne$Y,
# backgroundValues = fsom.all$metaclustering)
FlowSOMmary(fsom = fsom.all,
plotFile = paste0(dir_results, "ALL_fsom_summary.pdf"))
saveRDS(fsom.all, paste(dir_RDS, "fsom_all.rds"))
fsom.all$prettyColnames
rm(fsom.all)
###All...###
###All...###
###All...###
###All...###
fsom_level1 <- FlowSOM(input = agg,
scale = F,
transform = T,
toTransform = c(4,7:17),
colsToUse = c("BUV395-A","BUV805-A"),
seed = seed,
nClus = 10)
PlotLabels(fsom_level1, labels = fsom_level1$metaclustering)
p <- PlotMarker(fsom_level1, "BUV805-A")
print(p, newpage = T)
p <- PlotMarker(fsom_level1, "BUV395-A")
print(p, newpage = T)
Plot2DScatters(fsom = fsom_level1,
channelpairs = list(c("BUV805-A", "BUV395-A")),
metaclusters = 1:8,
plotFile = paste0(dir_results, "mCD45-hCD45_level1.png"))
FlowSOMmary(fsom = fsom_level1,
plotFile = paste0(dir_results, "L1_fsom_summary.pdf"))
saveRDS(fsom_level1, paste(dir_RDS, "fsom_level1.rds"))
#MC 5,7,8 are the huCD45+
#MC 1,2,3,4 are the mCD45+
# Subset the original fcs file
fsom_tmp <- NewData(fsom_level1, agg)
clustering <- GetMetaclusters(fsom_tmp)
agg_tmp_hCD45 <- agg[clustering %in% c(5,6,10,8,7),]
#agg_tmp_mCD45 <- agg[clustering %in% c(),]
#HUMAN: Create hCD45 subset
fsom_level2_hCD45$prettyColnames
fsom_level2_hCD45 <- FlowSOM(input = agg_tmp_hCD45,
scale = F,
transform = T,
toTransform = c(4,7:17),
colsToUse = c(4,7:17),
#colsToUse = c(7:12,15:17),
seed = 1985,
nClus = 10)
PlotLabels(fsom_level2_hCD45, labels = fsom_level2_hCD45$metaclustering)
p <- PlotMarker(fsom_level2_hCD45, "BUV805-A")
print(p, newpage = T)
p <- PlotMarker(fsom_level2_hCD45, "BUV395-A")
print(p, newpage = T)
PlotStars(fsom = fsom_level2_hCD45,
backgroundValues = fsom_level2_hCD45$metaclustering)
FlowSOMmary(fsom = fsom_level2_hCD45,
plotFile = paste0(dir_results, "L2_hCD45_fsom_summary.pdf"))
saveRDS(fsom_level2_hCD45, paste(dir_RDS, "fsom_level2_hCD45.rds"))
#########L3
#########L3
#########L3
fsom_tmp <- NewData(fsom_level2_hCD45, agg)
clustering <- GetMetaclusters(fsom_tmp)
agg_tmp_hCD45 <- agg[clustering %in% c(1:6,8,9,10),]
#agg_tmp_mCD45 <- agg[clustering %in% c(),]
#HUMAN: Create hCD45 subset
fsom_level3_hCD45 <- FlowSOM(input = agg_tmp_hCD45,
scale = F,
transform = T,
toTransform = c(4,7:17),
colsToUse = c(4,7:17),
#colsToUse = c(7:12,15:17),
seed = 2023,
nClus = 12)
PlotStars(fsom = fsom_level3_hCD45,
backgroundValues = fsom_level3_hCD45$metaclustering)
FlowSOMmary(fsom = fsom_level3_hCD45,
plotFile = paste0(dir_results, "L3_hCD45_fsom_summary.pdf"))
saveRDS(fsom_level3_hCD45, paste(dir_RDS, "fsom_level3_hCD45.rds"))
#########L3
#########L3
#########L3
#MOUSE: Create mCD45 subset
#fsom_level2_mCD45 <- FlowSOM(input = agg_tmp_mCD45,
# scale = F,
# colsToUse = c(7:17),
# seed = 2020)
#saveRDS(fsom_level2_mCD45, paste(dir_RDS, "fsom_level2_mCD45.rds"))
