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cell_downsample_simulation.R
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cell_downsample_simulation.R
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rm(list=ls())
library(deMULTIplex2)
savePath <- "./data-raw/benchmark_res/simulation_model/"; dir.create(savePath)
rdsPath <- paste0(savePath, "rds/");dir.create(rdsPath)
plotPath <- paste0(savePath, "plots/"); dir.create(plotPath)
test_text <- "simulation_model_cell_ds_"
#cur_text <- paste0("simulation_model_update_", "sim", 1, "_", "n.bc_", 5, "_")
#cur_text <- paste0("simulation_model_update_", "sim", 2, "_", "n.bc_", 10, "_")
#cur_text <- paste0("simulation_model_update_", "sim", 3, "_", "n.bc_", 10, "_")
#cur_text <- paste0("simulation_model_update_", "sim", 4, "_", "n.bc_", 30, "_")
cur_text <- paste0("simulation_model_update_", "sim", 5, "_", "n.bc_", 30, "_")
sim_res <- readRDS(paste0(rdsPath, cur_text, "sim_res.rds"))
tag_mtx <- sim_res$final.umi.mtx
true_label = sapply(strsplit(rownames(tag_mtx), "_"), function(x){x[1]})
cell_comp <- sapply(strsplit(rownames(tag_mtx), "[|]"), function(x) {
if(length(x) >= 2) {
x = unlist(strsplit(x[[2]], "_"))
if(x[1] == x[3]) x[1] else NA
} else NA
})
true_label[!is.na(cell_comp)] <- cell_comp[!is.na(cell_comp)]
names(true_label) =rownames(tag_mtx)
tag_mapping <- data.frame(tag = colnames(tag_mtx), true_label = colnames(tag_mtx))
print(nrow(tag_mtx))
#cur_ratio = 0.5
#cur_ratio = 0.1
#cur_ratio = 0.05
cur_ratio = 0.01
set.seed(1)
use_cell_num <- round(nrow(tag_mtx) * cur_ratio)
use_cell <- sample(1:nrow(tag_mtx), use_cell_num)
tag_mtx <- tag_mtx[use_cell, ]
true_label <- true_label[use_cell]
print(length(use_cell))
print(table(true_label))
print(median(table(true_label)))
bc_demux2 <- benchmark_demultiplex2(tag_mtx, true_label,
tag_mapping = tag_mapping,
true.multiplet = "doublet",
plot.path = plotPath, plot.name = paste0(cur_text, '_cell_ds_',cur_ratio), width = 3.5, height = 2.5,
seed = 1,
init.cos.cut = .5,
converge.threshold = 1e-3,
prob.cut = 0.5,
min.cell.fit = 1,
max.cell.fit = 5000,
max.iter = 15,
min.quantile.fit = 0.05, # Remove cells with total umi less than the specified quantile, which could be beads
max.quantile.fit = 0.95, # Remove cells with total umi greater than the specified quantile, which could be multiplets
residual.type = "rqr",
plot.umap = "none",
plot.diagnostics = F)
saveRDS(bc_demux2, paste0(rdsPath, paste0(cur_text, '_cell_ds_',cur_ratio), "bc_demux2.rds"))
# 1. deMULTIplex
library(deMULTIplex)
library(ggplot2)
bc_demux1 <- benchmark_demultiplex1(as.matrix(tag_mtx), true_label,
tag_mapping = tag_mapping,
true.multiplet = "doublet",
plot.path = plotPath, plot.name = paste0(paste0(cur_text, '_cell_ds_',cur_ratio), 'deMULTIplex1_'))
# 2. demuxEM
# Save data to required format
# library(DropletUtils)
# tag_mtx_write <-as.data.frame(t(tag_mtx)) %>% add_rownames('tag')
# write.csv(tag_mtx_write, paste0(rdsPath, "tag_mtx.csv"), quote = F, row.names = F)
#
# write10xCounts(paste0(rdsPath, "rna_mtx.h5"), t(tag_mtx), version='3')
#
# # Run code on docker
# #scCloud demuxEM -p 8 --hash-type nuclei-hashing --min-num-genes 100 --generate-diagnostic-plots tag_mtx.csv rna_mtx.h5 simulation_b1
# library(anndata)
# adata <- read_h5ad(paste0(rdsPath, "simulation_b1_demux.h5ad"))
# demuxem_res <- adata$obs
# demuxem_call <- as.character(demuxem_res$assignment)
# demuxem_call[demuxem_res$demux_type!