# Gender QC
# Normalized with limma::voom

source("__ - Preloader.R", verbose=T)


# The analysis
norm.expr.data <- expression.data %>%
    tibble::column_to_rownames("Gene")
norm.expr.data <- norm.expr.data[rowSums(norm.expr.data) >= 10,] %>%
    limma::voom() %>%
    as.matrix()

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
        ) |
        hgnc_symbol %in% c(
            "XIST",
            "TSIX",
            "KDM6A",
            "ZFX",
            "KDM5C",
            "ZFY-AS1",
            "ARSDP1",
            "GYG2P1",
            "RBMY2JP",
            "ARSLP1"
        )
    )

gender.qc.data <- as.data.frame(norm.expr.data) %>%
    rownames_to_column("ensembl.id") %>%
    tidyr::gather(
        key = "rna.seq.sample.id",
        value = "expr.value",
        -ensembl.id
    ) %>%
    dplyr::filter(
        ensembl.id %in% gender.qc.genes.to.plot$ensembl.id
    ) %>%
    dplyr::left_join(
        y = gender.qc.patients,
        by = c("rna.seq.sample.id" = "GenomeScan_ID")
    ) %>%
    #dplyr::filter(
    #    !is.na(gender)
    #) %>%
    readr::write_csv(
        file.path(results.dir.gender.plot, "plot.data.voom.csv")
    )

for (chr in gender.qc.genes.to.plot$chromosome_name) {
    current.gender.qc.genes.to.plot <- gender.qc.genes.to.plot %>%
        dplyr::filter(chromosome_name == chr)
    chromosome_name <- chr
    i <- 0
    for (current.ensembl.id in current.gender.qc.genes.to.plot$ensembl.id) {
        i <- i + 1

        hgnc_symbol <- gene.data %>%
            dplyr::filter(
                ensembl_gene_id == current.ensembl.id
            ) %>%
            dplyr::pull(hgnc_symbol)

        # calculate outliers, kinda
        plot.data <- gender.qc.data %>%
            dplyr::filter(
                ensembl.id == current.ensembl.id
            ) %>%
            dplyr::mutate(
                gender = dplyr::case_when(
                    is.na(gender) | (stringr::str_trim(gender) == "") ~ "other",
                    TRUE ~ gender
                )
            )

        if (nrow(plot.data) <= 0) {
            next
        }

        outliers <- boxplot(
            formula = expr.value ~ gender,
            data = plot.data,
            plot = FALSE
        )$out

        result.to.annotate <- plot.data %>%
            dplyr::filter(
                expr.value %in% outliers
            )

        # Visual: plot range (for t-test p-value)
        plot.y.range <- c(
            "min" = as.integer(min(plot.data$expr.value) - 1) ,
            "max" = as.integer(max(plot.data$expr.value) + 1)
        )
        plot.margin <- ((plot.y.range["max"] + (plot.y.range["min"] * -1)) * 0.05)
        plot.y.range["min"] <- plot.y.range["min"] - plot.margin
        plot.y.range["max"] <- plot.y.range["max"] + plot.margin

        # Plot the damn thing as if it is Graphpad Prism
        stat.table <- rstatix::t_test(plot.data, expr.value ~ gender)
        plt <- plot.data %>%
            ggplot2::ggplot(
                mapping = ggplot2::aes(
                    x = gender,
                    y = expr.value
                ) 
            ) +
            ggplot2::geom_jitter(
                mapping = ggplot2::aes(
                    colour = gender,
                    shape = gender
                ),
                width = 0.1
            ) +
            ggrepel::geom_text_repel(
                data = result.to.annotate,
                mapping = ggplot2::aes(
                    label = sample.id
                ),
                size = 2,
                box.padding = unit(0.35, "lines"),
                point.padding = unit(0.3, "lines")
            ) +
              ggplot2::stat_summary(
                  fun = "mean",
                  geom = "crossbar",
                  width = 0.3,
                  size = 0.3
              ) + 
            ggplot2::scale_y_continuous(
                limits = plot.y.range,
                guide = "prism_offset"
            ) +
            #ggprism::add_pvalue(
            #    stat.table,
            #    y.position = plot.y.range["max"]
            #) +
            ggprism::theme_prism() + 
            ggprism::scale_colour_prism() + 
            ggprism::scale_shape_prism() +
            ggplot2::theme(
                legend.position = "none"
            ) + 
            ggplot2::labs(
                subtitle = paste0("Gender Check: ", hgnc_symbol, " (chr. ", chromosome_name, ")"),
                x = "Gender",
                y = "Normalised Expression Values"
            )

        ggplot2::ggsave(
            filename = file.path(results.dir.gender.plot, paste0(chromosome_name, ".", i, ".", hgnc_symbol, ".png")),
            plot = plt,
            width = 12.5,
            height = 12.5,
            unit = "cm"
        )
    }
}

# Let's try a GSVA
gsva.groups <- list(
    X = gender.qc.genes.to.plot %>%
        dplyr::filter(chromosome_name == "X") %>%
        dplyr::pull(ensembl.id),
    Y = gender.qc.genes.to.plot %>%
        dplyr::filter(chromosome_name == "Y") %>%
        dplyr::pull(ensembl.id)
)

gsva_res = GSVA::gsva(
  norm.expr.data,
  gsva.groups,
  mx.diff = TRUE,
  verbose = FALSE,
  parallel.sz = 1
)


gender.qc.gsva.data <- as.data.frame(gsva_res) %>%
    rownames_to_column("gsva.group") %>%
    tidyr::gather(
        key = "rna.seq.sample.id",
        value = "gsva.value",
        -gsva.group
    ) %>%
    dplyr::left_join(
        y = gender.qc.patients %>%
          dplyr::select(
              GenomeScan_ID,
              sample.id,
              gender
          ),
        by = c("rna.seq.sample.id" = "GenomeScan_ID")
    ) %>%
    readr::write_csv(
        file.path(results.dir.gender.plot, "plot.data.gsva.csv")
    )


for (c.gender in unique(gender.qc.gsva.data$gender)) {
    if (is.na(c.gender)) {
        next
    }

    c.plot.data <- gender.qc.gsva.data %>%
        dplyr::filter(
            gender == c.gender
        )

    outliers <- boxplot(
        formula = gsva.value ~ gsva.group,
        data = c.plot.data,
        plot = FALSE
    )$out

    result.to.annotate <- c.plot.data %>%
        dplyr::filter(
            gsva.value %in% outliers
        )

    plt <- c.plot.data %>%
        ggplot2::ggplot(
            mapping = ggplot2::aes(
                x = gsva.group,
                y = gsva.value
            ) 
        ) +
        ggplot2::geom_boxplot() +
        ggrepel::geom_text_repel(
            data = result.to.annotate,
            mapping = ggplot2::aes(
                label = sample.id
            ),
            size = 2,
            box.padding = unit(0.35, "lines"),
            point.padding = unit(0.3, "lines")
        ) +
        ggprism::theme_prism() + 
        ggprism::scale_colour_prism() + 
        ggprism::scale_shape_prism() +
        ggplot2::theme(
            legend.position = "none"
        ) + 
        ggplot2::labs(
            subtitle = paste0("", toupper(c.gender)),
            x = "Chromosome",
            y = "GSVA Values"
        )

    ggplot2::ggsave(
        filename = file.path(results.dir.gender.plot, paste0("gsva.", c.gender, ".png")),
        plot = plt,
        width = 12.5,
        height = 12.5,
        unit = "cm"
    )
}