add dask suppport and valis alignment (#69)

- add dask support for loading larger than RAM transcript dataframes
- refactor alignment methods (decouple alignment matrix loading from alignment)
- add Valis registration support to the processinig pipeline
- cleanup of unused functions/impots
- hestcore 1.0.3 -> 1.0.4 (better documentation)

.gitignore

@@ -1,58 +1,23 @@
-__pycache__
-data
-align_coord_img.ipynb
-src/hiSTloader/yolov8n.pt
-.vscode/launch.json
-dist
-src/hest.egg-info
-make.bat
-Makefile
-*.parquet
-bench_data
-cell_seg
-filtered
-src/hest/bench/timm_ctp
-my_notebooks
-.gitattributes
-cells_xenium.geojson
-nuclei_xenium.geojson
-nuclei.tif
-cells.tif
-src_slides
-data64.h5
-tests/assets
-config
-tests/output_tests
-tissue_seg
-
-results
-atlas
-figures/test_paul
-bench_data.zip
-old_bench_data
-tutorials/downloads
-tutorials/processed
-bench_config/my_bench_config.yaml
-src/hest/bench/private
-str.csv
-bench_data_old
-ST_data_emb/
-ST_pred_results/
-hest_data
-fm_v1
-cufile.log
-int.csv
-docs/build
-docs/source/generated
-local
-hest_vis
-hest_vis2
-hest_vis
-vis
-vis2
-models/deeplabv3*
-htmlcov
-models/CellViT-SAM-H-x40.pth
-debug_seg
-replace_seg
-test_vis
+data
+.vscode/launch.json
+dist
+src/hest.egg-info
+bench_data
+.gitattributes
+tests/assets
+config
+tests/output_tests
+HEST/
+
+results
+atlas
+ST_data_emb/
+ST_pred_results/
+hest_data
+fm_v1
+docs/build
+docs/source/generated
+local
+models/deeplabv3*
+htmlcov
+models/CellViT-SAM-H-x40.pth

pyproject.toml

@@ -28,7 +28,7 @@ dependencies = [
     "spatial_image >= 0.3.0",
     "datasets",
     "mygene",
-    "hestcore == 1.0.3"
+    "hestcore == 1.0.4"
 ]
 
 requires-python = ">=3.9"

src/hest/HESTData.py

@@ -4,18 +4,17 @@
 import os
 import shutil
 import warnings
-from typing import Dict, Iterator, List, Union
+from typing import Dict, List, Union
 
 import cv2
 import geopandas as gpd
 import numpy as np
+from loguru import logger
 from hestcore.wsi import (WSI, CucimWarningSingleton, NumpyWSI,
                           contours_to_img, wsi_factory)
 from loguru import logger
 
-
-
-from hest.io.seg_readers import TissueContourReader
+from hest.io.seg_readers import TissueContourReader, write_geojson
 from hest.LazyShapes import LazyShapes, convert_old_to_gpd, old_geojson_to_new
 from hest.segmentation.TissueMask import TissueMask, load_tissue_mask
 
@@ -31,7 +30,7 @@
 from tqdm import tqdm
 
 from .utils import (ALIGNED_HE_FILENAME, check_arg, deprecated,
-                    find_first_file_endswith, get_path_from_meta_row,
+                    find_first_file_endswith, get_k_genes_from_df, get_path_from_meta_row,
                     plot_verify_pixel_size, tiff_save, verify_paths)
 
 
@@ -100,7 +99,7 @@ class representing a single ST profile + its associated WSI image
         else:
             self._tissue_contours = tissue_contours
 
-        if 'total_counts' not in self.adata.var_names:
+        if 'total_counts' not in self.adata.var_names and len(self.adata) > 0:
             sc.pp.calculate_qc_metrics(self.adata, inplace=True)
 
 
@@ -133,7 +132,7 @@ def load_wsi(self) -> None:
         self.wsi = NumpyWSI(self.wsi.numpy())
 
