Add Biostar Handbook code

rnaseq/code/combine_genes.r

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+#
+# Combine transcripts into genes with the tximport package.
+# 
+# https://bioconductor.org/packages/release/bioc/html/tximport.html
+#
+
+# Load the packages
+library(tximport)
+
+# The directory where the counts for each sample are located.
+data_dir <- 'salmon'
+
+# The sample file is in CSV format and must have the headers "sample" and "condition".
+design_file = "design.csv"
+
+# What software created the mappings.
+method <- "salmon"
+
+# The ouput file name.
+output_file = "counts.csv"
+
+# The name of the file that contains transcript to gene mapping.
+# See our guide on how to make a mapping file.
+tx2gene_file = "tx2gene.csv"
+
+# Inform the user.
+print("# Tool: Combine genes")
+print(paste("# Sample: ", design_file))
+print(paste("# Data dir: ", data_dir))
+print(paste("# Gene map: ", tx2gene_file))
+
+# Read the sample file
+sample_data <- read.csv(design_file, stringsAsFactors=F)
+
+# Isolate the sample names.
+sample_names <- sample_data$sample
+
+# Generate the file names that contain the quantification data.
+files <- file.path(data_dir,  sample_names, "quant.sf")
+
+# Read the transcript to gene mapping file.
+tx2gene = read.csv(tx2gene_file)
+
+# Summarize over transcripts.
+tx <- tximport(files, type=method, tx2gene=tx2gene)
+
+# Transform into a dataframe.
+df <- data.frame(tx$counts)
+
+# Set the column names
+colnames(df) <- sample_names
+
+# Add a rowname by gene id
+df$ensembl_gene_id = row.names(df)
+
+# Create a smaller data frame that connects gene to name.
+id2name = tx2gene[, c("ensembl_gene_id", "external_gene_name")]
+
+# Need to de-duplicate so the merge won't create new entries.
+id2name = id2name[!duplicated(id2name),]
+
+# Add the gene names to the list
+df = merge(df, id2name, by="ensembl_gene_id", all.x = TRUE, all.y = FALSE)
+
+# This will be the new column order.
+cols <- c("ensembl_gene_id", "external_gene_name", sample_names)
+
+# Reorganize the columns.
+df <- df[, cols]
+
+# Save the  resulting summarized counts.
+write.csv(df, file=output_file,  row.names = FALSE, quote = FALSE)
+
+# Inform the user.
+print(paste("# Results: ", output_file))

rnaseq/code/combine_transcripts.r

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+#
+# Combine transcripts with the tximport package. 
+# 
+# https://bioconductor.org/packages/release/bioc/html/tximport.html
+#
+# Transcript level summarization.
+#
+
+# Load the packages
+library(tximport)
+
+# The directory where the counts for each sample are located.
+data_dir <- 'salmon'
+
+# The sample file must be in CSV format and must have the headers "sample" and "condition".
+desing_file = "design.csv"
+
+# What software created the mappings.
+method <- "salmon"
+
+# The output file name.
+output_file = "counts.csv"
+
+# Inform the user.
+print("# Tool: Combine transcripts")
+print(paste("# Sample: ", desing_file))
+print(paste("# Data dir: ", data_dir))
+
+# Read the sample file
+sample_data <- read.csv(desing_file, stringsAsFactors=F)
+
+# Isolate the sample names.
+sample_names <- sample_data$sample
+
+# Generate the file names that contain the quantification data.
+files <- file.path(data_dir,  sample_names, "quant.sf")
+
+# Summarize over transcripts.
+tx <- tximport(files, type=method, txOut=TRUE)
+
+# Transform counts into a dataframe.
+df <- data.frame(tx$counts)
+
+# Set the column names.
+colnames(df) <- sample_names
+
+# Create a new column for transcript ids.
+df$ensembl_transcript_id = rownames(df)
+
+# List the desired column order.
+cols <- c("ensembl_transcript_id", sample_names)
+  
+# Reorganize the columns.
+df <- df[, cols]
+
+# Save the  resulting summarized counts.
+write.csv(df, file=output_file,  row.names = FALSE, quote = FALSE)
+
+# Inform the user.
+print(paste("# Results: ", output_file))

