Merge pull request #76 from kbestak/multiqc_report_fix

Fixed multiqc report

.gitignore

@@ -6,3 +6,5 @@ results/
 testing/
 testing*
 *.pyc
+.nf-test.log
+.nf-test/

assets/multiqc_config.yml

@@ -1,4 +1,3 @@
-custom_logo: "$projectdir/assets/nf-core-molkart_logo_light.png"
 custom_logo_url: https://github.com/nf-core/molkart/
 custom_logo_title: "nf-core/molkart"
 
@@ -7,6 +6,10 @@ report_comment: >
   analysis pipeline. For information about how to interpret these results, please see the
   <a href="https://nf-co.re/molkart/dev/docs/output" target="_blank">documentation</a>.
 report_section_order:
+  segmentation_stats:
+    order: 800
+  my_custom_content_image:
+    order: 800
   "nf-core-molkart-methods-description":
     order: -1000
   software_versions:
@@ -16,16 +19,20 @@ report_section_order:
 
 export_plots: true
 
+run_module:
+  - custom_content
+
 custom_data:
   my_custom_content_image:
-    section_name: "Crop overview"
+    section_name: "Crop selection overview"
   segmentation_stats:
-    id: "segmentation_stats"
+    file_format: "csv"
+    plot_type: "table"
     section_name: "QC statistics from segmentation"
-    pconfig:
-      id: "segmentation_stats_table"
-      namespace: "Segmentation stats"
     headers:
+      sample_id:
+        title: sample_id
+        Description: "Sample"
       segmentation_method:
         title: Segmentation method
         description: "Segmentation method"
@@ -60,10 +67,11 @@ custom_data:
         title: Number of removed large cells
         description: "Total number of labels above max_area"
 sp:
+  my_custom_content_image:
+    fn: "*overview.png"
   segmentation_stats:
-    fn: "final_QC.all_samples.csv"
+    fn: "*spot_QC.csv"
     shared: true
-  my_custom_content_image:
-    fn: "*.png"
 ignore_images: false
+
 disable_version_detection: true

conf/modules.config

@@ -25,7 +25,6 @@ process {
             mode: params.publish_dir_mode,
             saveAs: { filename -> filename.equals('versions.yml') ? null : filename }
         ]
-        container = "community.wave.seqera.io/library/multiqc:1.22.1--4886de6095538010"
     }
 
     withName: 'MOLKARTQC' {

conf/test.config

@@ -26,4 +26,9 @@ params {
     mindagap_loopnum = 100
     clahe_pyramid_tile = 368
     segmentation_method = "mesmer,cellpose"
+
+    // Only used if additionally params.create_training_subset is used
+    crop_size_x = 30
+    crop_size_y = 30
+    crop_amount = 2
 }

modules.json

@@ -37,7 +37,7 @@
                     },
                     "multiqc": {
                         "branch": "master",
-                        "git_sha": "8f2062e7b4185590fb9f43c275381a31a6544fc0",
+                        "git_sha": "06c8865e36741e05ad32ef70ab3fac127486af48",
                         "installed_by": ["modules"]
                     }
                 }

workflows/molkart.nf

@@ -298,19 +298,14 @@ workflow MOLKART {
     //
     // MODULE: MultiQC
     //
-    //workflow_summary       = WorkflowMolkart.paramsSummaryMultiqc(workflow, summary_params)
-    //ch_workflow_summary    = Channel.value(workflow_summary)
-    //methods_description    = WorkflowMolkart.methodsDescriptionText(workflow, ch_multiqc_custom_methods_description, params)
-    //ch_methods_description = Channel.value(methods_description)
-
     ch_multiqc_config        = Channel.fromPath(
         "$projectDir/assets/multiqc_config.yml", checkIfExists: true)
     ch_multiqc_custom_config = params.multiqc_config ?
         Channel.fromPath(params.multiqc_config, checkIfExists: true) :
         Channel.empty()
     ch_multiqc_logo          = params.multiqc_logo ?
         Channel.fromPath(params.multiqc_logo, checkIfExists: true) :
-        Channel.empty()
+        Channel.fromPath("$projectDir/assets/nf-core-molkart_logo_light.png", checkIfExists: true)
 
     summary_params      = paramsSummaryMap(
         workflow, parameters_schema: "nextflow_schema.json")
@@ -342,14 +337,16 @@ workflow MOLKART {
     } else {
         ch_multiqc_files = ch_multiqc_files.mix(
             MOLKARTQC.out.qc.map{it[1]}
-            .collectFile(name: 'final_QC.all_samples.csv', keepHeader: true, storeDir: "${params.outdir}/multiqc"))
+            .collectFile())
     }
 
     MULTIQC (
         ch_multiqc_files.collect(),
         ch_multiqc_config.toList(),
         ch_multiqc_custom_config.toList(),
-        ch_multiqc_logo.toList()
+        ch_multiqc_logo.toList(),
+        [],
+        []
     )
 
     emit: