fastqc: simple comment. trimming: reverted back. overall qc: simplified the scripts and made sure to add instructions.
This commit is contained in:
Jos van Nijnatten
parent
135f231c86
commit
648cd09a0e
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@ -1,34 +1,5 @@
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#!/bin/bash
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#SBATCH --job-name=fastqc
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#SBATCH --time=0-12:00:00
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#SBATCH --ntasks=1
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#SBATCH --mem=15G
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#SBATCH --qos=regular
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# The command to do a FastQC on a fastq file is
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file="file_to_analyse.fq.gz"
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fastqc_out="./path/to/fastqc/output/dir"
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set -e
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set -u
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set -x
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set -o pipefail
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module purge
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module load Java
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module load FastQC
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dir_raw_fastq="$(pwd)/fastq/raw"
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dir_fastqc="$(pwd)/fastqc"
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[ -d "${dir_fastqc}" ] || mkdir -p "${dir_fastqc}"
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# Run FastQC
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files=$(find -L "$dir_raw_fastq" -type f -iname "*.fastq.gz")
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for file in $files; do
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filename=$(basename "$file")
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fastqc -o "$dir_fastqc" "$file" &
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done
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wait
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fastqc -o "$fastqc_out" "$file"
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@ -1,64 +0,0 @@
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#!/bin/bash
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#SBATCH --job-name=trimming
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#SBATCH --time=2-00:00:00
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#SBATCH --ntasks=1
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#SBATCH --mem=15G
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#SBATCH --qos=regular
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set -e
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set -u
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set -x
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set -o pipefail
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module purge
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module load TrimGalore
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dir_raw_fastq="$(pwd)/fastq/raw"
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dir_trimmed_fastq="$(pwd)/fastq/trimmed"
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dir_trimmed_fastq_reports="$(pwd)/fastq/trimmed/reports"
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adapter_3p="TGGAATTCTCGG" # is _R1
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adapter_5p="GATCGTCGGACT" # is _R2
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[ -d "${dir_trimmed_fastq_reports}" ] || mkdir -p "${dir_trimmed_fastq_reports}"
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# Trim all adapters from the sequences
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while IFS=, read -r GSID Sample
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do
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echo ">> Executing trimming of $GSID (${#GSID})"
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if [ "${#GSID}" == "18" ]; then # check length - don't include sample pools
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for fqFile in $(find -L "$dir_raw_fastq" -maxdepth 1 -type f -iname "*${GSID}*.fastq.gz"); do
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newFilename=${fqFile/.fastq.gz/_trimmed.fq.gz}
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if [ -f "${newFilename}" ]; then
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echo ">>File exists ${newFilename}. Skipping"
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else
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echo ">>File: ${fqFile}"
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if grep -q "_R1" <<< $(basename "$fqFile"); then
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adapter_seq=$adapter_3p
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elif grep -q "_R2" <<< $(basename "$fqFile"); then
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adapter_seq=$adapter_5p
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fi
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echo ">>Trimming with sequence ${adapter_seq}"
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trim_galore \
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--adapter "$adapter_seq" \
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--length $(printf "$adapter_seq" | wc -m) \
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--output_dir "${dir_trimmed_fastq}" \
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--fastqc_args "--noextract" \
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"${fqFile}" &
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fi
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done
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else
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echo "Skipped."
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fi
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done < samples.csv
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wait
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mv ${dir_trimmed_fastq}/*.zip "${dir_trimmed_fastq_reports}"
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mv ${dir_trimmed_fastq}/*.txt "${dir_trimmed_fastq_reports}"
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mv ${dir_trimmed_fastq}/*.html "${dir_trimmed_fastq_reports}"
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@ -1,7 +1,15 @@
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# Principle Component Analysis
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# Normalized with limma::voom
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library(limma)
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library(tidyverse)
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source("__ - Preloader.R", verbose=T)
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# expression.data. Has columns:
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# - Gene (gene identifier)
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# - [Sample identifiers]
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expression.data <- "mrna_counts_table.csv"
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# results.dir. The directory of where to put the resulting tables (and later your plots.)
