bayesPrism.R

library(BayesPrism)
library(Biobase)
library(dplyr)
library(rjson)

config <- fromJSON(file="configs/glob_config.json")
workdir <- paste(config[["experiment_folder"]], "/results/datasets", sep="")
setwd(workdir)
files <- list.files()

subject_path <- FALSE

for (i in 1:length(files)){
    if (grepl("test", files[i], fixed=TRUE)){
            test_path <- files[i]}
    if (grepl("counts", files[i], fixed=TRUE)){
            ref_counts_path <- files[i]
        }
    if (grepl("celltypes", files[i], fixed=TRUE)){
            ref_celltypes_path <- files[i]
        }
    if (grepl("subject", files[i], fixed=TRUE)){
            subject_path <- files[i]
        }
}

# Bulk
df_bulk <- read.csv(test_path, row.names=1)
df_bulk <- t(df_bulk)

# single cell
df <- read.csv(ref_counts_path, row.names=1)
df_celltypes <- read.csv(ref_celltypes_path, row.names=1)
celltype_labels <- df_celltypes$cellType

scrna.filtered <-  select.gene.type(scrna, gene.type = "protein_coding")

scrna.filtered <- cleanup.genes(input=as.matrix(scrna),
                                  input.type="count.matrix",
                                  species="hs", 
                                  gene.group=c("Rb","Mrp","MALAT1", "other_Rb","chrM","chrX","chrY") ,
                                  exp.cells=5)

myPrism <- new.prism(reference=scrna.filtered, 
                      mixture=df_bulk,
                      input.type="count.matrix", 
                      cell.type.labels = celltype_labels, 
                      cell.state.labels = celltype_labels,
                      key=NULL,
                      outlier.cut=0.01,
                      outlier.fraction=0.1)

bp.res <- run.prism(prism = myPrism, n.cores=50)
preds <- get.fraction (bp=bp.res,
            which.theta="final",
            state.or.type="type")

write.csv(preds, "results/bayesPrism_fractions.csv")