#FlowSOMmary(fsom = fsom_level2_mCD45,
# plotFile = paste0(dir_results, "L2_mCD45_fsom_summary.pdf"))
########### 6. Test Quality ###########
########### ###########
fsom <- fsom_level2_hCD45
agg <- agg_tmp_hCD45
# 20. Check the FlowSOM quality
# 20.(A) Make 2D scatter plots
# 20.(A)(i) Specify the parameters
fsom$prettyColnames
channel_pairs = list(c("FSC-A", "SSC-A"),
c("BB700-P-A", "BB515-A"),
c("BV421-A", "APC-Cy7-A"),
c("APC-A", "BV786-A"),
c("PE-CF594-A", "BV786-A"),
c("BV786-A", "PE-A"),
c("BUV805-A", "BUV395-A"))
metaclusters_of_interest <- seq_len(10)
clusters_of_interest <- NULL
# 20.(A)(ii) Make the 2D scatter plots
Plot2DScatters(fsom = fsom,
channelpairs = channel_pairs,
metaclusters = metaclusters_of_interest,
clusters = clusters_of_interest,
plotFile = paste0(dir_results, "fsom_2D_scatters.png"))
########### 7. Test with Manual Gating ###########
###########
fsom <- fsom_level2_hCD45
agg <- agg_tmp_hCD45
fsom$prettyColnames
wspFile = "C:/Users/edmondsonef/Desktop/Humanized/Flow/Aggregate/19-331-122 Full Dataset WSP 220414.wsp"
ws <- open_flowjo_xml(wspFile)
ws
fj_ws_get_samples(ws, group_id = 7)
files <- list.files(path = dir_prepr, pattern = "Samples")
files
# 20.(B) Check the consistency with manual labeling
# 20.(B)(i) Extract the gating information from the wsp file
gating <- GetFlowJoLabels(files = files, cellTypes = "hCD45+",
wspFile = wspFile, group =4,
path = dir_raw, additional.sampleID = TRUE)
####*EFE TIP: PHYSICALLY REMOVE CONTROLS, etc####
# 20.(B)(ii) Get an overview of the gatenames and define the cell types of interest
print(levels(gating[[1]][["manual"]]))
cell_types_of_interest <- c(#"Unlabeled",
#"hCD45+",
"Q6: CD3+ , CD4 [PCP55]+",
"Q7: CD3+ , CD4 [PCP55]-",
"Q8: CD3- , CD4 [PCP55]-",
"Q9: CD3- , CD8 [FITC]+",
"Q10: CD3+ , CD8 [FITC]+",
"Q11: CD3+ , CD8 [FITC]-",
"Q12: CD3- , CD8 [FITC]-",
"Q13: CD3- , CD19 [AFire750]+",
"Q14: CD3+ , CD19 [AFire750]+",
"Q15: CD3+ , CD19 [AFire750]-",
"Q16: CD3- , CD19 [AFire750]-",
"Q17: CD3- , CD56+",
"Q18: CD3+ , CD56+",
"Q19: CD3+ , CD56-",
"Q20: CD3- , CD56-",
"Q29: CD66b [PEDazz]- , CD11b [AF647]+",
"Q30: CD66b [PEDazz]+ , CD11b [AF647]+",
"Q31: CD66b [PEDazz]+ , CD11b [AF647]-",
"Q32: CD66b [PEDazz]- , CD11b [AF647]-",
"Q33: CD33- , CD11b [AF647]+",
"Q34: CD33+ , CD11b [AF647]+",
"Q35: CD33+ , CD11b [AF647]-",
"Q36: CD33- , CD11b [AF647]-",
"Q37: CD25- , CD3+",
"Q38: CD25+ , CD3+",
"Q39: CD25+ , CD3-",
"Q40: CD25- , CD3-")
cell_types_of_interest <- c("hCD45+", "Q6: CD3+ , CD4 [PCP55]+",
"Q10: CD3+ , CD8 [FITC]+",
"Q13: CD3- , CD19 [AFire750]+",
"Q30: CD66b [PEDazz]+ , CD11b [AF647]+",
"Q34: CD33+ , CD11b [AF647]+")
cell_types_of_interest <- c("Q15: CD3+ , CD19 [AFire750]-",
"Q31: CD66b [PEDazz]+ , CD11b [AF647]-",
"Q10: CD3+ , CD8 [FITC]+",
