.gitignore

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+
+.DS_Store
+*.nosync
+*.RData

rnaseq/step1_fastqc/snippet.sh

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+# The command to do a FastQC on a fastq file is
+file="file_to_analyse.fq.gz"
+fastqc_out="./path/to/fastqc/output/dir"
+
+fastqc -o "$fastqc_out" "$file"

rnaseq/step6_overall_QC/01 - Principle Component Analysis.R

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+# Principle Component Analysis
+# Normalized with limma::voom
+library(limma)
+library(tidyverse)
+
+
+# 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
+do.center = TRUE
+do.scale = FALSE
+
+
+# The analysis
+# 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,] %>%
+	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,
+
+
+# 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

rnaseq/step6_overall_QC/02 - Gender Check.R

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+# 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.

rnaseq/step6_overall_QC/03 - Sample Counts.R

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+# Total counts per sample
+# Normalized with limma::voom
+library(limma)
+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 calculate the number of mapped reads 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"))
+
+
+# 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.
+