# Gender QC
# Normalized with limma::voom
library(GSVA)
library(limma)
library(edgeR)
library(tidyverse)


# expression.data. Has columns:
# - Gene (gene identifier)
# - [Sample identifiers]
expression.data <- "mrna_counts_table.csv"
# master.Table. Has columns:
# - GenomeScan_ID
# - gender, levels = c("male", "female")
# - age
# - factor(smoking.status, levels = c("Ex-smoker", "Current smoker"))
master.Table <- "patient_table.csv"
# results.dir. The directory of where to put the resulting tables (and later your plots.)
results.dir <- "results"


# The analysis
# We first do a differential expression analysis on gender using EdgeR.
# Afterwards you should plot these results.
x.genes <- gene.data %>%
    dplyr::filter(chromosome_name == "X") %>%
    dplyr::pull(ensembl_gene_id)

y.genes <- gene.data %>%
    dplyr::filter(chromosome_name == "Y") %>%
    dplyr::pull(ensembl_gene_id)

# Gender QC
results.dir.gender <- file.path(results.dir, "gender.check")
dir.create(results.dir.gender, recursive=TRUE)

# Differential Expression on Gender
gender.qc.patients <- master.Table %>%
    dplyr::filter(
        !is.na(GenomeScan_ID)
    ) %>%
    dplyr::mutate(
        gender = factor(gender, levels = c("male", "female")),
        age = as.numeric(age),
        smoking.status = factor(smoking.status, levels = c("Ex-smoker", "Current smoker"))
    ) %>%
    dplyr::filter(
        !is.na(gender)
    )
gender.qc.sample.order <- gender.qc.patients %>%
    dplyr::pull(GenomeScan_ID)

gender.qc.expression.data <- expression.data %>%
    tibble::column_to_rownames("Gene") %>%
    select.columns.in.order(gender.qc.sample.order) %>%
    as.matrix()

design <- model.matrix( ~0 + gender, data = gender.qc.patients)

DGEL <- edgeR::DGEList(gender.qc.expression.data)
keep <- edgeR::filterByExpr(DGEL)
keep[names(keep) %in% x.genes] <- TRUE
keep[names(keep) %in% y.genes] <- TRUE
DGEL <- DGEL[keep, , keep.lib.sizes=FALSE]
DGEL <- edgeR::calcNormFactors(DGEL, method = "TMM")

DGEL <- edgeR::estimateDisp(DGEL, design)
fit <- edgeR::glmQLFit(DGEL,design)

contrasts <- limma::makeContrasts(
    gender = gendermale - genderfemale,
    levels = design
)
qlf   <- edgeR::glmQLFTest(fit, contrast = contrasts[,"gender"])

gender.qc.results <- edgeR::topTags(
        qlf,
        n=nrow(DGEL)
    )$table %>%
    tibble::rownames_to_column("ensembl.id") %>%
    dplyr::left_join(
        y = gene.data,
        by = c("ensembl.id" = "ensembl_gene_id")
    ) %>%
    readr::write_csv(
        file.path(results.dir.gender, "differential.expression.on.gender.csv")
    )

# Plotting of gender expression
results.dir.gender.plot <- file.path(results.dir.gender, "img")
dir.create(results.dir.gender.plot, recursive=TRUE)

gender.qc.genes.to.plot <- gender.qc.results %>%
    dplyr::arrange(PValue) %>%
    dplyr::group_by(chromosome_name) %>%
    dplyr::filter(
        (
            chromosome_name %in% c("X", "Y") &
            FDR < 0.05 &
            dplyr::row_number() <= 5
        )
    )


# Next thing to do:
# - Plot the normalized expressino values for the genes in gender.qc.genes.to.plot in a boxplot, split and colored by gender.
# - (Optional) Do a GSVA with as genesets the genes found in gender.qc.genes.to.plot. Plot the boxplots as per the previous point.
# - (Optional) Plot the number of Y-chromosome reads devided by the number of X chromosome reads in a boxplot as per the first point.
#     x=$(samtools view -q 20 -f 2 $bam_file X | wc -l)
#     y=$(samtools view -q 20 -f 2 $bam_file Y | wc -l)
# - (Optional) Plot the number of Y-chromosome SNPs devided by the number of X chromosome SNPs in a boxplot as per the first point.