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Dockerfile
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0
Dockerfile
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111
main.nf
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111
main.nf
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@@ -1,105 +1,42 @@
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#!/usr/bin/env nextflow
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nextflow.enable.dsl=2
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// Parameters
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params.TPM = '/omic/eureka/corto/20002_1289_female_patient_0_TPM.csv'
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params.regulon = '/omic/eureka/corto/regulon.rda'
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params.outdir = '/omic/eureka/corto/output'
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process CORTO {
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container "${params.container}"
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containerOptions "${params.containerOptions}"
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publishDir "${params.outdir}/${params.project_name}", mode: 'copy'
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container 'harbor.cluster.omic.ai/omic/digital-patients/corto:latest'
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publishDir params.outdir, mode: 'copy'
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debug true
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// maxForks 1
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stageInMode 'copy'
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input:
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path gene_expression_matrix
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path centroid_list
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path cnv_data // This could be optional
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path TPM
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path regulon
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// Define output channels
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output:
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path "regulon.rda", emit: regulon
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path "*.csv", emit: csv_regulon
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path "*.log", optional: true, emit: logs // if you have log files
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path "*_metabolome.csv", emit: csv_metabol
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script:
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"""
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#!/bin/bash
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# Create an R script
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cat <<EOF > corto_analysis.R
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# Print the R version
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print(R.version.string)
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# Load the corto library
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#!/usr/bin/Rscript
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library(corto)
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library(data.table)
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# Function to load data
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loadData <- function(file_name, expected_var) {
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load(file_name)
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if (exists(expected_var)) {
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data <- get(expected_var)
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} else {
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stop(paste("Object", expected_var, "not found in", file_name))
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}
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return(data)
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}
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TPM <- as.matrix(fread("$TPM"),rownames=1)
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# Load the input matrix
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inmat <- loadData("${gene_expression_matrix}", "inmat")
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print("Dimensions of inmat before any operation:")
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print(dim(inmat))
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load("$regulon")
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# Load the centroids
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centroids <- loadData("${centroid_list}", "centroids")
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print("Length of centroids:")
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print(length(centroids))
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# Run corto with specified parameters
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regulon <- corto(inmat, centroids=centroids, nbootstraps=10, p=1e-30, nthreads=2)
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# Save the regulon object for later use
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save(regulon, file="regulon.rda")
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# Transform regulon into a data frame
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regulon_to_df <- function(regulon) {
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result_df <- data.frame(TF = character(),
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Target = character(),
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TFMode = numeric(),
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Likelihood = numeric(),
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stringsAsFactors = FALSE)
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for (tf in names(regulon)) {
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tf_data <- regulon[[tf]]
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if (is.null(tf_data\$tfmode) || is.null(tf_data\$likelihood)) next
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for (i in seq_along(tf_data\$tfmode)) {
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tf_mode <- tf_data\$tfmode[[i]]
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likelihood <- tf_data\$likelihood[[i]]
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target_name <- names(tf_data\$tfmode)[i]
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tf_df <- data.frame(TF = tf,
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Target = target_name,
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TFMode = tf_mode,
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Likelihood = likelihood,
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stringsAsFactors = FALSE)
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result_df <- rbind(result_df, tf_df)
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}
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}
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return(result_df)
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}
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# Check if regulon is a list and transform it
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if (is.list(regulon)) {
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regulon_df <- regulon_to_df(regulon)
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write.csv(regulon_df, file="regulon.csv", row.names=FALSE)
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} else {
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warning("Regulon object is not a list. Custom transformation needed.")