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results.dir <- "results"
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# PCA variables
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@ -10,10 +18,7 @@ do.scale = FALSE
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# The analysis
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master.Table %>% readr::write_csv(
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file.path(results.dir, "patient.table.csv")
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)
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# We ise prcomp to calculate the PCAs. Afterwards you should plot the results.
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norm.expr.data <- expression.data %>%
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tibble::column_to_rownames("Gene")
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norm.expr.data <- norm.expr.data[rowSums(norm.expr.data) >= 10,] %>%
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@ -69,132 +74,8 @@ data.frame(
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# Not saved: pcs.summery$sdev,
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# Plot PCAs
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# https://github.com/kevinblighe/PCAtools
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results.dir.pca.plot <- file.path(results.dir.pca, "img")
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dir.create(results.dir.pca.plot)
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metadata <- master.Table %>%
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dplyr::filter(
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!is.na(GenomeScan_ID)
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) %>%
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tibble::column_to_rownames("GenomeScan_ID") %>%
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select.rows.in.order(
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colnames(norm.expr.data)
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)
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p <- pca(norm.expr.data,
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metadata = metadata,
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center = do.center,
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scale = do.scale
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)
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elbow <- findElbowPoint(p$variance)
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metavars <- c('Age','Gender','Smoking_status','COPD_Y_or_N','SEO_COPD_Y_or_N','GOLD_stage')
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png(filename = file.path(results.dir.pca.plot,"scree_plot.png"),
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width = 800, height = 800,
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units = "px", pointsize = 12,
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type = "Xlib")
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print(screeplot(p,
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axisLabSize = 12,
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titleLabSize = 12,
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components = getComponents(p, 1:(elbow+5)),
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vline = c(elbow)
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) +
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geom_label(
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aes(
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x = elbow + 1,
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y = 50,
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label = 'Elbow method',
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vjust = -1,
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size = 8
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)
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))
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dev.off()
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png(filename = file.path(results.dir.pca.plot, "eigen_corr_plot.png"),
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width = 1200, height = 1200,
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units = "px", pointsize = 12,
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type = "Xlib")
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print(eigencorplot(p,
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metavars = metavars
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))
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dev.off()
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dir.create(file.path(results.dir.pca.plot, "pairsplot"))
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for (var in metavars) {
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png(
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filename = file.path(results.dir.pca.plot, "pairsplot", paste0(var,".png")),
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width = 1200, height = 1200,
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units = "px", pointsize = 12,
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type = "Xlib"
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)
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print(pairsplot(p,
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components = getComponents(p, c(1:(elbow+1))),
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triangle = TRUE,
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trianglelabSize = 12,
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hline = 0, vline = 0,
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pointSize = 0.4,
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gridlines.major = FALSE,
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gridlines.minor = FALSE,
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colby = var,
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title = paste0('Pairs plot: ',var),
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plotaxes = TRUE
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))
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dev.off()
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}
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# Plot PCAs - old failure
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pca.combinations <- combinations(
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n = (elbow+1),
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r = 2,
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v = 1:(elbow+1),
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repeats.allowed = FALSE
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)
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dir.create(file.path(results.dir.pca.plot, "biplots"))
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for (var in metavars) {
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for (i in 1:nrow(pca.combinations)) {
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pca.combi <- pca.combinations[i,]
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pca.title <- paste(paste0("PC", pca.combi), collapse="_")
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png(
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filename = file.path(results.dir.pca.plot, "biplots", paste0(var, "-", pca.title, ".png")),
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width = 800, height = 800,
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units = "px", pointsize = 12,
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type = "Xlib"
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)
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print(
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autoplot(
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norm.expr.data.pcs,
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data = master.Table %>%
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dplyr::filter(
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!is.na(GenomeScan_ID)
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) %>%
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tibble::column_to_rownames("GenomeScan_ID") %>%
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select.rows.in.order(
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rownames(norm.expr.data.pcs$x)