rnaseq/code/compare_results.r

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+#
+# Compare two RNA seq data analysis result files.
+#
+
+# The results files to be compared.
+file1 = "results1.csv"
+file2 = "results2.csv"
+
+# Read the data files.
+data1 <- read.csv(file1)
+data2 <- read.csv(file2)
+
+# Select the rows where the FDR is under a cutoff.
+sig1 <- subset(data1, FDR <= 0.5)
+sig2 <- subset(data2, FDR <= 0.5)
+
+# Extract the gene names only
+name1 = sig1$name
+name2 = sig2$name
+
+# Intersect: common elements.
+isect <- intersect(name1, name2)
+
+# Elements from both.
+uni <- union(name1, name2)
+
+# Elements in file 1 only.
+only1 <- setdiff(name1, name2)
+
+# Elements in file 2 only.
+only2 <- setdiff(name2, name1)
+
+# Report the differences
+print("# Tool: compare_results.r")
+print("")
+print(paste("# File 1:", length(name1), file1))
+print(paste("# File 2:", length(name2), file2))
+print("")
+print(paste("# Union:", length(uni)))
+print(paste("# Intersect:", length(isect)))
+print(paste("# File 1 only:", length(only1)))
+print(paste("# File 2 only:", length(only2)))
+
+print("----")
+
+print("Only 1:")
+print(paste( only1))
+
+print("----")
+
+print("Only 2:")
+print(paste( only2))
+
+expect = subset(data1, grepl("UP|DOWN", name))$name

rnaseq/code/create_heatmap.r

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+#
+# Create heat map from a differential expression count table.
+#
+# Load the library.
+suppressPackageStartupMessages(library(gplots))
+
+# The name of the file that contains the counts.
+count_file = "results.csv"
+
+# The name of the output file.
+output_file = "heatmap.pdf"
+
+# Inform the user.
+print("# Tool: Create Heatmap ")
+print(paste("# Input: ", count_file))
+print(paste("# Output: ", output_file))
+
+# FDR cutoff.
+MIN_FDR = 0.05
+
+# Plot width
+WIDTH = 12
+
+# Plot height.
+HEIGHT = 13
+
+# Set the margins
+MARGINS = c(9, 12)
+
+# Relative heights of the rows in the plot.
+LHEI = c(1, 5)
+
+# Read normalized counts from the standard input.
+data = read.csv(count_file, header=T, as.is=TRUE)
+
+# Subset data for values under a treshold.
+data = subset(data, data$FDR <= MIN_FDR)
+
+# The heatmap row names will be taken from the first column.
+row_names = data[, 1]
+
+# The code assumes that the normalized data matrix is listed to the right of the falsePos column.
+idx = which(colnames(data) == "falsePos") + 1
+
+# The normalized counts are on the right size.
+counts = data[, idx : ncol(data)]
+
+# Load the data from the second column on.
+values = as.matrix(counts)
+
+# Adds a little noise to each element to avoid the
+# clustering function failure on zero variance rows.
+values = jitter(values, factor = 1, amount = 0.00001)
+
+# Normalize each row to a z-score
+zscores = NULL
+for (i in 1 : nrow(values)) {
+  row = values[i,]
+  zrow = (row - mean(row)) / sd(row)
+  zscores = rbind(zscores, zrow)
+}
+
+# Set the row names on the zscores.
+row.names(zscores) = row_names
+
+# Turn the data into a matrix for heatmap2.
+zscores = as.matrix(zscores)
+
+# Set the color palette.
+col = greenred
+
+# Create a PDF device
+pdf(output_file, width = WIDTH, height = HEIGHT)
+
+heatmap.2(zscores, col=col, density.info="none", Colv=NULL,
+    dendrogram="row", trace="none", margins=MARGINS, lhei=LHEI)
+
+#dev.off()
+

rnaseq/code/create_pca.r

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+#!/usr/bin/env Rscript
+#
+# This script makes a pca of all samples and a heatmap of sample distances.
+#
+
+# The inputs to the script are counts file, design file and the number of samples.
+#
+# How to run?
+# Rscript summary_plots.r <counts_file> <design_file> <number_of_samples>
+# Example: Rscript summary_plots.r counts.txt design.txt 6
+# Design file example:
+#	Sample	Condition
+#	MCVS450	MCVS
+#	MCVS515	MCVS
+#	MCVS520	MCVS
+#	MNS456	MNS
+#	MNS486	MNS
+#	MNS580	MNS
+
+
+read <- function(counts_file, design_file,number_of_samples){
+  counts = read.table(counts_file, header=TRUE, sep="\t", row.names=1 )
+  idx = ncol(counts) - number_of_samples
+  
+  # Cut out the valid columns.
+  if (idx > 0) counts = counts[-c(1:idx)] else counts=counts
+
+  numeric_idx = sapply(counts, mode) == 'numeric'
+  counts[numeric_idx] = round(counts[numeric_idx], 0)
+  
+  colData = read.table(design_file, header=TRUE, sep="\t", row.names=1 )
+  
+  # Create DESEq2 dataset.
+  dds = DESeqDataSetFromMatrix(countData=counts, colData=colData, design = ~1)
+  
+  # Variance Stabilizing Transformation.
+  vsd = vst(dds)
+  names = colnames(counts)
+  groups = colnames(colData)
+  
+  rlist <- list("vsd" = vsd, "names"=names, "groups" =groups)
+  return(rlist)
+  
+}
+
+# Command line argument.
+args = commandArgs(trailingOnly=TRUE)
+
+
+if (length(args)!=3) {
+  stop("Counts file, Design file and the number of samples must be specified at the commandline", call.=FALSE)
+}
+
+
+# Load the library while suppressing verbose messages.
+suppressPackageStartupMessages(library(DESeq2))
+suppressPackageStartupMessages(library(ggplot2))
+
+# Set the plot dimensions.
+WIDTH = 12
+HEIGHT = 8
+
+
+# The first argument to the script -counts file
+infile = args[1]
+
+# The second  argument to the script - design file
+coldata_file = args[2]
+
+# The third argument to the script - total number of samples.
+sno = args[3]
+sno= as.numeric(sno)
+
+res = read(infile, coldata_file, sno)
+vsd= res$vsd
+names = res$names
+groups = res$groups
+
+# Open the drawing device.
+pdf('pca.pdf', width = WIDTH, height = HEIGHT)
+par(mfrow = c(2,1))
+nudge <- position_nudge(y = 0.5)
+
+z=plotPCA(vsd, intgroup=c(groups)) 
+z+ geom_text(aes(label = names), position=nudge, size = 2.5) +ggtitle(aes("PCA"))
+dev.off()
+
+#
+# Plot heatmap of sample distances
+#
+library(pheatmap)
+library("RColorBrewer")
+
+sampleDists = dist(t(assay(vsd)))
+sampleDistMatrix = as.matrix(sampleDists)
+
+colnames(sampleDistMatrix) = NULL
+
+colors = colorRampPalette( rev(brewer.pal(9, "Blues")) )(255)
+
+
+# Open the drawing device.
+
+pdf('heatmap.pdf', width = 8, height = HEIGHT)
+
+pheatmap(sampleDistMatrix,
+         clustering_distance_rows=sampleDists,
+         clustering_distance_cols=sampleDists,
+         col=colors)  + geom_label(aes(label = names))
+
+dev.off()
+

rnaseq/code/create_tx2gene.r

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+#
+#  Obtain gene names for transcripts with the biomaRt package.
+#
+# https://bioconductor.org/packages/release/bioc/html/biomaRt.html
+#
+
+# Load the biomart manager
+library(biomaRt)
+
+# The biomaRt dataset name.
+dataset <- "drerio_gene_ensembl"
+
+# The ouput file name.
+output_file = "tx2gene.csv"
+
+# Make a connection to the validated dataset.
+mart <- useEnsembl(dataset = dataset, biomart = 'ensembl')
+
+# The attributes that we want to obtain.
+# The first column must match the feature id used during quantification.
+attributes <- c(
+  "ensembl_transcript_id_version", 
+  "ensembl_gene_id",
+  "ensembl_transcript_id", 
+  "transcript_length",
+  "external_gene_name"
+)
+
+# Perform the query.
+data <- biomaRt::getBM(attributes = attributes, mart = mart)
+
+# Save the data into a file.
+write.csv(data, file=output_file, row.names=FALSE, quote=FALSE)
+
+# Inform the user.
+print("# Tool: Create tx2gene mapping")
+print(paste("# Dataset: ", dataset))
+print(paste("# Output: ", output_file))

