Update for Decoupler

docs/Usage_Principles.md

@@ -54,13 +54,13 @@ sc.pl.tsne(adata, color="leiden")
 
 ### Decoupler-GPU
 
-`dcg` offers accelerated drop in replacements for {func}`~rapids_singlecell.dcg.run_mlm` and {func}`~rapids_singlecell.dcg.run_wsum`
+`dcg` offers accelerated drop in replacements for {func}`~rapids_singlecell.dcg.mlm`, {func}`~rapids_singlecell.dcg.ulm` and {func}`~rapids_singlecell.dcg.aucell`
 
 Example:
 ```
 import decoupler as dc
-model = dc.get_progeny(organism='human', top=100)
-rsc.dcg.run_mlm(mat=adata, net=net, source='source', target='target', weight='weight', verbose=True)
-acts_mlm = dc.get_acts(adata, obsm_key='mlm_estimate')
-sc.pl.umap(acts_mlm, color=['KLF5',"FOXA1", 'CellType'], cmap='coolwarm', vcenter=0)
+model = dc.op.resource("PanglaoDB", organism="human")
+rsc.dcg.ulm(adata, model , tmin=3)
+acts_mlm = dc.pp.get_obsm(adata, key="score_ulm")
+sc.pl.umap(acts_mlm, color=['NK cells'], cmap='coolwarm', vcenter=0)
 ```

docs/api/decoupler_gpu.md

@@ -1,6 +1,6 @@
 # decoupler-GPU: `dcg`
 
-{mod}`decoupler` contains different statistical methods to extract biological activities. {mod}`rapids_singlecell.dcg` accelerates some of these methods.
+{mod}`decoupler.mt` contains different statistical methods to extract biological activities. {mod}`rapids_singlecell.dcg` accelerates some of these methods.
 
 ```{eval-rst}
 .. module:: rapids_singlecell.dcg
@@ -9,8 +9,7 @@
 .. autosummary::
     :toctree: generated
 
-    dcg.run_mlm
-    dcg.run_ulm
-    dcg.run_wsum
-    dcg.run_aucell
+    dcg.mlm
+    dcg.ulm
+    dcg.aucell
 ```

docs/conf.py

@@ -113,7 +113,7 @@
     "scanpy": ("https://scanpy.readthedocs.io/en/stable/", None),
     "squidpy": ("https://squidpy.readthedocs.io/en/stable/", None),
     "seaborn": ("https://seaborn.pydata.org/", None),
-    "decoupler": ("https://decoupler-py.readthedocs.io/en/latest/", None),
+    "decoupler": ("https://decoupler.readthedocs.io/en/latest/", None),
     "rmm": ("https://docs.rapids.ai/api/rmm/stable/", None),
     "statsmodels": ("https://www.statsmodels.org/stable/", None),
     "omnipath": ("https://omnipath.readthedocs.io/en/latest/", None),

docs/release-notes/0.13.0.md

@@ -5,6 +5,7 @@
 * Add support for aggregate operations on CSC matrices, Fortran-ordered arrays, and Dask with sparse CSR and dense matrices {pr}`395` {smaller}`S Dicks`
 * Adds dask support for `tl.score_genes` & `tl.score_genes_cell_cycle` {pr}`408` {smaller}`S Dicks`
 * Adds dask support for `tl.rank_genes_groups_logreg` {pr}`413` {smaller}`S Dicks`
+* Updates {mod}`dcg` to work `Decoupler 2.0.0` {pr}`418` {smaller}`S Dicks`
 
 ```{rubric} Performance
 ```
@@ -15,3 +16,4 @@
 
 ```{rubric} Misc
 ```
+* Removes `wsum` {pr}`418` {smaller}`S Dicks`

docs/release-notes/0.9.0.md

@@ -23,7 +23,7 @@
 
 * Fix `rapids_singlecell.preprocessing._utils._get_mean_var` {pr}`60` {smaller}`S Dicks`
 * Fix {func}`~rapids_singlecell.pp.harmony_integrate` reproducibility {pr}`60` {smaller}`S Dicks`
-* Fix {mod}`~rapids_singlecell.dcg` to work with {mod}`decoupler` version 1.5.0 {pr}`60` {smaller}`S Dicks`
+* Fix {mod}`~rapids_singlecell.dcg` to work with `decoupler` version 1.5.0 {pr}`60` {smaller}`S Dicks`
 
 
 ```{rubric} Misc

pyproject.toml

@@ -23,6 +23,7 @@ dependencies = [
     "tqdm",
     "statsmodels>=0.12.0",
     "scikit-image",
+    "docrep",
 ]
 
 [project.optional-dependencies]
@@ -44,6 +45,7 @@ test = [
     "profimp",
     "scanpy>=1.10.0",
     "bbknn",
+    "decoupler",
 ]
 
 [project.urls]

src/rapids_singlecell/decoupler_gpu/__init__.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-from ._method_aucell import run_aucell
-from ._method_mlm import run_mlm
-from ._method_ulm import run_ulm
-from ._method_wsum import run_wsum
+from ._method_aucell import aucell
+from ._method_mlm import mlm
+from ._method_ulm import ulm

src/rapids_singlecell/decoupler_gpu/_helper/_Method.py

@@ -0,0 +1,102 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import pandas as pd
+
+from rapids_singlecell.decoupler_gpu._helper._run import _run
+
+if TYPE_CHECKING:
+    from collections.abc import Callable
+
+    from rapids_singlecell.decoupler_gpu._helper._data import DataType
+
+
+class MethodMeta:
+    def __init__(
+        self,
+        name: str,
+        desc: str,
+        func: Callable,
+        *,
+        stype: str,
+        adj: bool,
+        weight: bool,
+        test: bool,
+        limits: tuple,
+        reference: str,
+    ):
+        self.name = name
+        self.desc = desc
+        self.func = func
+        self.stype = stype
+        self.adj = adj
+        self.weight = weight
+        self.test = test
+        self.limits = limits
+        self.reference = reference
+
+    def meta(self) -> pd.DataFrame:
+        meta = pd.DataFrame(
+            [
+                {
+                    "name": self.name,
+                    "desc": self.desc,
+                    "stype": self.stype,
+                    "weight": self.weight,
+                    "test": self.test,
+                    "limits": self.limits,
+                    "reference": self.reference,
+                }
+            ]
+        )
+        return meta
+
+
+class Method(MethodMeta):
+    def __init__(
+        self,
+        _method: MethodMeta,
+    ):
+        super().__init__(
+            name=_method.name,
+            desc=_method.desc,
+            func=_method.func,
+            stype=_method.stype,
+            adj=_method.adj,
+            weight=_method.weight,
+            test=_method.test,
+            limits=_method.limits,
+            reference=_method.reference,
+        )
+        self._method = _method
+        self.__doc__ = self.func.__doc__
+
+    def __call__(
+        self,
+        data: DataType,
+        net: pd.DataFrame,
+        *,
+        tmin: int | float = 5,
+        raw: bool = False,
+        empty: bool = True,
+        bsize: int | float = 5000,
+        verbose: bool = False,
+        pre_load: bool = False,
+        **kwargs,
+    ):
+        return _run(
+            name=self.name,
+            func=self.func,
+            adj=self.adj,
+            test=self.test,
+            data=data,
+            net=net,
+            tmin=tmin,
+            raw=raw,
+            empty=empty,
+            bsize=bsize,
+            verbose=verbose,
+            pre_load=pre_load,
+            **kwargs,
+        )