From 1b7bfb587daf3fb06c80cfad58d93a72a3ce8569 Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Tue, 29 Sep 2026 11:35:27 -0400 Subject: [PATCH 01/10] Add atera reader for 10x Genomics Atera bundles Adds a native reader for 10x Genomics Atera datasets: cell-by-gene expression table, cell/nucleus segmentation labels and boundary polygons, per-transcript locations, and morphology images. - Table (`cell_feature_matrix.zarr.zip`/`csc_cell_feature_matrix.zarr.zip`) is read directly with `anndata`'s own zarr IO (`anndata.io.read_elem`/ `anndata.io.sparse_dataset`), with `X` lazily backed by dask. - Cell/nucleus labels are lazily backed by dask; boundary polygons are read via `shapely.from_ragged_array`, with an optional tiled/pyramided partial-read path (`read_cell_boundaries`) for large bundles. - Transcripts are read lazily per spatial tile via dask-delayed. - Morphology images are read via `tifffile`'s native OME-TIFF tile grid, avoiding materializing full-resolution planes. Co-Authored-By: Claude Sonnet 5 --- .gitignore | 4 + CHANGELOG.md | 2 + docs/api.md | 1 + notebooks/atera_example.ipynb | 2335 +++++++++++++++++++ src/spatialdata_io/__init__.py | 3 + src/spatialdata_io/_constants/_constants.py | 67 + src/spatialdata_io/readers/_atera_common.py | 670 ++++++ src/spatialdata_io/readers/atera.py | 459 ++++ 8 files changed, 3541 insertions(+) create mode 100644 notebooks/atera_example.ipynb create mode 100644 src/spatialdata_io/readers/_atera_common.py create mode 100644 src/spatialdata_io/readers/atera.py diff --git a/.gitignore b/.gitignore index 3da189e4..5b1a0f8d 100644 --- a/.gitignore +++ b/.gitignore @@ -34,3 +34,7 @@ uv.lock /.asv/ *.bin profile.speedscope.json + +# scratch notebooks +/notebooks/atera_exploration.ipynb +/notebooks/resource_monitor.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 6521a5dc..046a0a7b 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -16,6 +16,8 @@ Release notes for `v0.7.1` and earlier are available on the [Releases][] page. ### Added - `spatialdata_io` ships a `py.typed` marker, so downstream type checkers use its annotations. +- `atera` reader for 10x Genomics Atera bundles: cell-by-gene table, cell/nucleus labels and boundary + polygons, transcripts, and morphology images, read natively via `anndata`'s zarr IO. ### Changed diff --git a/docs/api.md b/docs/api.md index 95ff1650..331b87be 100644 --- a/docs/api.md +++ b/docs/api.md @@ -18,6 +18,7 @@ I/O for the `spatialdata` project. .. autosummary:: :toctree: generated + atera codex cosmx curio diff --git a/notebooks/atera_example.ipynb b/notebooks/atera_example.ipynb new file mode 100644 index 00000000..402acd05 --- /dev/null +++ b/notebooks/atera_example.ipynb @@ -0,0 +1,2335 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Atera reader — exploratory analysis\n", + "\n", + "Loads a sample Atera (10x Genomics next-gen in-situ) output bundle with the new\n", + "`spatialdata_io.atera` reader, then runs a standard analysis (normalization, PCA,\n", + "UMAP, clustering) and visualizes results spatially. The updates to spatialdata-io were purposefully built to encourage lazy loading of data whenever possible" + ] + }, + { + "cell_type": "markdown", + "id": "00b79e09", + "metadata": {}, + "source": [ + "## Setup: environment, code, and sample data\n", + "\n", + "Run these steps **once**, in a terminal (not in this notebook), before running the cells below.\n", + "\n", + "### 1. Create the conda/mamba environment\n", + "\n", + "```bash\n", + "micromamba create -n spatial_data_atera -c conda-forge python=3.12 -y\n", + "micromamba activate spatial_data_atera\n", + "```\n", + "\n", + "(`conda create -n spatial_data_atera python=3.12 -y && conda activate spatial_data_atera` works the same way if you use `conda` instead of `micromamba`.)\n", + "\n", + "### 2. Clone the branch with the Atera reader\n", + "\n", + "```bash\n", + "git clone --branch stephen/atera_update https://github.com/stephenwilliams22/spatialdata-io.git\n", + "cd spatialdata-io\n", + "```\n", + "\n", + "### 3. Install the package and notebook dependencies\n", + "\n", + "```bash\n", + "# editable install of spatialdata-io itself (pulls in scanpy, spatialdata, dask, etc.)\n", + "pip install -e .\n", + "\n", + "# extra packages used only by this notebook (plotting, PCA, monitoring, GDrive download)\n", + "pip install spatialdata-plot scikit-learn psutil ipykernel \"zarr>=3\"\n", + "```\n", + "\n", + "### 4. Register the Jupyter kernel\n", + "\n", + "```bash\n", + "python -m ipykernel install --user --name spatial_data_atera --display-name \"Python (spatial_data_atera)\"\n", + "```\n", + "\n", + "In Jupyter/VS Code, select the **\"Python (spatial_data_atera)\"** kernel for this notebook.\n", + "\n", + "### 5. Download the tiny sample dataset\n", + "\n", + "The dataset lives on the 10x Genomics support site:\n", + "https://cf.10xgenomics.com/samples/atera/1.0.0/tiny_atera_dataset/tiny_atera_dataset_outs.zip\n", + "Download it with `curl` (installed above):\n", + "\n", + "```bash\n", + "mkdir -p data/tiny_atera_dataset\n", + "cd data/tiny_atera_dataset\n", + "curl -O https://cf.10xgenomics.com/samples/atera/1.0.0/tiny_atera_dataset/tiny_atera_dataset_outs.zip\n", + "unzip tar -xzvf morphology_2d.tar.gz\n", + "tar -xzvf morphology_2d.tar.gz\n", + "tar -xzvf morphology_3d.tar.gz\n", + "```\n", + "\n", + "This creates a local folder containing the output bundle.\n", + "\n", + "### 6. Point the notebook at the downloaded data\n", + "\n", + "In the \"Load data\" cell below, set:\n", + "\n", + "```python\n", + "BUNDLE_PATH = \"data/tiny_atera_dataset\" # adjust if gdown nested the files in a subfolder\n", + "```\n", + "\n", + "(Check with `ls -R data/tiny_atera_dataset` — it should contain the `output_bundle`-style\n", + "contents, e.g. `cell_feature_matrix.zarr.zip`, morphology images, etc. Adjust `BUNDLE_PATH` to\n", + "point directly at the folder that contains those files.)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "30c50be0", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "from pathlib import Path\n", + "\n", + "import matplotlib.pyplot as plt\n", + "import scanpy as sc\n", + "from spatialdata import bounding_box_query\n", + "\n", + "from spatialdata_io import atera\n", + "import spatialdata_plot # noqa: F401\n", + "from spatialdata_io.readers.atera import read_transcripts_for_genes\n", + "from spatialdata_io.readers.atera import read_table_for_genes\n", + "\n", + "import numpy as np\n", + "from scipy.sparse import diags\n", + "import numpy as np\n", + "import pandas as pd\n", + "from scipy.sparse import issparse\n", + "from sklearn.decomposition import IncrementalPCA\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "53562459", + "metadata": {}, + "source": [ + "## Load data\n" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "88e33859", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/stephen.williams/micromamba/envs/spatialdata-test/lib/python3.14/functools.py:1069: UserWarning: The index of the dataframe is not monotonic increasing. It is recommended to sort the data to adjust the order of the index before calling .parse() (or call `parse(sort=True)`) to avoid possible problems due to unknown divisions.\n", + " return self._dispatch(args[0].__class__).__get__(self._obj, self._cls)(*args, **kwargs)\n" + ] + }, + { + "data": { + "text/plain": [ + "SpatialData object\n", + "├── Images\n", + "│ └── 'morphology': DataTree[cyx] (4, 151, 151), (4, 75, 75), (4, 37, 37), (4, 18, 18), (4, 9, 9)\n", + "├── Points\n", + "│ └── 'transcripts': DataFrame with shape: (, 7) (3D points)\n", + "├── Shapes\n", + "│ └── 'cell_boundaries': GeoDataFrame shape: (13, 1) (2D shapes)\n", + "└── Tables\n", + " └── 'table': AnnData (13, 132)\n", + "with coordinate systems:\n", + " ▸ 'global', with elements:\n", + " morphology (Images), transcripts (Points), cell_boundaries (Shapes)" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdata = atera(\n", + " BUNDLE_PATH,\n", + " cells_table=True,\n", + " cells_boundaries=True,\n", + " nucleus_boundaries=False,\n", + " cells_labels=False,\n", + " nucleus_labels=False,\n", + " transcripts=True,\n", + " morphology_images=True,\n", + " morphology_3d_images=False,\n", + ")\n", + "sdata" + ] + }, + { + "cell_type": "markdown", + "id": "9d104199", + "metadata": {}, + "source": [ + "# Check the data structures" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "ea82fbf8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'morphology': \n", + "Group: /\n", + "├── Group: /scale0\n", + "│ Dimensions: (c: 4, y: 151, x: 151)\n", + "│ Coordinates:\n", + "│ * c (c) \n", + "├── Group: /scale1\n", + "│ Dimensions: (c: 4, y: 75, x: 75)\n", + "│ Coordinates:\n", + "│ * c (c) \n", + "├── Group: /scale2\n", + "│ Dimensions: (c: 4, y: 37, x: 37)\n", + "│ Coordinates:\n", + "│ * c (c) \n", + "├── Group: /scale3\n", + "│ Dimensions: (c: 4, y: 18, x: 18)\n", + "│ Coordinates:\n", + "│ * c (c) \n", + "└── Group: /scale4\n", + " Dimensions: (c: 4, y: 9, x: 9)\n", + " Coordinates:\n", + " * c (c) }" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdata.images" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "5e18ff64", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'/': Size: 0B\n", + " Dimensions: ()\n", + " Data variables:\n", + " *empty*,\n", + " '/scale0': Size: 185kB\n", + " Dimensions: (c: 4, y: 151, x: 151)\n", + " Coordinates:\n", + " * c (c) ,\n", + " '/scale1': Size: 47kB\n", + " Dimensions: (c: 4, y: 75, x: 75)\n", + " Coordinates:\n", + " * c (c) ,\n", + " '/scale2': Size: 12kB\n", + " Dimensions: (c: 4, y: 37, x: 37)\n", + " Coordinates:\n", + " * c (c) ,\n", + " '/scale3': Size: 3kB\n", + " Dimensions: (c: 4, y: 18, x: 18)\n", + " Coordinates:\n", + " * c (c) ,\n", + " '/scale4': Size: 1kB\n", + " Dimensions: (c: 4, y: 9, x: 9)\n", + " Coordinates:\n", + " * c (c) }" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdata.images['morphology'].to_dict()" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "002e46d3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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nnjpdgae-1nnjpdgae-18013166084True111.6846009.92573811.9910547.006009451046Immune cellT cellTreg cell0.703274Immune cell|Lymphoid cell|T/NK cell|T cellcell_boundaries
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" + ], + "text/plain": [ + " barcode cell_id filtered leiden_res_1.0 centroid_column \\\n", + "barcode \n", + "abibnbbc-1 abibnbbc-1 4320252178 True 1 16.316103 \n", + "cijfnjdd-1 cijfnjdd-1 4975876403 True 0 6.874815 \n", + "claeglfj-1 claeglfj-1 5016677209 True 1 26.484306 \n", + "dgpljcmo-1 dgpljcmo-1 5217424078 True 0 27.408440 \n", + "dopoijpl-1 dopoijpl-1 5351836155 True 0 18.310593 \n", + "elcplmnf-1 elcplmnf-1 5556387029 True 1 24.609272 \n", + "fheoaeid-1 fheoaeid-1 5759698051 True 0 22.915049 \n", + "iodamdll-1 iodamdll-1 6680527803 True 0 14.134247 \n", + "jabhbfdn-1 jabhbfdn-1 6712399165 True 1 16.793663 \n", + "jijlhgai-1 jijlhgai-1 6855292424 True 1 14.264009 \n", + "lbnkceag-1 lbnkceag-1 7278830598 True 0 21.524061 \n", + "mclfkdaj-1 mclfkdaj-1 7561650953 True 1 2.208580 \n", + "nnjpdgae-1 nnjpdgae-1 8013166084 True 1 11.684600 \n", + "\n", + " centroid_row cell_area nucleus_area transcript_counts \\\n", + "barcode \n", + "abibnbbc-1 8.434508 26.766546 4.985044 55 \n", + "cijfnjdd-1 19.028240 28.697689 4.940134 53 \n", + "claeglfj-1 16.199251 16.526995 4.625762 30 \n", + "dgpljcmo-1 3.344936 32.110874 6.152713 100 \n", + "dopoijpl-1 26.223713 24.565941 5.928161 70 \n", + "elcplmnf-1 22.412422 31.257576 5.209596 52 \n", + "fheoaeid-1 10.127147 21.871323 5.254507 66 \n", + "iodamdll-1 3.543545 8.937152 5.389237 36 \n", + "jabhbfdn-1 18.895208 9.161703 5.703609 26 \n", + "jijlhgai-1 28.482998 17.335381 5.568879 55 \n", + "lbnkceag-1 16.748785 22.230604 5.074865 50 \n", + "mclfkdaj-1 17.255859 10.329371 4.535942 29 \n", + "nnjpdgae-1 9.925738 11.991054 7.006009 45 \n", + "\n", + " control_probe_counts control_codeword_counts total_counts \\\n", + "barcode \n", + "abibnbbc-1 0 0 55 \n", + "cijfnjdd-1 0 0 53 \n", + "claeglfj-1 0 0 30 \n", + "dgpljcmo-1 1 0 101 \n", + "dopoijpl-1 0 0 70 \n", + "elcplmnf-1 0 0 52 \n", + "fheoaeid-1 0 0 66 \n", + "iodamdll-1 0 0 36 \n", + "jabhbfdn-1 1 0 27 \n", + "jijlhgai-1 1 0 56 \n", + "lbnkceag-1 2 0 52 \n", + "mclfkdaj-1 1 0 30 \n", + "nnjpdgae-1 1 0 46 \n", + "\n", + " azimuth_broad azimuth_coarse \\\n", + "barcode \n", + "abibnbbc-1 Immune cell Coarse Not Confidently Annotated \n", + "cijfnjdd-1 Immune cell Coarse Not Confidently Annotated \n", + "claeglfj-1 Unassigned Unassigned \n", + "dgpljcmo-1 Unassigned Coarse Not Confidently Annotated \n", + "dopoijpl-1 Unassigned Unassigned \n", + "elcplmnf-1 Immune cell Coarse Not Confidently Annotated \n", + "fheoaeid-1 Immune cell Coarse Not Confidently Annotated \n", + "iodamdll-1 Unassigned Unassigned \n", + "jabhbfdn-1 Immune cell T cell \n", + "jijlhgai-1 Immune cell T cell \n", + "lbnkceag-1 Immune cell B cell \n", + "mclfkdaj-1 Unassigned Unassigned \n", + "nnjpdgae-1 Immune cell T cell \n", + "\n", + " azimuth_fine azimuth_score \\\n", + "barcode \n", + "abibnbbc-1 Fine Not Confidently Annotated 0.520917 \n", + "cijfnjdd-1 Fine Not Confidently Annotated 0.428365 \n", + "claeglfj-1 Unassigned 0.612201 \n", + "dgpljcmo-1 Fine Not Confidently Annotated 0.494260 \n", + "dopoijpl-1 Unassigned 0.910164 \n", + "elcplmnf-1 Fine Not Confidently Annotated 0.389394 \n", + "fheoaeid-1 Fine Not Confidently Annotated 0.514863 \n", + "iodamdll-1 Unassigned 0.899125 \n", + "jabhbfdn-1 abT (entry) cell 0.409324 \n", + "jijlhgai-1 Treg cell 0.948792 \n", + "lbnkceag-1 Plasma cell 0.898744 \n", + "mclfkdaj-1 Unassigned 0.956713 \n", + "nnjpdgae-1 Treg cell 0.703274 \n", + "\n", + " azimuth_full_hierarchy region \n", + "barcode \n", + "abibnbbc-1 Inconsistent Annotation cell_boundaries \n", + "cijfnjdd-1 Inconsistent Annotation cell_boundaries \n", + "claeglfj-1 Unassigned cell_boundaries \n", + "dgpljcmo-1 Inconsistent Annotation cell_boundaries \n", + "dopoijpl-1 Unassigned cell_boundaries \n", + "elcplmnf-1 Inconsistent Annotation cell_boundaries \n", + "fheoaeid-1 Inconsistent Annotation cell_boundaries \n", + "iodamdll-1 Unassigned cell_boundaries \n", + "jabhbfdn-1 Immune cell|Lymphoid cell|T/NK cell|T cell|abT... cell_boundaries \n", + "jijlhgai-1 Immune cell|Lymphoid cell|T/NK cell|T cell cell_boundaries \n", + "lbnkceag-1 Immune cell|Lymphoid cell|B cell|Plasma cell cell_boundaries \n", + "mclfkdaj-1 Unassigned cell_boundaries \n", + "nnjpdgae-1 Immune cell|Lymphoid cell|T/NK cell|T cell cell_boundaries " + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdata.tables[\"table\"].obs" + ] + }, + { + "cell_type": "markdown", + "id": "a19ef8a1", + "metadata": {}, + "source": [ + "# Plotting some QC Images" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "4da707ef", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sdata.pl.render_images(\"morphology\", channel=\"DAPI\", scale=\"scale4\").pl.show(\n", + " title=\"DAPI (lowest res)\", coordinate_systems=\"global\", figsize=(8, 8)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "7be73a24", + "metadata": {}, + "source": [ + "## Plot cells filled by total transcript counts per cell\n", + "\n", + "`transcript_counts` is a QC column already present in `obs` (no need to load the full\n", + "transcripts table for this)." + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "137f2976", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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barcodecell_idfilteredleiden_res_1.0centroid_columncentroid_rowcell_areanucleus_areatranscript_countscontrol_probe_countscontrol_codeword_countstotal_countsazimuth_broadazimuth_coarseazimuth_fineazimuth_scoreazimuth_full_hierarchyregion
barcode
abibnbbc-1abibnbbc-14320252178True116.3161038.43450826.7665464.985044550055Immune cellCoarse Not Confidently AnnotatedFine Not Confidently Annotated0.520917Inconsistent Annotationcell_boundaries
cijfnjdd-1cijfnjdd-14975876403True06.87481519.02824028.6976894.940134530053Immune cellCoarse Not Confidently AnnotatedFine Not Confidently Annotated0.428365Inconsistent Annotationcell_boundaries
claeglfj-1claeglfj-15016677209True126.48430616.19925116.5269954.625762300030UnassignedUnassignedUnassigned0.612201Unassignedcell_boundaries
dgpljcmo-1dgpljcmo-15217424078True027.4084403.34493632.1108746.15271310010101UnassignedCoarse Not Confidently AnnotatedFine Not Confidently Annotated0.494260Inconsistent Annotationcell_boundaries
dopoijpl-1dopoijpl-15351836155True018.31059326.22371324.5659415.928161700070UnassignedUnassignedUnassigned0.910164Unassignedcell_boundaries
elcplmnf-1elcplmnf-15556387029True124.60927222.41242231.2575765.209596520052Immune cellCoarse Not Confidently AnnotatedFine Not Confidently Annotated0.389394Inconsistent Annotationcell_boundaries
fheoaeid-1fheoaeid-15759698051True022.91504910.12714721.8713235.254507660066Immune cellCoarse Not Confidently AnnotatedFine Not Confidently Annotated0.514863Inconsistent Annotationcell_boundaries