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),
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x = pca.combi[1],
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y = pca.combi[2],
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colour = var,
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loadings = FALSE,
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loadings.label = FALSE,
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#label = FALSE,
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label.size = 3
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) +
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ggprism::theme_prism() +
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#ggprism::scale_colour_prism() +
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ggprism::scale_shape_prism() +
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ggplot2::labs(subtitle = paste0(str_to_title(var), " (", pca.title,")"))
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)
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dev.off()
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}
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}
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# Next thing to do:
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# - (Optional) scree plot - to determine the optimal cutoff for PCA inclusion based on explaination of variance
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# - (Optional) eigencorplot - to correlate PCAs to clinical variables so that you know which PCA to include for which analysis
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# - (Optional) pairsplot - plot multiple PCAs against each other in a single figure
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# - Plot the first couple of PCAs against each other
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@ -1,16 +1,28 @@
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# Gender QC
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# Normalized with limma::voom
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library(GSVA)
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library(limma)
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library(edgeR)
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library(tidyverse)
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source("__ - Preloader.R", verbose=T)
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# expression.data. Has columns:
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# - Gene (gene identifier)
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# - [Sample identifiers]
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expression.data <- "mrna_counts_table.csv"
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# master.Table. Has columns:
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# - GenomeScan_ID
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# - gender, levels = c("male", "female")
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# - age
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# - factor(smoking.status, levels = c("Ex-smoker", "Current smoker"))
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master.Table <- "patient_table.csv"
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# results.dir. The directory of where to put the resulting tables (and later your plots.)
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results.dir <- "results"
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# The analysis
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norm.expr.data <- expression.data %>%
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tibble::column_to_rownames("Gene")
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norm.expr.data <- norm.expr.data[rowSums(norm.expr.data) >= 10,] %>%
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limma::voom() %>%
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as.matrix()
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# We first do a differential expression analysis on gender using EdgeR.
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# Afterwards you should plot these results.
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x.genes <- gene.data %>%
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dplyr::filter(chromosome_name == "X") %>%
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dplyr::pull(ensembl_gene_id)
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@ -87,248 +99,12 @@ gender.qc.genes.to.plot <- gender.qc.results %>%
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chromosome_name %in% c("X", "Y") &
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FDR < 0.05 &
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dplyr::row_number() <= 5
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) |
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hgnc_symbol %in% c(
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"XIST",
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"TSIX",
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"KDM6A",
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"ZFX",
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"KDM5C",
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"ZFY-AS1",
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"ARSDP1",
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"GYG2P1",
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"RBMY2JP",
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"ARSLP1"
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)
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)
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gender.qc.data <- as.data.frame(norm.expr.data) %>%
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rownames_to_column("ensembl.id") %>%
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tidyr::gather(
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key = "rna.seq.sample.id",
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value = "expr.value",
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-ensembl.id
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) %>%
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dplyr::filter(
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ensembl.id %in% gender.qc.genes.to.plot$ensembl.id
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) %>%
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dplyr::left_join(
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y = gender.qc.patients,
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by = c("rna.seq.sample.id" = "GenomeScan_ID")
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) %>%
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#dplyr::filter(
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# !is.na(gender)
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#) %>%
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readr::write_csv(
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file.path(results.dir.gender.plot, "plot.data.voom.csv")
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)
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for (chr in gender.qc.genes.to.plot$chromosome_name) {
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current.gender.qc.genes.to.plot <- gender.qc.genes.to.plot %>%
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dplyr::filter(chromosome_name == chr)
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chromosome_name <- chr
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i <- 0
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for (current.ensembl.id in current.gender.qc.genes.to.plot$ensembl.id) {
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i <- i + 1
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hgnc_symbol <- gene.data %>%
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dplyr::filter(
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ensembl_gene_id == current.ensembl.id
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) %>%
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dplyr::pull(hgnc_symbol)
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# calculate outliers, kinda
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plot.data <- gender.qc.data %>%
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dplyr::filter(
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ensembl.id == current.ensembl.id
