diff --git a/rnaseq/step1_fastqc/snippet.sh b/rnaseq/step1_fastqc/snippet.sh
index 0eb5213..b0d7dcb 100644
--- a/rnaseq/step1_fastqc/snippet.sh
+++ b/rnaseq/step1_fastqc/snippet.sh
@@ -1,34 +1,5 @@
-#!/bin/bash
-#SBATCH --job-name=fastqc
-#SBATCH --time=0-12:00:00
-#SBATCH --ntasks=1
-#SBATCH --mem=15G
-#SBATCH --qos=regular
+# The command to do a FastQC on a fastq file is
+file="file_to_analyse.fq.gz"
+fastqc_out="./path/to/fastqc/output/dir"
 
-
-set -e
-set -u
-set -x
-set -o pipefail
-
-
-module purge
-module load Java
-module load FastQC
-
-
-dir_raw_fastq="$(pwd)/fastq/raw"
-dir_fastqc="$(pwd)/fastqc"
-
-
-[ -d "${dir_fastqc}" ] || mkdir -p "${dir_fastqc}"
-
-
-# Run FastQC
-files=$(find -L "$dir_raw_fastq" -type f -iname "*.fastq.gz")
-for file in $files; do
-	filename=$(basename "$file")
-	fastqc -o "$dir_fastqc" "$file" &
-done
-
-wait
+fastqc -o "$fastqc_out" "$file"
diff --git a/rnaseq/step2_trim/snippet.sh b/rnaseq/step2_trim/snippet.sh
index 6c4eb2e..e69de29 100644
--- a/rnaseq/step2_trim/snippet.sh
+++ b/rnaseq/step2_trim/snippet.sh
@@ -1,64 +0,0 @@
-#!/bin/bash
-#SBATCH --job-name=trimming
-#SBATCH --time=2-00:00:00
-#SBATCH --ntasks=1
-#SBATCH --mem=15G
-#SBATCH --qos=regular
-
-
-set -e
-set -u
-set -x
-set -o pipefail
-
-
-module purge
-module load TrimGalore
-
-
-dir_raw_fastq="$(pwd)/fastq/raw"
-dir_trimmed_fastq="$(pwd)/fastq/trimmed"
-dir_trimmed_fastq_reports="$(pwd)/fastq/trimmed/reports"
-
-adapter_3p="TGGAATTCTCGG" # is _R1
-adapter_5p="GATCGTCGGACT" # is _R2
-
-
-[ -d "${dir_trimmed_fastq_reports}" ] || mkdir -p "${dir_trimmed_fastq_reports}"
-
-
-# Trim all adapters from the sequences
-while IFS=, read -r GSID Sample
-do
-  echo ">> Executing trimming of $GSID (${#GSID})"
-  if [ "${#GSID}" == "18" ]; then # check length - don't include sample pools
-    for fqFile in $(find -L "$dir_raw_fastq" -maxdepth 1 -type f -iname "*${GSID}*.fastq.gz"); do
-      newFilename=${fqFile/.fastq.gz/_trimmed.fq.gz}
-      if [ -f "${newFilename}" ]; then
-        echo ">>File exists ${newFilename}. Skipping"
-      else
-        echo ">>File: ${fqFile}"
-        if grep -q "_R1" <<< $(basename "$fqFile"); then
-          adapter_seq=$adapter_3p
-        elif grep -q "_R2" <<< $(basename "$fqFile"); then
-          adapter_seq=$adapter_5p
-        fi
-        echo ">>Trimming with sequence ${adapter_seq}"
-        trim_galore \
-          --adapter "$adapter_seq" \
-          --length $(printf "$adapter_seq" | wc -m) \
-          --output_dir "${dir_trimmed_fastq}" \
-          --fastqc_args "--noextract" \
-          "${fqFile}" &
-      fi
-    done
-  else
-    echo "Skipped."
-  fi
-done < samples.csv
-
-
-wait
-mv ${dir_trimmed_fastq}/*.zip "${dir_trimmed_fastq_reports}"
-mv ${dir_trimmed_fastq}/*.txt "${dir_trimmed_fastq_reports}"
-mv ${dir_trimmed_fastq}/*.html "${dir_trimmed_fastq_reports}"
\ No newline at end of file
diff --git a/rnaseq/step6_overall_QC/01 - Principle Component Analysis.R b/rnaseq/step6_overall_QC/01 - Principle Component Analysis.R
index 09e4dff..0c42844 100644
--- a/rnaseq/step6_overall_QC/01 - Principle Component Analysis.R	
+++ b/rnaseq/step6_overall_QC/01 - Principle Component Analysis.R	
@@ -1,7 +1,15 @@
 # Principle Component Analysis
 # Normalized with limma::voom
+library(limma)
+library(tidyverse)
 
