Merge branch 'master' into functional_analysis

nf-core · Jan 16, 2025 · ca61db0 · ca61db0
2 parents 12b7f8c + 2b977a4
commit ca61db0
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Showing 2 changed files with 35 additions and 37 deletions.
diff --git a/subworkflows/nf-core/abundance_differential_filter/main.nf b/subworkflows/nf-core/abundance_differential_filter/main.nf
@@ -31,18 +31,16 @@ workflow ABUNDANCE_DIFFERENTIAL_FILTER {
 
     // Set up how the channels crossed below will be used to generate channels for processing
     def criteria = multiMapCriteria { meta_input, abundance, analysis_method, fc_threshold, stat_threshold, meta_exp, samplesheet, meta_contrasts, variable, reference, target ->
+        def meta_for_diff = mergeMaps(meta_contrasts, meta_input) + [ 'method': analysis_method ]
+        def meta_input_new = meta_input + [ 'method': analysis_method ]
         samples_and_matrix:
-            meta_map = meta_input + [ 'method': analysis_method ]
-            [meta_map, samplesheet, abundance]
-        contrasts:
-            meta_map = mergeMaps(meta_contrasts, meta_input) + [ 'method': analysis_method ]
-            [ meta_map, variable, reference, target ]
+            [ meta_input_new, samplesheet, abundance ]
+        contrasts_for_diff:
+            [ meta_for_diff, variable, reference, target ]
         filter_params:
-            meta_map = mergeMaps(meta_contrasts, meta_input) + [ 'method': analysis_method ]
-            [meta_map, [ 'fc_threshold': fc_threshold, 'stat_threshold': stat_threshold ]]
+            [ meta_for_diff, [ 'fc_threshold': fc_threshold, 'stat_threshold': stat_threshold ]]
         contrasts_for_norm:
-            meta_map = meta_input + [ 'method': analysis_method ]
-            [ meta_map, variable, reference, target ]
+            [ meta_input_new, variable, reference, target ]
     }
 
     // For DIFFERENTIAL modules we need to cross the things we're iterating so we
@@ -61,13 +59,13 @@ workflow ABUNDANCE_DIFFERENTIAL_FILTER {
     // depending on the contrast setting etc, these modules may subset matrices,
     // hence not returning the full normalized matrix as NORM modules would do.
     LIMMA_NORM(
-        inputs.contrasts_for_norm.filter{it[0].method == 'limma'}.unique(),
-        inputs.samples_and_matrix.filter{it[0].method == 'limma'}.unique()
+        norm_inputs.contrasts_for_norm.filter{it[0].method == 'limma'},
+        norm_inputs.samples_and_matrix.filter{it[0].method == 'limma'}
     )
 
     LIMMA_DIFFERENTIAL(
-        inputs.contrasts.filter{it[0].method == 'limma'},
-        inputs.samples_and_matrix.filter { it[0].method == 'limma' }
+        inputs.contrasts_for_diff.filter{ it[0].method == 'limma' },
+        inputs.samples_and_matrix.filter{ it[0].method == 'limma' }
     )
 
     // ----------------------------------------------------
@@ -79,14 +77,14 @@ workflow ABUNDANCE_DIFFERENTIAL_FILTER {
     // depending on the contrast setting etc, these modules may subset matrices,
     // hence not returning the full normalized matrix as NORM modules would do.
     DESEQ2_NORM(
-        inputs.contrasts_for_norm.filter{it[0].method == 'deseq2'}.unique(),
-        inputs.samples_and_matrix.filter{it[0].method == 'deseq2'}.unique(),
+        norm_inputs.contrasts_for_norm.filter{it[0].method == 'deseq2'},
+        norm_inputs.samples_and_matrix.filter{it[0].method == 'deseq2'},
         ch_control_features.first(),
         ch_transcript_lengths.first()
     )
 