="singlet"] = as.character(demuxem_res$demux_type[demuxem_res$demux_type!="singlet"])
# use_levels = sort(unique(demuxem_call))
# use_levels <- c(use_levels[!use_levels %in% c("doublet", "unknown")], c("doublet", "unknown"))
# demuxem_call <- factor(demuxem_call, levels = use_levels)
# names(demuxem_call) <- paste0(rownames(demuxem_res))
#
# bc_demuxEM = confusion_stats(demuxem_call, true_label,
# call.multiplet = "doublet", call.negative = "unknown",
# true.multiplet = "doublet", true.negative = "unknown",
# plot.path =plotPath, plot.name = paste0(test_text, 'demuxEM_'), width = 3.5, height = 2.5)
# 3. GMM-Demux
# The code to run this software is a bit cumbersome, generate script from R
# Step 1, save the tag matrix to the csv file
bcs <- make.names(colnames(tag_mtx))
tag_mtx_write <- as.matrix(tag_mtx)
colnames(tag_mtx_write) <- bcs
write.csv(tag_mtx_write, paste0(rdsPath, "tag_mtx_gmm.csv"), quote=F)
resPath <- paste0(savePath, test_text, "gmmdemux_res/"); dir.create(resPath)
cmds <- paste0("GMM-demux -c ./rds/tag_mtx_gmm.csv ", paste0(bcs, collapse = ","), " -x ", bcs, " -o ", paste0(cur_text, '_cell_ds_',cur_ratio), "gmmdemux_res/", bcs)
fileConn<-file(paste0(savePath, "run_gmm.sh"))
writeLines(cmds, fileConn)
close(fileConn)
# Get results from assignment
bcs <- make.names(colnames(tag_mtx))
gmm_res <- lapply(bcs, function(bc) {
print(bc)
barcode.path <- paste0(savePath, paste0(cur_text, '_cell_ds_',cur_ratio),"gmmdemux_res/", bc, "/barcodes.tsv.gz")
tryCatch({
cells <<- read.delim(barcode.path,
header = FALSE,
stringsAsFactors = FALSE)
colnames(cells) <- "cell_barcode"
cells$assignment = bc
}, error = function(e) {
cells <<- data.frame(matrix(ncol = 2, nrow = 0))
})
return(cells)
})
gmm_res <- do.call(rbind, gmm_res)
gmm_res_call <- gmm_res$assignment
names(gmm_res_call) <- gmm_res$cell_barcode
gmm_res_call <- gsub("[.]", "-", gmm_res_call) # Correction
bc_gmm <- confusion_stats(gmm_res_call, true_label,
tag_mapping,
call.multiplet = 'Multiplet',
true.multiplet = "doublet",
plot.path =plotPath, plot.name = paste0(paste0(cur_text, '_cell_ds_',cur_ratio), 'gmmdemux_'), width = 3.5, height = 2.5)
# 4. HTODemux
bc_seu <- benchmark_HTODemux(tag_mtx, rna_mtx = t(tag_mtx), true_label = true_label,
tag_mapping = tag_mapping,
true.multiplet = "doublet",
plot.path = plotPath, plot.name = paste0(paste0(cur_text, '_cell_ds_',cur_ratio), 'HTODemux_'), width = 3.5, height = 2.5)
# 5. hashedDrops
require(DropletUtils)
demux_res <- hashedDrops(t(tag_mtx))
demux_res$assign <- colnames(tag_mtx)[demux_res$Best]
demux_res$assign[!demux_res$Confident] = 'ambiguous'
demux_res$assign[demux_res$Doublet] = 'doublet'
hashed_call <- demux_res$assign
names(hashed_call) <- rownames(demux_res)
bc_hashed = confusion_stats(hashed_call, true_label,
tag_mapping = tag_mapping,
call.multiplet = 'doublet',
true.multiplet = "doublet",
plot.path =plotPath, plot.name = paste0(paste0(cur_text, '_cell_ds_',cur_ratio), 'hashedDrops_'), width = 3.5, height = 2.5)
# 6. demuxmix
require(demuxmix)
#bc_mixfull <- benchmark_demuxmix_full(tag_mtx, rna_mtx, true_label, plot.path = plotPath, plot.name = paste0(paste0(cur_text, '_cell_ds_',cur_ratio), 'demuxmixfull_'), width = 3.5, height = 2.5)