 
-    def save(self, path: str, save_img=True, pyramidal=True, bigtiff=False, plot_pxl_size=False):
+    def save(self, path: str, save_img=True, pyramidal=True, bigtiff=False, plot_pxl_size=False, **kwargs):
         """Save a HESTData object to `path` as follows:
             - aligned_adata.h5ad (contains expressions for each spots + their location on the fullres image + a downscaled version of the fullres image)
             - metrics.json (contains useful metrics)
@@ -155,6 +154,8 @@ def save(self, path: str, save_img=True, pyramidal=True, bigtiff=False, plot_pxl
             self.adata.write(os.path.join(path, 'aligned_adata.h5ad'))
         except:
             # workaround from https://github.com/theislab/scvelo/issues/255
+            import traceback
+            traceback.print_exc()
             self.adata.__dict__['_raw'].__dict__['_var'] = self.adata.__dict__['_raw'].__dict__['_var'].rename(columns={'_index': 'features'})
             self.adata.write(os.path.join(path, 'aligned_adata.h5ad'))
 
@@ -172,7 +173,8 @@ def save(self, path: str, save_img=True, pyramidal=True, bigtiff=False, plot_pxl
         downscaled_img = self.adata.uns['spatial']['ST']['images']['downscaled_fullres']
         down_fact = self.adata.uns['spatial']['ST']['scalefactors']['tissue_downscaled_fullres_scalef']
         down_img = Image.fromarray(downscaled_img)
-        down_img.save(os.path.join(path, 'downscaled_fullres.jpeg'))
+        if len(downscaled_img) > 0:
+            down_img.save(os.path.join(path, 'downscaled_fullres.jpeg'))
 
 
         if plot_pxl_size:
@@ -748,7 +750,9 @@ def __init__(
         xenium_nuc_seg: pd.DataFrame=None,
         xenium_cell_seg: pd.DataFrame=None,
         cell_adata: sc.AnnData=None, # type: ignore
-        transcript_df: pd.DataFrame=None
+        transcript_df: pd.DataFrame=None,
+        dapi_path: str=None,
+        alignment_file_path: str=None
     ):
         """
         class representing a single ST profile + its associated WSI image
@@ -765,16 +769,31 @@ class representing a single ST profile + its associated WSI image
             xenium_cell_seg (pd.DataFrame): content of a xenium cell contour file as a dataframe (cell_boundaries.parquet)
             cell_adata (sc.AnnData): ST cell data, each row in adata.obs is a cell, each row in obsm is the cell location on the H&E image in pixels
             transcript_df (pd.DataFrame): dataframe of transcripts, each row is a transcript, he_x and he_y is the transcript location on the H&E image in pixels
+            dapi_path (str): path to a dapi focus image
+            alignment_file_path (np.ndarray): path to xenium alignment path
         """
         super().__init__(adata=adata, img=img, pixel_size=pixel_size, meta=meta, tissue_seg=tissue_seg, tissue_contours=tissue_contours, shapes=shapes)
 
         self.xenium_nuc_seg = xenium_nuc_seg
         self.xenium_cell_seg = xenium_cell_seg
         self.cell_adata = cell_adata
         self.transcript_df = transcript_df
-
-
-    def save(self, path: str, save_img=True, pyramidal=True, bigtiff=False, plot_pxl_size=False):
+        self.dapi_path = dapi_path
+        self.alignment_file_path = alignment_file_path
+
+
+    def save(
+            self, 
+            path: str, 
+            save_img=True, 
+            pyramidal=True, 
+            bigtiff=False, 
+            plot_pxl_size=False, 
+            save_transcripts=False, 
+            save_cell_seg=False, 
+            save_nuclei_seg=False,
+            **kwargs
+        ):
         """Save a HESTData object to `path` as follows:
             - aligned_adata.h5ad (contains expressions for each spots + their location on the fullres image + a downscaled version of the fullres image)
             - metrics.json (contains useful metrics)
@@ -795,21 +814,18 @@ def save(self, path: str, save_img=True, pyramidal=True, bigtiff=False, plot_pxl
         if self.cell_adata is not None:
             self.cell_adata.write_h5ad(os.path.join(path, 'aligned_cells.h5ad'))
 