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}
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EOF
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# Execute the R script
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Rscript corto_analysis.R
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predicted<-mra(TPM, regulon=regulon)
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name = strsplit(strsplit("$TPM", split = "/")[[1]][length(strsplit("$TPM", split = "/")[[1]])], split = "_TPM.csv")[[1]][1]
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name = paste(name, "_metabolome.csv", sep="")
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write.csv(predicted, name)
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"""
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}
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workflow {
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CORTO(Channel.of(file(params.TPM)), Channel.of(file(params.regulon)))
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}
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29
nextflow.config
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nextflow.config
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@@ -1,15 +1,34 @@
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manifest {
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name = 'corto'
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author = 'omic'
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recurseSubmodules = true
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homePage = 'https://gitlab.com/omic/next/registry/tools/corto'
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description = ''
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homePage = 'https://trs-gitea.cluster.omic.ai/omic/corto'
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description = 'CORTO - Correlation Tool for gene regulatory network analysis'
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mainScript = 'main.nf'
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nextflowVersion = '!>=21.04.3'
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version = '1.0.0'
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defaultBranch = 'master'
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}
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docker {
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params {
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TPM = null
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regulon = null
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outdir = null
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}
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profiles {
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standard {
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docker {
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enabled = true
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temp = 'auto'
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}
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}
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k8s {
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process {
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container = 'harbor.cluster.omic.ai/omic/digital-patients/corto:latest'
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}
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}
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}
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process {
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cpus = 1
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memory = '4 GB'
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}
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49
params.json
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params.json
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{
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"params": {
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"TPM": {
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"type": "file",
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"description": "Path to TPM (Transcripts Per Million) CSV file",
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"default": "s3://omic/eureka/corto/20002_1289_female_patient_0_TPM.csv",
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"required": true,
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"pipeline_io": "input",
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"var_name": "params.TPM",
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"examples": [
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"s3://omic/eureka/corto/20002_1289_female_patient_0_TPM.csv"
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],
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"pattern": ".*\\.csv$",
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"enum": [],
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"validation": {},
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"notes": "A CSV file containing TPM values with ENSG IDs as rows and samples as columns"
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},
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"regulon": {
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"type": "file",
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"description": "Path to regulon RDA file",
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"default": "s3://omic/eureka/corto/regulon.rda",
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"required": true,
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"pipeline_io": "input",
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"var_name": "params.regulon",
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"examples": [
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"s3://omic/eureka/corto/regulon.rda"
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],
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"pattern": ".*\\.rda$",
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"enum": [],
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"validation": {},
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"notes": "An R data file containing regulon information for the CORTO analysis"
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},
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"outdir": {
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"type": "folder",
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"description": "Directory for CORTO analysis results",
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"default": "s3://omic/eureka/corto/output",
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"required": true,
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"pipeline_io": "output",
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"var_name": "params.outdir",
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"examples": [
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"s3://omic/eureka/corto/output"
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],
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"pattern": ".*",
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"enum": [],
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"validation": {},
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"notes": "Directory where metabolome prediction results will be stored"
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}
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}
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}
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15
test.nf
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15
test.nf
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@@ -1,22 +1,17 @@
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nextflow.enable.dsl=2
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// GLOBAL FPSIM2 PARAMS
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params.container = 'corto:latest'
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params.container_corto = 'corto:latest'
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params.containerOptions = '--gpus all --rm -v /mnt:/mnt'
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params.outdir = '/mnt/OmicNAS/private/old/gabe/corto/outputs'
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params.project_name = 'test'
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// // INPUTS
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params.gene_expression_matrix = '/mnt/OmicNAS/private/old/gabe/corto/inputs/inmat.rda'
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params.centroid_list = '/mnt/OmicNAS/private/old/gabe/corto/inputs/centroids.rda'
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params.cnv_data = '/mnt/OmicNAS/private/old/gabe/corto/inputs/cnvmat.rda'
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params.TPM = '/data/olamide/corto/corto_metabolite_prediction/20002_1289_female_patient_0_TPM.csv'
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params.regulon = '/data/olamide/corto/corto_metabolite_prediction/regulon.rda'
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//params.TPM_REFERENCE = '/data/olamide/corto/corto_metabolite_prediction/TPM_ENSG_NO_MUTATIONS.csv'
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include {CORTO} from './main.nf'
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workflow {
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gene_expression_matrix = Channel.fromPath(params.gene_expression_matrix)
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centroid_list = Channel.fromPath(params.centroid_list)
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cnv_data = Channel.fromPath(params.cnv_data)
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CORTO(gene_expression_matrix, centroid_list, cnv_data)
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CORTO(params.TPM, params.regulon)// , params.TPM_REFERENCE)
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}
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