corto up and running!

main.nf

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+nextflow.enable.dsl=2
+
+process CORTO {
+  container "${params.container}"
+  containerOptions "${params.containerOptions}"
+  publishDir "${params.outdir}/${params.project_name}", mode: 'copy'
+  debug true
+  // maxForks 1
+  stageInMode 'copy'
+
+  input:
+    path gene_expression_matrix
+    path centroid_list
+    path cnv_data // This could be optional
+
+  // Define output channels
+  output:
+    path "regulon.rda", emit: regulon
+    path "*.csv", emit: csv_regulon
+    path "*.log", optional: true, emit: logs // if you have log files
+
+    script:
+    """
+    #!/bin/bash
+
+    # Create an R script
+    cat <<EOF > corto_analysis.R
+    # Print the R version
+    print(R.version.string)
+
+    # Load the corto library
+    library(corto)
+
+    # Function to load data
+    loadData <- function(file_name, expected_var) {
+        load(file_name)
+        if (exists(expected_var)) {
+            data <- get(expected_var)
+        } else {
+            stop(paste("Object", expected_var, "not found in", file_name))
+        }
+        return(data)
+    }
+
+    # Load the input matrix
+    inmat <- loadData("${gene_expression_matrix}", "inmat")
+    print("Dimensions of inmat before any operation:")
+    print(dim(inmat))
+
+    # Load the centroids
+    centroids <- loadData("${centroid_list}", "centroids")
+    print("Length of centroids:")
+    print(length(centroids))
+
+    # Run corto with specified parameters
+    regulon <- corto(inmat, centroids=centroids, nbootstraps=10, p=1e-30, nthreads=2)
+
+    # Save the regulon object for later use
+    save(regulon, file="regulon.rda")
+
+    # Transform regulon into a data frame
+    regulon_to_df <- function(regulon) {
+        result_df <- data.frame(TF = character(),
+                                Target = character(),
+                                TFMode = numeric(),
+                                Likelihood = numeric(),
+                                stringsAsFactors = FALSE)
+
+        for (tf in names(regulon)) {
+            tf_data <- regulon[[tf]]
+            if (is.null(tf_data\$tfmode) || is.null(tf_data\$likelihood)) next
+
+            for (i in seq_along(tf_data\$tfmode)) {
+                tf_mode <- tf_data\$tfmode[[i]]
+                likelihood <- tf_data\$likelihood[[i]]
+                target_name <- names(tf_data\$tfmode)[i]
+                tf_df <- data.frame(TF = tf,
+                                    Target = target_name,
+                                    TFMode = tf_mode,
+                                    Likelihood = likelihood,
+                                    stringsAsFactors = FALSE)
+                result_df <- rbind(result_df, tf_df)
+            }
+        }
+        return(result_df)
+    }
+
+    # Check if regulon is a list and transform it
+    if (is.list(regulon)) {
+        regulon_df <- regulon_to_df(regulon)
+        write.csv(regulon_df, file="regulon.csv", row.names=FALSE)
+    } else {
+        warning("Regulon object is not a list. Custom transformation needed.")
+    }
+
+    EOF
+
+    # Execute the R script
+    Rscript corto_analysis.R
+
+    """
+
+
+
+}