# ---- Load Data ----

library(scran)

library(scater)

library(randomForest)

library(ggplot2)

library(cowplot)

adtf <- "../data/combined_dec_ADT_MNNcorrected.RDS"

adt <- readRDS(adtf)

library(optparse)

parser <- OptionParser()

parser <- add_option(parser, c("-d", "--ds"), type="numeric",

                     help="Fraction of cells to use for initating spheres")

opt <- parse_args(parser)

# ---- Subset to D0 cells ----

# Rm patient with B-Cell Lymphoma

bc <- c("CV0198")

adt <- adt[,adt$patient_id != bc & ! (adt$Status_on_day_collection_summary %in% c("Non_covid","LPS")) & adt$Collection_Day =="D0"]

# removing anything with less than 500 cells

drpSmps <- names(which(table(adt$patient_id)<500))

adt <- adt[,!adt$patient_id %in% drpSmps]

colData(adt) <- droplevels(colData(adt))

m.adt <- assay(adt,"reconstructed")

# ---- CYDAR ----

library(cydar)

# Create list

smps <- as.character(unique(adt$patient_id))

mrks <- rownames(adt)

mrks <- mrks[!grepl("Iso",mrks)]

mtx.list <- lapply(smps, function(SMP) {

       cells <- colnames(adt)[adt$patient_id==SMP]

       message(SMP)

       m  <- t(m.adt[,cells])

       return(m)

})

names(mtx.list) <- smps

cd <- prepareCellData(mtx.list,markers=mrks)

distances <- neighborDistances(cd,as.tol=TRUE)

req <- 20

med <- round(median(distances[,req-1]),2)

library(BiocParallel)

param <- MulticoreParam(workers=3)

set.seed(42)

n <- opt$ds

cd <- countCells(cd, tol=med, downsample=n, BPPARAM=param)

saveRDS(cd,paste0("../data/HyperSpheres_",n,".RDS"))