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Improved readability of edgeR code
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@robinpaul85
robinpaul85 committed Jan 14, 2025
1 parent dc22a64 commit e0405b8
Showing 1 changed file with 101 additions and 176 deletions.
277 changes: 101 additions & 176 deletions server/utils/edge.R
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# Usage: echo <in_json> | Rscript edge.R > <out_json>

# in_json: [string] input data in JSON format. Streamed through stdin.
# out_json: [string] clustering results in JSON format. Streamed to stdout.


# json='{"case":"SJMB066856,SJMB069601,SJMB030827,SJMB030838,SJMB031131,SJMB031227,SJMB077221,SJMB077223","control":"SJMB069596,SJMB069587,SJMB074736,SJMB030488,SJMB030825,SJMB031110,SJMB032998,SJMB033002","input_file":"/Users/rpaul1/pp_data/files/hg38/sjmb12/rnaseq/geneCounts.txt"}' && time echo $json | Rscript edge.R

# json='{"case":"SJMB030827,SJMB030838,SJMB064540,SJMB064538,SJMB064520,SJMB064535,SJMB031131,SJMB031227","control":"SJMB030488,SJMB030825,SJMB064537,SJMB064510,SJMB064533,SJMB064534,SJMB031110","input_file":"/Users/rpaul1/pp_data/files/hg38/sjmb12/rnaseq/geneCounts.txt"}' && time echo $json | Rscript edge.R

# Checking if all R packages are installed or not, if not installing each one of them

#jsonlite_path <- system.file(package='jsonlite')
#if (nchar(jsonlite_path) == 0) {
# install.packages("jsonlite", repos='https://cran.case.edu/')
#}
#
#edgeR_path <- system.file(package='edgeR')
#if (nchar(edgeR_path) == 0) {
# BiocManager::install("edgeR")
#}
#
#readr_path <- system.file(package='readr')
#if (nchar(readr_path) == 0) {
# install.packages("readr", repos='https://cran.case.edu/')
#}


library(jsonlite)
library(rhdf5)
library(stringr)
library(readr)
# Load required packages
suppressWarnings({
library(jsonlite)
library(rhdf5)
library(stringr)
library(readr)
suppressPackageStartupMessages(library(edgeR))
suppressPackageStartupMessages(library(dplyr))
})

# Read JSON input from stdin
read_json_time <- system.time({
con <- file("stdin", "r")
json <- readLines(con, warn=FALSE)
close(con)
input <- fromJSON(json)
#print (input)
#print (input$output_path)

cases <- unlist(strsplit(input$case, ","))
controls <- unlist(strsplit(input$control, ","))
combined <- c("geneID","geneSymbol",cases,controls)
#data %>% select(all_of(combined))
#read_file_time_start <- Sys.time()
con <- file("stdin", "r")
json <- readLines(con, warn=FALSE)
close(con)
input <- fromJSON(json)

cases <- unlist(strsplit(input$case, ","))
controls <- unlist(strsplit(input$control, ","))
combined <- c("geneID", "geneSymbol", cases, controls)
})
print ("Time to read json")
print (read_json_time)
cat("Time to read JSON: ", read_json_time[3], " seconds\n")

case_sample_list <- c()
control_sample_list <- c()
if (exists(input$storage_type)==FALSE) {
# Read counts data
read_counts_time <- system.time({
if (input$storage_type == "HDF5") {
#print(h5ls(input$input_file))
geneIDs <- h5read(input$input_file, "gene_names") # Get geneID (i.e ENSG's) from HDF5 file
geneSymbols <- h5read(input$input_file, "gene_symbols") # Get gene symbols from HDF5 file
samples <- h5read(input$input_file, "samples") # Get sample names from HDF5 file

# Get the column numbers (in the HDF5 file) corresponding to the sample ID for case cohort
parse_sample_indices_time <- system.time({
samples_indicies <- c()
for (sample in cases) {
sample_index <- which(samples == sample)
if (length(sample_index) == 1) {
samples_indicies <- c(samples_indicies,sample_index)
case_sample_list <- c(case_sample_list,sample)
} else {
print (paste(sample,"not found"))
quit(status = 1)
}
geneIDs <- h5read(input$input_file, "gene_names")
geneSymbols <- h5read(input$input_file, "gene_symbols")
samples <- h5read(input$input_file, "samples")