rnaseq/code/deseq2.r

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+# 
+# Differential expression analysis with the DESeq2 package.
+# 
+# https://bioconductor.org/packages/release/bioc/html/DESeq2.html
+#
+
+# Load the library.
+suppressPackageStartupMessages(library(DESeq2))
+
+# The name of the file that contains the counts.
+counts_file = "counts.csv"
+
+# The sample file is in CSV format and must have the headers "sample" and "condition".
+design_file = "design.csv"
+
+# The final result file.
+output_file = "results.csv"
+
+# Read the sample file.
+colData <- read.csv(design_file, stringsAsFactors=F)
+
+# Turn conditions into factors.
+colData$condition = factor(colData$condition)
+
+# The first level should correspond to the first entry in the file!
+# Required later when building a model.
+colData$condition = relevel(colData$condition, toString(colData$condition[1]))
+
+# Isolate the sample names.
+sample_names <- colData$sample
+
+# Read the data from the standard input.
+df = read.csv(counts_file, header=TRUE, row.names=1 )
+
+# Created rounded integers for the count data
+countData = round(df[, sample_names])
+
+# Other columns in the dataframe that are not sample information. 
+otherCols = df[!(names(df) %in% sample_names)]
+
+#
+# Running DESeq2
+#
+
+# Create DESEq2 dataset.
+dds = DESeqDataSetFromMatrix(countData=countData, colData=colData, design = ~condition)
+
+# Run deseq
+dse = DESeq(dds)
+
+# Format the results.
+res = results(dse)
+
+#
+# The rest of the code is about formatting the output dataframe.
+#
+
+# Turn the DESeq2 results into a data frame.
+data = cbind(otherCols, data.frame(res))
+
+# Create the foldChange column.
+data$foldChange = 2 ^ data$log2FoldChange
+
+# Rename columns to better reflect reality.
+names(data)[names(data)=="pvalue"] <-"PValue"
+names(data)[names(data)=="padj"] <- "FDR"
+
+# Create a real adjusted pvalue
+data$PAdj = p.adjust(data$PValue, method="hochberg")
+
+# Sort the data by PValue to compute false discovery counts.
+data = data[with(data, order(PValue, -foldChange)), ]
+
+# Compute the false discovery counts on the sorted table.
+data$falsePos = 1:nrow(data) * data$FDR
+
+# Create the additional columns that we wish to present.
+data$baseMeanA = 1
+data$baseMeanB = 1
+
+# Get the normalized counts.
+normed = counts(dse, normalized=TRUE)
+
+# Round normalized counts to a single digit.
+normed = round(normed, 1)
+
+# Merge the two datasets by row names.
+total <- merge(data, normed, by=0)
+
+# Sort again for output.
+total = total[with(total, order(PValue, -foldChange)), ]
+
+# Sample names for condition A
+col_names_A = data.frame(split(colData, colData$condition)[1])[,1]
+
+# Sample names for condition B
+col_names_B = data.frame(split(colData, colData$condition)[2])[,1]
+
+# Create the individual baseMean columns.
+total$baseMeanA = rowMeans(total[, col_names_A])
+total$baseMeanB = rowMeans(total[, col_names_B])
+
+# Bringing some sanity to numbers. Round columns to fewer digits.
+total$foldChange = round(total$foldChange, 3)
+total$log2FoldChange = round(total$log2FoldChange, 1)
+total$baseMean  = round(total$baseMean, 1)
+total$baseMeanA = round(total$baseMeanA, 1)
+total$baseMeanB =  round(total$baseMeanB, 1)
+total$lfcSE = round(total$lfcSE, 2)
+total$stat = round(total$stat, 2)
+total$FDR = round(total$FDR, 4)
+total$falsePos = round(total$falsePos, 0)
+
+# Reformat these columns as string.
+total$PAdj = formatC(total$PAdj, format = "e", digits = 1)
+total$PValue = formatC(total$PValue, format = "e", digits = 1)
+
+# Rename the first column.
+colnames(total)[1] <- "name"
+
+# Reorganize columns names to make more sense.
+new_cols = c("name", names(otherCols), "baseMean","baseMeanA","baseMeanB","foldChange",
+             "log2FoldChange","lfcSE","stat","PValue","PAdj", "FDR","falsePos",col_names_A, col_names_B)
+
+# Slice the dataframe with new columns.
+total = total[, new_cols]
+
+# Write the results to the standard output.
+write.csv(total, file=output_file, row.names=FALSE, quote=FALSE)
+
+# Inform the user.
+print("# Tool: DESeq2")
+print(paste("# Design: ", design_file))
+print(paste("# Input: ", counts_file))
+print(paste("# Output: ", output_file))
+
+

