##
suppressWarnings(suppressPackageStartupMessages(library(xbioc)))
suppressWarnings(suppressPackageStartupMessages(library(MuSiC)))
suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
suppressWarnings(suppressPackageStartupMessages(library(cowplot)))
## We use this script to generate a clustering dendrogram of cell
## types, using the prior labelling from scRNA.

read_list <- function(lfile) {
    if (lfile == "None") {
        return(NULL)
    }
    return(read.table(file = lfile, header = FALSE,
                      stringsAsFactors = FALSE)$V1)
}

args <- commandArgs(trailingOnly = TRUE)
source(args[1])

## We then perform bulk tissue cell type estimation with pre-grouping
## of cell types: C, list_of_cell_types, marker genes name, marker
## genes list.
## data.to.use = list(
##     "C1" = list(cell.types = c("Neutro"),
##                 marker.names=NULL,
##                 marker.list=NULL),
##     "C2" = list(cell.types = c("Podo"),
##                 marker.names=NULL,
##                 marker.list=NULL),
##     "C3" = list(cell.types = c("Endo","CD-PC","LOH","CD-IC","DCT","PT"),
##                 marker.names = "Epithelial",
##                 marker.list = read_list("../test-data/epith.markers")),
##     "C4" = list(cell.types = c("Macro","Fib","B lymph","NK","T lymph"),
##                 marker.names = "Immune",
##                 marker.list = read_list("../test-data/immune.markers"))
## )
grouped_celltypes <- lapply(data.to.use, function(x) {
    x$cell.types
})
marker_groups <- lapply(data.to.use, function(x) {
    x$marker.list
})
names(marker_groups) <- names(data.to.use)


## Perform the estimation
## Produce the first step information
sub.basis <- music_basis(scrna_eset, clusters = celltypes_label,
                         samples = samples_label,
                         select.ct = celltypes)

## Plot the dendrogram of design matrix and cross-subject mean of
## realtive abundance
par(mfrow = c(1, 2))
d <- dist(t(log(sub.basis$Disgn.mtx + 1e-6)), method = "euclidean")
## Hierarchical clustering using Complete Linkage
hc1 <- hclust(d, method = "complete")
## Plot the obtained dendrogram
plot(hc1, cex = 0.6, hang = -1, main = "Cluster log(Design Matrix)")
d <- dist(t(log(sub.basis$M.theta + 1e-8)), method = "euclidean")
## Hierarchical clustering using Complete Linkage
hc2 <- hclust(d, method = "complete")
## Plot the obtained dendrogram
pdf(file = outfile_pdf, width = 8, height = 8)
plot(hc2, cex = 0.6, hang = -1, main = "Cluster log(Mean of RA)")

cl_type <- as.character(scrna_eset[[celltypes_label]])

for (cl in seq_len(length(grouped_celltypes))) {
  cl_type[cl_type %in% grouped_celltypes[[cl]]] <- names(grouped_celltypes)[cl]
}
pData(scrna_eset)[[clustertype_label]] <- factor(
    cl_type, levels = c(names(grouped_celltypes),
                        "CD-Trans", "Novel1", "Novel2"))

est_bulk <- music_prop.cluster(
    bulk.eset = bulk_eset, sc.eset = scrna_eset,
    group.markers = marker_groups, clusters = celltypes_label,
    groups = clustertype_label, samples = samples_label,
    clusters.type = grouped_celltypes)

write.table(est_bulk, file = outfile_tab, quote = F, col.names = NA, sep = "\t")
dev.off()