"Q17: CD3- , CD56+",
"Q34: CD33+ , CD11b [AF647]+","Q6: CD3+ , CD4 [PCP55]+",
"Q13: CD3- , CD19 [AFire750]+",
"Q29: CD66b [PEDazz]- , CD11b [AF647]+")
# 20.(B)(iii) Compile the labels of the aggregate file
aggregate_labels <- c()
for (file in unique(exprs(agg)[, "File"])) {
aggregate_labels <- c(aggregate_labels,
as.character(ManualVector(gating[[file]][["matrix"]],
cell_types_of_interest)
[exprs(agg)[, "Original_ID"]
[exprs(agg)[, "File"] == file]]))
}
# 20.(B)(iv) Show the manual labeling on the FlowSOM tree
PlotPies(fsom = fsom,
cellTypes = factor(aggregate_labels, levels = c("Unlabeled",
cell_types_of_interest)))
ggsave(paste0(dir_results, "Human_fsom_Labeled.pdf"))
# 19.(B)(v) Calculate the purity of the FlowSOM clustering
Purity(realClusters = aggregate_labels,
predictedClusters = GetClusters(fsom))
# 20.(C) Inspect the file contribution per cluster
# 20.(C)(i) Specify a color vector (optional)
file_colors <- c("#990000", "#cc0000", "#ff0000", #Different shades within the groups
"#1d1d77", "#2b3b92", "#3859ac", "#4677c7")
file_colors <- c("#990000", "#4677c7")
# 20.(C)(ii) Show the file contribution
p <- PlotPies(fsom = fsom,
cellTypes = factor(files[fsom$data[,"File"]]),
#cellTypes = cell_types_of_interest,
colorPalette = file_colors)
AddStarsPies(p = p, # Legend to show how it should be
arcs = data.frame(
x0 = rep(0, length(files)),
y0 = rep(0, length(files)),
start = seq(0, 2 * pi, length.out = 8)[-8],
end = seq(0, 2 * pi, length.out = 8)[-1],
value = rep(1, length(files)),
Markers = files),
colorPalette = file_colors)
ggsave(paste0(dir_results, "fsom_filecontribution.pdf"))
PlotManualBars(fsom, list_insteadof_plots = T,
manualVector = factor(aggregate_labels, levels = c(cell_types_of_interest)))
ggsave(paste0(dir_results, "fsom_filecontribution.pdf"))
########## 8. Discovery and downstream analysis ###########
##########
fsom <- readRDS("C:/Users/edmondsonef/Desktop/Humanized/Flow/6-10Mar2022/3-RDS/ fsom_level2_hCD45.rds")
fsom <- readRDS("C:/Users/edmondsonef/Desktop/Humanized/Flow/7-24Mar2022/3-RDS/ fsom_level2_hCD45.rds")
# 21. Explore the FlowSOM result
# 21.(B) Look for nodes with a specific pattern
# 21.(B)(i) Specify the query
query <- list("B cells" = c("CD19 [APC-F750]" = "high", "CD3" = "low"),
#"Activated T cells" = c("CD3" = "high", "CD25"="high"),
"CD11b-hi/CD66b-hi" = c("CD66b [PE-Dazz]" = "high","CD11b [AF647]" = "high"),
"CD11b-hi/CD66-lo" = c("CD11b [AF647]" = "high","CD66b [PE-Dazz]" ="low"),
"CD33-hi/CD11b-hi/" = c("CD33"="high", "CD11b [AF647]"="high"),
"CD33-hi/CD11b-hi" = c("CD33"="high", "CD11b [AF647]"="high"),
"CD33-hi/CD11b-hi" = c("CD33"="high", "CD11b [AF647]"="high"),
"CD33-hi/CD11b-lo" = c("CD33"="high", "CD11b [AF647]"="low"),
#"B cells Activated" = c("CD19 [APC-F750]" = "high", "CD25"="high"),