rnaseq/code/edger.r

@@ -0,0 +1,134 @@
+#
+# Differential expression analysis with the edgeR package.
+# 
+# https://bioconductor.org/packages/release/bioc/html/edgeR.html
+#
+
+# Load the library
+library(edgeR)
+
+# The name of the file that contains the counts.
+counts_file = "counts.csv"
+
+# The sample file is in CSV format and must have the headers "sample" and "condition".
+design_file = "design.csv"
+
+# The final result file.
+output_file = "results.csv"
+
+# Read the sample file.
+colData <- read.csv(design_file, stringsAsFactors=F)
+
+# Turn conditions into factors.
+colData$condition = factor(colData$condition)
+
+# The first level should correspond to the first entry in the file!
+# Required when building a model.
+colData$condition = relevel(colData$condition, toString(colData$condition[1]))
+
+# Isolate the sample names.
+sample_names <- colData$sample
+
+# Read the data from the standard input.
+df = read.csv(counts_file, header=TRUE, row.names=1 )
+
+# Created rounded integers for the count data
+counts = round(df[, sample_names])
+
+# Other columns in the dataframe that are not sample information. 
+otherCols = df[!(names(df) %in% sample_names)]
+
+# Using the same naming as in the library.
+group <- colData$condition
+
+# Creates a DGEList object from a table of counts and group.
+dge <- DGEList(counts=counts, group=group)
+
+# Maximizes the negative binomial conditional common likelihood to estimate a common dispersion value across all genes.
+dis <- estimateCommonDisp(dge)
+
+# Estimates tagwise dispersion values by an empirical Bayes method based on weighted conditional maximum likelihood.
+tag <- estimateTagwiseDisp(dis)
+
+# Compute genewise exact tests for differences in the means between the groups.
+etx <- exactTest(tag)
+
+# Extracts the most differentially expressed genes.
+etp <- topTags(etx, n=nrow(counts))
+
+# Get the scale of the data
+scale = dge$samples$lib.size * dge$samples$norm.factors
+
+# Get the normalized counts
+normed = round(t(t(counts)/scale) * mean(scale))
+
+# Turn the DESeq2 results into a data frame.
+data = merge(otherCols, etp$table, by="row.names")
+
+# Create column placeholders.
+data$baseMean = 1
+data$baseMeanA = 1
+data$baseMeanB = 1
+data$foldChange = 2 ^ data$logFC
+data$falsePos = 1
+
+# Rename the column.
+names(data)[names(data)=="logFC"] <-"log2FoldChange"
+
+# Compute the adjusted p-value
+data$PAdj = p.adjust(data$PValue, method="hochberg")
+
+# Rename the first columns for consistency with other methods.
+colnames(data)[1] <- "name"
+
+# Create a merged output that contains the normalized counts.
+total <- merge(data, normed, by.x='name', by.y="row.names")
+
+# Sort the data for the output. 
+total = total[with(total, order(PValue, -foldChange)), ]
+
+# Compute the false discovery counts on the sorted table.
+data$falsePos = 1:nrow(data) * data$FDR
+
+# Sample names for condition A
+col_names_A = data.frame(split(colData, colData$condition)[1])[,1]
+
+# Sample names for condition B
+col_names_B = data.frame(split(colData, colData$condition)[2])[,1]
+
+# Create the individual baseMean columns.
+total$baseMeanA = rowMeans(total[, col_names_A])
+total$baseMeanB = rowMeans(total[, col_names_B])
+total$baseMean = total$baseMeanA + total$baseMeanB
+
+# Round the numbers
+total$foldChange = round(total$foldChange, 3)
+total$FDR = round(total$FDR, 4)
+total$PAdj = round(total$PAdj, 4)
+total$logCPM = round(total$logCPM, 1)
+total$log2FoldChange = round(total$log2FoldChange, 1)
+total$baseMean = round(total$baseMean, 1)
+total$baseMeanA = round(total$baseMeanA, 1)
+total$baseMeanB = round(total$baseMeanB, 1)
+total$falsePos = round(total$falsePos, 0)
+
+# Reformat these columns as string.
+total$PAdj = formatC(total$PAdj, format = "e", digits = 1)
+total$PValue = formatC(total$PValue, format = "e", digits = 1)
+
+# Reorganize columns names to make more sense.
+new_cols = c("name", names(otherCols), "baseMean","baseMeanA","baseMeanB","foldChange",
+         "log2FoldChange","logCPM","PValue","PAdj", "FDR","falsePos",col_names_A, col_names_B)
+
+# Slice the dataframe with new columns.
+total = total[, new_cols]
+
+# Write the result to the standard output.
+write.csv(total, file=output_file, row.names=FALSE, quote=FALSE)
+
+# Inform the user.
+print("# Tool: edgeR")
+print(paste("# Design: ", design_file))
+print(paste("# Input: ", counts_file))
+print(paste("# Output: ", output_file))
+

