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8d_power_requirements_part3.R
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suppressMessages(library(data.table, warn.conflicts = F))
suppressMessages(library(ggplot2, warn.conflicts = F))
suppressMessages(library(dplyr, warn.conflicts = F))
suppressMessages(library(ggpubr, warn.conflicts = F))
section_name <- "8d_power_requirements"
HomeFolder <- "/lustre/scratch123/hgi/teams/parts/cf14/crispr_scrnaseq_hipsci/"
ExperimentName <- "Magpie"
#for testing
#target_gene_name <- "PAF1"
#n_cells <- 8
date <- "2022-08-15"
#num_cells <- 2:50
#n_iterations <- 100
REDO <- F
args <- commandArgs(trailingOnly = TRUE)
for (ind in 1:length(args)){
arg <- args[[ind]]
##needed arguments
if (arg == "--date"){ date <- args[[ind + 1]] }
if (arg == "--home_folder"){ HomeFolder <- args[[ind + 1]] }
if (arg == "--experiment_name"){ ExperimentName <- args[[ind + 1]] }
## not needed
if (arg == "--utils_path"){ utils_path <- args[[ind + 1]] }
if (arg == "--io_path"){ io_path <- args[[ind + 1]] }
}
# set relevent i/o paths
source(paste0(HomeFolder, "scripts/io/Magpie_io.R"))
source(paste0(HomeFolder, "scripts/io/Magpie_Utils.R"))
outdir <- file.path(OutFolder, section_name)
plotsdir <- file.path(HTMLFolder, "pipeline/", section_name)
if(!dir.exists(outdir)) dir.create(outdir)
if(!dir.exists(plotsdir)) dir.create(plotsdir)
#if(!dir.exists(paste0(plotsdir, "/cor_plots/"))) dir.create(paste0(plotsdir, "/cor_plots/"))
## ---- LoadData
GuideMetadata <- fread(GuideMetadataPath)
target_genes <- unique(GuideMetadata$gene)
target_meta <- fread(paste0(OutFolder, "/7b_target_summary/", date, "_target_meta_data.csv"))
target_gene_list <- list.files(paste0(OutFolder, "/8d_power_requirements/by_gene/"))
## ---- Subsampling stats
if (REDO == F){
fnms <- grep(list.files(paste0(OutFolder, "/8d_power_requirements/"), pattern = "tsv"),
pattern = "targets2consider", invert = T, value = T)
dfs4plot <- mapply(fnms, FUN = function(fnm){
rtn <- fread(paste0(OutFolder, "/8d_power_requirements/", fnm))
}, SIMPLIFY = F)
target_gene_list <- gsub(fnms, pattern = paste0(date, "_|[.]tsv"), replacement = "")
names(dfs4plot) <- target_gene_list
} else {
lfcs_by_target <- fread(paste0(OutFolder, "/", "6b_calc_lfcs_transcriptome_wide_by_gene", "/", date, "_combined_with_adj_pval.tsv.gz"))
lfcs_by_target <- split.data.frame(lfcs_by_target, f =lfcs_by_target$target)
all_downstream_genes <- lfcs_by_target[[1]]$downstream_gene
n_downstream_genes <- length(all_downstream_genes)
dfs4plot <- mapply(target_gene_list, FUN = function(target_gene_name){
fnm_out <- paste0(outdir, "/", date, "_", target_gene_name, ".tsv")
if (REDO == T | !(file.exists(fnm_out))){
lfcs <- lfcs_by_target[[target_gene_name]]
lfcs$lfc_upper <- 1.1*lfcs$lfc; lfcs$lfc_lower <- 0.9*lfcs$lfc
lfcs$pval_adj <- p.adjust(lfcs$pval_lm, method = "BH")
degs_0.05 <- filter(lfcs, pval_adj < 0.05 ) %>% .$downstream_gene
degs_0.1 <- filter(lfcs, pval_adj < 0.1 ) %>% .$downstream_gene
degs <- filter(lfcs, pval_adj < sig_pval_thresh)