-        if self.transcript_df is not None:
+        if save_transcripts and self.transcript_df is not None:
             self.transcript_df.to_parquet(os.path.join(path, 'aligned_transcripts.parquet'))
+
+        if save_cell_seg:
+            he_cells = self.get_shapes('tenx_cell', 'he').shapes
+            he_cells.to_parquet(os.path.join(path, 'he_cell_seg.parquet'))
+            write_geojson(he_cells, os.path.join(path, f'he_cell_seg.geojson'), '', chunk=True)
 
-        if self.xenium_nuc_seg is not None:
-            print('Saving Xenium nucleus boundaries... (can be slow)')
-            with open(os.path.join(path, 'nuclei_xenium.geojson'), 'w') as f:
-                json.dump(self.xenium_nuc_seg, f, indent=4)
-
-        if self.xenium_cell_seg is not None:
-            print('Saving Xenium cells boundaries... (can be slow)')
-            with open(os.path.join(path, 'cells_xenium.geojson'), 'w') as f:
-                json.dump(self.xenium_cell_seg, f, indent=4)
-
-
-        # TODO save segmentation    
+        if save_nuclei_seg:
+            he_nuclei = self.get_shapes('tenx_nucleus', 'he').shapes
+            he_nuclei.to_parquet(os.path.join(path, 'he_nucleus_seg.parquet'))
+            write_geojson(he_nuclei, os.path.join(path, f'he_nucleus_seg.geojson'), '', chunk=True)
 
 
 def read_HESTData(
@@ -936,19 +952,33 @@ def mask_and_patchify_bench(meta_df: pd.DataFrame, save_dir: str, use_mask=True,
         i += 1
 
 
-def create_benchmark_data(meta_df, save_dir:str, K, adata_folder, use_mask, keep_largest=None):
+def create_benchmark_data(meta_df, save_dir:str, K):
     os.makedirs(save_dir, exist_ok=True)
-    if K is not None:
-        splits = meta_df.groupby('patient')['id'].agg(list).to_dict()
-        create_splits(os.path.join(save_dir, 'splits'), splits, K=K)
+
+    meta_df['patient'] = meta_df['patient'].fillna('Patient 1')
+
+    get_k_genes_from_df(meta_df, 50, 'var', os.path.join(save_dir, 'var_50genes.json'))
+
+    splits = meta_df.groupby(['dataset_title', 'patient'])['id'].agg(list).to_dict()
+    create_splits(os.path.join(save_dir, 'splits'), splits, K=K)
 
     os.makedirs(os.path.join(save_dir, 'patches'), exist_ok=True)
-    mask_and_patchify_bench(meta_df, os.path.join(save_dir, 'patches'), use_mask=use_mask, keep_largest=keep_largest)
+    #mask_and_patchify_bench(meta_df, os.path.join(save_dir, 'patches'), use_mask=use_mask, keep_largest=keep_largest)
 
+    os.makedirs(os.path.join(save_dir, 'patches_vis'), exist_ok=True)
     os.makedirs(os.path.join(save_dir, 'adata'), exist_ok=True)
-    for index, row in meta_df.iterrows():
+    for _, row in meta_df.iterrows():
         id = row['id']
-        src_adata = os.path.join(adata_folder, id + '.h5ad')
+        path = os.path.join(get_path_from_meta_row(row), 'processed')
+        src_patch = os.path.join(path, 'patches.h5')
+        dst_patch = os.path.join(save_dir, 'patches', id + '.h5')
+        shutil.copy(src_patch, dst_patch)
+
+        src_vis = os.path.join(path, 'patches_patch_vis.png')
+        dst_vis = os.path.join(save_dir, 'patches_vis', id + '.png')
+        shutil.copy(src_vis, dst_vis)
+
+        src_adata = os.path.join(path, 'aligned_adata.h5ad')
         dst_adata = os.path.join(save_dir, 'adata', id + '.h5ad')
         shutil.copy(src_adata, dst_adata)
 
