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v0.1 #11

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Dec 19, 2024
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v0.1 #11

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14 changes: 7 additions & 7 deletions README.md
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---

<p align="center">
<a href="https://scispy.readthedocs.io/en/latest/" target="_blank">Documentation</a>
<a href="https://scispy.readthedocs.io/en/latest/notebooks/tutorial.html" target="_blank">Examples</a>
<a href="https://scispy.readthedocs.io/en/latest/" target="_blank">Documentation</a> •
<a href="https://scispy.readthedocs.io/en/latest/docs/notebooks/example.ipynb" target="_blank">Examples</a> •
<a href="https://www.biorxiv.org/" target="_blank">Preprint</a>
</p>

[![Tests][badge-tests]][link-tests]
[![Documentation][badge-docs]][link-docs]

[badge-tests]: https://img.shields.io/github/actions/workflow/status/bfxomics/scispy/test.yaml?branch=main
[link-tests]: https://github.com/bfxomics/scispy/actions/workflows/test.yml
[badge-tests]: https://img.shields.io/github/actions/workflow/status/cobioda/scispy/test.yaml?branch=main
[link-tests]: https://github.com/cobioda/scispy/actions/workflows/test.yml
[badge-docs]: https://img.shields.io/readthedocs/scispy

</div>
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</p>

<p align="center">
<img src="https://github.com/cobioda/scispy/docs/_static/scispy.png" width="500px">
<img src="https://github.com/cobioda/scispy/docs/_static/scispy.png" width="300px">
</p>

## Features

- **Read in-situ spatial-omics assays experiments**: build on top of spatialdata package
- **Automatic cell type annotation**: scanvi implementation
- **Import anatomical .csv shape file from xenium explorer**: as anndata observation
- **Import anatomical .csv shape file from xenium explorer**: as anndata observations
- **Automatic run pseudobulk data analysis**: using decoupler and pydeseq2 packages
- **Compute cell type proportion in region**: integrating statistical test in case of replicates
- **Produce high quality spatial figures**: build on top of spatialdata_plot package
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Please refer to the [documentation][link-docs]. In particular, the

- [API documentation][link-api].
- [Tutorials][link-tutorial]
- [Tutorials][https://scispy.readthedocs.io/en/latest/docs/notebooks/example.ipynb]

## Installation

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175 changes: 171 additions & 4 deletions docs/notebooks/example.ipynb
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"cell_type": "markdown",
"metadata": {},
"source": [
"# Example notebook"
"# xenium tutorial"
]
},
{
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"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import scispy as scis\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"import scanpy as sc\n",
"import anndata as ad\n",
"from anndata import AnnData\n",
"import scispy"
"import spatialdata as sd\n",
"from spatialdata import SpatialData\n",
"from spatialdata import polygon_query\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### load raw data and perform scanpy analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sdata = scis.io.load_xenium('xenium_output_dir')\n",
"scis.pp.run_scanpy(sdata)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### annotate your spatial cells using a single-cell reference"
]
},
{
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"metadata": {},
"outputs": [],
"source": [
"adata = AnnData(np.random.normal(size=(20, 10)))"
"scref = sc.read_h5ad('scref.h5ad')\n",
"scis.pp.scvi_annotate(sdata['table'], \n",
" scref, \n",
" label_ref='celltype', \n",
" label_key='celltype')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### add anatomical regions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scis.tl.add_to_shapes(sdata, \n",
" shape_key='shapes', \n",
" scale_factor=1/0.2125, \n",
" target_coordinates='global', \n",
" shape_file='coordinates.csv')\n",
"\n",
"sdata['shapes'].name = sdata['shapes'].name.astype('category')\n",
"\n",
"f, ax = plt.subplots(figsize=(8, 5))\n",
"sdata.pl.render_images(cmap='Blues').pl.show(ax=ax)\n",
"sdata.pl.render_shapes(elements='shapes', color='name', fill_alpha=0.8).pl.show(\"global\",ax=ax)\n",
"ax.set_title('my_sample')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sdata['table'].obs['anatomy'] = 'unknown'\n",
"for i, shape in sdata['shapes'].iterrows():\n",
" try:\n",
" sd_shape = polygon_query(sdata, polygon=shape.geometry, target_coordinate_system=\"global\")\n",
" sdata['table'].obs.anatomy[sdata['table'].obs['cell_id'].isin(sd_shape.table.obs['cell_id'])] = shape['name']\n",
" except:\n",
" print(shape['name'], \" contains invalid coordinates\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### save sdata object"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"sdata.write(\"sdata_1\")\n",
"\n",
"#import spatialdata_xenium_explorer\n",
"#spatialdata_xenium_explorer.write(\"explorer_1\", sdata, shapes_key='cell_boundaries', points_key='transcripts', gene_column='feature_name', layer='counts', polygon_max_vertices=40, mode='-it')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### concatenate sdata objects"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"adatas = []\n",
"samples = ['sdata_1','sdata_2','sdata_3','sdata_4']\n",
"for s in samples:\n",
" sdata = SpatialData.read(s)\n",
" adatas += [sdata.table]\n",
"\n",
"adata = ad.concat(adatas)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### visualize cell type proportions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"scis.tl.scis_prop(adata, \n",
" celltype='celltype', \n",
" zone='anatomy', \n",
" top=10, \n",
" replicate='sample', \n",
" condition='genotype', \n",
" condition_order=['KO','CTRL'],\n",
" figsize=(6,4))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### run pseudobulk analysis"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = scis.tl.run_pseudobulk(sdata['table'], \n",
" replicate_key='sample', \n",
" cond_key='genotype', \n",
" cond_1='KO', \n",
" cond_2='CTRL', \n",
" groups_key='celltype')\n",
"\n",
"# plot results as seaborn heatmap\n",
"pivlfc = pd.pivot_table(df, values=[\"log2FoldChange\"], index=[\"index\"], columns=['celltype'], fill_value=0)\n",
"sns.clustermap(pivlfc, cmap=\"vlag\", figsize=(2, 8))"
]
}
],
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