sig_lfcs <- lfcs$lfc[match(degs$downstream_gene, lfcs$downstream_gene)]
num_cells <- grep(gsub(list.files(paste0(OutFolder, "/8d_power_requirements/by_gene/", target_gene_name, "/"), pattern = date),
pattern = paste0(target_gene_name, "_", date, "_|-cells_.*"), replacement = ""),
pattern = "residuals", value = T, invert = T) %>%
unique() %>%
as.numeric() %>% sort()
df4plot <- mapply(num_cells, FUN = function(n_cells){
fnms <- list.files(paste0(OutFolder, "/8d_power_requirements/by_gene/", target_gene_name, "/"), pattern = paste0(date, "_", n_cells, "-cells"))
cor_per_cell_no <- mapply(fnms, FUN = function(fnm){
df <- fread(paste0(OutFolder, "/8d_power_requirements/by_gene/", target_gene_name, "/", fnm))
df$pval_adj <- p.adjust(df$pval_lm, method = "BH")
cor_deg <- cor(sig_lfcs, lfcs4cor_sig)
cor_all <- cor(df$lfc, lfcs$lfc)
subsampled_degs_0.05 <- filter(df, pval_adj < 0.05) %>% .$downstream_gene
subsampled_degs_0.1 <- filter(df, pval_adj < 0.1) %>% .$downstream_gene
## fraction of DEGs within 10% of true value
n_genes_within_10pct <- length(which(df$lfc < lfcs$lfc_upper & df$lfc > lfcs$lfc_lower))
frac_genes_within_10pct <- n_de_genes_within_10pct/length(df$lfc)
n_de_genes_within_10pct <- length(which(df$lfc < lfcs$lfc_upper & df$lfc > lfcs$lfc_lower & abs(df$lfc) > 0.1))
frac_de_genes_within_10pct <- n_de_genes_within_10pct/length(which(abs(df$lfc) > 0.1))
#fraction of DEGs within 25% of true value
n_genes_within_25pct <- length(which(df$lfc < lfcs$lfc*1.25 & df$lfc > lfcs$lfc*0.75))
frac_genes_within_25pct <- n_de_genes_within_10pct/length(df$lfc)
n_de_genes_within_25pct <- length(which(df$lfc < lfcs$lfc*1.25 & df$lfc > lfcs$lfc*0.75 & abs(df$lfc) > 0.1))
frac_de_genes_within_25pct <- n_de_genes_within_25pct/length(which(abs(df$lfc) > 0.1))
## compare recovery of DEGs
rtn1 <- data.frame(
gene = target_gene_name,
n_cells = n_cells,
it = gsub(fnm, pattern = paste0(".*cells_|[.]tsv[.]gz"), replacement = ""),
cor_deg = cor_deg,
cor_all = cor_all,
n_deg_unsampled_0.05 = length(degs_0.05),
n_deg_sampled_0.05 = length(subsampled_degs_0.05),
tp_0.05 = length(which(subsampled_degs_0.05 %in% degs_0.05)),
n_deg_unsampled_0.1 = length(degs_0.1),
n_deg_sampled_0.1=length(subsampled_degs_0.1),
tp_0.1 = length(which(subsampled_degs_0.1 %in% degs_0.1)),
n_genes_within_10pct = n_genes_within_10pct,
n_de_genes_within_10pct = n_de_genes_within_10pct,
n_genes_within_25pct = n_genes_within_25pct,
n_de_genes_within_25pct = n_de_genes_within_25pct
)
return(rtn1)
}, SIMPLIFY = F) %>% bind_rows %>% as.data.frame()
fdr_df <- cor_per_cell_no %>%
mutate(fn_0.05 = n_deg_unsampled_0.05 - tp_0.05,
fp_0.05 = n_deg_sampled_0.05 - tp_0.05,
tn_0.05 = n_downstream_genes - n_deg_sampled_0.05 - n_deg_unsampled_0.05 + tp_0.05,
fn_0.1 = n_deg_unsampled_0.1 - tp_0.1,
fp_0.1 = n_deg_sampled_0.1 - tp_0.1,
tn_0.1 = n_downstream_genes - n_deg_sampled_0.1 - n_deg_unsampled_0.1 + tp_0.1) %>%
mutate(
frac_deg_recovered_0.05 = tp_0.05/(tp_0.05 + fn_0.05),
frac_false_deg_recovered_0.05 = fp_0.05/(fp_0.05 + tp_0.05),
