Move main_coda logic into differentialabundance as experimental study…

… type

nextflow.config

@@ -21,6 +21,10 @@ params {
     control_features           = null
     sizefactors_from_controls  = false
 
+    // Experimental analysis options
+    tools   = null
+    pathway = null
+
     // Reporting
     logo_file               = "$projectDir/docs/images/nf-core-differentialabundance_logo_light.png"
     css_file                = "$projectDir/assets/nf-core_style.css"

nextflow_schema.json

@@ -24,7 +24,7 @@
                     "default": "rnaseq",
                     "description": "A string identifying the technology used to produce the data",
                     "help_text": "Currently 'rnaseq' or 'affy_array' may be specified.",
-                    "enum": ["rnaseq", "affy_array", "maxquant", "geo_soft_file"],
+                    "enum": ["rnaseq", "affy_array", "maxquant", "geo_soft_file", "experimental"],
                     "fa_icon": "far fa-keyboard"
                 },
                 "input": {
@@ -58,7 +58,15 @@
                     "default": "counts",
                     "description": "Type of abundance measure used, platform-dependent",
                     "fa_icon": "fas fa-keyboard"
-                },
+                }
+            }
+        },
+        "experimental_analysis_options": {
+            "title": "Experimental analysis, including CoDa",
+            "type": "object",
+            "fa_icon": "fas fa-terminal",
+            "description": "Values required for experimental analysis",
+            "properties": {
                 "tools": {
                     "type": "string",
                     "format": "file-path",
@@ -68,6 +76,12 @@
                     "pattern": "^\\S+\\.(csv|tsv|yaml)$",
                     "description": "Path to comma-separated file containing samplesheet",
                     "help_text": "This file defines possible combinations of tools, which are to be run by the pipeline"
+                },
+                "pathway": {
+                    "type": "string",
+                    "fa_icon": "fas fa-border-all",
+                    "description": "(experimantal only): Choose a (list of) pathway from those predefined in the tool sheet",
+                    "help_text": "Choose the a subset of pathways to run. Pathways are defined in the tool sheet."
                 }
             }
         },
@@ -1298,6 +1312,9 @@
         {
             "$ref": "#/definitions/input_output_options"
         },
+        {
+            "$ref": "#/definitions/experimental_analysis_options"
+        },
         {
             "$ref": "#/definitions/abundance_values"
         },

subworkflows/local/correlation/main.nf

@@ -1,8 +1,8 @@
-
-// include nf-core modules
+//
+// Perform correlation analysis
+//
 include {PROPR_PROPR as PROPR} from "../../../modules/nf-core/propr/propr/main.nf"
 
-
 workflow CORRELATION {
     take:
     ch_counts
@@ -17,30 +17,24 @@ workflow CORRELATION {
                 [ metacounts+metatools, counts ]
         }
         .branch {
-            propr:     it[0]["cor_method"] == "propr"
+            propr:  it[0]["cor_method"] == "propr"
         }
         .set { ch_counts_cor }
 
-    // Hacer un branch del channel para coger las counts normales cuando no hay variable selection
-
+    // Create a branch of the channel to retrieve the normal counts when there is no variable selection.
     ch_counts_cor.propr
         .branch{
-            no_sel:    it[0]["sel_method"] == null
-            sel:       it[0]["sel_method"] != null
+            no_sel: it[0]["sel_method"] == null
+            sel:    it[0]["sel_method"] != null
         }
         .set { ch_counts_selection }
 
-    //ch_counts_selection.no_sel.view()
-    //ch_counts_filtered.view()
-
     ch_propr = ch_counts_filtered.mix(ch_counts_selection.no_sel)
-    //ch_propr.view()
 
     PROPR(ch_propr)
     ch_matrix = PROPR.out.matrix
     ch_adjacency = PROPR.out.adj
 
-
     emit:
     matrix = ch_matrix
     adjacency = ch_adjacency

subworkflows/local/differential/main.nf

@@ -1,7 +1,8 @@
-// include modules
+//
+// Perform differential analysis
+//
 include {PROPR_PROPD as PROPD} from "../../../modules/nf-core/propr/propd/main.nf"
 
-
 workflow DIFFERENTIAL {
     take:
     ch_counts
@@ -15,9 +16,8 @@ workflow DIFFERENTIAL {
             metacounts, counts, meta ->
                 [ metacounts+meta, counts ]
         }
-        //.view()
         .branch {
-            propd:    it[0]["diff_method"] == "propd"
+            propd:  it[0]["diff_method"] == "propd"
             deseq2: it[0]["diff_method"] == "deseq2"
         }
         .set { ch_counts_tools }

subworkflows/local/enrichment/main.nf

@@ -1,19 +1,17 @@
-// include modules
-
+//
+// Perform enrichment analysis
+//
 include { PROPR_GREA as GREA_DIFF } from "../../../modules/nf-core/propr/grea/main.nf"
 include { PROPR_GREA as GREA_COR  } from "../../../modules/nf-core/propr/grea/main.nf"
 include { MYGENE } from "../../../modules/nf-core/mygene/main.nf"
 