#"Mouse CD45+" = c("mCD45" = "high"),
#"Human CD45+" = c("huCD45" = "high"),
"NK Cell" = c("CD56" = "high", "CD3" = "low"),
"NK T Cell" = c("CD56" = "high", "CD3" = "high"),
#"CD4 T Cell Activated" = c("CD4 [PerCPCy55]" = "high", "CD3"="high", "CD25"="high"),
"CD4 T Cell" = c("CD4 [PerCPCy55]" = "high", "CD3"="high"),
"CD8 T Cell" = c("CD8 [FITC]" = "high", "CD3"="high"))#,
#"CD8 T Cell Activated" = c("CD8 [FITC]" = "high", "CD3"="high", "CD25"="high"))#,
#"T cells" = c("CD3" = "high"))
# 21.(B)(ii) Retrieve the cluster labels based on the query
labels <- QueryMultiple(fsom = fsom,
cellTypes = query,
plotFile = paste0(dir_results, "fsom_QueryStarPlot.pdf"))
# 21.(B)(iii) Show the retrieved labels on the FlowSOM tree
library(RColorBrewer)
#display.brewer.all()
PlotVariable(fsom = fsom,
colorPalette = brewer.pal(n = 10, name = "Paired"),
variable = labels)
ggsave(paste0(dir_results, "fsom_query.pdf"))
########## 9. Compare Groups ###########
##########
data_dir <- "C:/Users/edmondsonef/Desktop/Humanized/Flow/"
#study_dir <- "1-05Jan2022/"
#study_dir <- "2-02Feb2022/"
#study_dir <- "3-15Feb2022/"
#study_dir <- "4-28Feb2022/"
#study_dir <- "5-02Mar2022/"
#study_dir <- "6-10Mar2022/"
study_dir <- "7-24Mar2022/"
dir_prepr <- paste0(data_dir,study_dir,"1-Preprocessed/") #where the preprocessed data will be stored
dir_QC <- paste0(data_dir,study_dir,"2-QC/") #where the data QC results will be stored
dir_RDS <- paste0(data_dir,study_dir,"3-RDS/") #where the R objects will be stored
dir_results <- paste0(data_dir,study_dir,"4-Results/") #where the results will be stored
dir_raw <- paste0(data_dir,study_dir) #where the raw data is located
#dir_results <- paste0(dir_raw,"/1-Preprocessed/NSG/")
#dir_results <- paste0(dir_raw,"/1-Preprocessed/NSG-IL15/")
dir_results <- paste0(dir_raw,"/1-Preprocessed/BM/")
#dir_results <- paste0(dir_raw,"/1-Preprocessed/")
s
files <- list.files(path = dir_prepr, pattern = "Samples")
files
fsom <- readRDS("C:/Users/edmondsonef/Desktop/Humanized/Flow/6-10Mar2022/3-RDS/ fsom_level2_hCD45.rds")
fsom <- readRDS("C:/Users/edmondsonef/Desktop/Humanized/Flow/7-24Mar2022/3-RDS/ fsom_level2_hCD45.rds")
# 22. Get features per fcs file
# Specify the variables of interest
types <- c("counts", "percentages", "MFIs")
MFIs <- c("CD3", "CD19 [APC-F750]", "huCD45",
"CD8 [FITC]","CD56","CD25","CD33",
"CD66b [PE-Dazz]","CD11b [AF647]","CD4 [PerCPCy55]")
MFIs <- c("CD56")
# Get the features
features <- GetFeatures(fsom = fsom,
files = paste0(dir_prepr, files[c(1:14,16,17,23,24,27,28,32,35,37,39,40,44,45,50,52,55,60,63,68,70,72,74:97)]),
filenames = files,
type = types,
MFI = MFIs)
files[1:14,16,17,23,24,27,28,32,35,37,39,40,44,45,50,52,55,60,63,68,70,72,74:97]
grouplist <- data1$Group
file_names = paste0(dir_prepr, files)
