Add tacco

src/methods_cell_type_annotation/tacco/config.vsh.yaml

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+__merge__: /src/api/comp_method_cell_type_annotation.yaml
+
+name: tacco
+label: "Tacco"
+summary: "Annotate cell types using Tacco"
+description: "Annotate cell types using Tacco"
+links:
+  documentation: "https://simonwm.github.io/tacco/"
+  repository: "https://github.com/simonwm/tacco"
+references:
+  doi: "10.1038/s41587-023-01657-3"
+
+resources:
+  - type: python_script
+    path: script.py
+
+engines:
+  - type: docker
+    image: openproblems/base_python:1.0.0
+    setup:
+      - type: python
+        pypi: [anndata, numpy, tacco]
+  - type: native
+
+runners:
+  - type: executable
+  - type: nextflow
+    directives:
+      label: [ midtime, midcpu, midmem ]

src/methods_cell_type_annotation/tacco/script.py

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+#!/usr/bin/env python3
+
+import anndata as ad
+import numpy as np
+import tacco
+
+## VIASH START
+par = {
+  'input_spatial_normalized_counts': 'resources_test/task_ist_preprocessing/mouse_brain_combined/spatial_normalized_counts.h5ad',
+  'input_scrnaseq_reference': 'resources_test/task_ist_preprocessing/mouse_brain_combined/scrnaseq_reference.h5ad',
+  'output': 'spatial_with_celltypes.h5ad',
+  'celltype_key': 'cell_type',
+}
+meta = {
+  'name': 'tacco',
+}
+## VIASH END
+
+# Optional parameter check: For this specific annotation method the par['input_spatial_normalized_counts'] and par['input_scrnaseq_reference'] are required
+assert par['input_spatial_normalized_counts'] is not None, 'Spatial input is required for this annotation method.'
+assert par['input_scrnaseq_reference'] is not None, 'Single cell input is required for this annotation method.'
+
+# Read input
+adata_sp = ad.read_h5ad(par['input_spatial_normalized_counts'])
+adata_sc = ad.read_h5ad(par['input_scrnaseq_reference'])
+
+# Switch to raw counts
+adata_sp.X = adata_sp.layers['counts']
+adata_sc.X = adata_sc.layers['counts']
+
+# Run tacco
+cell_type_assignment = tacco.tl.annotate(
+    adata=adata_sp,
+    reference=adata_sc,
+    annotation_key=par['celltype_key']
+)
+
+# Tacco stores the cell type proportions in a n_obs x n_celltypes matrix, so we have to extract the celltype with highest consensus
+cell_types = cell_type_assignment.columns
+highest_score_idx = np.argmax(cell_type_assignment, axis=1)
+adata_sp.obs[par['celltype_key']] = cell_types[highest_score_idx]
+
+# Write output
+adata_sp.write_h5ad(par['output'])