# Find indices of case and control samples in the HDF5 file
case_indices <- match(cases, samples)
control_indices <- match(controls, samples)

# Check for missing samples
if (any(is.na(case_indices))) {
missing_cases <- cases[is.na(case_indices)]
stop(paste(missing_cases, "not found"))
}

# Get the column numbers (in the HDF5 file) corresponding to the sample ID for control cohort
for (sample in controls) {
sample_index <- which(samples == sample)
if (length(sample_index) == 1) {
samples_indicies <- c(samples_indicies,sample_index)
control_sample_list <- c(control_sample_list,sample)
} else {
print (paste(sample,"not found"))
quit(status = 1)
}
if (any(is.na(control_indices))) {
missing_controls <- controls[is.na(control_indices)]
stop(paste(missing_controls, "not found"))
}
})
print ("Time to parse sample indices")
print (parse_sample_indices_time)
# Getting read counts corresponding to the sample indices
read_counts <- t(h5read(input$input_file,"counts",index=list(samples_indicies, 1:length(geneIDs))))
colnames(read_counts) <- c(cases,controls)

samples_indices <- c(case_indices, control_indices)
read_counts <- t(h5read(input$input_file, "counts", index = list(samples_indices, 1:length(geneIDs))))
colnames(read_counts) <- c(cases, controls)
} else if (input$storage_type == "text") {
suppressWarnings({
suppressMessages({
read_counts <- read_tsv(input$input_file, col_names = TRUE, col_select = combined)
})
suppressMessages({
read_counts <- read_tsv(input$input_file, col_names = TRUE, col_select = combined)
})
})
geneIDs <- unlist(read_counts[1])
geneSymbols <- unlist(read_counts[2])
read_counts <- select(read_counts, -geneID)
read_counts <- select(read_counts, -geneSymbol)
} else {
print ("Unknown storage type")
stop("Unknown storage type")
}
} else { # If not defined, parse data from a text file
suppressWarnings({
suppressMessages({
read_counts <- read_tsv(input$input_file, col_names = TRUE, col_select = combined)
})
})
geneIDs <- unlist(read_counts[1])
geneSymbols <- unlist(read_counts[2])
read_counts <- select(read_counts, -geneID)
read_counts <- select(read_counts, -geneSymbol)
}
#print ("case_sample_list:")
#print (case_sample_list)
#print ("control_sample_list:")
#print (control_sample_list)
#read_file_time_stop <- Sys.time()
#print (read_file_time_stop - read_file_time_start)
})
cat("Time to read counts data: ", read_counts_time[3], " seconds\n")

diseased <- rep("Diseased", length(cases))
control <- rep("Control", length(controls))
conditions <- c(diseased, control)
tabs <- rep("\t",length(geneIDs))
gene_id_symbols <- paste0(geneIDs,tabs,geneSymbols)
time_generate_dge_list <- system.time({
# Create conditions vector
conditions <- c(rep("Diseased", length(cases)), rep("Control", length(controls)))
gene_id_symbols <- paste0(geneIDs, "\t", geneSymbols)

# Create DGEList object
dge_list_time <- system.time({
y <- DGEList(counts = read_counts, group = conditions, genes = gene_id_symbols)
})
print ("Time to generate dge list")
print (time_generate_dge_list)
cat("Time to generate DGEList: ", dge_list_time[3], " seconds\n")

# Filter and normalize counts
filter_time <- system.time({
keep <- filterByExpr(y, min.count = input$min_count, min.total.count = input$min_total_count) # This will be replaced by its Rust version in the future
keep <- filterByExpr(y, min.count = input$min_count, min.total.count = input$min_total_count)
})
print ("Time to filter by expression")
print (filter_time)
cat("Time to filter by expression: ", filter_time[3], " seconds\n")

normalization_time <- system.time({
y <- y[keep, keep.lib.sizes = FALSE] # This will be replaced by its Rust version in the future
y <- calcNormFactors(y, method = "TMM") # This will be replaced by its Rust version in the future
y <- y[keep, keep.lib.sizes = FALSE]
y <- calcNormFactors(y, method = "TMM")
})
print ("Normalization time")
print (normalization_time)
#print (y)
cat("Normalization time: ", normalization_time[3], " seconds\n")