fsom <- fsom_level2_hCD45
# 23. Define the groups and feature you would want to compare.
feature <- "MFIs"
stat <- "fold changes"
grouplist = list("NSG-IL15" = files[1:4], "NSG" = files[5:8])
# 24. Compare the 2 groups of interest
stats <- GroupStats(features$cluster_MFIs,
#groups = grouplist)
groups = grouplist)
# 25. Show the findings of step 24 on the trees
# Define the plotting variables
stat_levels <- c(paste0(names(grouplist)[2], " underrepresented compared to ",
names(grouplist)[1]),
paste0(names(grouplist)[1], " underrepresented compared to ",
names(grouplist)[2]),
"--")
colors <- c("blue", "red", "white")
# Show statistical findings on FlowSOM trees
cluster_stat <- stats[stat,]
cluster_stat <- factor(ifelse(cluster_stat < -2.5, stat_levels[1],
ifelse(cluster_stat > 2.5, stat_levels[2],
stat_levels[3])),
levels = stat_levels)
cluster_stat[is.na(cluster_stat)] <- stat_levels[3]
gr_1 <- PlotStars(fsom = fsom, title = names(grouplist)[1],
nodeSizes = stats[paste0("medians ", names(grouplist)[1]),],
backgroundValues = cluster_stat,
backgroundColors = colors,
list_insteadof_ggarrange = TRUE)
gr_2 <- PlotStars(fsom = fsom, title = names(grouplist)[2],
nodeSizes = stats[paste0("medians ", names(grouplist)[2]),],
backgroundValues = cluster_stat,
backgroundColors = colors,
list_insteadof_ggarrange = TRUE)
ggpubr::ggarrange(plotlist = list(gr_1$tree, gr_2$tree, gr_2$starLegend,
gr_2$backgroundLegend),
heights = c(3,1))
ggsave(paste0(dir_results, "NSG_vs_IL15_fsom_groups.pdf"), width = 10, height = 7.5)
p <- PlotVariable(fsom, title = "Fold change group 1 vs. group 2",
variable = C_stats["fold changes", ])
print(p, newpage = FALSE)
########## 10. Map new data onto FlowSOM object ###########
##########
# 26. Map new data on the FlowSOM object
for (file in files){
ff_prepr <- read.FCS(paste0(dir_prepr, file))
ff_raw <- read.FCS(paste0(dir_raw, file))
fsom_tmp <- NewData(fsom = fsom,
input = ff_prepr)
clustering <- GetClusters(fsom_tmp)
clustering_raw <- matrix(data = rep(0, nrow(exprs(ff_raw))),
ncol = 1, dimnames = list(c(), "FlowSOM"))
clustering_raw[exprs(ff_prepr)[,"Original_ID"]] <- clustering
ff_tmp <- flowCore::fr_append_cols(ff_raw, clustering_raw)
write.FCS(ff_tmp, paste0(dir_prepr, "FlowSOM_", file))
}
########## 11. Additional ###########
########## Applying FlowSOM to files or groups separately and then meta-cluster on all ####
# Compute separate FlowSOM objects
fsom_KO <- FlowSOM(input = paste0(dir_prepr, files[1:3]),
scale = FALSE, colsToUse = channels_of_interest,
seed = 2020)
fsom_WT <- FlowSOM(input = paste0(dir_prepr, files[4:7]),
scale = FALSE, colsToUse = channels_of_interest,
seed = 2020)
# Extract the cluster median fluorescence intensity values (MFIs)
MFI_KO <- GetClusterMFIs(fsom = fsom_KO, prettyColnames = TRUE, colsUsed = TRUE)
rownames(MFI_KO) <- paste0("KO", rownames(MFI_KO))
MFI_WT <- GetClusterMFIs(fsom = fsom_WT, prettyColnames = TRUE, colsUsed = TRUE)
rownames(MFI_WT) <- paste0("WT", rownames(MFI_WT))
# Obtain the meta-clusters by hierarchical clustering
all_clusters <- rbind(MFI_KO, MFI_WT)
hclust <- hclust(dist(all_clusters))
metaclustering <- cutree(hclust, 15) #MC 14 corresponds to the NK cells
# Generate one clustering heatmap from all clusters
ann <- data.frame(cohort = rep(c("KO", "WT"), each = 100),