@@ -1200,6 +1230,13 @@ def unify_gene_names(adata: sc.AnnData, species="human", drop=False) -> sc.AnnDa
         mask = ~adata.var_names.duplicated(keep='first')
         adata = adata[:, mask]
 
+    duplicated_genes_after = adata.var_names[adata.var_names.duplicated()]
+    if len(duplicated_genes_after) > len(duplicated_genes_before):
+        logger.warning(f"duplicated genes increased from {len(duplicated_genes_before)} to {len(duplicated_genes_after)} after resolving aliases")
+    logger.info('deduplicating...')
+    mask = ~adata.var_names.duplicated(keep='first')
+    adata = adata[:, mask]
+
     if drop:
         adata = adata[:, ~remaining]
 

src/hest/LazyShapes.py

@@ -2,24 +2,30 @@
 import pandas as pd
 from shapely import Polygon
 
-from hest.io.seg_readers import read_gdf
+from hest.io.seg_readers import GDFReader, read_gdf
 from hest.utils import verify_paths
 
 
 class LazyShapes:
 
     path: str = None
 
-    def __init__(self, path: str, name: str, coordinate_system: str):
+    def __init__(self, path: str, name: str, coordinate_system: str, reader: GDFReader=None, reader_kwargs = {}):
         verify_paths([path])
         self.path = path
         self.name = name
         self.coordinate_system = coordinate_system
         self._shapes = None
+        self.reader_kwargs = reader_kwargs
+        self.reader = reader
 
     def compute(self) -> None:
         if self._shapes is None:
-            self._shapes = read_gdf(self.path)
+            if self.reader is None:
+                self._shapes = read_gdf(self.path, self.reader_kwargs)
+            else:
+                self._shapes = self.reader(**self.reader_kwargs).read_gdf(self.path)
+
 
     @property
     def shapes(self) -> gpd.GeoDataFrame:

src/hest/SlideReaderAdapter.py

@@ -0,0 +1,46 @@
+# Slide Adapter class for Valis compatibility
+import os
+
+import numpy as np
+from valis import slide_tools
+from valis.slide_io import PIXEL_UNIT, MetaData, SlideReader
+
+from hestcore.wsi import wsi_factory
+
+
+class SlideReaderAdapter(SlideReader):
+    def __init__(self, src_f, *args, **kwargs):
+        super().__init__(src_f, *args, **kwargs)
+        self.wsi = wsi_factory(src_f)
+        self.metadata = self.create_metadata()
+
+    def create_metadata(self):
+        meta_name = f"{os.path.split(self.src_f)[1]}_Series(0)".strip("_")
+        slide_meta = MetaData(meta_name, 'SlideReaderAdapter')
+
+        slide_meta.is_rgb = True
+        slide_meta.channel_names = self._get_channel_names('NO_NAME')
+        slide_meta.n_channels = 1
+        slide_meta.pixel_physical_size_xyu = [0.25, 0.25, PIXEL_UNIT]
+        level_dim = self.wsi.level_dimensions() #self._get_slide_dimensions()
+        slide_meta.slide_dimensions = np.array([list(item) for item in level_dim])
+
+        return slide_meta
+
+    def slide2vips(self, level, xywh=None, *args, **kwargs):
+        img = self.slide2image(level, xywh=xywh, *args, **kwargs)
+        vips_img = slide_tools.numpy2vips(img)
+
+        return vips_img
+
+    def slide2image(self, level, xywh=None, *args, **kwargs):
+        level_dim = self.wsi.level_dimensions()[level]
+        img = self.wsi.get_thumbnail(level_dim[0], level_dim[1])
+
+        if xywh is not None:
+            xywh = np.array(xywh)
+            start_c, start_r = xywh[0:2]
+            end_c, end_r = xywh[0:2] + xywh[2:]
+            img = img[start_r:end_r, start_c:end_c]
+
+        return img

src/hest/bench/st_dataset.py

@@ -42,4 +42,4 @@ def load_adata(expr_path, genes = None, barcodes = None, normalize=False):
         adata = adata[:, genes]
     if normalize:
         adata = normalize_adata(adata)
-    return adata.to_df()
+    return adata.to_df()