# Differential expression analysis
if (length(input$conf1) == 0) { # No adjustment of confounding factors
calculate_dispersion_time <- system.time({
suppressWarnings({
suppressMessages({
dge <- estimateDisp(y = y)
dispersion_time <- system.time({
suppressWarnings({
suppressMessages({
y <- estimateDisp(y)
})
})
})
})
print ("Dispersion Time")
print (calculate_dispersion_time)
exact_test_time <- system.time({
et <- exactTest(object = dge)
})
print ("Exact time")
print (exact_test_time)
} else { # Adjusting for confounding factors. This has been adapted based on the protocol described here: http://larionov.co.uk/deg_ebi_tutorial_2020/edger-analysis-1.html#calculate-degs
})
cat("Dispersion time: ", dispersion_time[3], " seconds\n")

exact_test_time <- system.time({
et <- exactTest(y)
})
cat("Exact test time: ", exact_test_time[3], " seconds\n")
} else { # Adjusting for confounding factors
y$samples <- data.frame(conditions = conditions, conf1 = input$conf1)
model_gen_time <- system.time({
design <- model.matrix(~ conf1 + conditions, data = y$samples)
})
print ("Time for making design matrix")
print (model_gen_time)
})
cat("Time for making design matrix: ", model_gen_time[3], " seconds\n")

dispersion_time <- system.time({
y <- estimateDisp(y, design)
})
print ("Dispersion Time")
print (dispersion_time)
# Fit the model
})
cat("Dispersion time: ", dispersion_time[3], " seconds\n")

fit_time <- system.time({
#fit <- glmQLFit(y, design)
fit <- glmFit(y, design)
})
print ("Fit time")
print (fit_time)
# Calculate the test statistics
})
cat("Fit time: ", fit_time[3], " seconds\n")

test_statistics_time <- system.time({
#et <- glmQLFTest(fit, coef = ncol(design))
et <- glmLRT(fit, coef = 2) # coef = 2 corresponds to the 'treatment' vs 'control' comparison
})
print ("Test statistics time")
print (test_statistics_time)
et <- glmLRT(fit, coef = 2)
})
cat("Test statistics time: ", test_statistics_time[3], " seconds\n")
}

# Multiple testing correction
multiple_testing_correction_time <- system.time({
logfc <- et$table$logFC
logcpm <- et$table$logCPM
pvalues <- et$table$PValue
genes_matrix <- str_split_fixed(unlist(et$genes),"\t",2)
geneids <- unlist(genes_matrix[,1])
genesymbols <- unlist(genes_matrix[,2])
adjust_p_values <- p.adjust(pvalues, method = "fdr")
output <- data.frame(geneids,genesymbols,logfc,-log10(pvalues),-log10(adjust_p_values))
names(output)[1] <- "gene_name"
names(output)[2] <- "gene_symbol"
names(output)[3] <- "fold_change"
names(output)[4] <- "original_p_value"
names(output)[5] <- "adjusted_p_value"
#write_csv(output,"DE_output.txt")
logfc <- et$table$logFC
logcpm <- et$table$logCPM
pvalues <- et$table$PValue
genes_matrix <- str_split_fixed(unlist(et$genes), "\t", 2)
geneids <- unlist(genes_matrix[, 1])
genesymbols <- unlist(genes_matrix[, 2])
adjust_p_values <- p.adjust(pvalues, method = "fdr")
output <- data.frame(geneids, genesymbols, logfc, -log10(pvalues), -log10(adjust_p_values))
names(output)[1] <- "gene_name"
names(output)[2] <- "gene_symbol"
names(output)[3] <- "fold_change"
names(output)[4] <- "original_p_value"
names(output)[5] <- "adjusted_p_value"
})
print ("Time for multiple testing correction")
print (multiple_testing_correction_time)
cat(paste0("adjusted_p_values:",toJSON(output)))
#output_json <- toJSON(output)
#print ("output_json")
#output_file <- paste0(input$output_path,"/r_output.txt")
#print (output_file)
#cat(output_json, file = output_file)
cat("Time for multiple testing correction: ", multiple_testing_correction_time[3], " seconds\n")

#top_degs = topTags(object = et, n = "Inf")
#print ("top_degs")
#print (top_degs)
# Output results
cat(paste0("adjusted_p_values:", toJSON(output)))

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