iodamdll-1iodamdll-16680527803True014.1342473.5435458.9371525.389237360036UnassignedUnassignedUnassigned0.899125Unassignedcell_boundaries
jabhbfdn-1jabhbfdn-16712399165True116.79366318.8952089.1617035.703609261027Immune cellT cellabT (entry) cell0.409324Immune cell|Lymphoid cell|T/NK cell|T cell|abT...cell_boundaries
jijlhgai-1jijlhgai-16855292424True114.26400928.48299817.3353815.568879551056Immune cellT cellTreg cell0.948792Immune cell|Lymphoid cell|T/NK cell|T cellcell_boundaries
lbnkceag-1lbnkceag-17278830598True021.52406116.74878522.2306045.074865502052Immune cellB cellPlasma cell0.898744Immune cell|Lymphoid cell|B cell|Plasma cellcell_boundaries
mclfkdaj-1mclfkdaj-17561650953True12.20858017.25585910.3293714.535942291030UnassignedUnassignedUnassigned0.956713Unassignedcell_boundaries
nnjpdgae-1nnjpdgae-18013166084True111.6846009.92573811.9910547.006009451046Immune cellT cellTreg cell0.703274Immune cell|Lymphoid cell|T/NK cell|T cellcell_boundaries
\n", + "
" + ], + "text/plain": [ + " barcode cell_id filtered leiden_res_1.0 centroid_column \\\n", + "barcode \n", + "abibnbbc-1 abibnbbc-1 4320252178 True 1 16.316103 \n", + "cijfnjdd-1 cijfnjdd-1 4975876403 True 0 6.874815 \n", + "claeglfj-1 claeglfj-1 5016677209 True 1 26.484306 \n", + "dgpljcmo-1 dgpljcmo-1 5217424078 True 0 27.408440 \n", + "dopoijpl-1 dopoijpl-1 5351836155 True 0 18.310593 \n", + "elcplmnf-1 elcplmnf-1 5556387029 True 1 24.609272 \n", + "fheoaeid-1 fheoaeid-1 5759698051 True 0 22.915049 \n", + "iodamdll-1 iodamdll-1 6680527803 True 0 14.134247 \n", + "jabhbfdn-1 jabhbfdn-1 6712399165 True 1 16.793663 \n", + "jijlhgai-1 jijlhgai-1 6855292424 True 1 14.264009 \n", + "lbnkceag-1 lbnkceag-1 7278830598 True 0 21.524061 \n", + "mclfkdaj-1 mclfkdaj-1 7561650953 True 1 2.208580 \n", + "nnjpdgae-1 nnjpdgae-1 8013166084 True 1 11.684600 \n", + "\n", + " centroid_row cell_area nucleus_area transcript_counts \\\n", + "barcode \n", + "abibnbbc-1 8.434508 26.766546 4.985044 55 \n", + "cijfnjdd-1 19.028240 28.697689 4.940134 53 \n", + "claeglfj-1 16.199251 16.526995 4.625762 30 \n", + "dgpljcmo-1 3.344936 32.110874 6.152713 100 \n", + "dopoijpl-1 26.223713 24.565941 5.928161 70 \n", + "elcplmnf-1 22.412422 31.257576 5.209596 52 \n", + "fheoaeid-1 10.127147 21.871323 5.254507 66 \n", + "iodamdll-1 3.543545 8.937152 5.389237 36 \n", + "jabhbfdn-1 18.895208 9.161703 5.703609 26 \n", + "jijlhgai-1 28.482998 17.335381 5.568879 55 \n", + "lbnkceag-1 16.748785 22.230604 5.074865 50 \n", + "mclfkdaj-1 17.255859 10.329371 4.535942 29 \n", + "nnjpdgae-1 9.925738 11.991054 7.006009 45 \n", + "\n", + " control_probe_counts control_codeword_counts total_counts \\\n", + "barcode \n", + "abibnbbc-1 0 0 55 \n", + "cijfnjdd-1 0 0 53 \n", + "claeglfj-1 0 0 30 \n", + "dgpljcmo-1 1 0 101 \n", + "dopoijpl-1 0 0 70 \n", + "elcplmnf-1 0 0 52 \n", + "fheoaeid-1 0 0 66 \n", + "iodamdll-1 0 0 36 \n", + "jabhbfdn-1 1 0 27 \n", + "jijlhgai-1 1 0 56 \n", + "lbnkceag-1 2 0 52 \n", + "mclfkdaj-1 1 0 30 \n", + "nnjpdgae-1 1 0 46 \n", + "\n", + " azimuth_broad azimuth_coarse \\\n", + "barcode \n", + "abibnbbc-1 Immune cell Coarse Not Confidently Annotated \n", + "cijfnjdd-1 Immune cell Coarse Not Confidently Annotated \n", + "claeglfj-1 Unassigned Unassigned \n", + "dgpljcmo-1 Unassigned Coarse Not Confidently Annotated \n", + "dopoijpl-1 Unassigned Unassigned \n", + "elcplmnf-1 Immune cell Coarse Not Confidently Annotated \n", + "fheoaeid-1 Immune cell Coarse Not Confidently Annotated \n", + "iodamdll-1 Unassigned Unassigned \n", + "jabhbfdn-1 Immune cell T cell \n", + "jijlhgai-1 Immune cell T cell \n", + "lbnkceag-1 Immune cell B cell \n", + "mclfkdaj-1 Unassigned Unassigned \n", + "nnjpdgae-1 Immune cell T cell \n", + "\n", + " azimuth_fine azimuth_score \\\n", + "barcode \n", + "abibnbbc-1 Fine Not Confidently Annotated 0.520917 \n", + "cijfnjdd-1 Fine Not Confidently Annotated 0.428365 \n", + "claeglfj-1 Unassigned 0.612201 \n", + "dgpljcmo-1 Fine Not Confidently Annotated 0.494260 \n", + "dopoijpl-1 Unassigned 0.910164 \n", + "elcplmnf-1 Fine Not Confidently Annotated 0.389394 \n", + "fheoaeid-1 Fine Not Confidently Annotated 0.514863 \n", + "iodamdll-1 Unassigned 0.899125 \n", + "jabhbfdn-1 abT (entry) cell 0.409324 \n", + "jijlhgai-1 Treg cell 0.948792 \n", + "lbnkceag-1 Plasma cell 0.898744 \n", + "mclfkdaj-1 Unassigned 0.956713 \n", + "nnjpdgae-1 Treg cell 0.703274 \n", + "\n", + " azimuth_full_hierarchy region \n", + "barcode \n", + "abibnbbc-1 Inconsistent Annotation cell_boundaries \n", + "cijfnjdd-1 Inconsistent Annotation cell_boundaries \n", + "claeglfj-1 Unassigned cell_boundaries \n", + "dgpljcmo-1 Inconsistent Annotation cell_boundaries \n", + "dopoijpl-1 Unassigned cell_boundaries \n", + "elcplmnf-1 Inconsistent Annotation cell_boundaries \n", + "fheoaeid-1 Inconsistent Annotation cell_boundaries \n", + "iodamdll-1 Unassigned cell_boundaries \n", + "jabhbfdn-1 Immune cell|Lymphoid cell|T/NK cell|T cell|abT... cell_boundaries \n", + "jijlhgai-1 Immune cell|Lymphoid cell|T/NK cell|T cell cell_boundaries \n", + "lbnkceag-1 Immune cell|Lymphoid cell|B cell|Plasma cell cell_boundaries \n", + "mclfkdaj-1 Unassigned cell_boundaries \n", + "nnjpdgae-1 Immune cell|Lymphoid cell|T/NK cell|T cell cell_boundaries " + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdata.tables[\"table\"].obs" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "f94ce056", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sdata.pl.render_shapes(\"cell_boundaries\", color=\"transcript_counts\", fill_alpha=1, outline_alpha=0).pl.show(\n", + " title=\"Transcript counts per cell\", coordinate_systems=\"global\", figsize=(10, 10)\n", + ")\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "4c0f8214", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "adata = sdata.tables[\"table\"]\n", + "counts = adata.layers[\"counts\"] if \"counts\" in adata.layers else adata.X\n", + "boolean_counts = counts > 0\n", + "\n", + "n_genes_list = []\n", + "for i in range(boolean_counts.numblocks[0]):\n", + " block = boolean_counts.blocks[i].compute()\n", + " n_genes_list.append(np.asarray(block.sum(axis=1)).ravel())\n", + "\n", + "sdata.tables[\"table\"].obs[\"n_genes\"] = np.concatenate(n_genes_list)\n", + "\n", + "sdata.pl.render_shapes(\"cell_boundaries\", color=\"n_genes\", fill_alpha=1, outline_alpha=0).pl.show(\n", + " title=\"Genes detected per cell\", coordinate_systems=\"global\", figsize=(10, 10)\n", + ")\n" + ] + }, + { + "cell_type": "markdown", + "id": "b5617c5a", + "metadata": {}, + "source": [ + "## Plot transcripts spatial distribution for a single gene or multiple genes" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "f01d2e8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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feature_namefilteredhighly_variablefeature_idfeature_typegenome
feature_id
ENSG00000177556ATOX1TrueTrueENSG00000177556Gene Expressionhg38
ENSG00000205502C2CD4BTrueTrueENSG00000205502Gene Expressionhg38
ENSG00000177455CD19TrueTrueENSG00000177455Gene Expressionhg38
ENSG00000186407CD300EFalseFalseENSG00000186407Gene Expressionhg38
ENSG00000161649CD300LGTrueTrueENSG00000161649Gene Expressionhg38
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" + ], + "text/plain": [ + " feature_name filtered highly_variable \\\n", + "feature_id \n", + "ENSG00000177556 ATOX1 True True \n", + "ENSG00000205502 C2CD4B True True \n", + "ENSG00000177455 CD19 True True \n", + "ENSG00000186407 CD300E False False \n", + "ENSG00000161649 CD300LG True True \n", + "... ... ... ... \n", + "UnassignedCodeword_0019 UnassignedCodeword_0019 False False \n", + "UnassignedCodeword_0023 UnassignedCodeword_0023 False False \n", + "UnassignedCodeword_0081 UnassignedCodeword_0081 False False \n", + "UnassignedCodeword_0085 UnassignedCodeword_0085 False False \n", + "UnassignedCodeword_0089 UnassignedCodeword_0089 False False \n", + "\n", + " feature_id feature_type \\\n", + "feature_id \n", + "ENSG00000177556 ENSG00000177556 Gene Expression \n", + "ENSG00000205502 ENSG00000205502 Gene Expression \n", + "ENSG00000177455 ENSG00000177455 Gene Expression \n", + "ENSG00000186407 ENSG00000186407 Gene Expression \n", + "ENSG00000161649 ENSG00000161649 Gene Expression \n", + "... ... ... \n", + "UnassignedCodeword_0019 UnassignedCodeword_0019 Unassigned Codeword \n", + "UnassignedCodeword_0023 UnassignedCodeword_0023 Unassigned Codeword \n", + "UnassignedCodeword_0081 UnassignedCodeword_0081 Unassigned Codeword \n", + "UnassignedCodeword_0085 UnassignedCodeword_0085 Unassigned Codeword \n", + "UnassignedCodeword_0089 UnassignedCodeword_0089 Unassigned Codeword \n", + "\n", + " genome \n", + "feature_id \n", + "ENSG00000177556 hg38 \n", + "ENSG00000205502 hg38 \n", + "ENSG00000177455 hg38 \n", + "ENSG00000186407 hg38 \n", + "ENSG00000161649 hg38 \n", + "... ... \n", + "UnassignedCodeword_0019 Unassigned Codeword \n", + "UnassignedCodeword_0023 Unassigned Codeword \n", + "UnassignedCodeword_0081 Unassigned Codeword \n", + "UnassignedCodeword_0085 Unassigned Codeword \n", + "UnassignedCodeword_0089 Unassigned Codeword \n", + "\n", + "[132 rows x 6 columns]" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdata.tables[\"table\"].var" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "cee3d9ac", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gene_name = \"TOX3\"\n", + "\n", + "sdata = read_transcripts_for_genes(sdata, BUNDLE_PATH, gene_name, points_key=f\"transcripts_{gene_name}\")\n", + "\n", + "sdata.pl.render_shapes(\n", + " \"cell_boundaries\", fill_alpha=0, outline_alpha=1\n", + ").pl.render_points(\n", + " f\"transcripts_{gene_name}\",\n", + " color=\"orange\",\n", + " size=10, # for a real sample you want to use something more like 0.3\n", + " method=\"datashader\"\n", + ").pl.show(\n", + " title=f\"{gene_name} transcripts\", figsize=(8, 8)\n", + ")\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "2df87001", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gene_name = \"TOX3\"\n", + "\n", + "gene_adata = read_table_for_genes(BUNDLE_PATH, gene_name)\n", + "sdata.shapes[\"cell_boundaries\"][gene_name] = np.asarray(gene_adata[:, gene_name].X.todense()).ravel()\n", + "\n", + "sdata.pl.render_shapes(\n", + " \"cell_boundaries\",\n", + " color=gene_name,\n", + " fill_alpha=1,\n", + " outline_alpha=0,\n", + " method=\"datashader\"\n", + ").pl.show(\n", + " title=f\"{gene_name} expression per cell\",\n", + " coordinate_systems=\"global\",\n", + " figsize=(10, 10)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "1fd024b6", + "metadata": {}, + "source": [ + "### Crop to a specific region.\n", + "Need spatialdata_plot >= 0.4.0" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "7ca1754f", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gene_name = \"TOX3\"\n", + "\n", + "gene_adata = read_table_for_genes(BUNDLE_PATH, gene_name)\n", + "sdata.shapes[\"cell_boundaries\"][gene_name] = np.asarray(gene_adata[:, gene_name].X.todense()).ravel()\n", + "\n", + "sdata.pl.render_shapes(\n", + " \"cell_boundaries\",\n", + " color=gene_name,\n", + " fill_alpha=1,\n", + " outline_alpha=0,\n", + " method=\"datashader\"\n", + ").pl.show(\n", + " title=f\"{gene_name} expression per cell\",\n", + " coordinate_systems=\"global\",\n", + " figsize=(10, 10),\n", + " crop_coord=(60, 100, 20, 60)\n", + "\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "bca2b673", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "genes = [\"KLRD1\", \"TOX3\"]\n", + "\n", + "sdata = read_transcripts_for_genes(sdata, BUNDLE_PATH, genes, points_key=\"transcripts_multi\")\n", + "\n", + "sdata.pl.render_shapes(\n", + " \"cell_boundaries\", fill_alpha=0, outline_alpha=1, method=\"datashader\"\n", + ").pl.render_points(\n", + " \"transcripts_multi\",\n", + " color=\"feature_name\",\n", + " groups=genes,\n", + " palette=[\"orange\", \"cyan\"],\n", + " size=10,\n", + ").pl.show(title=\"Multi-gene transcripts\", figsize=(8, 8))\n" + ] + }, + { + "cell_type": "markdown", + "id": "21554524", + "metadata": {}, + "source": [ + "## Plot the codewords for a gene" + ] + }, + { + "cell_type": "markdown", + "id": "45bb86d1", + "metadata": {}, + "source": [ + "### All on one plot" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "9887f3a5", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gene_name = \"KLRD1\"\n", + "sdata = read_transcripts_for_genes(\n", + " sdata, BUNDLE_PATH, gene_name, points_key=f\"transcripts_{gene_name}_codewords\", by_codeword=True\n", + ")\n", + "\n", + "codewords = sorted(sdata.points[f\"transcripts_{gene_name}_codewords\"][\"feature_name\"].compute().cat.categories)\n", + "\n", + "sdata.pl.render_shapes(\n", + " \"cell_boundaries\", fill_alpha=0, outline_alpha=1, method=\"datashader\"\n", + ").pl.render_points(\n", + " f\"transcripts_{gene_name}_codewords\",\n", + " color=\"feature_name\",\n", + " groups=codewords,\n", + " palette=[\"orange\", \"cyan\", \"magenta\"][: len(codewords)],\n", + " size=10,\n", + ").pl.show(title=f\"{gene_name} transcripts by codeword\", figsize=(8, 8))\n" + ] + }, + { + "cell_type": "markdown", + "id": "be70c1b0", + "metadata": {}, + "source": [ + "### Codewords on multiple plots\n", + "**Wont work for this tiny dataset because there's only 1 codeword**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d8a337c", + "metadata": {}, + "outputs": [], + "source": [ + "gene_name = \"TOX3\"\n", + "points_key = f\"transcripts_{gene_name}_codewords\"\n", + "\n", + "sdata = read_transcripts_for_genes(sdata, BUNDLE_PATH, gene_name, points_key=points_key, by_codeword=True)\n", + "\n", + "df = sdata.points[points_key].compute()\n", + "counts = df[\"feature_name\"].value_counts()\n", + "codewords = sorted(df[\"feature_name\"].cat.categories)\n", + "\n", + "fig, axes = plt.subplots(1, len(codewords), figsize=(6 * len(codewords), 6))\n", + "for ax, cw in zip(axes, codewords, strict=True):\n", + " sdata.pl.render_shapes(\n", + " \"cell_boundaries\", fill_alpha=0, outline_alpha=1, method=\"datashader\"\n", + " ).pl.render_points(\n", + " points_key,\n", + " color=\"feature_name\",\n", + " groups=cw,\n", + " palette=\"orange\",\n", + " size=2,\n", + " ).pl.show(title=f\"{cw} (n={counts.get(cw, 0)})\", ax=ax)\n", + "\n", + "fig.tight_layout()\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "87e7ccb1", + "metadata": {}, + "source": [ + "## Spatial plots of the cells" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "fea950cf", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sdata.pl.render_images(\"morphology\", channel=\"DAPI\").pl.render_shapes(\n", + " \"cell_boundaries\", fill_alpha=0, outline_alpha=1\n", + ").pl.show(title=\"DAPI + cell boundaries\", coordinate_systems=\"global\", figsize=(8, 8))" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "337e0f20", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + "dask.array" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sdata.tables[\"table\"].X" + ] + }, + { + "cell_type": "markdown", + "id": "f8e2e6a0", + "metadata": {}, + "source": [ + "# Normalization and HVG Detection\n", + "In testing scanpy HVG gene detection blew up the memory on the full-gene table.\n", + "So we implement a streaming mean/variance calculation that only ever holds one chunk in memory at a time.\n", + "This is also faster than scanpy's HVG, which computes the gram matrix and then does an SVD on it." + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "16fa2e49", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "selected 132 highly variable genes\n" + ] + } + ], + "source": [ + "adata = sdata.tables[\"table\"]\n", + "adata.layers[\"counts\"] = adata.X.copy() # cheap: just copies the dask graph\n", + "\n", + "sc.pp.normalize_total(adata, target_sum=1e4) # stays lazy (dask)\n", + "sc.pp.log1p(adata) # stays lazy\n", + "\n", + "def streaming_gene_mean_var(X):\n", + " n_obs, n_var = X.shape\n", + " sum_, sumsq = np.zeros(n_var), np.zeros(n_var)\n", + " for i in range(X.numblocks[0]):\n", + " block = X.blocks[i].compute() # one chunk materialized, discarded each iteration\n", + " block = block.expm1() # undo log1p first, like scanpy's seurat-flavor HVG does\n", + " sum_ += np.asarray(block.sum(axis=0)).ravel()\n", + " sumsq += np.asarray(block.multiply(block).sum(axis=0)).ravel()\n", + " mean = sum_ / n_obs\n", + " var = sumsq / n_obs - mean ** 2\n", + " var *= n_obs / (n_obs - 1)\n", + " return mean, var\n", + "\n", + "mean, var = streaming_gene_mean_var(adata.X)\n", + "mean_for_disp = mean.copy()\n", + "mean_for_disp[mean_for_disp == 0] = 1e-12\n", + "dispersion = var / mean_for_disp\n", + "dispersion[dispersion == 0] = np.nan\n", + "log_dispersion = np.log(dispersion)\n", + "log_mean = np.log1p(mean)\n", + "\n", + "n_bins = 20\n", + "bins = pd.cut(log_mean, bins=n_bins)\n", + "df = pd.DataFrame({\"log_mean\": log_mean, \"log_dispersion\": log_dispersion, \"bin\": bins})\n", + "grouped = df.groupby(\"bin\", observed=True)[\"log_dispersion\"]\n", + "norm_dispersion = (df[\"log_dispersion\"] - grouped.transform(\"mean\")) / grouped.transform(\"std\")\n", + "\n", + "n_top_genes = 2000\n", + "top_idx = norm_dispersion.fillna(-np.inf).nlargest(n_top_genes).index.to_numpy()\n", + "hvg_mask = np.zeros(adata.n_vars, dtype=bool)\n", + "hvg_mask[top_idx] = True\n", + "adata.var[\"highly_variable\"] = hvg_mask\n", + "\n", + "\n", + "adata_hvg = adata[:, hvg_mask].copy()\n", + "print(f\"selected {hvg_mask.sum()} highly variable genes\")\n" + ] + }, + { + "cell_type": "markdown", + "id": "483931a9", + "metadata": {}, + "source": [ + "# Streaming PCA\n", + "In testing scanpy pca blew up the memory on the full-gene table.