frac_deg_recovered_0.1 = tp_0.1/(tp_0.1 + fn_0.1),
frac_false_deg_recovered_0.1 = fp_0.1/(fp_0.1 + tp_0.1)
) %>% select(c("gene", "n_cells", "it",
"n_deg_sampled_0.05", "tp_0.05",
"n_deg_sampled_0.1", "tp_0.1",
"n_deg_unsampled_0.05", "frac_deg_recovered_0.05", "frac_false_deg_recovered_0.05",
"n_deg_unsampled_0.1", "frac_deg_recovered_0.1", "frac_false_deg_recovered_0.1"))
rtn <- data.frame(
target = target_gene_name,
n_cells = n_cells,
n_deg = length(degs$downstream_gene),
median_sensitivity_0.05 = median(fdr_df$frac_deg_recovered_0.05),
min_sensitivity_0.05 = quantile(fdr_df$frac_deg_recovered_0.05, 0.1),
mean_sensitivity_0.05 = median(fdr_df$frac_deg_recovered_0.05),
max_sensitivity_0.05 = quantile(fdr_df$frac_deg_recovered_0.05, 0.9),
median_sensitivity_0.1 = median(fdr_df$frac_deg_recovered_0.1),
min_sensitivity_0.1 = quantile(fdr_df$frac_deg_recovered_0.1, 0.1),
mean_sensitivity_0.1 = median(fdr_df$frac_deg_recovered_0.1),
max_sensitivity_0.1 = quantile(fdr_df$frac_deg_recovered_0.1, 0.9),
median_fdr_0.05 = median(fdr_df$frac_false_deg_recovered_0.05),
min_fdr_0.05 = quantile(fdr_df$frac_false_deg_recovered_0.05, 0.1),
mean_fdr_0.05 = median(fdr_df$frac_false_deg_recovered_0.05),
max_fdr_0.05 = quantile(fdr_df$frac_false_deg_recovered_0.05, 0.9),
median_fdr_0.1 = median(fdr_df$frac_false_deg_recovered_0.1),
min_fdr_0.1 = quantile(fdr_df$frac_false_deg_recovered_0.1, 0.1),
mean_fdr_0.1 = median(fdr_df$frac_false_deg_recovered_0.1),
max_fdr_0.1 = quantile(fdr_df$frac_false_deg_recovered_0.1, 0.9),
min_conf_deg = quantile(cor_per_cell_no$cor_deg, 0.1),
average_deg = mean(cor_per_cell_no$cor_deg),
med_deg = median(cor_per_cell_no$cor_deg),
max_conf_deg = quantile(cor_per_cell_no$cor_deg, 0.9),
min_conf_all = quantile(cor_per_cell_no$cor_all, 0.1),
average_all = mean(cor_per_cell_no$cor_all),
med_all = median(cor_per_cell_no$cor_all),
max_conf_all = quantile(cor_per_cell_no$cor_all, 0.9)
)
return(rtn)
}, SIMPLIFY = F)
df4plot <- as.data.frame(bind_rows(df4plot))
fwrite(df4plot, fnm_out, quote = F, sep = "\t")
} else{
df4plot <- fread(fnm_out)
}
rtn <- df4plot
return(rtn)
}, SIMPLIFY = F)
}
## ---- Downsampled_Gene_Metadata
#info on the genes that were subsampled
n_deg <- data.frame(
target = names(dfs4plot),
total_cells = target_meta$n_cells[match(names(dfs4plot), target_meta$gene)],
n_deg = target_meta$n_downstream_excl_target[match(names(dfs4plot), target_meta$gene)]
) %>% mutate(
n_deg_bin = ifelse(n_deg > 250, "N > 250",
ifelse(
n_deg > 75, "N > 75",
ifelse(
n_deg > 50, "N > 50",
ifelse (
n_deg > 25, "25 < N < 50", "10 < N < 25"
)
)
))
)
## ---- Correlation
cor_df <- mapply(1:length(dfs4plot), FUN = function(j){
df <- dfs4plot[[j]]$med_all
}) %>% as.data.frame()
colnames(cor_df) <- names(dfs4plot)
cor_df <- cor_df %>% mutate(n_cells = 2:50)
cor_df <- melt(cor_df, id.vars = c("n_cells"))
cor_df <- left_join(cor_df, n_deg,
by = c("variable" = "target"))
ggplot(cor_df, aes(x = n_cells, y = value,
col = n_deg_bin)) +
ggtitle("Correlation of Downstream Effect due to Knockdown") + ylab("Correlation") + xlab("Number of Cells") +
geom_point() + theme_bw()
## ---- FDR
## large effect
fdr_df <- mapply(1:length(dfs4plot), FUN = function(j){
df <- dfs4plot[[j]]$median_fdr_0.05
}) %>% as.data.frame()
colnames(fdr_df) <- names(dfs4plot)
fdr_df <- fdr_df %>% mutate(n_cells = 2:50)
fdr_df <- melt(fdr_df, id.vars = c("n_cells"))
fdr_df$n_total <- target_meta$n_downstream_excl_target[match(fdr_df$variable, target_meta$gene)]
ggplot(fdr_df, aes(x = n_cells, y = value,
col = n_total)) +
ylab("FDR") +
geom_point()
mapply(n_cells = 2:50, FUN = function(i){
})
#ggplot(df4plot, aes(x = n_cells, y = med)) +
# geom_point()
## Sensitivity (fraction of deg correctly recovered)
pdf(paste0(plotsdir, "/", date, "_fdr.pdf"), width = 9, height = 4)
mapply(target_gene_list, FUN = function(target_gene_name){
df4plot <- dfs4plot[[target_gene_name]]
p1 <- ggplot(df4plot, aes(x = n_cells, y = median_sensitivity_0.1, ymin = min_sensitivity_0.1, ymax = max_sensitivity_0.1)) +
geom_line(color = "navy", size = 2) +
geom_ribbon(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = 10, col = "red", lty = 3) +
ylab("Sensitivity (DEG, p-value < 0.1)") + xlab("Number of Cells") + ggtitle(paste0("Subsampling of ", target_gene_name)) +
theme_classic()
p2 <- ggplot(df4plot, aes(x = n_cells, y = median_fdr_0.1, ymin = min_fdr_0.1, ymax = max_fdr_0.1)) +
geom_line(color = "navy", size = 2) +
geom_ribbon(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = 10, col = "red", lty = 3) +
ylab("FDR (DEG, p-value < 0.1)") + xlab("Number of Cells") + ggtitle(paste0("Subsampling of ", target_gene_name)) +
theme_classic()
return(ggarrange(p1, p2, nrow = 1))
}, SIMPLIFY = F)
dev.off()
## Sensitivity (fraction of deg correctly recovered)
pdf(paste0(plotsdir, "/", date, "_fdr.pdf"), width = 9, height = 4)
mapply(target_gene_list, FUN = function(target_gene_name){
df4plot <- dfs4plot[[target_gene_name]]
p1 <- ggplot(df4plot, aes(x = n_cells, y = median_sensitivity_0.05, ymin = min_sensitivity_0.05, ymax = max_sensitivity_0.05)) +
geom_line(color = "navy", size = 2) +
geom_ribbon(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = 10, col = "red", lty = 3) +
ylab("Sensitivity (DEG, p-value < 0.1)") + xlab("Number of Cells") + ggtitle(paste0("Subsampling of ", target_gene_name)) +
theme_classic()
p2 <- ggplot(df4plot, aes(x = n_cells, y = median_fdr_0.05, ymin = min_fdr_0.05, ymax = max_fdr_0.05)) +
geom_line(color = "navy", size = 2) +
geom_ribbon(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = 10, col = "red", lty = 3) +
ylab("FDR (DEG, p-value < 0.1)") + xlab("Number of Cells") + ggtitle(paste0("Subsampling of ", target_gene_name)) +
theme_classic()
return(ggarrange(p1, p2, nrow = 1))
}, SIMPLIFY = F)
dev.off()
df1 <- fread("/lustre/scratch123/hgi/projects/crispri_scrnaseq/outs/Magpie/8d_power_requirements/by_gene/CNOT1/CNOT1_2022-08-15_17-cells_60.tsv.gz")
df2 <- magpie_lfcs_split %>%
bind_rows() %>% as.data.frame() %>%
filter(target == "CNOT1")
df4plot <- data.frame(
gene = df1$downstream_gene,