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) %>%
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dplyr::mutate(
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gender = dplyr::case_when(
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is.na(gender) | (stringr::str_trim(gender) == "") ~ "other",
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TRUE ~ gender
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)
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)
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if (nrow(plot.data) <= 0) {
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next
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}
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outliers <- boxplot(
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formula = expr.value ~ gender,
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data = plot.data,
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plot = FALSE
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)$out
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result.to.annotate <- plot.data %>%
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dplyr::filter(
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expr.value %in% outliers
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)
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# Visual: plot range (for t-test p-value)
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plot.y.range <- c(
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"min" = as.integer(min(plot.data$expr.value) - 1) ,
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"max" = as.integer(max(plot.data$expr.value) + 1)
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)
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plot.margin <- ((plot.y.range["max"] + (plot.y.range["min"] * -1)) * 0.05)
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plot.y.range["min"] <- plot.y.range["min"] - plot.margin
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plot.y.range["max"] <- plot.y.range["max"] + plot.margin
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# Plot the damn thing as if it is Graphpad Prism
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stat.table <- rstatix::t_test(plot.data, expr.value ~ gender)
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plt <- plot.data %>%
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ggplot2::ggplot(
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mapping = ggplot2::aes(
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x = gender,
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y = expr.value
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)
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) +
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ggplot2::geom_jitter(
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mapping = ggplot2::aes(
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colour = gender,
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shape = gender
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),
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width = 0.1
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) +
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ggrepel::geom_text_repel(
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data = result.to.annotate,
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mapping = ggplot2::aes(
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label = sample.id
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),
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size = 2,
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box.padding = unit(0.35, "lines"),
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point.padding = unit(0.3, "lines")
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) +
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ggplot2::stat_summary(
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fun = "mean",
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geom = "crossbar",
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width = 0.3,
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size = 0.3
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) +
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ggplot2::scale_y_continuous(
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limits = plot.y.range,
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guide = "prism_offset"
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) +
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#ggprism::add_pvalue(
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# stat.table,
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# y.position = plot.y.range["max"]
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#) +
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ggprism::theme_prism() +
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ggprism::scale_colour_prism() +
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ggprism::scale_shape_prism() +
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ggplot2::theme(
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legend.position = "none"
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) +
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ggplot2::labs(
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subtitle = paste0("Gender Check: ", hgnc_symbol, " (chr. ", chromosome_name, ")"),
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x = "Gender",
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y = "Normalised Expression Values"
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)
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ggplot2::ggsave(
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filename = file.path(results.dir.gender.plot, paste0(chromosome_name, ".", i, ".", hgnc_symbol, ".png")),
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plot = plt,
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width = 12.5,
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height = 12.5,
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unit = "cm"
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)
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}
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}
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# Let's try a GSVA
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gsva.groups <- list(
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X = gender.qc.genes.to.plot %>%
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dplyr::filter(chromosome_name == "X") %>%
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dplyr::pull(ensembl.id),
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Y = gender.qc.genes.to.plot %>%
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dplyr::filter(chromosome_name == "Y") %>%
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dplyr::pull(ensembl.id)
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)
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gsva_res = GSVA::gsva(
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norm.expr.data,
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gsva.groups,
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mx.diff = TRUE,
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verbose = FALSE,
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parallel.sz = 1
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)
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gender.qc.gsva.data <- as.data.frame(gsva_res) %>%
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rownames_to_column("gsva.group") %>%
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tidyr::gather(
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key = "rna.seq.sample.id",
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value = "gsva.value",
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-gsva.group
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) %>%
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dplyr::left_join(
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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.
|
||||
|
|
|
|||
|
|
@ -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.
|
||||
|
||||
|
|
|
|||
|
|
@ -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)
|
||||
)
|
||||
Loading…
Reference in New Issue