Address review comments and rewrite bbknn_ts in the component style

CHANGELOG.md

@@ -11,7 +11,7 @@
 
 * Added `ARI_batch` and `NMI_batch` to `metrics/clustering_overlap` (PR #68).
 
-* Added `methods/bbknn_ts` component (PR #84).
+* Added `methods/era_combat_bbknn` component (PR #84).
 
 * Added `metrics/cilisi` new metric component (PR #57).
     - ciLISI measures batch mixing in a cell type-aware manner by computing iLISI within each cell type and normalizing

src/methods/era_combat_bbknn/config.vsh.yaml

@@ -0,0 +1,64 @@
+__merge__: ../../api/comp_method.yaml
+
+name: era_combat_bbknn
+label: ERA ComBat + BBKNN
+summary: "A combination of ComBat and BBKNN discovered and implemented by the ERA AI system."
+description: |
+  Standard scRNA-seq preprocessing steps are applied to the raw counts, including
+  total count normalization, log-transformation and scaling of the gene expression
+  data. Batch effect correction is performed using `scanpy.pp.combat` directly on the
+  gene expression matrix (before dimensionality reduction). Dimensionality reduction
+  is then applied using PCA on the ComBat-corrected data, and this PCA embedding is
+  returned as the integrated embedding.
+
+  A custom batch-aware nearest neighbors graph is constructed based on this integrated
+  embedding: for each cell, neighbors are independently identified within its own batch
+  and other batches, up to `n_neighbors_per_batch`. These candidate neighbors are
+  merged and the top `total_k_neighbors` are selected for each cell. Finally, a
+  symmetric sparse distance matrix and a binary connectivities matrix are generated to
+  represent the integrated neighborhood graph.
+
+  The integration was devised by the AI system described in the associated
+  publication. The original implementation was rewritten to fit the component
+  structure, but produces the same embedding and graph.
+references:
+  # Aygün, E., Belyaeva, A., Comanici, G. et al.
+  # An AI system to help scientists write expert-level empirical software.
+  # Nature 654, 909-916 (2026). https://doi.org/10.1038/s41586-026-10658-6
+  doi: 10.1038/s41586-026-10658-6
+links:
+  documentation: https://google-research.github.io/era/
+  repository: https://github.com/google-research/era
+
+info:
+  method_types: [embedding, graph]
+  preferred_normalization: counts
+
+arguments:
+  - name: "--n_pca_components"
+    type: "integer"
+    default: 100
+    description: "Number of PCA components."
+  - name: "--n_neighbors_per_batch"
+    type: "integer"
+    default: 10
+    description: "Number of neighbors to use within each batch."
+  - name: "--total_k_neighbors"
+    type: "integer"
+    default: 50
+    description: "Total number of nearest neighbors to retain for the final graph."
+
+resources:
+  - type: python_script
+    path: script.py
+  - path: /src/utils/read_anndata_partial.py
+
+engines:
+  - type: docker
+    image: openproblems/base_python:1
+
+runners:
+  - type: executable
+  - type: nextflow
+    directives:
+      label: [hightime, highmem, midcpu]