-source("__ - Preloader.R", verbose=T)
+
+# expression.data. Has columns:
+# - Gene (gene identifier)
+# - [Sample identifiers]
+expression.data <- "mrna_counts_table.csv"
+# results.dir. The directory of where to put the resulting tables (and later your plots.)
+results.dir <- "results"
 
 
 # PCA variables
@@ -10,10 +18,7 @@ do.scale = FALSE
 
 
 # The analysis
-master.Table %>% readr::write_csv(
-	file.path(results.dir, "patient.table.csv")
-)
-
+# We ise prcomp to calculate the PCAs. Afterwards you should plot the results.
 norm.expr.data <- expression.data %>%
 	tibble::column_to_rownames("Gene")
 norm.expr.data <- norm.expr.data[rowSums(norm.expr.data) >= 10,] %>%
@@ -69,132 +74,8 @@ data.frame(
 # Not saved: pcs.summery$sdev,
 
 
-
-# Plot PCAs
-# https://github.com/kevinblighe/PCAtools
-results.dir.pca.plot <- file.path(results.dir.pca, "img")
-dir.create(results.dir.pca.plot)
-
-metadata <- master.Table %>%
-	dplyr::filter(
-		!is.na(GenomeScan_ID)
-	) %>%
-	tibble::column_to_rownames("GenomeScan_ID") %>%
-	select.rows.in.order(
-		colnames(norm.expr.data)
-	)
-
-
-p <- pca(norm.expr.data,
-	metadata = metadata, 
-  	center = do.center,
-  	scale = do.scale
-)
-elbow <- findElbowPoint(p$variance)
-metavars <- c('Age','Gender','Smoking_status','COPD_Y_or_N','SEO_COPD_Y_or_N','GOLD_stage')
-
-
-png(filename = file.path(results.dir.pca.plot,"scree_plot.png"),
-    width = 800, height = 800,
-    units = "px", pointsize = 12,
-    type = "Xlib")
-print(screeplot(p,
-		axisLabSize = 12,
-		titleLabSize = 12,
-	    components = getComponents(p, 1:(elbow+5)),
-	    vline = c(elbow)
-    ) +
-    geom_label(
-    	aes(
-    		x = elbow + 1,
-    		y = 50,
-    		label = 'Elbow method',
-    		vjust = -1,
-    		size = 8
-    	)
-    ))
-dev.off()
-
-
-png(filename = file.path(results.dir.pca.plot, "eigen_corr_plot.png"),
-    width = 1200, height = 1200,
-    units = "px", pointsize = 12,
-    type = "Xlib")
-print(eigencorplot(p,
-    metavars = metavars
-))
-dev.off()
-
-
-dir.create(file.path(results.dir.pca.plot, "pairsplot"))
-for (var in metavars) {
-	png(
-		filename = file.path(results.dir.pca.plot, "pairsplot", paste0(var,".png")),
-	    width = 1200, height = 1200,
-	    units = "px", pointsize = 12,
-	    type = "Xlib"
-    )
-	print(pairsplot(p,
-	    components = getComponents(p, c(1:(elbow+1))),
-	    triangle = TRUE,
-	    trianglelabSize = 12,
-	    hline = 0, vline = 0,
-	    pointSize = 0.4,
-	    gridlines.major = FALSE,
-	    gridlines.minor = FALSE,
-	    colby = var,
-	    title = paste0('Pairs plot: ',var),
-	    plotaxes = TRUE
-	))
-	dev.off()
-}
-
-
-
-# Plot PCAs - old failure
-pca.combinations <- combinations(
-		n = (elbow+1),
-		r = 2,
-		v = 1:(elbow+1),
-		repeats.allowed = FALSE
-	)
-
-dir.create(file.path(results.dir.pca.plot, "biplots"))
-for (var in metavars) {
-	for (i in 1:nrow(pca.combinations)) {
-		pca.combi <- pca.combinations[i,]
-		pca.title <- paste(paste0("PC", pca.combi), collapse="_")
-		png(
-			filename = file.path(results.dir.pca.plot, "biplots", paste0(var, "-", pca.title, ".png")),
-		    width = 800, height = 800,
-		    units = "px", pointsize = 12,
-		    type = "Xlib"
-	    )
-		print(
-			autoplot(
-				norm.expr.data.pcs,
-				data = master.Table %>%
-					dplyr::filter(
-						!is.na(GenomeScan_ID)
-					) %>%
-					tibble::column_to_rownames("GenomeScan_ID") %>%
-					select.rows.in.order(
-						rownames(norm.expr.data.pcs$x)
-					),
-				x = pca.combi[1],
-				y = pca.combi[2],
-				colour = var,
-				loadings = FALSE,
-				loadings.label = FALSE,
-				#label = FALSE,
-				label.size = 3
-		    ) +
-		    ggprism::theme_prism() + 
-		    #ggprism::scale_colour_prism() + 
-		    ggprism::scale_shape_prism() +
-		    ggplot2::labs(subtitle = paste0(str_to_title(var), " (", pca.title,")"))
-		)
-		dev.off()
-	}
-}
-
+# Next thing to do:
+# - (Optional) scree plot - to determine the optimal cutoff for PCA inclusion based on explaination of variance
+# - (Optional) eigencorplot - to correlate PCAs to clinical variables so that you know which PCA to include for which analysis
+# - (Optional) pairsplot - plot multiple PCAs against each other in a single figure
+# - Plot the first couple of PCAs against each other
\ No newline at end of file
diff --git a/rnaseq/step6_overall_QC/02 - Gender Check.R b/rnaseq/step6_overall_QC/02 - Gender Check.R
index af7536d..f02f19d 100644
--- a/rnaseq/step6_overall_QC/02 - Gender Check.R	
+++ b/rnaseq/step6_overall_QC/02 - Gender Check.R	
@@ -1,16 +1,28 @@
 # Gender QC
 # Normalized with limma::voom
+library(GSVA)
+library(limma)
+library(edgeR)
+library(tidyverse)
 