bc_mixnaive <- benchmark_demuxmix_naive(tag_mtx, true_label = true_label,
tag_mapping = tag_mapping,
true.multiplet = "doublet",
plot.path = plotPath, plot.name = paste0(paste0(cur_text, '_cell_ds_',cur_ratio), 'demuxmixnaive_'), width = 3.5, height = 2.5)
# 7. BFF raw and BFF cluster
library(cellhashR)
cellhashR_calls <- GenerateCellHashingCalls(barcodeMatrix = t(tag_mtx),c("bff_raw", "bff_cluster"),
doTSNE = FALSE,
doUMAP = FALSE, doHeatmap = FALSE, bff_raw.min_average_reads=0, bff_cluster.min_average_reads=0)
bff_raw_call <- cellhashR_calls$bff_raw; names(bff_raw_call) <- cellhashR_calls$cellbarcode
bff_cluster_call <-cellhashR_calls$bff_cluster; names(bff_cluster_call) <- cellhashR_calls$cellbarcode
bc_bffraw <- confusion_stats(bff_raw_call, true_label,
tag_mapping,
call.multiplet = 'Doublet',
true.multiplet = "doublet",
plot.path =plotPath, plot.name = paste0(paste0(cur_text, '_cell_ds_',cur_ratio), 'bffraw_'), width = 3.5, height = 2.5)
bc_bffcluster <- confusion_stats(bff_cluster_call, true_label,
tag_mapping,
call.multiplet = 'Doublet',
true.multiplet = "doublet",
plot.path =plotPath, plot.name = paste0(paste0(cur_text, '_cell_ds_',cur_ratio), 'bffcluster_'), width = 3.5, height = 2.5)
nsn <- function(x){
if(exists(x[1])) {
if(length(x) ==1) {
return(get(x))
} else {
return(get(x[2], get(x[1])))
}
} else {
return(NULL)
}
}
# Combine all results
bc_all <- list(
deMULTIplex2 = nsn("bc_demux2"),
deMULTIplex = nsn("bc_demux1"),
#demuxEM = nsn("bc_demuxEM"),
GMM_Demux = nsn("bc_gmm"),
HTODemux = nsn("bc_seu"),
hashedDrops = nsn("bc_hashed"),
#demuxmix = nsn("bc_mixfull"),
demuxmix_naive = nsn("bc_mixnaive"),
bff_raw = nsn("bc_bffraw"),
bff_cluster = nsn("bc_bffcluster")
)
saveRDS(bc_all, paste0(rdsPath, paste0(cur_text, '_cell_ds_',cur_ratio), "bc_all.rds"))
# Plot summary plots
summary_df <- sapply(bc_all, function(x) {
if(!is.null(x)) {
x$singlet_avg_stats
} else {
return(c(0,0,0))
}
})
write.csv(round(t(summary_df),3), paste0(plotPath, paste0(cur_text, '_cell_ds_',cur_ratio),"summary_df.csv"))
plot_df <- reshape2::melt(summary_df)
colnames(plot_df) <- c("stats", "method", "value")
g1<-ggplot(plot_df, aes(method, value)) +
geom_col() +
facet_wrap(~stats, scales = 'free') +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1))
ggsave(paste0(plotPath, paste0(cur_text, '_cell_ds_',cur_ratio), 'bc_all_bar.pdf'), g1, width = 8, height = 8)
# Summarize across ratios
show_ratios <- c(1, 0.5, 0.1, 0.05, 0.01)
#show_ratios <- c(1, 0.5, 0.1, 0.05)
bc_res <- lapply(show_ratios, function(cur_ratio) {
if(cur_ratio == 1) {
res = readRDS(paste0(rdsPath, cur_text, "bc_all.rds"))
} else {
res = readRDS(paste0(rdsPath, paste0(paste0(cur_text, '_cell_ds_'),cur_ratio), "bc_all.rds"))
}
res <- res[!names(res) == "demuxmix"]
return(res)
})
names(bc_res) <- as.character(show_ratios)
singlet_stats <- lapply(bc_res, function(x) {
sapply(x, function(y) {
if(is.null(y)) return(c(NA,NA,NA)) else y$singlet_avg_stats
})
})
singlet_dfs <- lapply(1:length(singlet_stats), function(i){
x <- singlet_stats[[i]]
x <- reshape2::melt(x)
colnames(x) <- c("stats", "method", "value")
x$dataset <- names(singlet_stats)[i]
return(x)
})
singlet_df_cbn <- do.call(rbind, singlet_dfs)
show_methods <- names(bc_res[[1]])
