diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..57ef2cc
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,4 @@
+
+.DS_Store
+*.nosync
+*.RData
diff --git a/rnaseq/.DS_Store b/rnaseq/.DS_Store
new file mode 100644
index 0000000..8933a0f
Binary files /dev/null and b/rnaseq/.DS_Store differ
diff --git a/rnaseq/step1_fastqc/snippet.sh b/rnaseq/step1_fastqc/snippet.sh
index e69de29..0eb5213 100644
--- a/rnaseq/step1_fastqc/snippet.sh
+++ b/rnaseq/step1_fastqc/snippet.sh
@@ -0,0 +1,34 @@
+#!/bin/bash
+#SBATCH --job-name=fastqc
+#SBATCH --time=0-12:00:00
+#SBATCH --ntasks=1
+#SBATCH --mem=15G
+#SBATCH --qos=regular
+
+
+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" "$file" &
+done
+
+wait
diff --git a/rnaseq/step2_trim/snippet.sh b/rnaseq/step2_trim/snippet.sh
index e69de29..6c4eb2e 100644
--- a/rnaseq/step2_trim/snippet.sh
+++ b/rnaseq/step2_trim/snippet.sh
@@ -0,0 +1,64 @@
+#!/bin/bash
+#SBATCH --job-name=trimming
+#SBATCH --time=2-00:00:00
+#SBATCH --ntasks=1
+#SBATCH --mem=15G
+#SBATCH --qos=regular
+
+
+set -e
+set -u
+set -x
+set -o pipefail
+
+
+module purge
+module load TrimGalore
+
+
+dir_raw_fastq="$(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
new file mode 100644
index 0000000..09e4dff
--- /dev/null
+++ b/rnaseq/step6_overall_QC/01 - Principle Component Analysis.R	
@@ -0,0 +1,200 @@
+# Principle Component Analysis
+# Normalized with limma::voom
+
+source("__ - Preloader.R", verbose=T)
+
+
+# PCA variables
+do.center = TRUE
+do.scale = FALSE
+
+
+# The analysis
+master.Table %>% readr::write_csv(
+	file.path(results.dir, "patient.table.csv")
+)
+
+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()
+
+# Principle Component analysis
+results.dir.pca <- file.path(results.dir, "principle.components")
+dir.create(results.dir.pca, recursive=TRUE)
+
+norm.expr.data.pcs <- norm.expr.data %>%
+  t() %>%
+  stats::prcomp(
+  	center = do.center,
+  	scale. = do.scale
+  )
+
+# Write summary of PCAs to files
+pcs.summery <- summary(norm.expr.data.pcs)
+pcs.summery$importance %>%
+	t() %>%
+	as.data.frame() %>%
+	tibble::rownames_to_column("PC.name") %>%
+	readr::write_csv(
+		file.path(results.dir.pca, "importance.csv")
+	)
+
+pcs.summery$x %>%
+	t() %>%
+	as.data.frame() %>%
+	tibble::rownames_to_column("ensembl.id") %>%
+	readr::write_csv(
+		file.path(results.dir.pca, "values.csv")
+	)
+
+pcs.summery$rotation %>%
+	t() %>%
+	as.data.frame() %>%
+	tibble::rownames_to_column("sample.id") %>%
+	readr::write_csv(
+		file.path(results.dir.pca, "rotation.csv")
+	)
+
+data.frame(
+		rownames = names(pcs.summery$center),
+		center = pcs.summery$center,
+		scale = pcs.summery$scale
+	) %>%
+	readr::write_csv(
+		file.path(results.dir.pca, "rest.csv")
+	)
+
+# 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()
+	}
+}
+
diff --git a/rnaseq/step6_overall_QC/02 - Gender Check.R b/rnaseq/step6_overall_QC/02 - Gender Check.R
new file mode 100644
index 0000000..af7536d
--- /dev/null
+++ b/rnaseq/step6_overall_QC/02 - Gender Check.R	
@@ -0,0 +1,334 @@
+# 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"
+    )
+}
diff --git a/rnaseq/step6_overall_QC/03 - Sample Counts.R b/rnaseq/step6_overall_QC/03 - Sample Counts.R
new file mode 100644
index 0000000..90e805a
--- /dev/null
+++ b/rnaseq/step6_overall_QC/03 - Sample Counts.R	
@@ -0,0 +1,163 @@
+# Total counts per sample
+# 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()
+
+# Total counts per sample
+total.count.per.sample <- expression.data %>%
+	tibble::column_to_rownames("Gene") %>%
+	colSums()
+
+data.frame(
+		sample = names(total.count.per.sample),
+		counts = as.numeric(total.count.per.sample)
+	) %>%
+	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"
+)
+
diff --git a/rnaseq/step6_overall_QC/__ - Preloader.R b/rnaseq/step6_overall_QC/__ - Preloader.R
new file mode 100644
index 0000000..ce96a0f
--- /dev/null
+++ b/rnaseq/step6_overall_QC/__ - Preloader.R	
@@ -0,0 +1,189 @@
+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)
+  )
diff --git a/rnaseq/step6_overall_QC/snippet.R b/rnaseq/step6_overall_QC/snippet.R
deleted file mode 100644
index e69de29..0000000