rnaseq/code/filter_counts.r

@@ -0,0 +1,55 @@
+#
+# Filter a count table to remove entries with low expression.
+#
+
+# The sample file is in CSV format and must have the headers "sample" and "condition".
+sample_file = "samples.csv"
+
+# The count file that contains the summarized counts.
+counts_file = "combined_genes.csv"
+
+# The result file name.
+output_file = "filtered_counts.csv"
+
+# Inform the user.
+print("# Tool: Filter counts")
+print(paste("# Sample: ", sample_file))
+print(paste("# Counts: ", counts_file))
+
+# Read the sample file
+sd <- read.csv(sample_file, stringsAsFactors=F)
+
+# Isolate the sample names.
+sn <- sd$sample
+
+# Read the count file.
+df = read.csv(counts_file, stringsAsFactors = FALSE)
+
+# Create a matrix from the sample names
+dm = as.matrix(df[,sn])
+
+#
+# The filtering condition below selects for at least 10 reads across all conditions.
+#
+min_count = 10
+
+# Apply the filter on the row sums.
+keep <- (rowSums(dm) >= min_count) 
+
+# Compute reporting counts.
+count_total = length(keep)
+count_good = sum(keep)
+count_removed = count_total - count_good
+
+# Notify the user on filtering.
+print (paste("# Minimum:", min_count, "Total:", count_total, "Kept:", count_good, "Removed:", count_removed))
+
+# Slice the input dataframe with the boolean vector.
+df = df[keep,]
+
+# Save the  resulting filtered counts.
+write.csv(df, file=output_file,  row.names = FALSE, quote = FALSE)
+
+# Inform the user.
+print(paste("# Results:", output_file))
+

