Merge branch 'main' into plot_example

Author: namsaraeva

docs/tutorials/notebooks

@@ -6,6 +6,7 @@
 from math import floor, nan
 from typing import TYPE_CHECKING, Any, Literal
 
+import anndata as ad
 import matplotlib.pyplot as plt
 import numpy as np
 import pandas as pd
@@ -140,8 +141,8 @@ def load(
             # filter samples according to label
             if condition_label is not None and treatment_label is not None:
                 print(f"Filtering samples with {condition_label} and {treatment_label} labels.")
-                adata = AnnData.concatenate(
-                    adata[adata.obs["label"] == condition_label], adata[adata.obs["label"] == treatment_label]
+                adata = ad.concat(
+                    [adata[adata.obs["label"] == condition_label], adata[adata.obs["label"] == treatment_label]]
                 )
             label_encoder = LabelEncoder()
             adata.obs["y_"] = label_encoder.fit_transform(adata.obs["label"])
@@ -235,7 +236,7 @@ def sample(self, adata: AnnData, categorical: bool, subsample_size: int, random_
                         random_state=random_state,
                     )
                 )
-            subsample = AnnData.concatenate(*label_subsamples, index_unique=None)
+            subsample = ad.concat([*label_subsamples], index_unique=None)
         else:
             subsample = sc.pp.subsample(adata[:, features], n_obs=subsample_size, copy=True, random_state=random_state)
 
@@ -414,17 +415,17 @@ def set_scorer(
         """
         if multiclass:
             return {
-                "augur_score": make_scorer(roc_auc_score, multi_class="ovo", needs_proba=True),
-                "auc": make_scorer(roc_auc_score, multi_class="ovo", needs_proba=True),
+                "augur_score": make_scorer(roc_auc_score, multi_class="ovo", response_method="predict_proba"),
+                "auc": make_scorer(roc_auc_score, multi_class="ovo", response_method="predict_proba"),
                 "accuracy": make_scorer(accuracy_score),
                 "precision": make_scorer(precision_score, average="macro", zero_division=zero_division),
                 "f1": make_scorer(f1_score, average="macro"),
                 "recall": make_scorer(recall_score, average="macro"),
             }
         return (
             {
-                "augur_score": make_scorer(roc_auc_score, needs_proba=True),
-                "auc": make_scorer(roc_auc_score, needs_proba=True),
+                "augur_score": make_scorer(roc_auc_score, response_method="predict_proba"),
+                "auc": make_scorer(roc_auc_score, response_method="predict_proba"),
                 "accuracy": make_scorer(accuracy_score),
                 "precision": make_scorer(precision_score, average="binary", zero_division=zero_division),
                 "f1": make_scorer(f1_score, average="binary"),

pertpy/tools/_augur.py

@@ -796,7 +796,7 @@ def __call__(self, X: np.ndarray, Y: np.ndarray, **kwargs) -> float:
         label = ["c"] * X.shape[0] + ["p"] * Y_train.shape[0]
         train = np.concatenate([X, Y_train])
 
-        reg = LogisticRegression()  # TODO dynamically pass this?
+        reg = LogisticRegression()
         reg.fit(train, label)
         test_labels = reg.predict_proba(Y_test)
         return np.mean(test_labels[:, 1])

pertpy/tools/_distances/_distances.py

@@ -1,7 +1,6 @@
 from __future__ import annotations
 
 import copy
-import warnings
 from collections import OrderedDict
 from typing import TYPE_CHECKING, Literal
 
@@ -10,7 +9,6 @@
 import scanpy as sc
 import seaborn as sns
 from matplotlib import pyplot as pl
-from rich import print
 from scanpy import get
 from scanpy._settings import settings
 from scanpy._utils import _check_use_raw, sanitize_anndata
@@ -477,15 +475,6 @@ def _get_perturbation_markers(
         return perturbation_markers
 
     def _get_column_indices(self, adata, col_names):
-        """Fetches the column indices in X for a given list of column names
-
-        Args:
-            adata: :class:`~anndata.AnnData` object
-            col_names: Column names to extract the indices for
-
-        Returns:
-            Set of column indices
-        """
         if isinstance(col_names, str):  # pragma: no cover
             col_names = [col_names]
 
@@ -827,7 +816,7 @@ def plot_perturbscore(  # pragma: no cover
                 )
                 pl.xlabel("Perturbation score", fontsize=16)
                 pl.ylabel("Cell density", fontsize=16)
-                pl.title("Density Plot using Seaborn and Matplotlib", fontsize=18)
+                pl.title("Density", fontsize=18)
                 pl.legend(title="mixscape class", title_fontsize=14, fontsize=12)
                 sns.despine()
 
@@ -855,19 +844,21 @@ def plot_violin(  # pragma: no cover
         ax: Axes | None = None,
         **kwargs,
     ):
-        """Violin plot using mixscape results. Requires `pt.tl.mixscape` to be run first.
+        """Violin plot using mixscape results.
+
+        Requires `pt.tl.mixscape` to be run first.
 