-source("__ - Preloader.R", verbose=T)
+
+# 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
-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()
-
+# 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)
@@ -87,248 +99,12 @@ gender.qc.genes.to.plot <- gender.qc.results %>%
             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"
-    )
-}
+# 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.
+# - (Optional) Plot the number of Y-chromosome SNPs devided by the number of X chromosome SNPs in a boxplot as per the first point.
diff --git a/rnaseq/step6_overall_QC/03 - Sample Counts.R b/rnaseq/step6_overall_QC/03 - Sample Counts.R
index 90e805a..3f4c5c2 100644
--- a/rnaseq/step6_overall_QC/03 - Sample Counts.R	
+++ b/rnaseq/step6_overall_QC/03 - Sample Counts.R	
@@ -1,17 +1,25 @@
 # Total counts per sample
 # Normalized with limma::voom
+library(limma)
+library(tidyverse)
 
-source("__ - Preloader.R", verbose=T)
+
+# 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
-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()
-
-# Total counts per sample
+# We calculate the number of mapped reads per sample.
 total.count.per.sample <- expression.data %>%
 	tibble::column_to_rownames("Gene") %>%
 	colSums()
@@ -23,141 +31,9 @@ data.frame(
 	readr::write_csv(file.path(results.dir, "total.counts.per.sample.csv"))
 