     DESEQ2_DIFFERENTIAL(
-        inputs.contrasts.filter{it[0].method == 'deseq2'},
+        inputs.contrasts_for_diff.filter{it[0].method == 'deseq2'},
         inputs.samples_and_matrix.filter{it[0].method == 'deseq2'},
         ch_control_features.first(),
         ch_transcript_lengths.first()
@@ -99,7 +97,7 @@ workflow ABUNDANCE_DIFFERENTIAL_FILTER {
     // NOTE that this method don't rely on normalization, hence it does
     // not produce a normalized matrix.
     PROPR_PROPD(
-        inputs.contrasts.filter{it[0].method == 'propd'},
+        inputs.contrasts_for_diff.filter{it[0].method == 'propd'},
         inputs.samples_and_matrix.filter { it[0].method == 'propd' }
     )
 

diff --git a/subworkflows/nf-core/abundance_differential_filter/tests/main.nf.test.snap b/subworkflows/nf-core/abundance_differential_filter/tests/main.nf.test.snap
@@ -28,7 +28,7 @@
                 ]
             ],
             [
-
+                
             ],
             [
                 [
@@ -48,9 +48,9 @@
         ],
         "meta": {
             "nf-test": "0.9.2",
-            "nextflow": "24.10.2"
+            "nextflow": "24.10.3"
         },
-        "timestamp": "2024-12-07T16:33:03.091762783"
+        "timestamp": "2025-01-14T16:51:17.162826924"
     },
     "deseq2 and limma - mouse - basic": {
         "content": [
@@ -234,9 +234,9 @@
         ],
         "meta": {
             "nf-test": "0.9.2",
-            "nextflow": "24.10.2"
+            "nextflow": "24.10.3"
         },
-        "timestamp": "2024-12-07T16:34:54.548033825"
+        "timestamp": "2025-01-14T16:53:11.245100955"
     },
     "limma - voom": {
         "content": [
@@ -308,9 +308,9 @@
         ],
         "meta": {
             "nf-test": "0.9.2",
-            "nextflow": "24.10.2"
+            "nextflow": "24.10.3"
         },
-        "timestamp": "2024-12-07T16:33:26.97858478"
+        "timestamp": "2025-01-14T16:51:32.460857013"
     },
     "deseq2 + limma-voom + propd - mouse - basic": {
         "content": [
@@ -568,9 +568,9 @@
         ],
         "meta": {
             "nf-test": "0.9.2",
-            "nextflow": "24.10.2"
+            "nextflow": "24.10.3"
         },
-        "timestamp": "2024-12-13T16:07:08.941008861"
+        "timestamp": "2025-01-14T16:53:54.80697542"
     },
     "stub": {
         "content": [
@@ -628,7 +628,7 @@
                     ]
                 ],
                 "2": [
-
+                    
                 ],
                 "3": [
                     [
@@ -677,7 +677,7 @@
                     "versions.yml:md5,05e3901f6d78f8839a7e07f422e9bc03"
                 ],
                 "adjacency": [
-
+                    
                 ],
                 "model": [
                     [
@@ -781,9 +781,9 @@
         ],
         "meta": {
             "nf-test": "0.9.2",
-            "nextflow": "24.10.2"
+            "nextflow": "24.10.3"
         },
-        "timestamp": "2024-12-13T15:08:45.989521056"
+        "timestamp": "2025-01-14T16:54:12.823125892"
     },
     "deseq2 - mouse - basic": {
         "content": [
@@ -888,9 +888,9 @@
         ],
         "meta": {
             "nf-test": "0.9.2",
-            "nextflow": "24.10.2"
+            "nextflow": "24.10.3"
         },
-        "timestamp": "2024-12-07T16:32:17.301539736"
+        "timestamp": "2025-01-14T16:50:35.085607799"
     },
     "deseq2 - with transcript lengths": {
         "content": [
@@ -995,9 +995,9 @@
         ],
         "meta": {
             "nf-test": "0.9.2",
-            "nextflow": "24.10.2"
+            "nextflow": "24.10.3"
         },
-        "timestamp": "2024-12-07T16:34:05.584119298"
+        "timestamp": "2025-01-14T16:52:07.822745077"
     },
     "propd - mouse - basic": {
         "content": [
@@ -1084,8 +1084,8 @@
         ],
         "meta": {
             "nf-test": "0.9.2",
-            "nextflow": "24.10.2"
+            "nextflow": "24.10.3"
         },
-        "timestamp": "2024-12-13T15:02:45.903371649"
+        "timestamp": "2025-01-14T16:52:25.336720308"
     }
-}
+}