subsampled = df1$lfc,
subsampled_pval = df1$pval_lm,
all = df2$lfc[match(df1$downstream_gene, df2$downstream_gene)],
all_pval = df2$pval_lm[match(df1$downstream_gene, df2$downstream_gene)]
) %>%
mutate(
deg_status = ifelse(all_pval < 0.05 & subsampled_pval < 0.05, "TP",
ifelse(all_pval > 0.05 & subsampled_pval < 0.05, "FP",
ifelse(all_pval < 0.05 & subsampled_pval > 0.05, "FN", "TN")))
)
ggplot(df4plot, aes(x = all, y = subsampled, col = deg_status)) +
ggtitle("CNOT1") + ylab("Subsampled (17 cells)") +
geom_point(alpha = 0.4) + theme_bw()
ggplot(df4plot, aes(x = all, y = subsampled)) +
ggtitle("CNOT1") + ylab("Subsampled (17 cells)") +
geom_point(alpha = 0.4) + theme_bw()
p <- ggplot(df4plot, aes(x = n_cells, y = median_sensitivity_0.05, ymin = min_sensitivity_0.05, ymax = max_sensitivity_0.05)) +
geom_line(color = "navy", size = 2) +
geom_ribbon(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = 10, col = "red", lty = 3) +
ylab("Sensitivity (DEG, p-value < 0.05)") + xlab("Number of Cells") + ggtitle(paste0("Subsampling of ", target_gene_name)) +
theme_classic()
print(p)
p <- ggplot(df4plot, aes(x = n_cells, y = median_fdr_0.05, ymin = min_fdr_0.05, ymax = max_fdr_0.05)) +
geom_line(color = "navy", size = 2) +
geom_ribbon(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = 10, col = "red", lty = 3) +
ylab("FDR (DEG, p-value < 0.05)") + xlab("Number of Cells") + ggtitle(paste0("Subsampling of ", target_gene_name)) +
theme_classic()
print(p)
print(p2)
pdf(paste0(plotsdir, "/", date, "_correlation_simulation_deg_only.pdf"), width = 6, height = 4)
mapply(target_gene_list, FUN = function(target_gene_name){
df4plot <- dfs4plot[[target_gene_name]]
p <- ggplot(df4plot, aes(x = n_cells, y = med_deg, ymin = min_conf_deg, ymax = max_conf_deg)) +
geom_line(color = "navy", size = 2) +
geom_ribbon(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = 10, col = "red", lty = 3) +
ylab("Median Correlation (DEG Only)") + xlab("Number of Cells") + ggtitle(paste0("Subsampling of ", target_gene_name)) +
theme_classic()
print(p)
})
dev.off()
pdf(paste0(plotsdir, "/", date, "_correlation_simulation_all_genes.pdf"), width = 6, height = 4)
mapply(target_gene_list, FUN = function(target_gene_name){
df4plot <- dfs4plot[[target_gene_name]]
p <- ggplot(df4plot, aes(x = n_cells, y = med_all, ymin = min_conf_all, ymax = max_conf_all)) +
geom_line(color = "navy", size = 2) +
geom_ribbon(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = 10, col = "red", lty = 3) +
ylab("Median Correlation (DEG Only)") + xlab("Number of Cells") + ggtitle(paste0("Subsampling of ", target_gene_name)) +
theme_classic()
print(p)
})
dev.off()
pdf(paste0(plotsdir, "/", date, "_correlation_simulation.pdf"), width = 15, height = 12)
df4plot <- as.data.frame(bind_rows(dfs4plot))
ggplot(df4plot, aes(x = n_cells, y = med_all, ymin = min_conf_all, ymax = max_conf_all)) +
geom_line(color = "navy", size = 2) +
geom_ribbon(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = 10, col = "red", lty = 3) +
facet_wrap(~target) +