rnaseq/code/mission-impossible.mk

@@ -0,0 +1,92 @@
+#
+# This Makefile perform the Mission Impossible RNA-seq analysis.
+#
+#
+# More info in the Biostar Handbook volume RNA-Seq by Example
+#
+
+# The reference genome.
+REF = refs/genome.fa
+
+# The file containing transcripts.
+TRX = refs/transcripts.fa
+
+# The name of the HISAT2 index.
+HISAT2_INDEX = idx/genome
+
+# The name of the salmon index
+SALMON_INDEX = idx/transcripts.salmon
+
+# The design file.
+DESIGN = design.csv
+
+# These targets are not files.
+.PHONY: data align results
+
+# Tell the user to read the source of this Makefile to understand it.
+usage:
+	@echo "#"
+	@echo "# Use the the source, Luke!"
+	@echo "#"
+
+# Create the design file and ids.txt file.
+${DESIGN}:
+
+	# The design file
+	@echo "sample,condition" > design.csv
+	@echo "BORED_1,bored" >> design.csv
+	@echo "BORED_2,bored" >> design.csv
+	@echo "BORED_3,bored" >> design.csv
+	@echo "EXCITED_1,excited" >> design.csv
+	@echo "EXCITED_2,excited" >> design.csv
+	@echo "EXCITED_3,excited" >> design.csv
+
+	# Create the ids.txt file (first column only, delete first line)
+	cat design.csv | cut -f1 -d , | sed 1d > ids.txt
+
+# Download the data for the analysis.
+data:
+	# Download the reference genome.
+	wget -nc http://data.biostarhandbook.com/books/rnaseq/data/golden.genome.tar.gz
+
+	# Unpack the reference genome.
+	tar xzvf golden.genome.tar.gz
+
+	# Download the sequencing reads.
+	wget -nc http://data.biostarhandbook.com/books/rnaseq/data/golden.reads.tar.gz
+
+	# Unpack the sequencing reads.
+	tar zxvf golden.reads.tar.gz
+
+# Build the HISAT2 and salmon index for the reference genomes
+index: ${REF}
+	mkdir -p idx
+	hisat2-build ${REF} ${HISAT2_INDEX}
+	salmon index -t ${TRX} -i ${SALMON_INDEX}
+
+# Runs a HISAT2 alignment.
+align: ${DESIGN} ${HISAT2_INDEX_FILE}
+	mkdir -p bam
+	cat ids.txt | parallel --progress --verbose "hisat2 -x ${HISAT2_INDEX} -1 reads/{}_R1.fq -2 reads/{}_R2.fq | samtools sort > bam/{}.bam"
+	cat ids.txt | parallel -j 1 echo "bam/{}.bam" | \
+		xargs featureCounts -p -a refs/features.gff -o counts.txt
+	RScript code/parse_featurecounts.r
+
+# Run a SALMON classification.
+classify: ${DESIGN} ${SALMON_INDEX}
+	mkdir -p salmon
+	cat ids.txt | parallel --progress --verbose "salmon quant -i ${SALMON_INDEX} -l A --validateMappings -1 reads/{}_R1.fq -2 reads/{}_R2.fq  -o salmon/{}"
+	RScript code/combine_transcripts.r
+
+# Runs an analysis on the aligned data.
+results:
+	mkdir -p res
+	RScript code/deseq2.r
+	RScript code/create_heatmap.r
+
+# Required software
+install:
+	@echo ""
+	@echo mamba install wget parallel samtools subread hisat2 salmon bioconductor-tximport bioconductor-edger bioconductor-biomart bioconductor-deseq2 r-gplots
+	@echo ""
+

rnaseq/code/parse_featurecounts.r

@@ -0,0 +1,49 @@
+#
+# Transform feature counts output to simple counts.
+#
+
+# The results files to be compared.
+
+# Count file produced by featurecounts.
+counts_file <- "counts.txt"
+
+# The sample file must be in CSV format and must have the headers "sample" and "condition".
+design_file = "design.csv"
+
+# The name of the output file.
+output_file = "counts.csv"
+
+# Inform the user.
+print("# Tool: Parse featurecounts")
+print(paste("# Design: ", design_file))
+print(paste("# Input: ", counts_file))
+
+# Read the sample file.
+sample_data <- read.csv(design_file, stringsAsFactors=F)
+
+# Turn conditions into factors.
+sample_data$condition <- factor(sample_data$condition)
+
+# The first level should correspond to the first entry in the file!
+# Required when building a model.
+sample_data$condition <- relevel(sample_data$condition, toString(sample_data$condition[1]))
+
+# Read the featurecounts output.
+df <- read.table(counts_file, header=TRUE)
+
+#
+# It is absolutely essential that the order of the featurecounts headers is the same
+# as the order of the sample names in the file! The code below will overwrite the headers!
+#
+
+# Subset the dataframe to the columns of interest.
+counts <- df[ ,c(1, 7:length(names(df)))]
+
+# Rename the columns
+names(counts) <- c("name", sample_data$sample)
+
+# Write the result to the standard output.
+write.csv(counts, file=output_file, row.names=FALSE, quote=FALSE)
+
+# Inform the user.
+print(paste("# Output: ", output_file))