[FEAT] Add new script dsb_normalize_adt.R

Add script that performs dsb normalization of ADT counts.
Script covers normalization without isoform controls.
Will be applied to single sample, without hashing.

workflow/scripts/dsb_normalize_adt.R

@@ -0,0 +1,87 @@
+##################################################
+#### File name: dsb_normalize_adt.R
+#### Author: Anne Bertolini
+#### October 2023
+#### R Version: 4
+##################################################
+
+library(dsb)
+library(Seurat)
+library(ggplot2)
+
+# command line arguments are parsed
+option_list <- list(
+  make_option("--adt_raw_dir", type = "character", help = "Path to directory from cellranger, raw output, \"raw_feature_bc_matrix\"."),
+  make_option("--adt_filtered_dir", type = "character", help = "Path to directory from cellranger, output with called cells, \"filtered_feature_bc_matrix\""),
+  make_option("--outdir", type = "character", help = "Path to the output directory."),
+  make_option("--sample", type = "character", help = "Sample identifier.")
+)
+opt_parser <- OptionParser(option_list = option_list)
+opt <- parse_args(opt_parser)
+
+# give out date, time and session info
+cat("\n\n")
+print(Sys.time())
+cat("\n\n\nPrint sessionInfo:\n\n")
+print(sessionInfo())
+cat("\n\n\n")
+cat("\nInput files:\n\n")
+print(opt)
+cat("\n\n")
+
+
+# read raw ADT data using the Seurat function "Read10X"
+raw_adt = Seurat::Read10X(opt$adt_raw_dir, strip.suffix = TRUE)
+cells_adt = Seurat::Read10X(opt$adt_filtered_dir, strip.suffix = TRUE)
+
+# define cell-containing barcodes and separate cells and empty drops
+stained_cells_adt = colnames(cells_adt)
+background_adt = setdiff(colnames(raw_adt), stained_cells_adt)
+
+# create metadata of droplet QC stats
+md = data.frame(
+  prot.size = log10(Matrix::colSums(raw_adt))
+)
+
+# add indicator for barcodes Cell Ranger called as cells
+md$drop.class = ifelse(rownames(md) %in% stained_cells_adt, 'cell', 'background')
+
+# remove barcodes with no evidence of capture in the experiment
+md = md[md$prot.size > 0, ]
+# remove barcodes from matrix
+raw_adt <- raw_adt[, colnames(raw_adt) %in% rownames(md)]
+
+# plot general QC
+plot_density <- ggplot2::ggplot(md, aes(x = seq_along(prot.size), y = prot.size )) +
+  theme_bw() +
+  geom_bin2d(bins = 300) +
+  scale_fill_viridis_c(option = "C") +
+  facet_wrap(~drop.class)
+filename <- paste0(opt$outdir, opt$sample, ".plot_adt_counts.png")
+ggsave(filename = filename, plot = plot_density)
+
+# have matrix of background and cells
+cells.adt.mtx = as.matrix(raw_adt[, colnames(raw_adt) %in% stained_cells_adt])
+background.adt.mtx = as.matrix(raw_adt[, colnames(raw_adt) %in% background_adt])
+
+
+# give out antibodies with lowest counts in cells, and highest
+cat("\n\n\nAntibodies with the lowest maximum counts in sample:\n\n")
+pm = sort(apply(cells.adt.mtx, 1, max))
+print(head(pm))
+cat("\n\n\nAntibodies with the highest maximum counts in sample:\n\n")
+print(tail(pm))
+
+
+
+# normalize with dsb (denoising is only possible with isoform controls) with
+cells.dsb.norm = dsb::DSBNormalizeProtein(
+  cell_protein_matrix = cells.adt.mtx,
+  empty_drop_matrix = background.adt.mtx,
+  denoise.counts = FALSE,
+  return.stats = TRUE
+)
+
+# write dsb-normalized ADT counts
+filename <- paste0(opt$outdir, opt$sample, ".dsb_normalize_adt.RDS")
+saveRDS(cells.dsb.norm, file = filename)