 
-norm.data <- norm.expr.data %>%
-  as.data.frame() %>%
-  tibble::rownames_to_column(
-    "Gene"
-  ) %>%
-  tidyr::gather(
-    key = "sample.id",
-    value = "expr.value",
-    -Gene
-  ) %>%
-  dplyr::left_join(
-    y = master.Table %>%
-      dplyr::filter(
-          !is.na(GenomeScan_ID)
-      ) %>%
-      dplyr::mutate(
-          id = dplyr::case_when(
-            stringr::str_trim(gender) == "" ~ paste0("Water ", dplyr::row_number()),
-            TRUE ~ sample.id
-          ),
-          gender = dplyr::case_when(
-            stringr::str_trim(gender) == "" ~ "water",
-            !is.na(gender) ~ as.character(gender)
-          )
-      ) %>%
-      dplyr::select(
-        GenomeScan_ID,
-        gender,
-        id
-      ),
-    by = c("sample.id" = "GenomeScan_ID")
-  )
-
-norm.plot <- norm.data %>%
-  ggplot2::ggplot(
-    mapping = ggplot2::aes(
-      x = id,
-      y = expr.value,
-      fill = gender
-    )
-  ) +
-  ggplot2::geom_boxplot() +
-  ggplot2::scale_fill_manual(
-    values = c(
-      "male" = "blue",
-      "female" = "red",
-      "water" = "green"
-    )
-  ) +
-  ggplot2::labs(
-    title = "Normalized expression values distribution",
-    y = "Normalized expression values (limma::voom)",
-    x = "Sample",
-    gender = "Gender"
-  ) +
-  ggprism::theme_prism() +
-  ggplot2::theme(
-    axis.text.x = ggplot2::element_text(angle = 90)
-  )
-
-ggplot2::ggsave(
-  filename = file.path(results.dir, "counts.per.sample.normalised.png"),
-  plot = norm.plot,
-  width = 40,
-  height = 20,
-  units = "cm"
-)
-
-
-
-expr.data <- expression.data %>%
-  tidyr::gather(
-    key = "sample.id",
-    value = "expr.value",
-    -Gene
-  ) %>%
-  dplyr::filter(
-    expr.value != 0
-  ) %>%
-  dplyr::left_join(
-    y = master.Table %>%
-      dplyr::filter(
-          !is.na(GenomeScan_ID)
-      ) %>%
-      dplyr::mutate(
-          id = dplyr::case_when(
-            stringr::str_trim(gender) == "" ~ paste0("Water ", dplyr::row_number()),
-            TRUE ~ sample.id
-          ),
-          gender = dplyr::case_when(
-            stringr::str_trim(gender) == "" ~ "water",
-            !is.na(gender) ~ as.character(gender)
-          )
-      ) %>%
-      dplyr::select(
-        GenomeScan_ID,
-        gender,
-        id
-      ),
-    by = c("sample.id" = "GenomeScan_ID")
-  )
-
-expr.plot <- expr.data %>%
-  ggplot2::ggplot(
-    mapping = ggplot2::aes(
-      x = id,
-      y = expr.value,
-      fill = gender
-    )
-  ) +
-  ggplot2::geom_boxplot() +
-  ggplot2::scale_fill_manual(
-    values = c(
-      "male" = "blue",
-      "female" = "red",
-      "water" = "green"
-    )
-  ) +
-  ggplot2::scale_y_continuous(trans='log2')  +
-  ggplot2::labs(
-    title = "Raw expression values distribution, without zero's",
-    y = "Expression values",
-    x = "Sample",
-    gender = "Gender"
-  ) +
-  ggprism::theme_prism() +
-  ggplot2::theme(
-    axis.text.x = ggplot2::element_text(angle = 90)
-  )
-
-ggplot2::ggsave(
-  filename = file.path(results.dir, "counts.per.sample.raw.zeros.removed.png"),
-  plot = expr.plot,
-  width = 40,
-  height = 20,
-  units = "cm"
-)
+# Next thing to do:
+# - Check the number of reads per sample in total.counts.per.sample.csv
+# - Plot the reads distribution (all reads) per sample in a boxplot.
+# - (Optional) Calculate the number of unmapped, multimapped, unique mapped to
+#   feature and unique mapped to no feature and plot these in a stacked bar graph.
 