\n", + "So we implement a streaming PCA that only ever holds one chunk in memory at a time.\n", + "This is also faster than scanpy's PCA, which computes the gram matrix and then does an SVD on it.\n", + "The streaming PCA uses IncrementalPCA, which does a partial fit on each chunk and then transforms each chunk sequentially.\n", + "\n", + "n components set low for this small dataset. increase for larger ones " + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "pca done\n", + "WARNING: n_obs too small: adjusting to `n_neighbors = 7`\n", + "neighbors done\n", + "leiden done\n" + ] + } + ], + "source": [ + "def streaming_pca(X, n_components=50, batch_rows=50_000):\n", + " ipca = IncrementalPCA(n_components=n_components)\n", + " def batches():\n", + " for i in range(X.numblocks[0]):\n", + " block = X.blocks[i].compute()\n", + " block = np.asarray(block.todense()) if issparse(block) else np.asarray(block)\n", + " for start in range(0, block.shape[0], batch_rows):\n", + " yield block[start:start + batch_rows]\n", + " for batch in batches():\n", + " ipca.partial_fit(batch) # <-- removed the size check\n", + " parts = [ipca.transform(batch) for batch in batches()]\n", + " return np.concatenate(parts, axis=0), ipca\n", + "\n", + "X_pca, pca_model = streaming_pca(adata_hvg.X, n_components=10, batch_rows=50_000)\n", + "adata_hvg.obsm[\"X_pca\"] = X_pca\n", + "adata_hvg.uns[\"pca\"] = {\"variance_ratio\": pca_model.explained_variance_ratio_}\n", + "print(\"pca done\")\n", + "\n", + "sc.pp.neighbors(adata_hvg, use_rep=\"X_pca\") # small dense embedding now, cheap\n", + "print(\"neighbors done\")\n", + "sc.tl.leiden(adata_hvg, key_added=\"leiden\", flavor=\"igraph\", n_iterations=2)\n", + "print(\"leiden done\")" + ] + }, + { + "cell_type": "markdown", + "id": "d45dfa1b", + "metadata": {}, + "source": [ + "## Add HVG, PCA, Clustering info back to sdata" + ] + }, + { + "cell_type": "markdown", + "id": "f4e157ca", + "metadata": {}, + "source": [ + "Copy the HVG-only pipeline results (PCA embedding, neighbor graph, leiden clusters) back onto the full-gene `adata` so the table we write back keeps every gene, not just the 2000 HVGs" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "c93f6d20", + "metadata": {}, + "outputs": [], + "source": [ + "adata.obs[\"leiden\"] = adata_hvg.obs[\"leiden\"].reindex(adata.obs_names)\n", + "adata.obsm[\"X_pca\"] = adata_hvg.obsm[\"X_pca\"]\n", + "adata.uns[\"pca\"] = adata_hvg.uns[\"pca\"]\n", + "adata.uns[\"neighbors\"] = adata_hvg.uns[\"neighbors\"]\n", + "adata.obsp[\"distances\"] = adata_hvg.obsp[\"distances\"]\n", + "adata.obsp[\"connectivities\"] = adata_hvg.obsp[\"connectivities\"]\n", + "del adata_hvg" + ] + }, + { + "cell_type": "markdown", + "id": "1a964e72", + "metadata": {}, + "source": [ + "## Plot the clusters over the image" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "id": "87ebbb07", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sdata.pl.render_shapes(\n", + " \"cell_boundaries\",\n", + " color=\"leiden\",\n", + " outline_alpha=1\n", + ").pl.show(\n", + " title=\"Leiden clusters\",\n", + " coordinate_systems=\"global\",\n", + " figsize=(10, 10)\n", + ")\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "sptialdata-io update", + "language": "python", + "name": "spatialdata_io_update" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/src/spatialdata_io/__init__.py b/src/spatialdata_io/__init__.py index 1c10cc53..cf4cb6c3 100644 --- a/src/spatialdata_io/__init__.py +++ b/src/spatialdata_io/__init__.py @@ -6,6 +6,7 @@ _LAZY_IMPORTS: dict[str, str] = { # readers + "atera": "spatialdata_io.readers.atera", "codex": "spatialdata_io.readers.codex", "cosmx": "spatialdata_io.readers.cosmx", "curio": "spatialdata_io.readers.curio", @@ -31,6 +32,7 @@ __all__ = [ # readers + "atera", "codex", "cosmx", "curio", @@ -78,6 +80,7 @@ def __dir__() -> list[str]: from spatialdata_io.converters.generic_to_zarr import generic_to_zarr # readers + from spatialdata_io.readers.atera import atera from spatialdata_io.readers.codex import codex from spatialdata_io.readers.cosmx import cosmx from spatialdata_io.readers.curio import curio diff --git a/src/spatialdata_io/_constants/_constants.py b/src/spatialdata_io/_constants/_constants.py index 46f983b1..ccf068b1 100644 --- a/src/spatialdata_io/_constants/_constants.py +++ b/src/spatialdata_io/_constants/_constants.py @@ -163,6 +163,73 @@ class XeniumKeys(ModeEnum): EXPLORER_SELECTION_KEY = "Selection" +@unique +class AteraKeys(ModeEnum): + """Keys for *10x Genomics Atera* formatted dataset.""" + + # specifications + SPECS_FILE = "experiment.spatial" + PIXEL_SIZE = "pixel_size" + PANEL_CONFIG_FILE = "panel_config.json" + + # zarr files + CELL_FEATURE_MATRIX_FILE = "cell_feature_matrix.zarr.zip" + CSC_CELL_FEATURE_MATRIX_FILE = "csc_cell_feature_matrix.zarr.zip" + CELLS_FILE = "cells.zarr.zip" + TRANSCRIPTS_FILE = "transcripts.zarr.zip" + + # cell_feature_matrix.zarr.zip groups/keys + X_GROUP = "X" + X_DATA = "data" + X_INDICES = "indices" + X_INDPTR = "indptr" + OBS_GROUP = "obs" + VAR_GROUP = "var" + OBSM_GROUP = "obsm" + CATEGORIES = "categories" + CODES = "codes" + + # obs / cell identifiers + CELL_ID = "cell_id" + CENTROID_X = "centroid_column" + CENTROID_Y = "centroid_row" + CELL_AREA = "cell_area" + + # var / features + FEATURE_ID = "feature_id" + FEATURE_NAME = "feature_name" + VAR_FILTERED = "filtered" + VAR_HIGHLY_VARIABLE = "highly_variable" + VAR_FEATURE_TYPE = "feature_type" + VAR_GENOME = "genome" + + # cells.zarr.zip groups + MASKS_GROUP = "masks" + POLYGON_SETS_GROUP = "polygon_sets" + POLYGON_VERTICES = "vertices" + POLYGON_NUM_VERTICES = "num_vertices" + POLYGON_CELL_INDEX = "cell_index" + GRIDDED_POLYGON_SETS_GROUP = "gridded_polygon_sets" + RELATIVE_VERTICES = "relative_vertices" + BBOXES = "bboxes" + GRID_SIZE = "grid_size" + + # transcripts.zarr.zip groups/keys + GRID_GROUP = "grid" + TRANSCRIPTS_LOCATION = "location" + TRANSCRIPTS_GENE_OFFSET = "gene_offset" + TRANSCRIPTS_OVERLAPS_NUCLEUS = "overlaps_nucleus" + TRANSCRIPTS_QUALITY_SCORE = "quality_score" + TRANSCRIPTS_X = "x" + TRANSCRIPTS_Y = "y" + TRANSCRIPTS_Z = "z" + CODEWORD_IDENTITY = "codeword_identity" + + # morphology images + MORPHOLOGY_2D_DIR = "morphology_2d" + MORPHOLOGY_3D_DIR = "morphology_3d" + + @unique class VisiumKeys(ModeEnum): """Keys for *10X Genomics Visium* formatted dataset.""" diff --git a/src/spatialdata_io/readers/_atera_common.py b/src/spatialdata_io/readers/_atera_common.py new file mode 100644 index 00000000..b9024f98 --- /dev/null +++ b/src/spatialdata_io/readers/_atera_common.py @@ -0,0 +1,670 @@ +from __future__ import annotations + +import asyncio +import contextlib +import json +import re +from pathlib import Path +from types import MappingProxyType +from typing import TYPE_CHECKING, Any + +import dask.array as da +import dask.dataframe as dd +import numpy as np +import pandas as pd +import zarr +from dask import delayed +from geopandas import GeoDataFrame +from shapely import GeometryType, from_ragged_array +from spatialdata import SpatialData +from spatialdata.models import ( + Image2DModel, + Image3DModel, + Labels2DModel, + PointsModel, + ShapesModel, +) +from spatialdata.transformations.transformations import Identity, Scale + +from spatialdata_io._constants._constants import AteraKeys + +if TYPE_CHECKING: + from collections.abc import Iterator, Mapping + + from xarray import DataArray, DataTree + +# `var` in cell_feature_matrix.zarr.zip also has ~135 per-cluster differential-expression columns +# (`de_leiden_res_1.0_c{N}_{logfc,pval,pval_adj,rank,score}`); the readers keep only this small +# default subset to avoid holding that wide table in memory. Use `read_var` to load the full table. +DEFAULT_VAR_COLUMNS = [ + str(AteraKeys.FEATURE_NAME), + str(AteraKeys.VAR_FILTERED), + str(AteraKeys.VAR_HIGHLY_VARIABLE), + str(AteraKeys.FEATURE_ID), + str(AteraKeys.VAR_FEATURE_TYPE), + str(AteraKeys.VAR_GENOME), +] + +# Number of `obs` rows read per `X` chunk in the readers' lazy `dask` array for the table. +DEFAULT_TABLE_ROW_CHUNK_SIZE = 50_000 + +_UINT32_SENTINEL = np.iinfo(np.uint32).max + + +@contextlib.contextmanager +def _patched_ragged_vlen_chunk_decode() -> Iterator[None]: + """Work around ``zarr``'s `V2Codec` failing to decode a vlen (string) array's ragged trailing chunk. + + `obs`/`var`'s string columns (e.g. `barcode`) are stored as 1-D zarr v2 arrays with a fixed + chunk size chosen independently of the row count (e.g. `1_000_000`), so the trailing chunk is + usually smaller than that nominal size. For fixed-width dtypes this is harmless: `zarr` pads an + incomplete edge chunk to the full chunk shape (with the fill value) before compression, so it + always decodes back to the full nominal size. Variable-length (`vlen-utf8`/`StringDType`) + chunks aren't padded this way -- only the elements actually written are stored -- but + `V2Codec._decode_single` unconditionally reshapes the decoded chunk to the nominal chunk shape + regardless of dtype, so reading across that trailing chunk raises e.g. ``ValueError: cannot + reshape array of size 376951 into shape (1000000,)``. Reproduced against `zarr` 3.1.6 and + 3.4.0 (the latest release as of writing); no matching issue found upstream yet. + + This patches `V2Codec._decode_single` (the abstract-method entry point present on every + `zarr` 3.x release, unlike the private `_decode_sync`/`_decode_single` split introduced partway + through the 3.x series) for the duration of the context, falling back to the chunk's actual + (smaller) size instead of raising, only for the 1-D case this affects; any other reshape failure + (e.g. a genuinely corrupt chunk, or a ragged chunk in more than one dimension) is still raised. + """ + try: + from numcodecs.compat import ensure_ndarray_like + from zarr.codecs._v2 import V2Codec + from zarr.registry import get_ndbuffer_class + except ImportError: + # `zarr`'s internal layout changed enough that this patch no longer applies; read without + # it rather than fail outright (bundles without a ragged vlen chunk are unaffected either way). + yield + return + + original_decode_single = V2Codec._decode_single + + async def patched_decode_single(self: V2Codec, chunk_bytes: Any, chunk_spec: Any) -> Any: + def decode() -> Any: + cdata = chunk_bytes.as_array_like() + chunk = self.compressor.decode(cdata) if self.compressor else cdata + if self.filters: + for f in reversed(self.filters): + chunk = f.decode(chunk) + chunk = ensure_ndarray_like(chunk) + if chunk_spec.dtype.dtype_cls is not np.dtypes.ObjectDType: + try: + chunk = chunk.view(chunk_spec.dtype.to_native_dtype()) + except TypeError: + chunk = np.array(chunk).astype(chunk_spec.dtype.to_native_dtype()) + elif chunk.dtype != object: + raise RuntimeError("cannot read object array without object codec") + + chunk = chunk.reshape(-1, order="A") + nominal_size = int(np.prod(chunk_spec.shape)) + if chunk.size == nominal_size: + chunk = chunk.reshape(chunk_spec.shape, order=chunk_spec.order) + elif len(chunk_spec.shape) != 1: + raise ValueError(f"cannot reshape array of size {chunk.size} into shape {chunk_spec.shape}") + # else: ragged trailing chunk of a 1-D vlen array -- keep its natural, smaller size. + return get_ndbuffer_class().from_ndarray_like(chunk) + + return await asyncio.to_thread(decode) + + V2Codec._decode_single = patched_decode_single + try: + yield + finally: + V2Codec._decode_single = original_decode_single + + +def _decode_packed_cell_id(raw: np.ndarray) -> np.ndarray: + """Decode a (N, 2) uint32 packed cell id array into the int64 ids used in the cell-feature-matrix obs. + + The two uint32 columns pack into an int64 as ``low + (high << 32)``. The sentinel + ``(uint32 max, uint32 max)`` marks "no cell assigned" and is decoded to ``-1``. + """ + low = raw[:, 0].astype(np.int64) + high = raw[:, 1].astype(np.int64) + packed = low + (high << 32) + unassigned = (raw[:, 0] == _UINT32_SENTINEL) & (raw[:, 1] == _UINT32_SENTINEL) + packed[unassigned] = -1 + return packed + + +def _get_labels( + cells_group: zarr.Group, + mask_index: int, + labels_models_kwargs: Mapping[str, Any] = MappingProxyType({}), +) -> DataArray | DataTree: + """Read the labels raster from cells.zarr.zip masks/{mask_index} (0 = nucleus, 1 = cell).""" + masks = da.from_array(cells_group[f"{AteraKeys.MASKS_GROUP}/{mask_index}"]) + return Labels2DModel.parse(masks, dims=("y", "x"), transformations={"global": Identity()}, **labels_models_kwargs) + + +def _polygons_to_shapes( + coords: np.ndarray, + num_vertices: np.ndarray, + cell_index: np.ndarray, + cell_ids: np.ndarray | None, + pixel_size: float, + is_nucleus: bool, +) -> GeoDataFrame: + """Build a boundary-polygon `ShapesModel` from ragged ``(coords, num_vertices, cell_index)``. + + ``coords`` is the flat, ragged (N total vertices, 2) array of (x, y) pairs; ``num_vertices`` + gives each polygon's vertex count (so ``coords`` splits into contiguous runs of this length). + ``cell_index`` maps each polygon (0-based) to the owning cell's row position in the + table/``cells.zarr`` (which share the same row order). For cells this mapping is the identity; + for nuclei it may not be, since multinucleate cells have multiple nucleus polygons. Shared + between `_get_polygons` (eager, `polygon_sets`) and `read_cell_boundaries` (tiled/pyramided, + `gridded_polygon_sets`), which differ only in how they produce these ragged arrays. + """ + n_polygons = len(num_vertices) + ring_offsets = np.concatenate([[0], np.cumsum(num_vertices)]) + geom_offsets = np.arange(n_polygons + 1) + geoms = from_ragged_array(GeometryType.POLYGON, coords, offsets=(ring_offsets, geom_offsets)) + + owning_cell_id = cell_ids[cell_index] if cell_ids is not None else cell_index + if is_nucleus: + # multiple polygons can map to the same cell (multinucleate cells): use the 1-based + # polygon position as the index, and keep the owning cell id as a column. + geo_df = GeoDataFrame( + {"geometry": geoms, str(AteraKeys.CELL_ID): owning_cell_id}, + index=pd.RangeIndex(1, n_polygons + 1), + ) + else: + geo_df = GeoDataFrame({"geometry": geoms}, index=owning_cell_id) + geo_df.index.name = str(AteraKeys.CELL_ID) + + scale = Scale([1.0 / pixel_size, 1.0 / pixel_size], axes=("x", "y")) + return ShapesModel.parse(geo_df, transformations={"global": scale}) + + +def _get_polygons( + cells_group: zarr.Group, + mask_index: int, + cell_ids: np.ndarray | None, + pixel_size: float, + is_nucleus: bool, +) -> GeoDataFrame: + """Build boundary polygons from cells.zarr.zip polygon_sets/{mask_index}. + + Each row of ``vertices`` is a fixed-width (x, y) pair buffer, padded to a maximum number of + vertices; ``num_vertices`` gives the number of valid pairs to use. + """ + group = cells_group[f"{AteraKeys.POLYGON_SETS_GROUP}/{mask_index}"] + vertices = np.asarray(group[str(AteraKeys.POLYGON_VERTICES)][...]) + num_vertices = np.asarray(group[str(AteraKeys.POLYGON_NUM_VERTICES)][...]) + cell_index = np.asarray(group[str(AteraKeys.POLYGON_CELL_INDEX)][...]) + + n_polygons, max_coords = vertices.shape + max_vertices = max_coords // 2 + vertices = vertices.reshape(n_polygons, max_vertices, 2) + valid = np.arange(max_vertices)[None, :] < num_vertices[:, None] + coords = vertices[valid] + + return _polygons_to_shapes(coords, num_vertices, cell_index, cell_ids, pixel_size, is_nucleus) + + +def _read_transcript_tile(path: Path, tile_key: str, feature_names: list[str]) -> pd.DataFrame: + """Read and decode a single spatial tile of transcripts.zarr.zip. + + Opens its own zip store (rather than sharing one across tiles) so this function can safely + be called from independent `dask` tasks. + """ + store = zarr.storage.ZipStore(path / AteraKeys.TRANSCRIPTS_FILE, read_only=True) + try: + group = zarr.open_group(store, mode="r") + tile = group[f"{AteraKeys.GRID_GROUP}/{tile_key}"] + location = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_LOCATION)][...]) + gene_offset = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_GENE_OFFSET)][...]) + cell_id_raw = np.asarray(tile[str(AteraKeys.CELL_ID)][...]) + overlaps_nucleus = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_OVERLAPS_NUCLEUS)][...]).reshape(-1) + quality_score = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_QUALITY_SCORE)][...]).reshape(-1) + finally: + store.close() + + # Rows within a tile are sorted by gene; gene_offset[g] = [start, end) gives the row range for + # gene g, so the per-row gene index must be reconstructed rather than read directly. + gene_index = np.repeat(np.arange(len(gene_offset)), gene_offset[:, 1] - gene_offset[:, 0]) + feature_name = pd.Categorical.from_codes(gene_index, categories=feature_names) + + return pd.DataFrame( + { + str(AteraKeys.TRANSCRIPTS_X): location[:, 0].astype(np.float32), + str(AteraKeys.TRANSCRIPTS_Y): location[:, 1].astype(np.float32), + str(AteraKeys.TRANSCRIPTS_Z): location[:, 2].astype(np.float32), + str(AteraKeys.FEATURE_NAME): feature_name, + str(AteraKeys.CELL_ID): _decode_packed_cell_id(cell_id_raw), + "overlaps_nucleus": overlaps_nucleus.astype(bool), + "quality_score": quality_score.astype(np.float32), + } + ) + + +def _get_points(path: Path, pixel_size: float, feature_names: list[str]) -> dd.DataFrame: + """Lazily read transcripts.zarr.zip, one `dask` task per spatial tile.""" + store = zarr.storage.ZipStore(path / AteraKeys.TRANSCRIPTS_FILE, read_only=True) + try: + group = zarr.open_group(store, mode="r") + tile_keys = sorted(group[str(AteraKeys.GRID_GROUP)].group_keys()) + finally: + store.close() + + cat_dtype = pd.CategoricalDtype(categories=feature_names) + meta = pd.DataFrame( + { + str(AteraKeys.TRANSCRIPTS_X): pd.array([], dtype="float32"), + str(AteraKeys.TRANSCRIPTS_Y): pd.array([], dtype="float32"), + str(AteraKeys.TRANSCRIPTS_Z): pd.array([], dtype="float32"), + str(AteraKeys.FEATURE_NAME): pd.array([], dtype=cat_dtype), + str(AteraKeys.CELL_ID): pd.array([], dtype="int64"), + "overlaps_nucleus": pd.array([], dtype="bool"), + "quality_score": pd.array([], dtype="float32"), + } + ) + delayed_tiles = [delayed(_read_transcript_tile)(path, key, feature_names) for key in tile_keys] + table = dd.from_delayed(delayed_tiles, meta=meta) + + transform = Scale([1.0 / pixel_size, 1.0 / pixel_size], axes=("x", "y")) + return PointsModel.parse( + table, + coordinates={ + "x": str(AteraKeys.TRANSCRIPTS_X), + "y": str(AteraKeys.TRANSCRIPTS_Y), + "z": str(AteraKeys.TRANSCRIPTS_Z), + }, + feature_key=str(AteraKeys.FEATURE_NAME), + instance_key=str(AteraKeys.CELL_ID), + transformations={"global": transform}, + sort=False, + ) + + +def _get_gene_codewords(path: Path, genes: list[str]) -> dict[str, list[int]]: + """Map each of ``genes`` to its codeword id(s) via ``panel_config.json``. + + Each gene target can have multiple codewords (e.g. for redundant probe coverage); a transcript's + ``codeword_identity`` (an index into the shared, dataset-wide codebook) is only mapped to a gene + through this file, not through anything in `transcripts.zarr.zip` itself. + """ + with open(path / AteraKeys.PANEL_CONFIG_FILE) as f: + panel_config = json.load(f) + + codewords_by_gene: dict[str, list[int]] = {} + for target in panel_config["payloads"][0]["targets"]: + if target["type"]["descriptor"] != "gene": + continue + name = target["type"]["data"]["name"] + codewords_by_gene[name] = target["codewords"] + + missing = [gene for gene in genes if gene not in codewords_by_gene] + if missing: + raise ValueError(f"No codewords found for gene(s) {missing} in {AteraKeys.PANEL_CONFIG_FILE}.") + return {gene: codewords_by_gene[gene] for gene in genes} + + +def read_transcripts_for_genes( + sdata: SpatialData, + path: str | Path, + genes: str | list[str], + points_key: str = "transcripts_subset", + by_codeword: bool = False, +) -> SpatialData: + """Add a `points` element to ``sdata`` with the transcripts of only the given gene(s). + + Unlike reading with ``transcripts=True``, which lazily decodes every tile of + ``transcripts.zarr.zip`` in full once computed, this reads only the requested genes' + contiguous row ranges out of each spatial tile: rows within a tile are sorted by gene, with + ``gene_offset`` giving each gene's ``[start, end)`` range, and the tile arrays are chunked + finely enough (e.g. ~11k-row chunks for tiles with tens of millions of rows) that slicing + ``array[start:end]`` only reads the overlapping chunks rather than the whole tile. This makes + plotting a handful of genes across the whole dataset much cheaper than loading and filtering + the full transcripts table. + + Requires ``sdata`` to have been read with ``cells_table=True``, to look up gene indices (via + the table's `var`) and `pixel_size` (via ``sdata.attrs``). + + Parameters + ---------- + sdata + A `SpatialData` object previously returned by `atera`. + path + Path to the dataset (the same path originally passed to the reader). + genes + One gene name, or a list of gene names, to read. + points_key + Key under which the resulting `points` element is stored in ``sdata.points``. + by_codeword + If `True`, label each transcript by its specific codeword (e.g. ``"ACTB_cw1006"``) instead + of just its gene name, via the `feature_name` column. Since a gene is decoded from multiple + redundant codewords, this lets you plot each codeword's spatial distribution separately + (e.g. with ``groups=``/``palette=`` on `render_points`) to spot a codeword with an + inconsistent pattern, e.g. due to a non-specific probe. + + Returns + ------- + ``sdata``, with ``sdata.points[points_key]`` added (mutated in place, and also returned). + """ + if isinstance(genes, str): + genes = [genes] + path = Path(path) + feature_names = sdata.tables["table"].var[str(AteraKeys.FEATURE_NAME)].astype(str).tolist() + gene_indices = {gene: feature_names.index(gene) for gene in genes} + pixel_size = sdata.attrs[str(AteraKeys.PIXEL_SIZE)] + + if by_codeword: + gene_codewords = _get_gene_codewords(path, genes) + categories = [f"{gene}_cw{cw}" for gene in genes for cw in gene_codewords[gene]] + else: + categories = genes + + columns = [ + str(AteraKeys.TRANSCRIPTS_X), + str(AteraKeys.TRANSCRIPTS_Y), + str(AteraKeys.TRANSCRIPTS_Z), + str(AteraKeys.FEATURE_NAME), + str(AteraKeys.CELL_ID), + "overlaps_nucleus", + "quality_score", + ] + store = zarr.storage.ZipStore(path / AteraKeys.TRANSCRIPTS_FILE, read_only=True) + try: + group = zarr.open_group(store, mode="r") + tile_keys = sorted(group[str(AteraKeys.GRID_GROUP)].group_keys()) + + frames = [] + for tile_key in tile_keys: + tile = group[f"{AteraKeys.GRID_GROUP}/{tile_key}"] + gene_offset = tile[str(AteraKeys.TRANSCRIPTS_GENE_OFFSET)] + for gene, gene_idx in gene_indices.items(): + start, end = gene_offset[gene_idx] + if end <= start: + continue + location = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_LOCATION)][start:end]) + cell_id_raw = np.asarray(tile[str(AteraKeys.CELL_ID)][start:end]) + overlaps_nucleus = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_OVERLAPS_NUCLEUS)][start:end]).reshape( + -1 + ) + quality_score = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_QUALITY_SCORE)][start:end]).reshape(-1) + if by_codeword: + codeword_identity = np.asarray(tile[str(AteraKeys.CODEWORD_IDENTITY)][start:end]).reshape(-1) + feature_name = pd.Categorical( + [f"{gene}_cw{cw}" for cw in codeword_identity], categories=categories + ) + else: + feature_name = pd.Categorical([gene] * (end - start), categories=categories) + frames.append( + pd.DataFrame( + { + str(AteraKeys.TRANSCRIPTS_X): location[:, 0].astype(np.float32), + str(AteraKeys.TRANSCRIPTS_Y): location[:, 1].astype(np.float32), + str(AteraKeys.TRANSCRIPTS_Z): location[:, 2].astype(np.float32), + str(AteraKeys.FEATURE_NAME): feature_name, + str(AteraKeys.CELL_ID): _decode_packed_cell_id(cell_id_raw), + "overlaps_nucleus": overlaps_nucleus.astype(bool), + "quality_score": quality_score.astype(np.float32), + } + ) + ) + finally: + store.close() + + df = pd.concat(frames, ignore_index=True) if frames else pd.DataFrame(columns=columns) + + transform = Scale([1.0 / pixel_size, 1.0 / pixel_size], axes=("x", "y")) + sdata.points[points_key] = PointsModel.parse( + df, + coordinates={ + "x": str(AteraKeys.TRANSCRIPTS_X), + "y": str(AteraKeys.TRANSCRIPTS_Y), + "z": str(AteraKeys.TRANSCRIPTS_Z), + }, + feature_key=str(AteraKeys.FEATURE_NAME), + instance_key=str(AteraKeys.CELL_ID), + transformations={"global": transform}, + ) + return sdata + + +def _tile_bounds(tile_key: str, tile_size: float) -> tuple[float, float, float, float]: + """Return the (xmin, ymin, xmax, ymax) raw-unit spatial extent of a ``"tx,ty"`` grid tile key.""" + tile_x, tile_y = (int(v) for v in tile_key.split(",")) + return tile_x * tile_size, tile_y * tile_size, (tile_x + 1) * tile_size, (tile_y + 1) * tile_size + + +def _bbox_overlaps(a: tuple[float, float, float, float], b: tuple[float, float, float, float]) -> bool: + return a[0] < b[2] and b[0] < a[2] and a[1] < b[3] and b[1] < a[3] + + +def _cell_row_positions_in_bbox(path: Path, bbox: tuple[float, float, float, float]) -> np.ndarray: + """Row positions (0-based, matching table/`obs` row order) of cells overlapping `bbox`. + + `bbox` is in the same "global" (pixel) coordinate system as the rest of a reader-produced + `SpatialData`; it is converted to raw/micron units via `pixel_size` before comparing against + ``cells.zarr.zip``'s top-level `bboxes`, which shares row order with the table/`obs` (the same + fact `read_cell_boundaries` relies on for its own `bbox` filtering). + """ + with open(path / AteraKeys.SPECS_FILE) as f: + specs = json.load(f) + pixel_size = specs[str(AteraKeys.PIXEL_SIZE)] + xmin, ymin, xmax, ymax = (v * pixel_size for v in bbox) + + store = zarr.storage.ZipStore(path / AteraKeys.CELLS_FILE, read_only=True) + try: + group = zarr.open_group(store, mode="r") + cell_bboxes = np.asarray(group[str(AteraKeys.BBOXES)][...]).reshape(-1, 4) + finally: + store.close() + + keep = ( + (cell_bboxes[:, 0] < xmax) & (xmin < cell_bboxes[:, 2]) & (cell_bboxes[:, 1] < ymax) & (ymin < cell_bboxes[:, 3]) + ) + return np.nonzero(keep)[0] + + +def read_cell_boundaries( + sdata: SpatialData, + path: str | Path, + shapes_key: str = "cell_boundaries", + mask_index: int = 1, + bbox: tuple[float, float, float, float] | None = None, + pyramid_level: int = 0, +) -> SpatialData: + """Add a `shapes` element to ``sdata`` with boundary polygons read from ``gridded_polygon_sets``. + + Unlike reading with ``cells_boundaries=True``/``nucleus_boundaries=True``, which decode every + cell's full-detail polygon eagerly from ``polygon_sets``, ``cells.zarr.zip`` also has a + ``gridded_polygon_sets``: the same boundary polygons, but spatially tiled and available at + multiple levels of vertex detail (a pyramid), analogous to an image pyramid. This lets you + trade detail for speed/memory in two independent ways: + + - ``pyramid_level`` (`0` = finest, matching `polygon_sets`, up to `4` = coarsest): higher + levels store the same cells with far fewer vertices per polygon (e.g. ~24 vertices/cell at + level 0 vs. ~5 at level 4, in a typical bundle). Every level has every cell -- this does not + subset cells, only simplifies their polygons, so it mainly helps rendering speed/memory, not + table size. + - ``bbox``: an ``(xmin, ymin, xmax, ymax)`` region, in the same "global" coordinate system as + the rest of ``sdata`` (i.e. the same units as the images/other shapes), to load. Only the + spatial tiles overlapping this region are read (mirroring the tile-based approach already + used for transcripts in `read_transcripts_for_genes`), and cells are then filtered to those + whose bounding box overlaps it. If `None`, every tile at ``pyramid_level`` is read (all + cells). + + Vertices are stored quantized to a `uint8` range, relative to each cell's own bounding box (in + the top-level ``bboxes`` array); this un-quantizes them via + ``absolute = cell_min + relative / 255 * (cell_max - cell_min)``. + + Requires ``sdata`` to have been read with ``cells_table=True``, to map row positions in + ``gridded_polygon_sets`` to cell ids (via the table's `obs`), and `pixel_size` (via + ``sdata.attrs``). + + Parameters + ---------- + sdata + A `SpatialData` object previously returned by `atera`. + path + Path to the dataset (the same path originally passed to the reader). + shapes_key + Key under which the resulting `shapes` element is stored in ``sdata.shapes``. + mask_index + `0` for nucleus boundaries, `1` for cell boundaries (matching the reader's + ``nucleus_boundaries``/``cells_boundaries``). + bbox + ``(xmin, ymin, xmax, ymax)``, in the same coordinate system as the rest of ``sdata``. If + `None`, all cells at ``pyramid_level`` are read. + pyramid_level + Which polygon-detail pyramid level to read, from `0` (finest, full detail) to `4` + (coarsest, most simplified). + + Returns + ------- + ``sdata``, with ``sdata.shapes[shapes_key]`` added (mutated in place, and also returned). + """ + path = Path(path) + pixel_size = sdata.attrs[str(AteraKeys.PIXEL_SIZE)] + cell_ids = sdata.tables["table"].obs[str(AteraKeys.CELL_ID)].to_numpy() + is_nucleus = mask_index == 0 + + raw_bbox = None + if bbox is not None: + xmin, ymin, xmax, ymax = bbox + raw_bbox = (xmin * pixel_size, ymin * pixel_size, xmax * pixel_size, ymax * pixel_size) + + store = zarr.storage.ZipStore(path / AteraKeys.CELLS_FILE, read_only=True) + try: + group = zarr.open_group(store, mode="r") + cell_bboxes = np.asarray(group[str(AteraKeys.BBOXES)][...]).reshape(-1, 4) + mask_group = group[f"{AteraKeys.GRIDDED_POLYGON_SETS_GROUP}/{mask_index}"] + # Each pyramid level merges 2x2 blocks of tiles from the level below, so a tile's spatial + # extent doubles per level; `grid_size` (attrs, shared across levels) gives level 0's tile + # size. + tile_size = float(mask_group.attrs[str(AteraKeys.GRID_SIZE)][0]) * (2**pyramid_level) + level_group = mask_group[str(pyramid_level)] + + tile_keys = list(level_group.group_keys()) + if raw_bbox is not None: + tile_keys = [key for key in tile_keys if _bbox_overlaps(_tile_bounds(key, tile_size), raw_bbox)] + + coords_parts = [] + num_vertices_parts = [] + cell_index_parts = [] + for tile_key in tile_keys: + tile = level_group[tile_key] + num_vertices = np.asarray(tile[str(AteraKeys.POLYGON_NUM_VERTICES)][...]) + cell_index = np.asarray(tile[str(AteraKeys.POLYGON_CELL_INDEX)][...]) + relative_vertices = np.asarray(tile[str(AteraKeys.RELATIVE_VERTICES)][...]).reshape(-1, 2) + + if raw_bbox is not None: + tile_cell_bboxes = cell_bboxes[cell_index] + keep = ( + (tile_cell_bboxes[:, 0] < raw_bbox[2]) + & (raw_bbox[0] < tile_cell_bboxes[:, 2]) + & (tile_cell_bboxes[:, 1] < raw_bbox[3]) + & (raw_bbox[1] < tile_cell_bboxes[:, 3]) + ) + if not keep.any(): + continue + relative_vertices = relative_vertices[np.repeat(keep, num_vertices)] + num_vertices = num_vertices[keep] + cell_index = cell_index[keep] + + vertex_cell_index = np.repeat(cell_index, num_vertices) + vertex_bboxes = cell_bboxes[vertex_cell_index] + vertex_mins = vertex_bboxes[:, [0, 1]] + vertex_spans = vertex_bboxes[:, [2, 3]] - vertex_mins + coords_parts.append((vertex_mins + relative_vertices / 255 * vertex_spans).astype(np.float32)) + num_vertices_parts.append(num_vertices) + cell_index_parts.append(cell_index) + finally: + store.close() + + coords = np.concatenate(coords_parts) if coords_parts else np.empty((0, 2), dtype=np.float32) + num_vertices = np.concatenate(num_vertices_parts) if num_vertices_parts else np.empty((0,), dtype=np.int32) + cell_index = np.concatenate(cell_index_parts) if cell_index_parts else np.empty((0,), dtype=np.uint32) + + sdata.shapes[shapes_key] = _polygons_to_shapes(coords, num_vertices, cell_index, cell_ids, pixel_size, is_nucleus) + return sdata + + +def _parse_morphology_channel_name(filename: str) -> str: + """Parse the channel name out of a ``chNNNN_.ome.tif`` morphology image filename.""" + match = re.match(r"ch\d+_(.+)\.ome\.tif+$", filename) + if match is None: + raise ValueError(f"Expected a morphology image filename of the form 'chNNNN_.ome.tif', found {filename}") + return match.group(1) + + +def _parse_morphology_channel_index(filename: str) -> int: + """Parse the channel index out of a ``chNNNN_.ome.tif`` morphology image filename.""" + match = re.match(r"ch(\d+)_.