create cell feature function(updated version of gen_batch_feature) (#39)

dance/transforms/graph_construct.py

@@ -1,13 +1,12 @@
 # Copyright 2022 DSE lab.  All rights reserved.
-
-import glob
 import itertools
 import os
 import pickle
-import random
 import time
 from collections import defaultdict
+from typing import List, Union
 
+import anndata as ad
 import dgl
 import networkx as nx
 import numba
@@ -27,6 +26,7 @@
 from torch.nn import functional as F
 
 import dance.transforms.preprocess
+from dance import logger
 
 
 @numba.njit("f4(f4[:], f4[:])")
@@ -486,6 +486,86 @@ def gen_batch_features(ad_inputs):
     return batch_features
 
 
+def generate_cell_features(
+    data: Union[ad.AnnData, List[ad.AnnData]],
+    *,
+    group_batch: bool = False,
+    batch_col_id: str = "batch",
+) -> torch.Tensor:
+    """Generate cell node features from anndata objects.
+
+    Parameters
+    ----------
+    data: Union[anndata.AnnData, List[anndata.AnnData]]
+        A list of or a single AnnData object(s).
+    group_batch: bool
+        If set to True, set features of cell within a batch to the mean values.
+    batch_col_id: str
+        Column ID corresponding to the batchs.
+
+    Returns
+    -------
+    cell_features: torch.Tensor
+        A cell feature matrix, each row represents the node features corresponding to a cell, generated based on
+        the statistics of the cell's gene expression profiles.
+
+    TODO
+    ----
+        Add option for providing call-backs for additional flexibility of generating different types of features.
+
+    """
+    data = data if isinstance(data, list) else [data]
+
+    cells = []
+    columns = [
+        "cell_mean", "cell_std", "nonzero_25%", "nonzero_50%", "nonzero_75%", "nonzero_max", "nonzero_count",
+        "nonzero_mean", "nonzero_std", "batch"
+    ]
+
+    for adata in data:
+        bcl = adata.obs[batch_col_id].tolist()
+        logger.info(f"Unique batches: {sorted(set(bcl))}")
+
+        for i, cell in enumerate(adata.X):
+            cell = cell.toarray()
+            nz = cell[np.nonzero(cell)]
+
+            if len(nz) == 0:
+                logger.warning("Encountered a cell with all zero features.")
+                cells.append([0] * (len(columns) - 1) + bcl[i])
+            else:
+                cells.append([
+                    cell.mean(),
+                    cell.std(),
+                    np.percentile(nz, 25),
+                    np.percentile(nz, 50),
+                    np.percentile(nz, 75),
+                    cell.max(),
+                    len(nz) / 1000,
+                    nz.mean(),
+                    nz.std(),
+                    bcl[i],
+                ])
+
+    features = pd.DataFrame(cells, columns=columns)
+    logger.debug(f"features=\n{features}")
+
+    if group_batch:
+        batch_source = features.groupby("batch", as_index=False).mean()
+        logger.info(f"Batch features:\n{batch_source.set_index('batch')}")
+
+        # Assign cell features with corresponding reduced batch features
+        b2i = {j: i for i, j in enumerate(batch_source["batch"].tolist())}
+        batch_source = batch_source.drop("batch", axis=1).to_numpy()
+        cell_batch_idxs = list(map(b2i.get, features["batch"]))
+        cell_features = batch_source[cell_batch_idxs]
+
+    else:
+        cell_features = features.drop("batch", axis=1).to_numpy()
+
+    return torch.tensor(cell_features, dtype=torch.float32)
+
+
 def construct_modality_prediction_graph(dataset, **kwargs):
     """Construct the cell-feature graph object for modality prediction task, based on
     the input dataset.