row.names = rownames(all_clusters))
p <- pheatmap::pheatmap(t(all_clusters), cluster_rows = F, cutree_cols = 15,
cellwidth = 5, fontsize_col = 3, annotation_col = ann,
cluster_cols = hclust)
ggsave(p, filename = paste0(dir_results, "Higher_level_clustering.pdf"), width = 17)
# Generate the meta-cluster percentages boxplots
fsom_KO$metaclustering <- factor(unname(metaclustering[1:100]), levels = 1:15)
fsom_WT$metaclustering <- factor(unname(metaclustering[101:200]), levels = 1:15)
perc_KO <- GetFeatures(fsom = fsom_KO,
files = paste0(dir_prepr, files[1:3]),
level = "metaclusters", type = "percentages",
filenames = files[1:3])
perc_WT <- GetFeatures(fsom = fsom_WT,
files = paste0(dir_prepr, files[4:7]),
level = "metaclusters", type = "percentages",
filenames = files[4:7])
df <- data.frame(rbind(perc_KO[[1]], perc_WT[[1]])*100,
cohort = rep(c("KO", "WT"), c(3, 4)), check.names = FALSE)
df_g <- tidyr::gather(df, "MC", "percentage", -cohort)
ggplot(df_g, aes(x = cohort, y = percentage)) +
geom_boxplot() +
facet_wrap(~MC, scales = "free") +
theme_minimal()
ggsave(filename = "Results/FlowSOM_boxplot.pdf", width = 10, height = 10)
########## 12. Subset and hierarchical approach ###########
wspFile = "C:/Users/edmondsonef/Desktop/Humanized/Flow/5-02Mar2022/15719 02Mar2022 Simone.wsp"
ws <- open_flowjo_xml("C:/Users/edmondsonef/Desktop/Humanized/Flow/5-02Mar2022/15719 02Mar2022 Simone.wsp")
ws
head(fj_ws_get_samples(ws, group_id = 5))
files <- list.files(path = dir_raw, pattern = "Samples")
files
# 20.(B) Check the consistency with manual labeling
# 20.(B)(i) Extract the gating information from the wsp file
gating <- GetFlowJoLabels(files = files,
wspFile = wspFile, group =5,
path = dir_raw)
# 20.(B)(ii) Get an overview of the gatenames and define the cell types of interest
print(levels(gating[[1]][["manual"]]))
cell_types_of_interest <- c("hCD45+","Q6: CD3+ , CD4 [PCP55]+",
"Q10: CD3+ , CD8 [FITC]+",
"Q13: CD3- , CD19 [AFire750]+",
"Q17: CD3- , CD56+",
"Q18: CD3+ , CD56+",
"Q29: CD66b [PEDazz]- , CD11b [AF647]+",
"Q30: CD66b [PEDazz]+ , CD11b [AF647]+",
"Q31: CD66b [PEDazz]+ , CD11b [AF647]-",
"Q33: CD33- , CD11b [AF647]+",
"Q38: CD25+ , CD3+",
"Q35: CD33+ , CD11b [AF647]-",
"Q39: CD25+ , CD3-")
# 20.(B)(iii) Compile the labels of the aggregate file
aggregate_labels <- c()
for (file in unique(exprs(agg)[, "File"])) {
aggregate_labels <- c(aggregate_labels,
as.character(ManualVector(gating[[file]][["matrix"]],
cell_types_of_interest)
[exprs(agg)[, "Original_ID"]
[exprs(agg)[, "File"] == file]]))
}
# Read in preprocessed fcs file, lymphocyte panel
ff <- read.FCS(paste0(data_dir,study_dir3,'1-Preprocessed/NSG/Samples_Tube_015 Animal 142_027.fcs'), truncate_max_range = FALSE)
#manual_labels <- readRDS(paste0(dir_raw, "attachments/lympho_labels.rds"))
# Perform a first level clustering to isolate the lymphocytes
fsom_level1 <- FlowSOM(input = ff,
scale = FALSE, nClus = 3,
colsToUse = c(13:14),
seed = 2020)
fsom_level1$prettyColnames
PlotStars(fsom = fsom_level1,
backgroundValues = fsom_level1$metaclustering)
# Inspect the 2D scatter plots to identify the meta-clusters of interest
Plot2DScatters(fsom = fsom_level1,
channelpairs = list(c("BUV805-A", "BUV395-A")),
metaclusters = 1:2,
plotFile = paste0(data_dir, "hierarchy_level1.png"))