diff --git a/rnaseq/step6_overall_QC/__ - Preloader.R b/rnaseq/step6_overall_QC/__ - Preloader.R
deleted file mode 100644
index ce96a0f..0000000
--- a/rnaseq/step6_overall_QC/__ - Preloader.R	
+++ /dev/null
@@ -1,189 +0,0 @@
-library(tidyverse)
-library(ggfortify)
-library(ggprism)
-library(limma)
-library(biomaRt)
-library(PCAtools)
-library(gtools)
-library(edgeR)
-library(ggprism)
-library(foreign)
-
-
-# Global variables
-results.dir <- file.path("results.nosync", "RNA-Seq QC")
-data.dir <- "Data"
-patient.dir <- file.path(data.dir, "Patients")
-sample.dir <- file.path(data.dir, "Samples")
-expression.dir <- file.path(data.dir, "mRNA - RNA-Seq")
-
-
-dir.create(results.dir, recursive = TRUE)
-
-
-####
-# Helper functions
-####
-select.columns.in.order <- function(dataframe, columns) {
-  dataframe[, columns]
-}
-
-select.rows.in.order <- function(dataframe, rows) {
-  dataframe[rows,]
-}
-
-getGenedataByEnsemblId38 <- function(ensemblIds, file.location) {
-  file.name <- file.path(file.location, "genes_info_hg38.csv")
-  if (!file.exists(file.name)) {
-    if (!("mart" %in% ls())) {
-      assign("mart", useEnsembl(
-        biomart = "ENSEMBL_MART_ENSEMBL",
-        dataset = "hsapiens_gene_ensembl"
-      ))
-    }
-    gene.list <- getBM(
-      filters = "ensembl_gene_id",
-      attributes = c(
-        "hgnc_symbol",
-        "ensembl_gene_id",
-        "ensembl_transcript_id",
-        "chromosome_name",
-        "start_position",
-        "end_position",
-        "strand",
-        "transcription_start_site",
-        "transcript_start",
-        "transcript_end",
-        "external_gene_name"
-      ),
-      values = as.character(ensemblIds),
-      mart = mart
-    )
-    readr::write_csv(gene.list, path = file.name)
-  }
-  return(
-    readr::read_csv(
-    	file.name,
-		col_types = readr::cols()
-	)
-  )
-}
-
-#remove.rows.with.count.less.then <- function(dataframe, minRowCount, columns.to.exclude) {
-#  dataframe %>%
-#  dplyr::filter(
-#    rowSums(dplyr::select(., -tidyselect::one_of(columns.to.exclude))) < minRowCount
-#  )
-#}
-
-limma.voom.convert.column <- function(dataframe, columnname) {
-  dataframe %>%
-  tibble::column_to_rownames(columnname) %>%
-  limma::voom() %>%
-  as.data.frame() %>%
-  tibble::rownames_to_column(columnname)
-}
-
-select.columns.in.order <- function(dataframe, columns) {
-  dataframe[, columns]
-}
-
-drop.columns.if.all.same.value <- function(dataframe) {
-  for (name in colnames(dataframe)) {
-    is.all.same <- (dataframe[, name] %>% unique() %>% length()) <= 1
-    if (is.all.same) {
-      dataframe <- dataframe %>%
-        dplyr::select(
-          -tidyselect::one_of(name)
-        )
-    }
-  }
-  dataframe
-}
-
-
-
-# Load data
-master.Table <- foreign::read.spss(
-		file.path(patient.dir, "PRESTO proteogenomics full data sat - ver 7.5.sav")
-	) %>%
-  as.data.frame() %>%
-  dplyr::mutate(
-    GenomeScan_ID = stringr::str_trim(GenomeScan_ID),
-    gender = forcats::fct_recode(
-      Gender,
-      female = "f",
-      male = "m",
-      other = ""
-    ),
-    age = as.numeric(Age),
-    smoking.status = forcats::fct_recode(
-      Smoking_status,
-      `Ex-smoker` = "ES        ",
-      `Current smoker` = "CS        ",
-      other = "          "
-    )
-  )
-
-expression.data <- readr::read_tsv(
-		file.path(expression.dir, "20200427_103972-001_rawcounts.txt"),
-		col_types = readr::cols()
-	)
-
-gene.data <- getGenedataByEnsemblId38(
-    ensemblIds = expression.data$Gene,
-    file.location = expression.dir
-  ) %>%
-  dplyr::group_by(hgnc_symbol) %>%
-  dplyr::filter(
-    dplyr::row_number() == 1,
-    !is.na(hgnc_symbol),
-    hgnc_symbol != ""
-  ) %>%
-  dplyr::ungroup() %>%
-  dplyr::select(
-    hgnc_symbol,
-    ensembl_gene_id,
-    chromosome_name,
-    transcript_start,
-    transcript_end
-  )
-
-
-master.Table <- master.Table %>%
-  dplyr::mutate(
-    Group_simple2 = stringr::str_trim(Group_simple2),
-    Group_simple  = stringr::str_trim(Group_simple),
-    T_number = as.character(T_number),
-    sample.id = stringr::str_trim(PRESTO_ID)
-  ) %>%
-  dplyr::filter(
-    # # According to Niek, I should not include this, for whatever reason
-    #!(GenomeScan_ID %in% c(
-    #    "T02-01796",
-    #    "T02-03095",
-    #    "T02-10683",
-    #    "T10-18671",
-    #    "T12-12036"
-    #  )
-    # ),
-    # 
-    # # (According to Niek, don't include) Water Controls
-    #!stringr::str_detect(T_number, pattern="Water"),
-    # 
-    # # (According to Niek, don't include)never smoker controles
-    #!(Group_simple2 == "NS_Ctrl"),
-    # 
-    # # (According to Niek, don't include)ALFA1 patiënten 
-    #!(Group_simple == "ALFA"),
-    # 
-    #stringr::str_trim(Passed_RNAseq_library_prep_QC_Y_N) == "Y",
-    GenomeScan_ID %in% colnames(expression.data),
-    !is.na(sample.id),
-    sample.id != ""
-  )
-
-expression.data <- expression.data %>%
-  select.columns.in.order(
-    c("Gene", master.Table$GenomeScan_ID)
-  )