Merge pull request #11 from cobioda/bego

v0.1
cobioda · Dec 19, 2024 · 2aa08b0 · 2aa08b0
2 parents e85a175 + 087843d
commit 2aa08b0
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diff --git a/README.md b/README.md
@@ -7,16 +7,16 @@
 ---
 
 <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>
@@ -28,14 +28,14 @@
 </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
@@ -45,7 +45,7 @@
 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
 

diff --git a/docs/notebooks/example.ipynb b/docs/notebooks/example.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Example notebook"
+    "# xenium tutorial"
    ]
   },
   {
@@ -13,9 +13,40 @@
    "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"
    ]
   },
   {
@@ -24,7 +55,143 @@
    "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))"
    ]
   }
  ],