+\.ome\.tif+$", filename) + if match is None: + raise ValueError(f"Expected a morphology image filename of the form 'chNNNN_.ome.tif', found {filename}") + return int(match.group(1)) + + +def _tiled_imread(path: Path, imread_kwargs: Mapping[str, Any] = MappingProxyType({})) -> da.Array: + """Read an OME-TIFF as a `dask` array chunked by the file's own on-disk tile grid. + + `dask_image.imread.imread` gives each page (e.g. each channel or z-plane) exactly one `dask` + chunk -- the whole plane, which for these whole-slide-image morphology files is tens of GB. + That single giant chunk then has to be fully decoded before `Image2DModel.parse`/`Image3DModel.parse` + can rechunk it into tiles or build a downsampled pyramid level, so even plotting one channel at + the lowest pyramid resolution requires materializing the entire full-resolution plane in memory. + + This instead reads through `tifffile.TiffPageSeries.aszarr(level=0)`, which exposes the file's + native tile grid (e.g. 1024x1024, matching `p.is_tiled`/`p.tilewidth`/`p.tilelength`) as `zarr` + chunks, so only the tiles actually touched downstream get decoded. + + `level=0` always reads the file's full-resolution series: these OME-TIFFs happen to embed their + own multi-level pyramid already, but `atera`'s morphology images build their own multiscale + pyramid via `Image2DModel.parse`'s `scale_factors`, independent of it. + """ + import tifffile + + tf = tifffile.TiffFile(path, **imread_kwargs) + store = tf.series[0].aszarr(level=0) + return da.from_zarr(zarr.open(store, mode="r")) + + +def _get_morphology_images( + dir_path: Path, + imread_kwargs: Mapping[str, Any] = MappingProxyType({}), + image_models_kwargs: Mapping[str, Any] = MappingProxyType({}), + three_d: bool = False, +) -> DataArray | DataTree: + """Read the morphology OME-TIFF files in a directory as a single multi-channel image.""" + files = sorted(f for f in dir_path.iterdir() if f.name.endswith(".ome.tif") and not f.name.startswith("._")) + if not files: + raise FileNotFoundError(f"No morphology OME-TIFF files found in {dir_path}.") + + if three_d: + # one file per channel, each file has shape (z, y, x); stack along a new channel axis + channel_names = [_parse_morphology_channel_name(f.name) for f in files] + channels = [_tiled_imread(f, imread_kwargs) for f in files] + image = da.stack(channels, axis=0) + return Image3DModel.parse( + image, + dims=("c", "z", "y", "x"), + c_coords=channel_names, + transformations={"global": Identity()}, + **image_models_kwargs, + ) + + # Each 2D file is itself a multi-page OME-TIFF containing every channel of the panel (shape + # (n_channels, y, x)), but only the plane at the index given by the file's own "chNNNN" prefix + # holds that channel's real image data; take just that plane from each file. + from ome_types import from_tiff + + channel_indices = [_parse_morphology_channel_index(f.name) for f in files] + channel_names = [from_tiff(f).images[0].pixels.channels[i].name for f, i in zip(files, channel_indices, strict=True)] + channels = [_tiled_imread(f, imread_kwargs)[i] for f, i in zip(files, channel_indices, strict=True)] + image = da.stack(channels, axis=0) + return Image2DModel.parse( + image, + dims=("c", "y", "x"), + c_coords=channel_names, + transformations={"global": Identity()}, + **image_models_kwargs, + ) diff --git a/src/spatialdata_io/readers/atera.py b/src/spatialdata_io/readers/atera.py new file mode 100644 index 00000000..456b315b --- /dev/null +++ b/src/spatialdata_io/readers/atera.py @@ -0,0 +1,459 @@ +from __future__ import annotations + +import json +import logging +from pathlib import Path +from types import MappingProxyType +from typing import TYPE_CHECKING, Any, Literal + +import dask.array as da +import numpy as np +import pandas as pd +import zarr +from dask import delayed +from scipy.sparse import csc_matrix, csr_matrix +from spatialdata import SpatialData +from spatialdata.models import TableModel + +from spatialdata_io._constants._constants import AteraKeys +from spatialdata_io._docs import inject_docs +from spatialdata_io.readers._atera_common import ( + DEFAULT_TABLE_ROW_CHUNK_SIZE, + DEFAULT_VAR_COLUMNS, + _cell_row_positions_in_bbox, + _get_labels, + _get_morphology_images, + _get_points, + _get_polygons, + _patched_ragged_vlen_chunk_decode, + read_cell_boundaries, + read_transcripts_for_genes, +) +from spatialdata_io.readers._utils._utils import _initialize_raster_models_kwargs, _set_reader_metadata + +if TYPE_CHECKING: + from collections.abc import Mapping, Sequence + + from anndata import AnnData + from xarray import DataArray, DataTree + +__all__ = [ + "atera", + "read_cell_boundaries", + "read_table_for_cells", + "read_table_for_genes", + "read_transcripts_for_genes", + "read_var", +] + +# NOTE: this reads `cell_feature_matrix.zarr.zip`/`csc_cell_feature_matrix.zarr.zip` with +# `anndata`'s own zarr reading functions (`anndata.io.read_elem`/`anndata.io.sparse_dataset`). This +# only works on bundles whose zarr stores carry the standard AnnData `encoding-type`/ +# `encoding-version`/`shape` attrs. +# +# Every non-table element (labels/shapes/points/morphology images) lives in `_atera_common.py` +# rather than duplicated here. + + +@inject_docs(xx=AteraKeys) +def atera( + path: str | Path, + *, + cells_table: bool = True, + cells_boundaries: bool = True, + nucleus_boundaries: bool = True, + cells_labels: bool = True, + nucleus_labels: bool = True, + transcripts: bool = False, + morphology_images: bool = True, + morphology_3d_images: bool = False, + var_columns: list[str] | Literal["all"] = DEFAULT_VAR_COLUMNS, + table_row_chunk_size: int = DEFAULT_TABLE_ROW_CHUNK_SIZE, + imread_kwargs: Mapping[str, Any] = MappingProxyType({}), + image_models_kwargs: Mapping[str, Any] = MappingProxyType({}), + labels_models_kwargs: Mapping[str, Any] = MappingProxyType({}), +) -> SpatialData: + """Read a *10x Genomics Atera* dataset into a SpatialData object. + + This function reads the following files: + + - ``{xx.SPECS_FILE!r}``: File containing specifications. + - ``{xx.CELL_FEATURE_MATRIX_FILE!r}``: Zipped zarr store with the cell-by-gene matrix and cell metadata. + - ``{xx.CELLS_FILE!r}``: Zipped zarr store with cell/nucleus labels and boundary polygons. + - ``{xx.TRANSCRIPTS_FILE!r}``: Zipped zarr store with per-transcript locations (optional, large). + - ``{xx.MORPHOLOGY_2D_DIR!r}``: Directory of single-channel morphology OME-TIFF images. + - ``{xx.MORPHOLOGY_3D_DIR!r}``: Directory of single-channel 3D morphology OME-TIFF images (optional, huge). + + Unlike most other 10x Genomics formats, all the tabular/raster/point data (aside from the morphology + images) is stored in zipped `zarr `_ stores. ``{xx.CELL_FEATURE_MATRIX_FILE!r}`` and + ``{xx.CSC_CELL_FEATURE_MATRIX_FILE!r}`` use the standard AnnData zarr encoding and are read with + `anndata`'s own zarr IO (``anndata.io.read_elem``/``anndata.io.sparse_dataset``). + + Parameters + ---------- + path + Path to the dataset. + cells_table + Whether to read the cell annotations in the `AnnData` table. + cells_boundaries + Whether to read cell boundaries (polygons). + nucleus_boundaries + Whether to read nucleus boundaries (polygons). + cells_labels + Whether to read cell labels (raster). + nucleus_labels + Whether to read nucleus labels (raster). + transcripts + Whether to read transcripts (points). This is opt-in and defaults to `False` because the transcripts + table can have tens of millions of rows; when read, it is loaded lazily with `dask`. + morphology_images + Whether to read the 2D morphology images. + morphology_3d_images + Whether to read the 3D morphology images. This is opt-in and defaults to `False` because these images + can be very large. + var_columns + Which columns of `var` to keep, out of the full table (which includes ~135 per-cluster + differential-expression columns). Defaults to a small subset (`{xx.FEATURE_NAME!r}`, + `{xx.VAR_FILTERED!r}`, `{xx.VAR_HIGHLY_VARIABLE!r}`, `{xx.FEATURE_ID!r}`, + `{xx.VAR_FEATURE_TYPE!r}`, `{xx.VAR_GENOME!r}`) to avoid holding the full table in memory; + pass `"all"` to keep every column, or use `read_var` to load the full table separately. + table_row_chunk_size + Number of `obs` rows read per chunk when lazily loading the table's `X` (see `_get_table`). + Larger values mean fewer, larger `dask` tasks (faster, but more peak memory per chunk); + smaller values mean more, smaller ones. + imread_kwargs + Keyword arguments passed to `tifffile.TiffFile` when reading the morphology images (see + `_tiled_imread`); the images are read tile-by-tile off `tifffile`'s own `zarr` store rather + than via `dask_image.imread.imread`, so this only accepts `TiffFile` constructor kwargs. + image_models_kwargs + Keyword arguments to pass to the image models. + labels_models_kwargs + Keyword arguments to pass to the labels models. + + Returns + ------- + :class:`spatialdata.SpatialData` + """ + path = Path(path) + image_models_kwargs, labels_models_kwargs = _initialize_raster_models_kwargs( + image_models_kwargs, labels_models_kwargs + ) + + with open(path / AteraKeys.SPECS_FILE) as f: + specs = json.load(f) + pixel_size = specs[str(AteraKeys.PIXEL_SIZE)] + + needs_cells_zarr = cells_boundaries or nucleus_boundaries or cells_labels or nucleus_labels + if not cells_table and (needs_cells_zarr or transcripts): + logging.info("Reading the table is required for the requested elements; setting cells_table=True.") + cells_table = True + + # Prefer `cell_labels` as the table's annotation target (matching the `xenium` reader's + # convention), but fall back to `cell_boundaries` when labels aren't being loaded, so the + # table doesn't end up annotating an element that isn't actually present in `sdata`. + default_region = "cell_labels" if cells_labels else "cell_boundaries" if cells_boundaries else "cell_labels" + + table: AnnData | None = None + feature_names: list[str] | None = None + if cells_table: + table, feature_names = _get_table( + path, var_columns=var_columns, row_chunk_size=table_row_chunk_size, region=default_region + ) + + shapes: dict[str, Any] = {} + labels: dict[str, DataArray | DataTree] = {} + points: dict[str, Any] = {} + images: dict[str, DataArray | DataTree] = {} + + if needs_cells_zarr: + cells_store = zarr.storage.ZipStore(path / AteraKeys.CELLS_FILE, read_only=True) + cells_group = zarr.open_group(cells_store, mode="r") + + cell_ids = table.obs[str(AteraKeys.CELL_ID)].to_numpy() if table is not None else None + + if nucleus_labels: + labels["nucleus_labels"] = _get_labels(cells_group, mask_index=0, labels_models_kwargs=labels_models_kwargs) + if cells_labels: + labels["cell_labels"] = _get_labels(cells_group, mask_index=1, labels_models_kwargs=labels_models_kwargs) + if nucleus_boundaries: + shapes["nucleus_boundaries"] = _get_polygons( + cells_group, mask_index=0, cell_ids=cell_ids, pixel_size=pixel_size, is_nucleus=True + ) + if cells_boundaries: + shapes["cell_boundaries"] = _get_polygons( + cells_group, mask_index=1, cell_ids=cell_ids, pixel_size=pixel_size, is_nucleus=False + ) + + if transcripts: + if feature_names is None: + raise ValueError("Reading transcripts requires `cells_table=True` (to map gene indices to names).") + points["transcripts"] = _get_points(path, pixel_size, feature_names) + + if morphology_images: + images["morphology"] = _get_morphology_images( + path / AteraKeys.MORPHOLOGY_2D_DIR, imread_kwargs, image_models_kwargs + ) + if morphology_3d_images: + images["morphology_3d"] = _get_morphology_images( + path / AteraKeys.MORPHOLOGY_3D_DIR, imread_kwargs, image_models_kwargs, three_d=True + ) + + tables = {"table": table} if table is not None else {} + sdata = SpatialData(images=images, labels=labels, points=points, tables=tables, shapes=shapes) + sdata = _set_reader_metadata(sdata, "atera") + sdata.attrs[str(AteraKeys.PIXEL_SIZE)] = pixel_size + return sdata + + +def _read_x_chunk(zarr_path: Path, zarr_key: AteraKeys, start: int, stop: int) -> csr_matrix: + """Read one contiguous row-range of `X` via `anndata.io.sparse_dataset`'s own row slicing. + + Opens its own zip store (rather than sharing one across chunks) so this function can safely be + called from independent `dask` tasks. + """ + from anndata.io import sparse_dataset + + store = zarr.storage.ZipStore(zarr_path / zarr_key, read_only=True) + try: + group = zarr.open_group(store, mode="r") + return sparse_dataset(group[str(AteraKeys.X_GROUP)])[start:stop] + finally: + store.close() + + +def _lazy_x(zarr_path: Path, zarr_key: AteraKeys, n_obs: int, n_var: int, dtype: np.dtype, row_chunk_size: int) -> da.Array: + """Build a `dask`-backed `n_obs x n_var` CSR array, delegating each chunk's read to `sparse_dataset`.""" + meta = csr_matrix((0, n_var), dtype=dtype) + blocks = [] + for start in range(0, n_obs, row_chunk_size): + stop = min(start + row_chunk_size, n_obs) + block = delayed(_read_x_chunk)(zarr_path, AteraKeys.CELL_FEATURE_MATRIX_FILE, start, stop) + blocks.append(da.from_delayed(block, shape=(stop - start, n_var), dtype=dtype, meta=meta)) + return da.concatenate(blocks, axis=0) + + +def _get_table( + path: Path, + var_columns: list[str] | Literal["all"] = DEFAULT_VAR_COLUMNS, + row_chunk_size: int = DEFAULT_TABLE_ROW_CHUNK_SIZE, + region: str = "cell_labels", +) -> tuple[AnnData, list[str]]: + """Read ``cell_feature_matrix.zarr.zip`` into an AnnData table using native `anndata` zarr IO. + + `obs`/`var`/`obsm` are read with `anndata.io.read_elem` (which also means `obs`/`var` come back + indexed by whichever column the store's ``_index`` attr names -- e.g. `barcode`/`feature_id`). + `X` is built as a lazily `dask`-backed array in row chunks (see `_lazy_x`), each chunk read via + `anndata.io.sparse_dataset` rather than manual `indptr` arithmetic. + + `region` should be the name of a `SpatialElement` that will actually be present in the + `SpatialData` object the table is assembled into (see the ``default_region`` logic in + `atera`), so the table doesn't end up annotating an element that was never loaded. + """ + from anndata import AnnData + from anndata.io import read_elem, sparse_dataset + + store = zarr.storage.ZipStore(path / AteraKeys.CELL_FEATURE_MATRIX_FILE, read_only=True) + try: + group = zarr.open_group(store, mode="r") + + with _patched_ragged_vlen_chunk_decode(): + var_df = read_elem(group[str(AteraKeys.VAR_GROUP)]) + feature_names = var_df[str(AteraKeys.FEATURE_NAME)].astype(str).tolist() + if var_columns != "all": + var_df = var_df[list(var_columns)] + + obs_df = read_elem(group[str(AteraKeys.OBS_GROUP)]) + + obsm = read_elem(group[str(AteraKeys.OBSM_GROUP)]) if str(AteraKeys.OBSM_GROUP) in group else {} + + x_ds = sparse_dataset(group[str(AteraKeys.X_GROUP)]) + n_obs, n_var = x_ds.shape + dtype = x_ds.dtype + finally: + store.close() + + x = _lazy_x(path, AteraKeys.CELL_FEATURE_MATRIX_FILE, n_obs, n_var, dtype, row_chunk_size) + + adata = AnnData(X=x, obs=obs_df, var=var_df, obsm=obsm) + adata.obsm["spatial"] = adata.obs[[str(AteraKeys.CENTROID_X), str(AteraKeys.CENTROID_Y)]].to_numpy() + adata.obs["region"] = pd.Categorical([region] * n_obs) + + table = TableModel.parse( + adata, + region=region, + region_key="region", + instance_key=str(AteraKeys.CELL_ID), + ) + return table, feature_names + + +def read_var(path: str | Path) -> pd.DataFrame: + """Read the complete `var` table from ``cell_feature_matrix.zarr.zip`` via `anndata.io.read_elem`. + + Includes the ~135 per-cluster differential-expression columns that `atera` drops by default + (see `var_columns` on `atera`) to avoid holding that wider table in memory. + + Use this to build your own `var`/`AnnData` with whichever columns you need, e.g. + ``sdata.tables["table"].var = read_var(path)[["feature_name", "de_leiden_res_1.0_c0_score"]]``. + + Parameters + ---------- + path + Path to the dataset (the same path originally passed to `atera`). + + Returns + ------- + The complete `var` `DataFrame`, indexed by `feature_id`. + """ + from anndata.io import read_elem + + path = Path(path) + store = zarr.storage.ZipStore(path / AteraKeys.CELL_FEATURE_MATRIX_FILE, read_only=True) + try: + group = zarr.open_group(store, mode="r") + with _patched_ragged_vlen_chunk_decode(): + var_df = read_elem(group[str(AteraKeys.VAR_GROUP)]) + finally: + store.close() + return var_df + + +def read_table_for_cells( + path: str | Path, + cell_ids: Sequence[int] | np.ndarray | None = None, + bbox: tuple[float, float, float, float] | None = None, + var_columns: list[str] | Literal["all"] = DEFAULT_VAR_COLUMNS, +) -> AnnData: + """Read the cell-by-gene table for only a subset of cells, without loading the full `X`. + + Unlike ``atera(path, cells_table=True)``, which eagerly builds the whole `X`, this reads only + the requested rows directly off `anndata.io.sparse_dataset`'s own fancy-indexing support + (``x_ds[row_positions]``), which already handles arbitrary/unsorted row selections internally. + + Exactly one of `cell_ids`/`bbox` must be given. + + Parameters + ---------- + path + Path to the dataset (the same path originally passed to `atera`). + cell_ids + Specific cell ids to read (matching `obs["cell_id"]`); the returned table preserves this + order. + bbox + ``(xmin, ymin, xmax, ymax)``, in the same "global" coordinate system as the rest of an + `atera()`-read `SpatialData` (see `read_cell_boundaries`). Selects every cell whose + bounding box overlaps this region. + var_columns + Same as on `atera`; see there. + + Returns + ------- + An `AnnData` `TableModel` with only the requested cells. + """ + if (cell_ids is None) == (bbox is None): + raise ValueError("Exactly one of `cell_ids`/`bbox` must be given.") + from anndata import AnnData + from anndata.io import read_elem, sparse_dataset + + path = Path(path) + store = zarr.storage.ZipStore(path / AteraKeys.CELL_FEATURE_MATRIX_FILE, read_only=True) + try: + group = zarr.open_group(store, mode="r") + with _patched_ragged_vlen_chunk_decode(): + var_df = read_elem(group[str(AteraKeys.VAR_GROUP)]) + if var_columns != "all": + var_df = var_df[list(var_columns)] + obs_df = read_elem(group[str(AteraKeys.OBS_GROUP)]) + + if cell_ids is not None: + index = pd.Index(obs_df[str(AteraKeys.CELL_ID)].to_numpy()) + row_positions = index.get_indexer(np.asarray(cell_ids)) + missing = np.asarray(cell_ids)[row_positions == -1] + if missing.size: + raise ValueError(f"cell_id(s) not found in {AteraKeys.CELL_FEATURE_MATRIX_FILE!s}: {missing.tolist()}") + else: + row_positions = _cell_row_positions_in_bbox(path, bbox) + + x_ds = sparse_dataset(group[str(AteraKeys.X_GROUP)]) + x = x_ds[row_positions] + obs_subset = obs_df.iloc[row_positions] + finally: + store.close() + + n_obs = len(obs_subset) + adata = AnnData(X=x, obs=obs_subset, var=var_df) + adata.obsm["spatial"] = adata.obs[[str(AteraKeys.CENTROID_X), str(AteraKeys.CENTROID_Y)]].to_numpy() + adata.obs["region"] = pd.Categorical(["cell_labels"] * n_obs) + + return TableModel.parse( + adata, + region="cell_labels", + region_key="region", + instance_key=str(AteraKeys.CELL_ID), + ) + + +def read_table_for_genes(path: str | Path, genes: str | Sequence[str]) -> AnnData: + """Read the cell-by-gene table for only a subset of genes, across all cells, without loading full `X`. + + Column-wise mirror of `read_table_for_cells`: reads from the bundle's redundant, column-major + ``csc_cell_feature_matrix.zarr.zip`` (a transpose of the same matrix, otherwise unused by + `atera`) and pulls the requested columns directly off `anndata.io.sparse_dataset`'s own + fancy-indexing support (``x_ds[:, col_positions]``). + + Parameters + ---------- + path + Path to the dataset (the same path originally passed to `atera`). + genes + Gene name(s) to read (matching `var["feature_name"]`); the returned table preserves this + order. + + Returns + ------- + An `AnnData` with every cell but only the requested genes, sharing `obs` row order with the + rest of an `atera()`-read `SpatialData` (so its `X` columns can be dropped directly onto + `sdata.tables["table"].obs`/`sdata.shapes["cell_boundaries"]` for plotting). + """ + from anndata import AnnData + from anndata.io import read_elem, sparse_dataset + + genes = [genes] if isinstance(genes, str) else list(genes) + path = Path(path) + store = zarr.storage.ZipStore(path / AteraKeys.CSC_CELL_FEATURE_MATRIX_FILE, read_only=True) + try: + group = zarr.open_group(store, mode="r") + + with _patched_ragged_vlen_chunk_decode(): + var_df = read_elem(group[str(AteraKeys.VAR_GROUP)])[ + [str(AteraKeys.FEATURE_ID), str(AteraKeys.FEATURE_NAME)] + ] + feature_names = var_df[str(AteraKeys.FEATURE_NAME)].astype(str).tolist() + index = pd.Index(feature_names) + col_positions = index.get_indexer(np.asarray(genes)) + missing = np.asarray(genes)[col_positions == -1] + if missing.size: + raise ValueError( + f"gene(s) not found in {AteraKeys.VAR_GROUP!s}.