         Args:
             adata: The annotated data object.
-            target_gene: Target gene name to plot.
+            target_gene_idents: Target gene name to plot.
             keys: Keys for accessing variables of `.var_names` or fields of `.obs`. Default is 'mixscape_class_p_ko'.
             groupby: The key of the observation grouping to consider. Default is 'mixscape_class'.
             log: Plot on logarithmic axis.
             use_raw: Whether to use `raw` attribute of `adata`. Defaults to `True` if `.raw` is present.
             stripplot: Add a stripplot on top of the violin plot.
             order: Order in which to show the categories.
-            xlabel: Label of the x axis. Defaults to `groupby` if `rotation` is `None`, otherwise, no label is shown.
-            ylabel: Label of the y axis. If `None` and `groupby` is `None`, defaults to `'value'`.
+            xlabel: Label of the x-axis. Defaults to `groupby` if `rotation` is `None`, otherwise, no label is shown.
+            ylabel: Label of the y-axis. If `None` and `groupby` is `None`, defaults to `'value'`.
                     If `None` and `groubpy` is not `None`, defaults to `keys`.
             show: Show the plot, do not return axis.
             save: If `True` or a `str`, save the figure. A string is appended to the default filename.
@@ -1072,11 +1063,9 @@ def plot_lda(  # pragma: no cover
             .. image:: ../_static/docstring_previews/mixscape_lda.png
         """
         if mixscape_class not in adata.obs:
-            raise ValueError(
-                f'Did not find `.obs["{mixscape_class!r}"]`. Please run the `mixscape` function first first.'
-            )
+            raise ValueError(f'Did not find `.obs["{mixscape_class!r}"]`. Please run the `mixscape` function first.')
         if lda_key not in adata.uns:
-            raise ValueError(f'Did not find `.uns["{lda_key!r}"]`. Run the `lda` function first.')
+            raise ValueError(f'Did not find `.uns["{lda_key!r}"]`. Please run the `lda` function first.')
 
         adata_subset = adata[
             (adata.obs[mixscape_class_global] == perturbation_type) | (adata.obs[mixscape_class_global] == control)

pertpy/tools/_mixscape.py

@@ -5,6 +5,7 @@
 import numpy as np
 import pandas as pd
 from anndata import AnnData
+from pynndescent import NNDescent
 from rich import print
 
 if TYPE_CHECKING:
@@ -25,7 +26,7 @@ def __init__(self):
     def compute_control_diff(  # type: ignore
         self,
         adata: AnnData,
-        target_col: str = "perturbations",
+        target_col: str = "perturbation",
         group_col: str = None,
         reference_key: str = "control",
         layer_key: str = None,
@@ -147,16 +148,16 @@ def add(
         perturbations: Iterable[str],
         reference_key: str = "control",
         ensure_consistency: bool = False,
-        target_col: str = "perturbations",
-    ) -> AnnData:
+        target_col: str = "perturbation",
+    ) -> tuple[AnnData, AnnData] | AnnData:
         """Add perturbations linearly. Assumes input of size n_perts x dimensionality
 
         Args:
             adata: Anndata object of size n_perts x dim.
             perturbations: Perturbations to add.
             reference_key: perturbation source from which the perturbation summation starts. Defaults to 'control'.
             ensure_consistency: If True, runs differential expression on all data matrices to ensure consistency of linear space.
-            target_col: .obs column name that stores the label of the perturbation applied to each cell. Defaults to 'perturbations'.
+            target_col: .obs column name that stores the label of the perturbation applied to each cell. Defaults to 'perturbation'.
 