-
 workflow ENRICHMENT {
     take:
     ch_diff_adjacency
     ch_cor_adjacency
     ch_counts
 
-
     main:
-
     MYGENE(ch_counts)
     ch_gmt = MYGENE.out.gmt
 
@@ -39,7 +37,6 @@ workflow ENRICHMENT {
     GREA_COR(ch_cor_grea.grea, ch_gmt.collect())
     ch_enriched_cor = GREA_COR.out.enrichedGO
 
-
     emit:
     enriched_diff = ch_enriched_diff
     enriched_cor = ch_enriched_cor

subworkflows/local/experimental/main.nf

@@ -1,5 +1,6 @@
-// include subworkflows
-
+//
+// Run experimental analysis
+//
 include { CORRELATION }         from '../correlation/main.nf'
 include { DIFFERENTIAL }        from '../differential/main.nf'
 include { VARIABLE_SELECTION }  from '../variable_selection/main.nf'
@@ -13,7 +14,7 @@ workflow EXPERIMENTAL {
 
 
     main:
-    // Dividir el ch_samples_and_matrix en un channel de samplesheet y otro de matrix (PROPD los coge por separado)
+    // Split the ch_samples_and_matrix into one channel for the samplesheet and another for the matrix (PROPD takes them separately).
     ch_samples_and_matrix
     .map {
         meta, samplesheet, counts ->
@@ -27,7 +28,6 @@ workflow EXPERIMENTAL {
                 [ meta, counts ]
         }
         .set { ch_counts }
-    // ch_counts.view()
 
     ch_counts
         .combine(ch_tools)
@@ -41,33 +41,26 @@ workflow EXPERIMENTAL {
     ch_out = Channel.empty()
 
     // Perform differential analysis
-
     DIFFERENTIAL(ch_counts, ch_tools, ch_samplesheet.collect())
     ch_diff_results = DIFFERENTIAL.out.results
     ch_diff_adjacency = DIFFERENTIAL.out.adjacency
 
     // Perform variable selection
     ch_counts_filtered = VARIABLE_SELECTION(ch_diff_adjacency, ch_counts)
 
-    //VARIABLE_SELECTION.out.count.view()
-
     // Perform correlation analysis
     CORRELATION(ch_counts, ch_tools, ch_counts_filtered)
     ch_matrix = CORRELATION.out.matrix
     ch_cor_adjacency = CORRELATION.out.adjacency
     ch_out.mix(ch_matrix)
 
-    //ch_diff_adjacency.view()
-    //ch_cor_adjacency.view()
-
     // Perform enrichment analysis
     ENRICHMENT(ch_diff_adjacency, ch_cor_adjacency, ch_counts)
     ch_enriched_cor = ENRICHMENT.out.enriched_cor
     ch_enriched_diff = ENRICHMENT.out.enriched_diff
 
     ch_out.mix(ch_enriched_diff, ch_enriched_cor)
 
-
     emit:
     output = ch_out
 }

subworkflows/local/variable_selection/main.nf

@@ -1,11 +1,12 @@
-// include modules
+//
+// Perform variable selection
+//
 include { FILTERVAR } from "../../../modules/local/filtervar/main.nf"
 
 workflow VARIABLE_SELECTION {
     take:
-    ch_adj//meta_tools, adj
-    ch_counts //meta_id, counts
-
+    ch_adj      //meta_tools, adj
+    ch_counts   //meta_id, counts
 
     main:
     ch_counts
@@ -14,26 +15,19 @@ workflow VARIABLE_SELECTION {
                 [counts]
         }
         .combine(ch_adj)
-        //.view()
         .map{
             counts, meta, adj ->
                 [ meta, counts, adj]
         }
-        //.view()
         .branch {
             filtervar:    it[0]["sel_method"] == "filtervar"
             deseqfilter:  it[0]["sel_method"] == "deseqfilter"
         }
         .set { ch_counts_adj_sel }
 
-    //ch_counts_adj_sel.nofilter.view()
-
-
     FILTERVAR(ch_counts_adj_sel.filtervar)
 
     ch_counts_cor = FILTERVAR.out.count
-    //ch_counts_cor.view()
-
 
     emit:
     count = ch_counts_cor