singlet_df_cbn <- singlet_df_cbn[singlet_df_cbn$method %in% show_methods,]
singlet_df_cbn$method <- factor(as.character(singlet_df_cbn$method), levels = show_methods)
singlet_df_cbn$dataset <- factor(singlet_df_cbn$dataset, levels= names(bc_res))
plot_df <- singlet_df_cbn[singlet_df_cbn$stats == "f_score", ]
use_color <- get_gradient_color("BlueGreenRed",5)
names(use_color) <- names(bc_res)
g1 <- ggplot(plot_df, aes(x = method, y = value, fill = dataset)) +
geom_bar(stat="identity",position ="dodge") +
scale_fill_manual(values = use_color) +
ylab("F score")+
theme(text=element_text(size=8),
strip.text = element_text(size = 8, margin = margin(2,2,2,2)),
legend.text=element_text(size=8),
axis.text = element_text(size=8),
axis.text.x = element_text(angle = 15, vjust = 1, hjust=1),
legend.margin=margin(0,0,0,0),
plot.margin = unit(c(0,0.4,0,0.1), "cm"))
ggsave(paste0(plotPath, paste0(cur_text, '_cell_ds_'), "_barv1.pdf"), g1, width = 5, height =2)
# Line plot
plot_df$value[is.na(plot_df$value)] = 0
g1 <- ggplot(plot_df, aes(x = dataset, y = value, color = method, group = method)) +
geom_line() +
geom_point()+
ylab("F score")+
theme_bw()+
guides(colour = guide_legend(override.aes = list(size=3),
keywidth=0.1,
keyheight=0.15,
default.unit="inch"))+
theme(text=element_text(size=8),
legend.text=element_text(size=8),
axis.text = element_text(size=8),
#axis.text.x = element_text(angle = 15, vjust = 1, hjust=1),
legend.margin=margin(0,0,0,0),
plot.margin = unit(c(0,0.4,0,0.1), "cm"))
ggsave(paste0(plotPath, paste0(cur_text, '_cell_ds_'), "_linev1.pdf"), g1, width = 4, height =1.8)
# Heatmap
library(pheatmap)
singlet_mtx <- sapply(singlet_stats, function(x) {
x["f_score",][show_methods]
})
breaksList <- seq(0,1,by=.1)
graphics.off() # Shut down open plotting devices if any left over
pdf(paste0(plotPath, paste0(cur_text, '_cell_ds_'), "singlet_summary_mtx", ".pdf"), width = 2.8, height = 1.8)
pheatmap(singlet_mtx, cluster_rows = F, cluster_cols = F, display_numbers =T, border_color=NA, number_format ="%.2f", number_color ='black', fontsize = 8, fontsize_number=8, color = get_gradient_color('RdBu', length(breaksList)), breaks = breaksList)
dev.off()
#cur_text <- paste0("simulation_model_update_", "sim", 1, "_", "n.bc_", 5, "_")
#cur_text <- paste0("simulation_model_update_", "sim", 2, "_", "n.bc_", 10, "_")
#cur_text <- paste0("simulation_model_update_", "sim", 3, "_", "n.bc_", 10, "_")
#cur_text <- paste0("simulation_model_update_", "sim", 4, "_", "n.bc_", 30, "_")
#cur_text <- paste0("simulation_model_update_", "sim", 5, "_", "n.bc_", 30, "_")
sim_res <- readRDS(paste0(rdsPath, cur_text, "sim_res.rds"))
for(cur_ratio in c(1,0.5,.1,.05,.01)) {
tag_mtx <- sim_res$final.umi.mtx
true_label = sapply(strsplit(rownames(tag_mtx), "_"), function(x){x[1]})
cell_comp <- sapply(strsplit(rownames(tag_mtx), "[|]"), function(x) {
if(length(x) >= 2) {
x = unlist(strsplit(x[[2]], "_"))
if(x[1] == x[3]) x[1] else NA
} else NA
})
true_label[!is.na(cell_comp)] <- cell_comp[!is.na(cell_comp)]
names(true_label) =rownames(tag_mtx)
#print(cur_ratio)
if(cur_ratio != 1) {
set.seed(1)
use_cell_num <- round(nrow(tag_mtx) * cur_ratio)
use_cell <- sample(1:nrow(tag_mtx), use_cell_num)
tag_mtx <- tag_mtx[use_cell, ]
true_label <- true_label[use_cell]
}
print(median(table(true_label)))
}