ylab("Median Correlation (All Genes)") + xlab("Number of Cells") +
theme_classic()
ggplot(df4plot, aes(x = n_cells, y = med_deg, ymin = min_conf_deg, ymax = max_conf_deg)) +
geom_line(color = "navy", size = 2) +
geom_ribbon(fill = "gray", alpha = 0.5) +
geom_vline(xintercept = 10, col = "red", lty = 3) +
facet_wrap(~target) +
ylab("Median Correlation (DEG Only)") + xlab("Number of Cells") +
theme_classic()
dev.off()
## median correlation for 10 and 25 cells, w.r.t. number of DEG
pdf(paste0(plotsdir, "/", date, "_correlation_vs_n_deg.pdf"), width = 8, height = 8)
for (n in c(10, 25)){
df4plot <- filter(as.data.frame(bind_rows(dfs4plot)), n_cells == n)
p1 <- ggplot(df4plot, aes(x = n_deg, y = med_deg)) +
xlab("Number of Downstream DE Genes") + ylab("Median Correlation (DEG Only)") +
ggtitle(paste0(n, ' cells')) +
geom_point() + theme_bw()
p2 <- ggplot(df4plot, aes(x = n_deg, y = med_all)) +
xlab("Number of Downstream DE Genes") + ylab("Median Correlation (All Genes)") +
ggtitle('') +
geom_point() + theme_bw()
p3 <- ggplot(df4plot, aes(x = n_deg, y = min_conf_deg)) +
xlab("Number of Downstream DE Genes") + ylab("Correlation (10%) (DEG Only)") +
geom_point() + theme_bw()
p4 <- ggplot(df4plot, aes(x = n_deg, y = min_conf_all)) +
xlab("Number of Downstream DE Genes") + ylab("Correlation (10%) (All Genes)") +
geom_point() + theme_bw()
p <- ggarrange(p1, p2, p3, p4, ncol = 2, nrow = 2)
print(p)
}
dev.off()
#### Scratch
lfcs_by_target <- fread(paste0(OutFolder, "/", "6b_calc_lfcs_transcriptome_wide_by_gene", "/", date, "_combined_with_adj_pval.tsv.gz"))
lfcs_by_target <- split.data.frame(lfcs_by_target, f =lfcs_by_target$target)
all_downstream_genes <- lfcs_by_target[[1]]$downstream_gene
n_downstream_genes <- length(all_downstream_genes)
iter <- 1; n_cells <- 40
fnms <- list.files(paste0(outdir, "/by_gene/"), pattern = paste0(n_cells, "-cells_", iter, '[.]tsv[.]gz'), recursive = T)
dfs <- mapply(paste0(outdir, "/by_gene/", fnms), FUN = fread, SIMPLIFY = F)
names(dfs) <- unlist(lapply(strsplit(fnms, "/"), "[[", 1))
plotlist <- mapply(1:length(dfs), FUN = function(i){
print(i)
tg <- names(dfs)[i]
#unsampled <- lfcs_by_target[[tg]]
#sampled <- dfs[[tg]]
rtn <- data.frame(
downstream_gene_name = lfcs_by_target[[tg]]$downstream_gene_name,
lfc_unsampled =lfcs_by_target[[tg]]$lfc,
lfc_unsampled_pval_adj = p.adjust(lfcs_by_target[[tg]]$pval_lm, method = "BH"),
lfc_sampled =dfs[[tg]]$lfc,
lfc_sampled_pval_adj = p.adjust(dfs[[tg]]$pval_lm, method = "BH")
) %>%
mutate(
err = abs(lfc_sampled - lfc_unsampled),
pct_err = abs((lfc_sampled - lfc_unsampled)/lfc_unsampled),
deg_stat = ifelse(lfc_unsampled_pval_adj < 0.05 & lfc_sampled_pval_adj < 0.05, "TP",
ifelse(lfc_unsampled_pval_adj < 0.05 & lfc_sampled_pval_adj > 0.05, "FN",
ifelse( lfc_unsampled_pval_adj > 0.05 & lfc_sampled_pval_adj < 0.05, "FP", "TN")))
)
p <- ggplot(rtn, aes(x = lfc_sampled, y = lfc_unsampled)) + geom_point(size = 0.4) + ggtitle(tg) + xlab("All") + ylab("Sampled") + theme_bw()
return(p)
}, SIMPLIFY = F)
ggarrange(plotlist = plotlist)