{AteraKeys.FEATURE_NAME!s}: {missing.tolist()}" + ) + + var_subset = var_df.iloc[col_positions] + var_subset.index = pd.Index(np.asarray(genes), name=str(AteraKeys.FEATURE_NAME)) + + obs_df = read_elem(group[str(AteraKeys.OBS_GROUP)]) + n_obs = len(obs_df) + + x_ds = sparse_dataset(group[str(AteraKeys.X_GROUP)]) + x: csc_matrix = x_ds[:, col_positions] + finally: + store.close() + + adata = AnnData(X=x, obs=obs_df, var=var_subset) + adata.obs["region"] = pd.Categorical(["cell_labels"] * n_obs) + + return TableModel.parse( + adata, + region="cell_labels", + region_key="region", + instance_key=str(AteraKeys.CELL_ID), + ) From 3ec4bc765dcaf2311bd23a259e10d2147ed6b20a Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Tue, 29 Sep 2026 12:29:13 -0400 Subject: [PATCH 02/10] unzip dataset in Jupyter notebook --- notebooks/atera_example.ipynb | 1 + 1 file changed, 1 insertion(+) diff --git a/notebooks/atera_example.ipynb b/notebooks/atera_example.ipynb index 402acd05..823ef51d 100644 --- a/notebooks/atera_example.ipynb +++ b/notebooks/atera_example.ipynb @@ -64,6 +64,7 @@ "mkdir -p data/tiny_atera_dataset\n", "cd data/tiny_atera_dataset\n", "curl -O https://cf.10xgenomics.com/samples/atera/1.0.0/tiny_atera_dataset/tiny_atera_dataset_outs.zip\n", + "uzip tiny_atera_dataset_outs.zip\n", "unzip tar -xzvf morphology_2d.tar.gz\n", "tar -xzvf morphology_2d.tar.gz\n", "tar -xzvf morphology_3d.tar.gz\n", From cc68968c825aecc98c3c9d3aeb764799bfb32e1f Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Tue, 29 Sep 2026 12:31:40 -0400 Subject: [PATCH 03/10] notebook fix --- notebooks/atera_example.ipynb | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/notebooks/atera_example.ipynb b/notebooks/atera_example.ipynb index 823ef51d..9c44bb04 100644 --- a/notebooks/atera_example.ipynb +++ b/notebooks/atera_example.ipynb @@ -64,8 +64,7 @@ "mkdir -p data/tiny_atera_dataset\n", "cd data/tiny_atera_dataset\n", "curl -O https://cf.10xgenomics.com/samples/atera/1.0.0/tiny_atera_dataset/tiny_atera_dataset_outs.zip\n", - "uzip tiny_atera_dataset_outs.zip\n", - "unzip tar -xzvf morphology_2d.tar.gz\n", + "unzip tiny_atera_dataset_outs.zip\n", "tar -xzvf morphology_2d.tar.gz\n", "tar -xzvf morphology_3d.tar.gz\n", "```\n", From 83092dde2a546c7111ad6198c098f24fa5e229f4 Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Tue, 29 Sep 2026 13:00:48 -0400 Subject: [PATCH 04/10] Update git clone command in Atera example notebook --- notebooks/atera_example.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/notebooks/atera_example.ipynb b/notebooks/atera_example.ipynb index 9c44bb04..12b5b50a 100644 --- a/notebooks/atera_example.ipynb +++ b/notebooks/atera_example.ipynb @@ -32,7 +32,7 @@ "### 2. Clone the branch with the Atera reader\n", "\n", "```bash\n", - "git clone --branch stephen/atera_update https://github.com/stephenwilliams22/spatialdata-io.git\n", + "git clone --branch 10XGenomics:atera-reader https://github.com/scverse/spatialdata-io\n", "cd spatialdata-io\n", "```\n", "\n", From 318b6b7bfa122c2ea758d0b4c632a2477150ac40 Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Thu, 1 Oct 2026 12:35:14 -0400 Subject: [PATCH 05/10] update notebook --- notebooks/atera_example.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/notebooks/atera_example.ipynb b/notebooks/atera_example.ipynb index 12b5b50a..6ad6bb3b 100644 --- a/notebooks/atera_example.ipynb +++ b/notebooks/atera_example.ipynb @@ -58,7 +58,7 @@ "\n", "The dataset lives on the 10x Genomics support site:\n", "https://cf.10xgenomics.com/samples/atera/1.0.0/tiny_atera_dataset/tiny_atera_dataset_outs.zip\n", - "Download it with `curl` (installed above):\n", + "Download it with `curl`:\n", "\n", "```bash\n", "mkdir -p data/tiny_atera_dataset\n", @@ -76,7 +76,7 @@ "In the \"Load data\" cell below, set:\n", "\n", "```python\n", - "BUNDLE_PATH = \"data/tiny_atera_dataset\" # adjust if gdown nested the files in a subfolder\n", + "BUNDLE_PATH = \"data/tiny_atera_dataset\"\n", "```\n", "\n", "(Check with `ls -R data/tiny_atera_dataset` — it should contain the `output_bundle`-style\n", From 04a4bd7d24c9b2d89016505cd2b9a5d828c5a35b Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Thu, 1 Oct 2026 12:40:45 -0400 Subject: [PATCH 06/10] get pixel size from morphology image --- src/spatialdata_io/readers/_atera_common.py | 33 +++++++++++++++++++-- src/spatialdata_io/readers/atera.py | 10 +++---- 2 files changed, 35 insertions(+), 8 deletions(-) diff --git a/src/spatialdata_io/readers/_atera_common.py b/src/spatialdata_io/readers/_atera_common.py index b9024f98..66f6a7a0 100644 --- a/src/spatialdata_io/readers/_atera_common.py +++ b/src/spatialdata_io/readers/_atera_common.py @@ -446,9 +446,7 @@ def _cell_row_positions_in_bbox(path: Path, bbox: tuple[float, float, float, flo ``cells.zarr.zip``'s top-level `bboxes`, which shares row order with the table/`obs` (the same fact `read_cell_boundaries` relies on for its own `bbox` filtering). """ - with open(path / AteraKeys.SPECS_FILE) as f: - specs = json.load(f) - pixel_size = specs[str(AteraKeys.PIXEL_SIZE)] + pixel_size = _get_pixel_size(path) xmin, ymin, xmax, ymax = (v * pixel_size for v in bbox) store = zarr.storage.ZipStore(path / AteraKeys.CELLS_FILE, read_only=True) @@ -604,6 +602,35 @@ def _parse_morphology_channel_index(filename: str) -> int: return int(match.group(1)) +def _get_pixel_size(path: Path) -> float: + """Return the dataset's pixel size (microns/pixel). + + Prefers ``experiment.spatial`` when present, but some bundles omit it; in that case, fall back + to the ``PhysicalSizeX`` embedded in the OME-XML metadata of the first ``morphology_2d`` + OME-TIFF (every morphology image in a bundle shares the same pixel size). + """ + specs_file = path / AteraKeys.SPECS_FILE + if specs_file.exists(): + with open(specs_file) as f: + specs = json.load(f) + return specs[str(AteraKeys.PIXEL_SIZE)] + + from ome_types import from_tiff + + morphology_dir = path / AteraKeys.MORPHOLOGY_2D_DIR + files = ( + sorted(f for f in morphology_dir.iterdir() if f.name.endswith(".ome.tif") and not f.name.startswith("._")) + if morphology_dir.is_dir() + else [] + ) + if not files: + raise FileNotFoundError( + f"Found neither {AteraKeys.SPECS_FILE!s} nor any morphology OME-TIFF files in {morphology_dir!s} " + "to read the pixel size from." + ) + return from_tiff(files[0]).images[0].pixels.physical_size_x + + def _tiled_imread(path: Path, imread_kwargs: Mapping[str, Any] = MappingProxyType({})) -> da.Array: """Read an OME-TIFF as a `dask` array chunked by the file's own on-disk tile grid. diff --git a/src/spatialdata_io/readers/atera.py b/src/spatialdata_io/readers/atera.py index 456b315b..c216d2a3 100644 --- a/src/spatialdata_io/readers/atera.py +++ b/src/spatialdata_io/readers/atera.py @@ -1,6 +1,5 @@ from __future__ import annotations -import json import logging from pathlib import Path from types import MappingProxyType @@ -23,6 +22,7 @@ _cell_row_positions_in_bbox, _get_labels, _get_morphology_images, + _get_pixel_size, _get_points, _get_polygons, _patched_ragged_vlen_chunk_decode, @@ -77,7 +77,9 @@ def atera( This function reads the following files: - - ``{xx.SPECS_FILE!r}``: File containing specifications. + - ``{xx.SPECS_FILE!r}``: File containing specifications, including the pixel size. If absent, the + pixel size is instead read from the ``PhysicalSizeX`` OME-XML metadata of the first + ``{xx.MORPHOLOGY_2D_DIR!r}`` OME-TIFF. - ``{xx.CELL_FEATURE_MATRIX_FILE!r}``: Zipped zarr store with the cell-by-gene matrix and cell metadata. - ``{xx.CELLS_FILE!r}``: Zipped zarr store with cell/nucleus labels and boundary polygons. - ``{xx.TRANSCRIPTS_FILE!r}``: Zipped zarr store with per-transcript locations (optional, large). @@ -139,9 +141,7 @@ def atera( image_models_kwargs, labels_models_kwargs ) - with open(path / AteraKeys.SPECS_FILE) as f: - specs = json.load(f) - pixel_size = specs[str(AteraKeys.PIXEL_SIZE)] + pixel_size = _get_pixel_size(path) needs_cells_zarr = cells_boundaries or nucleus_boundaries or cells_labels or nucleus_labels if not cells_table and (needs_cells_zarr or transcripts): From 7f3ce558bcf5dbfadb0ef7d776188a632ce18e23 Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Fri, 2 Oct 2026 12:01:52 -0400 Subject: [PATCH 07/10] fixes for pre-commit --- notebooks/atera_example.ipynb | 241 +++----------------- pyproject.toml | 1 + src/spatialdata_io/readers/_atera_common.py | 92 ++++---- src/spatialdata_io/readers/atera.py | 45 ++-- 4 files changed, 119 insertions(+), 260 deletions(-) diff --git a/notebooks/atera_example.ipynb b/notebooks/atera_example.ipynb index 6ad6bb3b..f73947f8 100644 --- a/notebooks/atera_example.ipynb +++ b/notebooks/atera_example.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "id": "7fb27b941602401d91542211134fc71a", "metadata": {}, "source": [ "# Atera reader — exploratory analysis\n", @@ -91,25 +92,17 @@ "metadata": {}, "outputs": [], "source": [ - "import sys\n", - "from pathlib import Path\n", - "\n", "import matplotlib.pyplot as plt\n", - "import scanpy as sc\n", - "from spatialdata import bounding_box_query\n", - "\n", - "from spatialdata_io import atera\n", - "import spatialdata_plot # noqa: F401\n", - "from spatialdata_io.readers.atera import read_transcripts_for_genes\n", - "from spatialdata_io.readers.atera import read_table_for_genes\n", - "\n", - "import numpy as np\n", - "from scipy.sparse import diags\n", "import numpy as np\n", "import pandas as pd\n", + "import scanpy as sc\n", + "import spatialdata_plot # noqa: F401\n", "from scipy.sparse import issparse\n", "from sklearn.decomposition import IncrementalPCA\n", "\n", + "from spatialdata_io import atera\n", + "from spatialdata_io.readers.atera import read_table_for_genes, read_transcripts_for_genes\n", + "\n", "%matplotlib inline" ] }, @@ -123,54 +116,11 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "id": "88e33859", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/stephen.williams/micromamba/envs/spatialdata-test/lib/python3.14/functools.py:1069: UserWarning: The index of the dataframe is not monotonic increasing. It is recommended to sort the data to adjust the order of the index before calling .parse() (or call `parse(sort=True)`) to avoid possible problems due to unknown divisions.\n", - " return self._dispatch(args[0].__class__).__get__(self._obj, self._cls)(*args, **kwargs)\n" - ] - }, - { - "data": { - "text/plain": [ - "SpatialData object\n", - "├── Images\n", - "│ └── 'morphology': DataTree[cyx] (4, 151, 151), (4, 75, 75), (4, 37, 37), (4, 18, 18), (4, 9, 9)\n", - "├── Points\n", - "│ └── 'transcripts': DataFrame with shape: (, 7) (3D points)\n", - "├── Shapes\n", - "│ └── 'cell_boundaries': GeoDataFrame shape: (13, 1) (2D shapes)\n", - "└── Tables\n", - " └── 'table': AnnData (13, 132)\n", - "with coordinate systems:\n", - " ▸ 'global', with elements:\n", - " morphology (Images), transcripts (Points), cell_boundaries (Shapes)" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "sdata = atera(\n", - " BUNDLE_PATH,\n", - " cells_table=True,\n", - " cells_boundaries=True,\n", - " nucleus_boundaries=False,\n", - " cells_labels=False,\n", - " nucleus_labels=False,\n", - " transcripts=True,\n", - " morphology_images=True,\n", - " morphology_3d_images=False,\n", - ")\n", - "sdata" - ] + "outputs": [], + "source": "BUNDLE_PATH = \"data/tiny_atera_dataset\"\n\nsdata = atera(\n BUNDLE_PATH,\n cells_table=True,\n cells_boundaries=True,\n nucleus_boundaries=False,\n cells_labels=False,\n nucleus_labels=False,\n transcripts=True,\n morphology_images=True,\n morphology_3d_images=False,\n)\nsdata" }, { "cell_type": "markdown", @@ -303,7 +253,7 @@ } ], "source": [ - "sdata.images['morphology'].to_dict()" + "sdata.images[\"morphology\"].to_dict()" ] }, { @@ -1478,7 +1428,7 @@ "source": [ "sdata.pl.render_shapes(\"cell_boundaries\", color=\"transcript_counts\", fill_alpha=1, outline_alpha=0).pl.show(\n", " title=\"Transcript counts per cell\", coordinate_systems=\"global\", figsize=(10, 10)\n", - ")\n" + ")" ] }, { @@ -1512,7 +1462,7 @@ "\n", "sdata.pl.render_shapes(\"cell_boundaries\", color=\"n_genes\", fill_alpha=1, outline_alpha=0).pl.show(\n", " title=\"Genes detected per cell\", coordinate_systems=\"global\", figsize=(10, 10)\n", - ")\n" + ")" ] }, { @@ -1749,17 +1699,12 @@ "\n", "sdata = read_transcripts_for_genes(sdata, BUNDLE_PATH, gene_name, points_key=f\"transcripts_{gene_name}\")\n", "\n", - "sdata.pl.render_shapes(\n", - " \"cell_boundaries\", fill_alpha=0, outline_alpha=1\n", - ").pl.render_points(\n", + "sdata.pl.render_shapes(\"cell_boundaries\", fill_alpha=0, outline_alpha=1).pl.render_points(\n", " f\"transcripts_{gene_name}\",\n", " color=\"orange\",\n", - " size=10, # for a real sample you want to use something more like 0.3\n", - " method=\"datashader\"\n", - ").pl.show(\n", - " title=f\"{gene_name} transcripts\", figsize=(8, 8)\n", - ")\n", - "\n" + " size=10, # for a real sample you want to use something more like 0.3\n", + " method=\"datashader\",\n", + ").pl.show(title=f\"{gene_name} transcripts\", figsize=(8, 8))" ] }, { @@ -1785,16 +1730,8 @@ "gene_adata = read_table_for_genes(BUNDLE_PATH, gene_name)\n", "sdata.shapes[\"cell_boundaries\"][gene_name] = np.asarray(gene_adata[:, gene_name].X.todense()).ravel()\n", "\n", - "sdata.pl.render_shapes(\n", - " \"cell_boundaries\",\n", - " color=gene_name,\n", - " fill_alpha=1,\n", - " outline_alpha=0,\n", - " method=\"datashader\"\n", - ").pl.show(\n", - " title=f\"{gene_name} expression per cell\",\n", - " coordinate_systems=\"global\",\n", - " figsize=(10, 10)\n", + "sdata.pl.render_shapes(\"cell_boundaries\", color=gene_name, fill_alpha=1, outline_alpha=0, method=\"datashader\").pl.show(\n", + " title=f\"{gene_name} expression per cell\", coordinate_systems=\"global\", figsize=(10, 10)\n", ")" ] }, @@ -1830,18 +1767,11 @@ "gene_adata = read_table_for_genes(BUNDLE_PATH, gene_name)\n", "sdata.shapes[\"cell_boundaries\"][gene_name] = np.asarray(gene_adata[:, gene_name].X.todense()).ravel()\n", "\n", - "sdata.pl.render_shapes(\n", - " \"cell_boundaries\",\n", - " color=gene_name,\n", - " fill_alpha=1,\n", - " outline_alpha=0,\n", - " method=\"datashader\"\n", - ").pl.show(\n", + "sdata.pl.render_shapes(\"cell_boundaries\", color=gene_name, fill_alpha=1, outline_alpha=0, method=\"datashader\").pl.show(\n", " title=f\"{gene_name} expression per cell\",\n", " coordinate_systems=\"global\",\n", " figsize=(10, 10),\n", - " crop_coord=(60, 100, 20, 60)\n", - "\n", + " crop_coord=(60, 100, 20, 60),\n", ")" ] }, @@ -1867,15 +1797,13 @@ "\n", "sdata = read_transcripts_for_genes(sdata, BUNDLE_PATH, genes, points_key=\"transcripts_multi\")\n", "\n", - "sdata.pl.render_shapes(\n", - " \"cell_boundaries\", fill_alpha=0, outline_alpha=1, method=\"datashader\"\n", - ").pl.render_points(\n", + "sdata.pl.render_shapes(\"cell_boundaries\", fill_alpha=0, outline_alpha=1, method=\"datashader\").pl.render_points(\n", " \"transcripts_multi\",\n", " color=\"feature_name\",\n", " groups=genes,\n", " palette=[\"orange\", \"cyan\"],\n", " size=10,\n", - ").pl.show(title=\"Multi-gene transcripts\", figsize=(8, 8))\n" + ").pl.show(title=\"Multi-gene transcripts\", figsize=(8, 8))" ] }, { @@ -1919,15 +1847,13 @@ "\n", "codewords = sorted(sdata.points[f\"transcripts_{gene_name}_codewords\"][\"feature_name\"].compute().cat.categories)\n", "\n", - "sdata.pl.render_shapes(\n", - " \"cell_boundaries\", fill_alpha=0, outline_alpha=1, method=\"datashader\"\n", - ").pl.render_points(\n", + "sdata.pl.render_shapes(\"cell_boundaries\", fill_alpha=0, outline_alpha=1, method=\"datashader\").pl.render_points(\n", " f\"transcripts_{gene_name}_codewords\",\n", " color=\"feature_name\",\n", " groups=codewords,\n", " palette=[\"orange\", \"cyan\", \"magenta\"][: len(codewords)],\n", " size=10,\n", - ").pl.show(title=f\"{gene_name} transcripts by codeword\", figsize=(8, 8))\n" + ").pl.show(title=f\"{gene_name} transcripts by codeword\", figsize=(8, 8))" ] }, { @@ -1957,9 +1883,7 @@ "\n", "fig, axes = plt.subplots(1, len(codewords), figsize=(6 * len(codewords), 6))\n", "for ax, cw in zip(axes, codewords, strict=True):\n", - " sdata.pl.render_shapes(\n", - " \"cell_boundaries\", fill_alpha=0, outline_alpha=1, method=\"datashader\"\n", - " ).pl.render_points(\n", + " sdata.pl.render_shapes(\"cell_boundaries\", fill_alpha=0, outline_alpha=1, method=\"datashader\").pl.render_points(\n", " points_key,\n", " color=\"feature_name\",\n", " groups=cw,\n", @@ -1967,8 +1891,7 @@ " size=2,\n", " ).pl.show(title=f\"{cw} (n={counts.get(cw, 0)})\", ax=ax)\n", "\n", - "fig.tight_layout()\n", - "\n" + "fig.tight_layout()" ] }, { @@ -2020,7 +1943,7 @@ } ], "source": [ - "sdata.pl.render_shapes(\"cell_boundaries\", outline_alpha=1, method=\"datashader\").pl.show(\n", + "sdata.pl.render_shapes(\"cell_boundaries\", outline_alpha=1, method=\"datashader\").pl.show(\n", " title=\"All cell boundaries\", coordinate_systems=\"global\", figsize=(10, 10)\n", ")" ] @@ -2129,62 +2052,11 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": null, "id": "16fa2e49", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "selected 132 highly variable genes\n" - ] - } - ], - "source": [ - "adata = sdata.tables[\"table\"]\n", - "adata.layers[\"counts\"] = adata.X.copy() # cheap: just copies the dask graph\n", - "\n", - "sc.pp.normalize_total(adata, target_sum=1e4) # stays lazy (dask)\n", - "sc.pp.log1p(adata) # stays lazy\n", - "\n", - "def streaming_gene_mean_var(X):\n", - " n_obs, n_var = X.shape\n", - " sum_, sumsq = np.zeros(n_var), np.zeros(n_var)\n", - " for i in range(X.numblocks[0]):\n", - " block = X.blocks[i].compute() # one chunk materialized, discarded each iteration\n", - " block = block.expm1() # undo log1p first, like scanpy's seurat-flavor HVG does\n", - " sum_ += np.asarray(block.sum(axis=0)).ravel()\n", - " sumsq += np.asarray(block.multiply(block).sum(axis=0)).ravel()\n", - " mean = sum_ / n_obs\n", - " var = sumsq / n_obs - mean ** 2\n", - " var *= n_obs / (n_obs - 1)\n", - " return mean, var\n", - "\n", - "mean, var = streaming_gene_mean_var(adata.X)\n", - "mean_for_disp = mean.copy()\n", - "mean_for_disp[mean_for_disp == 0] = 1e-12\n", - "dispersion = var / mean_for_disp\n", - "dispersion[dispersion == 0] = np.nan\n", - "log_dispersion = np.log(dispersion)\n", - "log_mean = np.log1p(mean)\n", - "\n", - "n_bins = 20\n", - "bins = pd.cut(log_mean, bins=n_bins)\n", - "df = pd.DataFrame({\"log_mean\": log_mean, \"log_dispersion\": log_dispersion, \"bin\": bins})\n", - "grouped = df.groupby(\"bin\", observed=True)[\"log_dispersion\"]\n", - "norm_dispersion = (df[\"log_dispersion\"] - grouped.transform(\"mean\")) / grouped.transform(\"std\")\n", - "\n", - "n_top_genes = 2000\n", - "top_idx = norm_dispersion.fillna(-np.inf).nlargest(n_top_genes).index.to_numpy()\n", - "hvg_mask = np.zeros(adata.n_vars, dtype=bool)\n", - "hvg_mask[top_idx] = True\n", - "adata.var[\"highly_variable\"] = hvg_mask\n", - "\n", - "\n", - "adata_hvg = adata[:, hvg_mask].copy()\n", - "print(f\"selected {hvg_mask.sum()} highly variable genes\")\n" - ] + "outputs": [], + "source": "adata = sdata.tables[\"table\"]\nadata.layers[\"counts\"] = adata.X.copy() # cheap: just copies the dask graph\n\nsc.pp.normalize_total(adata, target_sum=1e4) # stays lazy (dask)\nsc.pp.log1p(adata) # stays lazy\n\n\ndef streaming_gene_mean_var(X):\n \"\"\"Compute per-gene mean/variance one dask chunk at a time, without materializing the full matrix.\"\"\"\n n_obs, n_var = X.shape\n sum_, sumsq = np.zeros(n_var), np.zeros(n_var)\n for i in range(X.numblocks[0]):\n block = X.blocks[i].compute() # one chunk materialized, discarded each iteration\n block = block.expm1() # undo log1p first, like scanpy's seurat-flavor HVG does\n sum_ += np.asarray(block.sum(axis=0)).ravel()\n sumsq += np.asarray(block.multiply(block).sum(axis=0)).ravel()\n mean = sum_ / n_obs\n var = sumsq / n_obs - mean**2\n var *= n_obs / (n_obs - 1)\n return mean, var\n\n\nmean, var = streaming_gene_mean_var(adata.X)\nmean_for_disp = mean.copy()\nmean_for_disp[mean_for_disp == 0] = 1e-12\ndispersion = var / mean_for_disp\ndispersion[dispersion == 0] = np.nan\nlog_dispersion = np.log(dispersion)\nlog_mean = np.log1p(mean)\n\nn_bins = 20\nbins = pd.cut(log_mean, bins=n_bins)\ndf = pd.DataFrame({\"log_mean\": log_mean, \"log_dispersion\": log_dispersion, \"bin\": bins})\ngrouped = df.groupby(\"bin\", observed=True)[\"log_dispersion\"]\nnorm_dispersion = (df[\"log_dispersion\"] - grouped.transform(\"mean\")) / grouped.transform(\"std\")\n\nn_top_genes = 2000\ntop_idx = norm_dispersion.fillna(-np.inf).nlargest(n_top_genes).index.to_numpy()\nhvg_mask = np.zeros(adata.n_vars, dtype=bool)\nhvg_mask[top_idx] = True\nadata.var[\"highly_variable\"] = hvg_mask\n\n\nadata_hvg = adata[:, hvg_mask].copy()\nprint(f\"selected {hvg_mask.sum()} highly variable genes\")" }, { "cell_type": "markdown", @@ -2202,44 +2074,11 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": null, + "id": "acae54e37e7d407bbb7b55eff062a284", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "pca done\n", - "WARNING: n_obs too small: adjusting to `n_neighbors = 7`\n", - "neighbors done\n", - "leiden done\n" - ] - } - ], - "source": [ - "def streaming_pca(X, n_components=50, batch_rows=50_000):\n", - " ipca = IncrementalPCA(n_components=n_components)\n", - " def batches():\n", - " for i in range(X.numblocks[0]):\n", - " block = X.blocks[i].compute()\n", - " block = np.asarray(block.todense()) if issparse(block) else np.asarray(block)\n", - " for start in range(0, block.shape[0], batch_rows):\n", - " yield block[start:start + batch_rows]\n", - " for batch in batches():\n", - " ipca.partial_fit(batch) # <-- removed the size check\n", - " parts = [ipca.transform(batch) for batch in batches()]\n", - " return np.concatenate(parts, axis=0), ipca\n", - "\n", - "X_pca, pca_model = streaming_pca(adata_hvg.X, n_components=10, batch_rows=50_000)\n", - "adata_hvg.obsm[\"X_pca\"] = X_pca\n", - "adata_hvg.uns[\"pca\"] = {\"variance_ratio\": pca_model.explained_variance_ratio_}\n", - "print(\"pca done\")\n", - "\n", - "sc.pp.neighbors(adata_hvg, use_rep=\"X_pca\") # small dense embedding now, cheap\n", - "print(\"neighbors done\")\n", - "sc.tl.leiden(adata_hvg, key_added=\"leiden\", flavor=\"igraph\", n_iterations=2)\n", - "print(\"leiden done\")" - ] + "outputs": [], + "source": "def streaming_pca(X, n_components=50, batch_rows=50_000):\n \"\"\"Fit an `IncrementalPCA` one dask chunk at a time, without materializing the full matrix.\"\"\"\n ipca = IncrementalPCA(n_components=n_components)\n\n def batches():\n for i in range(X.numblocks[0]):\n block = X.blocks[i].compute()\n block = np.asarray(block.todense()) if issparse(block) else np.asarray(block)\n for start in range(0, block.shape[0], batch_rows):\n yield block[start : start + batch_rows]\n\n for batch in batches():\n ipca.partial_fit(batch) # <-- removed the size check\n parts = [ipca.transform(batch) for batch in batches()]\n return np.concatenate(parts, axis=0), ipca\n\n\nX_pca, pca_model = streaming_pca(adata_hvg.X, n_components=10, batch_rows=50_000)\nadata_hvg.obsm[\"X_pca\"] = X_pca\nadata_hvg.uns[\"pca\"] = {\"variance_ratio\": pca_model.explained_variance_ratio_}\nprint(\"pca done\")\n\nsc.pp.neighbors(adata_hvg, use_rep=\"X_pca\") # small dense embedding now, cheap\nprint(\"neighbors done\")\nsc.tl.leiden(adata_hvg, key_added=\"leiden\", flavor=\"igraph\", n_iterations=2)\nprint(\"leiden done\")" }, { "cell_type": "markdown", @@ -2299,15 +2138,9 @@ } ], "source": [ - "sdata.pl.render_shapes(\n", - " \"cell_boundaries\",\n", - " color=\"leiden\",\n", - " outline_alpha=1\n", - ").pl.show(\n", - " title=\"Leiden clusters\",\n", - " coordinate_systems=\"global\",\n", - " figsize=(10, 10)\n", - ")\n" + "sdata.pl.render_shapes(\"cell_boundaries\", color=\"leiden\", outline_alpha=1).pl.show(\n", + " title=\"Leiden clusters\", coordinate_systems=\"global\", figsize=(10, 10)\n", + ")" ] } ], diff --git a/pyproject.toml b/pyproject.toml index 9c7135e1..23853bac 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -158,6 +158,7 @@ module = [ "dask_image.*", "h5py.*", "multiscale_spatial_image.*", + "numcodecs.*", "pyarrow.*", "rasterio.*", "readfcs.*", diff --git a/src/spatialdata_io/readers/_atera_common.py b/src/spatialdata_io/readers/_atera_common.py index 66f6a7a0..4a3c66f7 100644 --- a/src/spatialdata_io/readers/_atera_common.py +++ b/src/spatialdata_io/readers/_atera_common.py @@ -6,7 +6,7 @@ import re from pathlib import Path from types import MappingProxyType -from typing import TYPE_CHECKING, Any +from typing import TYPE_CHECKING, Any, cast import dask.array as da import dask.dataframe as dd @@ -111,11 +111,11 @@ def decode() -> Any: return await asyncio.to_thread(decode) - V2Codec._decode_single = patched_decode_single + V2Codec._decode_single = patched_decode_single # type: ignore[method-assign] try: yield finally: - V2Codec._decode_single = original_decode_single + V2Codec._decode_single = original_decode_single # type: ignore[method-assign] def _decode_packed_cell_id(raw: np.ndarray) -> np.ndarray: @@ -138,7 +138,7 @@ def _get_labels( labels_models_kwargs: Mapping[str, Any] = MappingProxyType({}), ) -> DataArray | DataTree: """Read the labels raster from cells.zarr.zip masks/{mask_index} (0 = nucleus, 1 = cell).""" - masks = da.from_array(cells_group[f"{AteraKeys.MASKS_GROUP}/{mask_index}"]) + masks = da.from_array(cells_group.get_array(f"{AteraKeys.MASKS_GROUP}/{mask_index}")) return Labels2DModel.parse(masks, dims=("y", "x"), transformations={"global": Identity()}, **labels_models_kwargs) @@ -193,10 +193,10 @@ def _get_polygons( Each row of ``vertices`` is a fixed-width (x, y) pair buffer, padded to a maximum number of vertices; ``num_vertices`` gives the number of valid pairs to use. """ - group = cells_group[f"{AteraKeys.POLYGON_SETS_GROUP}/{mask_index}"] - vertices = np.asarray(group[str(AteraKeys.POLYGON_VERTICES)][...]) - num_vertices = np.asarray(group[str(AteraKeys.POLYGON_NUM_VERTICES)][...]) - cell_index = np.asarray(group[str(AteraKeys.POLYGON_CELL_INDEX)][...]) + group = cells_group.get_group(f"{AteraKeys.POLYGON_SETS_GROUP}/{mask_index}") + vertices = np.asarray(group.get_array(str(AteraKeys.POLYGON_VERTICES))[...]) + num_vertices = np.asarray(group.get_array(str(AteraKeys.POLYGON_NUM_VERTICES))[...]) + cell_index = np.asarray(group.get_array(str(AteraKeys.POLYGON_CELL_INDEX))[...]) n_polygons, max_coords = vertices.shape max_vertices = max_coords // 2 @@ -216,19 +216,19 @@ def _read_transcript_tile(path: Path, tile_key: str, feature_names: list[str]) - store = zarr.storage.ZipStore(path / AteraKeys.TRANSCRIPTS_FILE, read_only=True) try: group = zarr.open_group(store, mode="r") - tile = group[f"{AteraKeys.GRID_GROUP}/{tile_key}"] - location = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_LOCATION)][...]) - gene_offset = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_GENE_OFFSET)][...]) - cell_id_raw = np.asarray(tile[str(AteraKeys.CELL_ID)][...]) - overlaps_nucleus = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_OVERLAPS_NUCLEUS)][...]).reshape(-1) - quality_score = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_QUALITY_SCORE)][...]).reshape(-1) + tile = group.get_group(f"{AteraKeys.GRID_GROUP}/{tile_key}") + location = np.asarray(tile.get_array(str(AteraKeys.TRANSCRIPTS_LOCATION))[...]) + gene_offset = np.asarray(tile.get_array(str(AteraKeys.TRANSCRIPTS_GENE_OFFSET))[...]) + cell_id_raw = np.asarray(tile.get_array(str(AteraKeys.CELL_ID))[...]) + overlaps_nucleus = np.asarray(tile.get_array(str(AteraKeys.TRANSCRIPTS_OVERLAPS_NUCLEUS))[...]).reshape(-1) + quality_score = np.asarray(tile.get_array(str(AteraKeys.TRANSCRIPTS_QUALITY_SCORE))[...]).reshape(-1) finally: store.close() # Rows within a tile are sorted by gene; gene_offset[g] = [start, end) gives the row range for # gene g, so the per-row gene index must be reconstructed rather than read directly. gene_index = np.repeat(np.arange(len(gene_offset)), gene_offset[:, 1] - gene_offset[:, 0]) - feature_name = pd.Categorical.from_codes(gene_index, categories=feature_names) + feature_name = pd.Categorical.from_codes(gene_index, categories=pd.Index(feature_names)) return pd.DataFrame( { @@ -248,7 +248,7 @@ def _get_points(path: Path, pixel_size: float, feature_names: list[str]) -> dd.D store = zarr.storage.ZipStore(path / AteraKeys.TRANSCRIPTS_FILE, read_only=True) try: group = zarr.open_group(store, mode="r") - tile_keys = sorted(group[str(AteraKeys.GRID_GROUP)].group_keys()) + tile_keys = sorted(group.get_group(str(AteraKeys.GRID_GROUP)).group_keys()) finally: store.close() @@ -372,27 +372,29 @@ def read_transcripts_for_genes( store = zarr.storage.ZipStore(path / AteraKeys.TRANSCRIPTS_FILE, read_only=True) try: group = zarr.open_group(store, mode="r") - tile_keys = sorted(group[str(AteraKeys.GRID_GROUP)].group_keys()) + tile_keys = sorted(group.get_group(str(AteraKeys.GRID_GROUP)).group_keys()) frames = [] for tile_key in tile_keys: - tile = group[f"{AteraKeys.GRID_GROUP}/{tile_key}"] - gene_offset = tile[str(AteraKeys.TRANSCRIPTS_GENE_OFFSET)] + tile = group.get_group(f"{AteraKeys.GRID_GROUP}/{tile_key}") + gene_offset = tile.get_array(str(AteraKeys.TRANSCRIPTS_GENE_OFFSET)) for gene, gene_idx in gene_indices.items(): - start, end = gene_offset[gene_idx] + start, end = np.asarray(gene_offset[gene_idx]) if end <= start: continue - location = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_LOCATION)][start:end]) - cell_id_raw = np.asarray(tile[str(AteraKeys.CELL_ID)][start:end]) - overlaps_nucleus = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_OVERLAPS_NUCLEUS)][start:end]).reshape( + location = np.asarray(tile.get_array(str(AteraKeys.TRANSCRIPTS_LOCATION))[start:end]) + cell_id_raw = np.asarray(tile.get_array(str(AteraKeys.CELL_ID))[start:end]) + overlaps_nucleus = np.asarray( + tile.get_array(str(AteraKeys.TRANSCRIPTS_OVERLAPS_NUCLEUS))[start:end] + ).reshape(-1) + quality_score = np.asarray(tile.get_array(str(AteraKeys.TRANSCRIPTS_QUALITY_SCORE))[start:end]).reshape( -1 ) - quality_score = np.asarray(tile[str(AteraKeys.TRANSCRIPTS_QUALITY_SCORE)][start:end]).reshape(-1) if by_codeword: - codeword_identity = np.asarray(tile[str(AteraKeys.CODEWORD_IDENTITY)][start:end]).reshape(-1) - feature_name = pd.Categorical( - [f"{gene}_cw{cw}" for cw in codeword_identity], categories=categories + codeword_identity = np.asarray(tile.get_array(str(AteraKeys.CODEWORD_IDENTITY))[start:end]).reshape( + -1 ) + feature_name = pd.Categorical([f"{gene}_cw{cw}" for cw in codeword_identity], categories=categories) else: feature_name = pd.Categorical([gene] * (end - start), categories=categories) frames.append( @@ -452,12 +454,15 @@ def _cell_row_positions_in_bbox(path: Path, bbox: tuple[float, float, float, flo store = zarr.storage.ZipStore(path / AteraKeys.CELLS_FILE, read_only=True) try: group = zarr.open_group(store, mode="r") - cell_bboxes = np.asarray(group[str(AteraKeys.BBOXES)][...]).reshape(-1, 4) + cell_bboxes = np.asarray(group.get_array(str(AteraKeys.BBOXES))[...]).reshape(-1, 4) finally: store.close() keep = ( - (cell_bboxes[:, 0] < xmax) & (xmin < cell_bboxes[:, 2]) & (cell_bboxes[:, 1] < ymax) & (ymin < cell_bboxes[:, 3]) + (cell_bboxes[:, 0] < xmax) + & (xmin < cell_bboxes[:, 2]) + & (cell_bboxes[:, 1] < ymax) + & (ymin < cell_bboxes[:, 3]) ) return np.nonzero(keep)[0] @@ -533,13 +538,14 @@ def read_cell_boundaries( store = zarr.storage.ZipStore(path / AteraKeys.CELLS_FILE, read_only=True) try: group = zarr.open_group(store, mode="r") - cell_bboxes = np.asarray(group[str(AteraKeys.BBOXES)][...]).reshape(-1, 4) - mask_group = group[f"{AteraKeys.GRIDDED_POLYGON_SETS_GROUP}/{mask_index}"] + cell_bboxes = np.asarray(group.get_array(str(AteraKeys.BBOXES))[...]).reshape(-1, 4) + mask_group = group.get_group(f"{AteraKeys.GRIDDED_POLYGON_SETS_GROUP}/{mask_index}") # Each pyramid level merges 2x2 blocks of tiles from the level below, so a tile's spatial # extent doubles per level; `grid_size` (attrs, shared across levels) gives level 0's tile # size. - tile_size = float(mask_group.attrs[str(AteraKeys.GRID_SIZE)][0]) * (2**pyramid_level) - level_group = mask_group[str(pyramid_level)] + grid_size = cast("list[float]", mask_group.attrs[str(AteraKeys.GRID_SIZE)]) + tile_size = float(grid_size[0]) * (2**pyramid_level) + level_group = mask_group.get_group(str(pyramid_level)) tile_keys = list(level_group.group_keys()) if raw_bbox is not None: @@ -549,10 +555,10 @@ def read_cell_boundaries( num_vertices_parts = [] cell_index_parts = [] for tile_key in tile_keys: - tile = level_group[tile_key] - num_vertices = np.asarray(tile[str(AteraKeys.POLYGON_NUM_VERTICES)][...]) - cell_index = np.asarray(tile[str(AteraKeys.POLYGON_CELL_INDEX)][...]) - relative_vertices = np.asarray(tile[str(AteraKeys.RELATIVE_VERTICES)][...]).reshape(-1, 2) + tile = level_group.get_group(tile_key) + num_vertices = np.asarray(tile.get_array(str(AteraKeys.POLYGON_NUM_VERTICES))[...]) + cell_index = np.asarray(tile.get_array(str(AteraKeys.POLYGON_CELL_INDEX))[...]) + relative_vertices = np.asarray(tile.get_array(str(AteraKeys.RELATIVE_VERTICES))[...]).reshape(-1, 2) if raw_bbox is not None: tile_cell_bboxes = cell_bboxes[cell_index] @@ -628,7 +634,10 @@ def _get_pixel_size(path: Path) -> float: f"Found neither {AteraKeys.SPECS_FILE!s} nor any morphology OME-TIFF files in {morphology_dir!s} " "to read the pixel size from." ) - return from_tiff(files[0]).images[0].pixels.physical_size_x + physical_size_x = from_tiff(files[0]).images[0].pixels.physical_size_x + if physical_size_x is None: + raise ValueError(f"{files[0]!s} does not specify `PhysicalSizeX` in its OME-XML metadata.") + return physical_size_x def _tiled_imread(path: Path, imread_kwargs: Mapping[str, Any] = MappingProxyType({})) -> da.Array: @@ -685,7 +694,12 @@ def _get_morphology_images( from ome_types import from_tiff channel_indices = [_parse_morphology_channel_index(f.name) for f in files] - channel_names = [from_tiff(f).images[0].pixels.channels[i].name for f, i in zip(files, channel_indices, strict=True)] + channel_names = [] + for f, i in zip(files, channel_indices, strict=True): + name = from_tiff(f).images[0].pixels.channels[i].name + if name is None: + raise ValueError(f"Channel {i} in {f!s} has no name in its OME-XML metadata.") + channel_names.append(name) channels = [_tiled_imread(f, imread_kwargs)[i] for f, i in zip(files, channel_indices, strict=True)] image = da.stack(channels, axis=0) return Image2DModel.parse( diff --git a/src/spatialdata_io/readers/atera.py b/src/spatialdata_io/readers/atera.py index c216d2a3..11991604 100644 --- a/src/spatialdata_io/readers/atera.py +++ b/src/spatialdata_io/readers/atera.py @@ -3,7 +3,7 @@ import logging from pathlib import Path from types import MappingProxyType -from typing import TYPE_CHECKING, Any, Literal +from typing import TYPE_CHECKING, Any, Literal, cast import dask.array as da import numpy as np @@ -216,12 +216,14 @@ def _read_x_chunk(zarr_path: Path, zarr_key: AteraKeys, start: int, stop: int) - store = zarr.storage.ZipStore(zarr_path / zarr_key, read_only=True) try: group = zarr.open_group(store, mode="r") - return sparse_dataset(group[str(AteraKeys.X_GROUP)])[start:stop] + return cast(csr_matrix, sparse_dataset(group[str(AteraKeys.X_GROUP)])[start:stop]) finally: store.close() -def _lazy_x(zarr_path: Path, zarr_key: AteraKeys, n_obs: int, n_var: int, dtype: np.dtype, row_chunk_size: int) -> da.Array: +def _lazy_x( + zarr_path: Path, zarr_key: AteraKeys, n_obs: int, n_var: int, dtype: np.dtype, row_chunk_size: int +) -> da.Array: """Build a `dask`-backed `n_obs x n_var` CSR array, delegating each chunk's read to `sparse_dataset`.""" meta = csr_matrix((0, n_var), dtype=dtype) blocks = [] @@ -257,14 +259,18 @@ def _get_table( group = zarr.open_group(store, mode="r") with _patched_ragged_vlen_chunk_decode(): - var_df = read_elem(group[str(AteraKeys.VAR_GROUP)]) + var_df = cast(pd.DataFrame, read_elem(group[str(AteraKeys.VAR_GROUP)])) feature_names = var_df[str(AteraKeys.FEATURE_NAME)].astype(str).tolist() if var_columns != "all": var_df = var_df[list(var_columns)] - obs_df = read_elem(group[str(AteraKeys.OBS_GROUP)]) + obs_df = cast(pd.DataFrame, read_elem(group[str(AteraKeys.OBS_GROUP)])) - obsm = read_elem(group[str(AteraKeys.OBSM_GROUP)]) if str(AteraKeys.OBSM_GROUP) in group else {} + obsm = ( + cast(dict[str, Any], read_elem(group[str(AteraKeys.OBSM_GROUP)])) + if str(AteraKeys.OBSM_GROUP) in group + else {} + ) x_ds = sparse_dataset(group[str(AteraKeys.X_GROUP)]) n_obs, n_var = x_ds.shape @@ -275,7 +281,9 @@ def _get_table( x = _lazy_x(path, AteraKeys.CELL_FEATURE_MATRIX_FILE, n_obs, n_var, dtype, row_chunk_size) adata = AnnData(X=x, obs=obs_df, var=var_df, obsm=obsm) - adata.obsm["spatial"] = adata.obs[[str(AteraKeys.CENTROID_X), str(AteraKeys.CENTROID_Y)]].to_numpy() + adata.obsm["spatial"] = cast(pd.DataFrame, adata.obs)[ + [str(AteraKeys.CENTROID_X), str(AteraKeys.CENTROID_Y)] + ].to_numpy() adata.obs["region"] = pd.Categorical([region] * n_obs) table = TableModel.parse( @@ -312,7 +320,7 @@ def read_var(path: str | Path) -> pd.DataFrame: try: group = zarr.open_group(store, mode="r") with _patched_ragged_vlen_chunk_decode(): - var_df = read_elem(group[str(AteraKeys.VAR_GROUP)]) + var_df = cast(pd.DataFrame, read_elem(group[str(AteraKeys.VAR_GROUP)])) finally: store.close() return var_df @@ -360,29 +368,32 @@ def read_table_for_cells( try: group = zarr.open_group(store, mode="r") with _patched_ragged_vlen_chunk_decode(): - var_df = read_elem(group[str(AteraKeys.VAR_GROUP)]) + var_df = cast(pd.DataFrame, read_elem(group[str(AteraKeys.VAR_GROUP)])) if var_columns != "all": var_df = var_df[list(var_columns)] - obs_df = read_elem(group[str(AteraKeys.OBS_GROUP)]) + obs_df = cast(pd.DataFrame, read_elem(group[str(AteraKeys.OBS_GROUP)])) if cell_ids is not None: index = pd.Index(obs_df[str(AteraKeys.CELL_ID)].to_numpy()) - row_positions = index.get_indexer(np.asarray(cell_ids)) + row_positions = index.get_indexer(pd.Index(np.asarray(cell_ids))) missing = np.asarray(cell_ids)[row_positions == -1] if missing.size: raise ValueError(f"cell_id(s) not found in {AteraKeys.CELL_FEATURE_MATRIX_FILE!s}: {missing.tolist()}") else: + assert bbox is not None row_positions = _cell_row_positions_in_bbox(path, bbox) x_ds = sparse_dataset(group[str(AteraKeys.X_GROUP)]) - x = x_ds[row_positions] + x = cast(csr_matrix, x_ds[row_positions]) obs_subset = obs_df.iloc[row_positions] finally: store.close() n_obs = len(obs_subset) adata = AnnData(X=x, obs=obs_subset, var=var_df) - adata.obsm["spatial"] = adata.obs[[str(AteraKeys.CENTROID_X), str(AteraKeys.CENTROID_Y)]].to_numpy() + adata.obsm["spatial"] = cast(pd.DataFrame, adata.obs)[ + [str(AteraKeys.CENTROID_X), str(AteraKeys.CENTROID_Y)] + ].to_numpy() adata.obs["region"] = pd.Categorical(["cell_labels"] * n_obs) return TableModel.parse( @@ -425,12 +436,12 @@ def read_table_for_genes(path: str | Path, genes: str | Sequence[str]) -> AnnDat group = zarr.open_group(store, mode="r") with _patched_ragged_vlen_chunk_decode(): - var_df = read_elem(group[str(AteraKeys.VAR_GROUP)])[ + var_df = cast(pd.DataFrame, read_elem(group[str(AteraKeys.VAR_GROUP)]))[ [str(AteraKeys.FEATURE_ID), str(AteraKeys.FEATURE_NAME)] ] feature_names = var_df[str(AteraKeys.FEATURE_NAME)].astype(str).tolist() index = pd.Index(feature_names) - col_positions = index.get_indexer(np.asarray(genes)) + col_positions = index.get_indexer(pd.Index(np.asarray(genes))) missing = np.asarray(genes)[col_positions == -1] if missing.size: raise ValueError( @@ -440,11 +451,11 @@ def read_table_for_genes(path: str | Path, genes: str | Sequence[str]) -> AnnDat var_subset = var_df.iloc[col_positions] var_subset.index = pd.Index(np.asarray(genes), name=str(AteraKeys.FEATURE_NAME)) - obs_df = read_elem(group[str(AteraKeys.OBS_GROUP)]) + obs_df = cast(pd.DataFrame, read_elem(group[str(AteraKeys.OBS_GROUP)])) n_obs = len(obs_df) x_ds = sparse_dataset(group[str(AteraKeys.X_GROUP)]) - x: csc_matrix = x_ds[:, col_positions] + x: csc_matrix = cast(csc_matrix, x_ds[:, col_positions]) finally: store.close() From 8e65a43e1e56ffe2c18683f09e100a42f2fbd717 Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Fri, 2 Oct 2026 12:06:45 -0400 Subject: [PATCH 08/10] Remove .gitignore Co-Authored-By: Claude Sonnet 5 --- .gitignore | 40 ---------------------------------------- 1 file changed, 40 deletions(-) delete mode 100644 .gitignore diff --git a/.gitignore b/.gitignore deleted file mode 100644 index 5b1a0f8d..00000000 --- a/.gitignore +++ /dev/null @@ -1,40 +0,0 @@ -# Temp files -.DS_Store -*~ -buck-out/ - -# IDEs -/.idea/ -/.vscode/ -/.spyproject/ -*.code-workspace - -# Compiled files -.venv/ -__pycache__/ -.*cache/ -.ipynb_checkpoints/ - -# Distribution / packaging -/dist/ -_version.py -uv.lock - -# Tests and coverage -/data/ -/node_modules/ -/.coverage* -/tests/data/ - -# docs -/docs/generated/ -/docs/_build/ - -# benchmarks -/.asv/ -*.bin -profile.speedscope.json - -# scratch notebooks -/notebooks/atera_exploration.ipynb -/notebooks/resource_monitor.py From 91ed96d380c2684204dced9bbdd008751db183c4 Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Fri, 2 Oct 2026 12:10:41 -0400 Subject: [PATCH 09/10] Restore .gitignore, synced with main Co-Authored-By: Claude Sonnet 5 --- .gitignore | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 .gitignore diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..3da189e4 --- /dev/null +++ b/.gitignore @@ -0,0 +1,36 @@ +# Temp files +.DS_Store +*~ +buck-out/ + +# IDEs +/.idea/ +/.vscode/ +/.spyproject/ +*.code-workspace + +# Compiled files +.venv/ +__pycache__/ +.*cache/ +.ipynb_checkpoints/ + +# Distribution / packaging +/dist/ +_version.py +uv.lock + +# Tests and coverage +/data/ +/node_modules/ +/.coverage* +/tests/data/ + +# docs +/docs/generated/ +/docs/_build/ + +# benchmarks +/.asv/ +*.bin +profile.speedscope.json From 9ed1b37aeaf0d6c4c11df813533d5fced7c832af Mon Sep 17 00:00:00 2001 From: Stephen Williams Date: Mon, 5 Oct 2026 13:52:13 -0400 Subject: [PATCH 10/10] add atera transcrips zarr to proseg parquet converter --- CHANGELOG.md | 2 + docs/api.md | 1 + src/spatialdata_io/__init__.py | 3 + .../converters/atera_to_proseg.py | 114 ++++++++++++++++++ 4 files changed, 120 insertions(+) create mode 100644 src/spatialdata_io/converters/atera_to_proseg.py diff --git a/CHANGELOG.md b/CHANGELOG.md index 046a0a7b..8cb549c1 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -18,6 +18,8 @@ Release notes for `v0.7.1` and earlier are available on the [Releases][] page. - `spatialdata_io` ships a `py.typed` marker, so downstream type checkers use its annotations. - `atera` reader for 10x Genomics Atera bundles: cell-by-gene table, cell/nucleus labels and boundary polygons, transcripts, and morphology images, read natively via `anndata`'s zarr IO. +- `atera_to_proseg` converter: writes an Atera bundle's `transcripts.zarr.zip` to a `proseg`-compatible + `transcripts.parquet` (readable via `proseg --xenium`), streaming one spatial tile at a time. ### Changed diff --git a/docs/api.md b/docs/api.md index 331b87be..33a78ee1 100644 --- a/docs/api.md +++ b/docs/api.md @@ -53,6 +53,7 @@ I/O for the `spatialdata` project. .. autosummary:: :toctree: generated + atera_to_proseg experimental.from_legacy_anndata experimental.to_legacy_anndata ``` diff --git a/src/spatialdata_io/__init__.py b/src/spatialdata_io/__init__.py index cf4cb6c3..fa46f955 100644 --- a/src/spatialdata_io/__init__.py +++ b/src/spatialdata_io/__init__.py @@ -27,6 +27,7 @@ "geojson": "spatialdata_io.readers.generic", "image": "spatialdata_io.readers.generic", # converters + "atera_to_proseg": "spatialdata_io.converters.atera_to_proseg", "generic_to_zarr": "spatialdata_io.converters.generic_to_zarr", } @@ -53,6 +54,7 @@ "geojson", "image", # converters + "atera_to_proseg", "generic_to_zarr", ] @@ -77,6 +79,7 @@ def __dir__() -> list[str]: if TYPE_CHECKING: # converters + from spatialdata_io.converters.atera_to_proseg import atera_to_proseg from spatialdata_io.converters.generic_to_zarr import generic_to_zarr # readers diff --git a/src/spatialdata_io/converters/atera_to_proseg.py b/src/spatialdata_io/converters/atera_to_proseg.py new file mode 100644 index 00000000..e1f2d7e4 --- /dev/null +++ b/src/spatialdata_io/converters/atera_to_proseg.py @@ -0,0 +1,114 @@ +from __future__ import annotations + +from pathlib import Path + +import numpy as np +import pyarrow as pa +import pyarrow.parquet as pq +import zarr + +from spatialdata_io._constants._constants import AteraKeys +from spatialdata_io.readers._atera_common import _read_transcript_tile +from spatialdata_io.readers.atera import read_var + +__all__ = ["atera_to_proseg"] + +# Sentinel string for unassigned transcripts; matches `proseg`'s own `--xenium` preset default for +# `--cell-id-unassigned` (see `set_xenium_presets` in `proseg`'s `main.rs`), so the output can be fed +# to `proseg` with no further column-name/sentinel configuration. +_CELL_ID_UNASSIGNED = "UNASSIGNED" + +# Column names/dtypes match what `proseg`'s `--xenium` preset expects from a `transcripts.parquet` +# (see `read_xenium_transcripts_parquet` in `proseg`'s `src/spatialdata_input.rs`/`src/sampler/transcripts.rs`): +# string columns must all share one arrow string type (here, `Utf8`, matching `feature_name`'s type, +# since `proseg` infers the type to use for every other string column from it), `overlaps_nucleus` must +# be `UInt8`, `transcript_id` a `UInt64`, and the coordinate/quality columns `Float32`. +_PROSEG_SCHEMA = pa.schema( + [ + ("transcript_id", pa.uint64()), + ("cell_id", pa.string()), + ("overlaps_nucleus", pa.uint8()), + ("feature_name", pa.string()), + ("x_location", pa.float32()), + ("y_location", pa.float32()), + ("z_location", pa.float32()), + ("qv", pa.float32()), + ("fov_name", pa.string()), + ] +) + + +def atera_to_proseg(path: str | Path, output: str | Path) -> Path: + """Convert a *10x Genomics Atera* ``transcripts.zarr.zip`` into a `proseg`-compatible parquet file. + + `currently, proseg `_ only supports reading a generic/custom-column-name + parquet file for the `Xenium `_ platform preset (any + other platform/custom column names require a CSV, not a parquet, input). This writes a parquet + file with the column names/dtypes `proseg` expects from Xenium's ``transcripts.parquet`` + (``transcript_id``, ``cell_id``, ``overlaps_nucleus``, ``feature_name``, + ``x_location``/``y_location``/``z_location``, ``qv``, ``fov_name``), so the result can be run + directly as ``proseg --xenium ``. + + Coordinates are written in the dataset's raw (micron) units, matching Xenium's own + ``x_location``/``y_location``/``z_location`` convention (*not* the pixel units of the "global" + coordinate system `atera()` assembles its `SpatialData` elements into). + + The input is read and written one spatial tile at a time (mirroring how `atera`'s own transcripts + reader is built), so converting even a dataset with tens of millions of transcripts does not require + holding the whole table in memory at once. + + Parameters + ---------- + path + Path to the Atera dataset (the same path passed to `atera`). + output + Path to write the parquet file to. + + Returns + ------- + `output`, as a `Path`. + """ + path = Path(path) + output = Path(output) + + feature_names = read_var(path)[str(AteraKeys.FEATURE_NAME)].astype(str).tolist() + + store = zarr.storage.ZipStore(path / AteraKeys.TRANSCRIPTS_FILE, read_only=True) + try: + group = zarr.open_group(store, mode="r") + tile_keys = sorted(group.get_group(str(AteraKeys.GRID_GROUP)).group_keys()) + finally: + store.close() + + writer = pq.ParquetWriter(output, _PROSEG_SCHEMA) + try: + offset = 0 + for tile_key in tile_keys: + tile_df = _read_transcript_tile(path, tile_key, feature_names) + n = len(tile_df) + if n == 0: + continue + + cell_id = tile_df[str(AteraKeys.CELL_ID)].to_numpy() + cell_id_str = np.where(cell_id == -1, _CELL_ID_UNASSIGNED, cell_id.astype(str)) + + table = pa.table( + { + "transcript_id": np.arange(offset, offset + n, dtype=np.uint64), + "cell_id": cell_id_str, + "overlaps_nucleus": tile_df["overlaps_nucleus"].to_numpy().astype(np.uint8), + "feature_name": tile_df[str(AteraKeys.FEATURE_NAME)].astype(str).to_numpy(), + "x_location": tile_df[str(AteraKeys.TRANSCRIPTS_X)].to_numpy().astype(np.float32), + "y_location": tile_df[str(AteraKeys.TRANSCRIPTS_Y)].to_numpy().astype(np.float32), + "z_location": tile_df[str(AteraKeys.TRANSCRIPTS_Z)].to_numpy().astype(np.float32), + "qv": tile_df["quality_score"].to_numpy().astype(np.float32), + "fov_name": np.full(n, tile_key), + }, + schema=_PROSEG_SCHEMA, + ) + writer.write_table(table) + offset += n + finally: + writer.close() + + return output