Reader for Curio files

docs/api.md

@@ -12,6 +12,7 @@ I/O for the `spatialdata` project.
 .. autosummary::
     :toctree: generated
 
+    curio
     cosmx
     visium
     xenium

src/spatialdata_io/__init__.py

@@ -1,12 +1,14 @@
 from importlib.metadata import version
 
 from spatialdata_io.readers.cosmx import cosmx
+from spatialdata_io.readers.curio import curio
 from spatialdata_io.readers.mcmicro import mcmicro
 from spatialdata_io.readers.steinbock import steinbock
 from spatialdata_io.readers.visium import visium
 from spatialdata_io.readers.xenium import xenium
 
 __all__ = [
+    "curio",
     "visium",
     "xenium",
     "cosmx",

src/spatialdata_io/_constants/_constants.py

@@ -3,6 +3,21 @@
 from spatialdata_io._constants._enum import ModeEnum
 
 
+@unique
+class CurioKeys(ModeEnum):
+    """Keys for *Curio* formatted dataset."""
+
+    # files and directories
+    ANNDATA_FILE = ".h5ad"
+    CLUSTER_ASSIGNMENT = "cluster_assignment.txt"
+    METRICS_FILE = "Metrics.csv"
+    VAR_FEATURES_CLUSTERS = "variable_features_clusters.txt"
+    VAR_FEATURES_MORANSI = "variable_features_moransi.txt"
+    # metadata
+    CATEGORY = "Category"
+    TOP_CLUSTER_DEFINING_FEATURES = "Top_cluster_defining_features"
+
+
 @unique
 class CosmxKeys(ModeEnum):
     """Keys for *Nanostring Cosmx* formatted dataset."""

src/spatialdata_io/readers/curio.py

@@ -0,0 +1,84 @@
+from __future__ import annotations
+
+from pathlib import Path
+from typing import Optional
+
+import anndata as ad
+import pandas as pd
+from spatialdata import SpatialData
+from spatialdata.models import TableModel
+
+from spatialdata_io._constants._constants import CurioKeys
+from spatialdata_io._docs import inject_docs
+
+__all__ = ["curio"]
+
+
+@inject_docs(vx=CurioKeys)
+def curio(
+    path: str | Path,
+    dataset_id: Optional[str] = None,
+) -> SpatialData:
+    """
+    Read *Curio* formatted dataset.
+
+    This function reads the following files:
+
+        - ``<dataset_id>_`{vx.ANNDATA_FILE!r}```: Counts and metadata file.
+        - ``<dataset_id>_`{vx.CLUSTER_ASSIGNMENT!r}```: Cluster assignment file.
+        - ``<dataset_id>_`{vx.METRICS_FILE!r}```: Metrics file.
+        - ``<dataset_id>_`{vx.VAR_FEATURES_CLUSTERS!r}```: Variable features clusters file.
+        - ``<dataset_id>_`{vx.VAR_FEATURES_MORANSI!r}```: Variable features Moran's I file.
+
+    .. seealso::
+
+        - `CODEX output <https://help.codex.bio/codex/processor/technical-notes/expected-output>`_.
+
+    Parameters
+    ----------
+    path
+        Path to the directory containing the data.
+    dataset_id
+        Dataset identifier.
+    imread_kwargs
+        Keyword arguments passed to :func:`dask_image.imread.imread`.
+    image_models_kwargs
+        Keyword arguments passed to :class:`spatialdata.models.Image2DModel`.
+
+    Returns
+    -------
+    :class:`spatialdata.SpatialData`
+    """
+    path = Path(path)
+    path_files = [
+        CurioKeys.ANNDATA_FILE,
+        CurioKeys.CLUSTER_ASSIGNMENT,
+        CurioKeys.METRICS_FILE,
+        CurioKeys.VAR_FEATURES_CLUSTERS,
+        CurioKeys.VAR_FEATURES_MORANSI,
+    ]
+
+    if dataset_id is not None:
+        file_names = [f"{dataset_id}_{file_name}" for file_name in path_files]
+    else:
+        file_names = []
+        for file_name in path_files:
+            file_names.extend(str(path.glob(file_name)))
+
+    adata = ad.read_h5ad(path / file_names[0])
+    cluster_assign = pd.read_csv(path / file_names[1], sep="\t", header=None)
+    metrics = pd.read_csv(path / file_names[2], sep=r"\,", header=0)
+    var_features_clusters = pd.read_csv(path / file_names[3], sep="\t", header=0)
+    var_features_moransi = pd.read_csv(path / file_names[4], sep="\t", header=0)
+
+    adata.obs = adata.obs.assign(cluster=cluster_assign[1].values)
+    categories = metrics[CurioKeys.CATEGORY].unique()
+    for cat in categories:
+        df = metrics.loc[metrics[CurioKeys.CATEGORY] == cat]
+        adata.uns[cat] = dict(zip(df.iloc[:, 0], df.iloc[:, 1]))
+    adata.uns[CurioKeys.TOP_CLUSTER_DEFINING_FEATURES] = var_features_clusters
+    adata.var.join(var_features_moransi, how="outer")
+
+    table = TableModel.parse(adata)
+
+    return SpatialData(table=table)