added Coreograph

conf/modules.config

@@ -69,6 +69,15 @@ process {
         ]
     }
 
+    withName: "COREOGRAPH" {
+        ext.args = { "--downsampleFactor ${Math.round(Math.log(0.65 * 32 / params.pixel_size) / Math.log(2))}" }
+        publishDir = [
+            path: { "${params.outdir}/tma_dearray" },
+            mode: params.publish_dir_mode,
+        ]
+
+    }
+
     /*
     withName: SCIMAP_MCMICRO {
         containerOptions = '-e NUMBA_CACHE_DIR=/tmp'

nextflow.config

@@ -13,6 +13,8 @@ params {
     input_sample               = null
     input_cycle                = null
     marker_sheet               = null
+    tma_dearray                = false
+    pixel_size                 = null
     segmentation               = 'mesmer'
     cellpose_model             = []
 

nextflow_schema.json

@@ -66,6 +66,10 @@
                     "enum": ["basicpy"],
                     "description": "input that defines type of illumination correction to be performed"
                 },
+                "tma_dearray": {
+                    "type": "boolean",
+                    "description": "If the sample is a tissue microarray (TMA), set this to 'true' to generate a separate output for each tissue core for pipeline steps beginning with cell segmentation. This also tends to speed up processing and reduce RAM usage."
+                },
                 "segmentation": {
                     "type": "string",
                     "pattern": "^((cellpose|mesmer)?,?)*(?<!,)$",
@@ -74,6 +78,11 @@
                 "cellpose_model": {
                     "type": "string",
                     "description": "optional model file for cellpose segmentation"
+                },
+                "pixel_size": {
+                    "type": "number",
+                    "description": "Pixel width of input image data, in microns",
+                    "help_text": "You must set this parameter when enabling TMA de-arraying with the `tma_dearray` parameter. This is required for the de-arraying module to correctly process the image."
                 }
             }
         },

subworkflows/local/utils_nfcore_mcmicro_pipeline/main.nf

@@ -180,6 +180,9 @@ def validateInputParameters() {
         error "You can only provide a cellpose model if you have selected cellpose as one of your segmentation methods"
     }
 
+    if (params.tma_dearray && !params.pixel_size) {
+        error "You must also provide the pixel_size parameter (image pixel width in microns) when enabling tma_dearray."
+    }
 }
 
 //

subworkflows/local/utils_nfcore_mcmicro_pipeline/tests/initialisation.nf.test

@@ -24,5 +24,25 @@ nextflow_workflow {
 
         },
 
+        test("Error on tma_dearray without pixel_size") {
+            when {
+                params {
+                    input_cycle = "${projectDir}/assets/samplesheet-test.csv"
+                    marker_sheet = "${projectDir}/assets/markers_1_sp.csv"
+                    tma_dearray = true
+
+                }
+                workflow {
+                    """
+                    input = [false, false, false, false, [], '$outputDir', params.input_cycle, [], params.marker_sheet]
+                    """
+                }
+            }
+            then {
+                assert workflow.failed
+                assert workflow.stdout*.contains("pixel_size").any()
+            }
+        },
+
     ]
 }

tests/main.nf.test

@@ -523,5 +523,46 @@ nextflow_workflow {
 
         },
 
+        test("cycle: no illumination correction, coreograph") {
+
+            when {
+                params {
+                    segmentation = "mesmer"
+                    tma_dearray = true
+                    pixel_size = 0.7
+                }
+                workflow {
+                    """
+                    input[0] = Channel.of(
+                        [
+                            [id:"TEST1", cycle_number:1, channel_count:4],
+                            "https://raw.githubusercontent.com/nf-core/test-datasets/modules/data/imaging/core_detection/single_core_dapi.tif",
+                            [],
+                            [],
+                        ],
+                    )
+                    input[1] = Channel.of(
+                        [
+                            [1,1,'DNA_6',[],[],[]],
+                        ],
+                    )
+                    """
+                }
+            }
+
+            then {
+                assertAll (
+                    {
+                        assert snapshot (
+                            path("$outputDir/registration/ashlar/TEST1.ome.tif"),
+                            path("$outputDir/segmentation/deepcell_mesmer/mask_TEST1_1.tif"),
+                            CsvUtils.summarizeCsv("$outputDir/quantification/mcquant/mesmer/1_mask_TEST1_1.csv"),
+                        ).match()
+                    },
+                    { assert workflow.success }
+                )
+            }
+        },
+
     ]
 }

tests/main.nf.test.snap

@@ -452,5 +452,32 @@
             "nextflow": "23.10.1"
         },
         "timestamp": "2024-04-22T15:59:42.197064018"
+    },
+    "cycle: no illumination correction, coreograph": {
+        "content": [
+            "TEST1.ome.tif:md5,755b8a7782f9c561bb9fd6bf5e19f798",
+            "mask_TEST1_1.tif:md5,525d07ef2a89a8323474ff3017737ac2",
+            {
+                "headers": [
+                    "CellID",
+                    "DNA_6",
+                    "X_centroid",
+                    "Y_centroid",
+                    "Area",
+                    "MajorAxisLength",
+                    "MinorAxisLength",
+                    "Eccentricity",
+                    "Solidity",
+                    "Extent",
+                    "Orientation"
+                ],
+                "rowCount": 4804
+            }
+        ],
+        "meta": {
+            "nf-test": "0.8.4",
+            "nextflow": "23.10.1"
+        },
+        "timestamp": "2024-08-01T17:44:21.535294636"
     }
 }

workflows/mcmicro.nf

@@ -16,6 +16,7 @@ include { BASICPY                } from '../modules/nf-core/basicpy/main'
 include { ASHLAR                 } from '../modules/nf-core/ashlar/main'
 include { BACKSUB                } from '../modules/nf-core/backsub/main'
 include { CELLPOSE               } from '../modules/nf-core/cellpose/main'
+include { COREOGRAPH             } from '../modules/nf-core/coreograph/main'
 include { DEEPCELL_MESMER        } from '../modules/nf-core/deepcell/mesmer/main'
 include { MCQUANT                } from '../modules/nf-core/mcquant/main'
 include { SCIMAP_MCMICRO         } from '../modules/nf-core/scimap/mcmicro/main'
@@ -88,6 +89,17 @@ workflow MCMICRO {
     }
     */
 
+    // Run Coreograph
+    if (params.tma_dearray) {
+        COREOGRAPH(ASHLAR.out.tif)
+        COREOGRAPH.out.cores
+            .transpose()
+            .map { meta, img -> [[id: meta.id + '_' + img.fileName.toString().tokenize('.')[0]], img]}
+            .set { ch_segmentation_input }
+    } else {
+        ch_segmentation_input = ASHLAR.out.tif
+    }
+
     // Run Segmentation
 
     ch_masks = Channel.empty()
@@ -120,7 +132,7 @@ workflow MCMICRO {
         }
         .collectFile(name: 'markers.csv', sort: false, newLine: true)
 
-    ASHLAR.out.tif
+    ch_segmentation_input
         .cross(ch_masks) { it[0]['id'] }
         .map{ t_ashlar, t_mask -> [t_mask[0], t_ashlar[1], t_mask[1]] }
         .combine(ch_mcquant_markers)