         Returns:
             Anndata object of size (n_perts+1) x dim, where the last row is the addition of the specified perturbations.
@@ -256,8 +257,8 @@ def subtract(
         perturbations: Iterable[str],
         reference_key: str = "control",
         ensure_consistency: bool = False,
-        target_col: str = "perturbations",
-    ) -> AnnData:
+        target_col: str = "perturbation",
+    ) -> tuple[AnnData, AnnData] | AnnData:
         """Subtract perturbations linearly. Assumes input of size n_perts x dimensionality
 
         Args:
@@ -358,3 +359,51 @@ def subtract(
             return new_perturbation, adata
 
         return new_perturbation
+
+    def label_transfer(
+        self,
+        adata: AnnData,
+        column: str = "perturbation",
+        target_val: str = "unknown",
+        n_neighbors: int = 5,
+        use_rep: str = "X_umap",
+    ) -> None:
+        """Impute missing values in the specified column using KNN imputation in the space defined by `use_rep`.
+
+        Args:
+            adata: The AnnData object containing single-cell data.
+            column: The column name in AnnData object to perform imputation on. Defaults to "perturbation".
+            target_val: The target value to impute. Defaults to "unknown".
+            n_neighbors: Number of neighbors to use for imputation. Defaults to 5.
+            use_rep: The key in `adata.obsm` where the embedding (UMAP, PCA, etc.) is stored. Defaults to 'X_umap'.
+
+        Examples:
+            >>> import pertpy as pt
+            >>> import scanpy as sc
+            >>> import numpy as np
+            >>> adata = sc.datasets.pbmc68k_reduced()
+            >>> rng = np.random.default_rng()
+            >>> adata.obs["perturbation"] = rng.choice(["A", "B", "C", "unknown"], size=adata.n_obs, p=[0.33, 0.33, 0.33, 0.01])
+            >>> sc.pp.neighbors(adata)
+            >>> sc.tl.umap(adata)
+            >>> ps = pt.tl.PseudobulkSpace()
+            >>> ps.label_transfer(adata, n_neighbors=5, use_rep="X_umap")
+        """
+        if use_rep not in adata.obsm:
+            raise ValueError(f"Representation {use_rep} not found in the AnnData object.")
+
+        embedding = adata.obsm[use_rep]
+
+        nnd = NNDescent(embedding, n_neighbors=n_neighbors)
+        indices, _ = nnd.query(embedding, k=n_neighbors)
+
+        perturbations = np.array(adata.obs[column])
+        missing_mask = perturbations == target_val
+
+        for idx in np.where(missing_mask)[0]:
+            neighbor_indices = indices[idx]
+            neighbor_categories = perturbations[neighbor_indices]
+            most_common = pd.Series(neighbor_categories).mode()[0]
+            perturbations[idx] = most_common
+
+        adata.obs[column] = perturbations

pertpy/tools/_perturbation_space/_perturbation_space.py

@@ -15,7 +15,7 @@ class CentroidSpace(PerturbationSpace):
     def compute(
         self,
         adata: AnnData,
-        target_col: str = "perturbations",
+        target_col: str = "perturbation",
         layer_key: str = None,
         embedding_key: str = "X_umap",
         keep_obs: bool = True,
@@ -115,7 +115,7 @@ class PseudobulkSpace(PerturbationSpace):
     def compute(
         self,
         adata: AnnData,
-        target_col: str = "perturbations",
+        target_col: str = "perturbation",
         layer_key: str = None,
         embedding_key: str = None,
         **kwargs,
@@ -133,13 +133,13 @@ def compute(
              AnnData object with one observation per perturbation.
 
         Examples:
-            >>> import pertpy as pp
+            >>> import pertpy as pt
             >>> mdata = pt.dt.papalexi_2021()
             >>> ps = pt.tl.PseudobulkSpace()
             >>> ps_adata = ps.compute(mdata["rna"], target_col="gene_target", groups_col="gene_target")
         """
         if "groups_col" not in kwargs:
-            kwargs["groups_col"] = "perturbations"
+            kwargs["groups_col"] = "perturbation"
 
         if layer_key is not None and embedding_key is not None:
             raise ValueError("Please, select just either layer or embedding for computation.")
@@ -244,7 +244,7 @@ def compute(  # type: ignore
         copy: bool = True,
         return_object: bool = False,
         **kwargs,
-    ) -> tuple[AnnData, object | AnnData]:
+    ) -> tuple[AnnData, object] | AnnData:
         """Computes a clustering using Density-based spatial clustering of applications (DBSCAN).
 
         Args:

pertpy/tools/_perturbation_space/_simple.py

@@ -0,0 +1,7 @@
+import numpy as np
+import pytest
+
+
+@pytest.fixture
+def rng():
+    return np.random.default_rng()

tests/conftest.py

@@ -64,7 +64,7 @@ def test_distance_axioms(self, adata, distance):
         assert all(np.diag(df.values) == 0)  # distance to self is 0
 
         # (M2) Positivity
-        assert len(df) == np.sum(df.values == 0)  # distance to other is not 0 (TODO)
+        assert len(df) == np.sum(df.values == 0)  # distance to other is not 0
         assert all(df.values.flatten() >= 0)  # distance is non-negative
 
         # (M3) Symmetry