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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "lightweight-rochester",
+   "metadata": {},
+   "source": [
+    "# Cell-cell communication analysis with LIANA+"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "rolled-saturn",
+   "metadata": {},
+   "source": [
+    "This notebook shows how to:\n",
+    " 1. fetch publicly available datasets from the [cellxgene portal](https://cellxgene.cziscience.com/collections)\n",
+    " 2. perform cell-cell communication analysis in \"steady state\" conditions (i.e. not in a multicondition (case/control) setting) with [LIANA+](https://www.biorxiv.org/content/10.1101/2023.08.19.553863v1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "available-devon",
+   "metadata": {},
+   "source": [
+    "### Requirements"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "earlier-montana",
+   "metadata": {},
+   "source": [
+    "This notebook requires python >= 3.8. \n",
+    "\n",
+    "This notebook uses ~12GB of RAM at peak time. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "documented-welding",
+   "metadata": {},
+   "source": [
+    "### Package imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "dried-broadway",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import cellxgene_census\n",
+    "import liana as li\n",
+    "import scanpy as sc"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "latest-stylus",
+   "metadata": {},
+   "source": [
+    "### Data loading with cellxgene census"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "substantial-comparative",
+   "metadata": {},
+   "source": [
+    "The cellxgene portal contains hundreds of standardized data collections from scRNA-seq, scATAC-seq, spatial transcriptomics and more technologies. There are two major strategies for fetching data from the cellxgene portal:\n",
+    "\n",
+    " * **Option 1**: If you only want data from one specific dataset, you can download the associated .h5ad object locally with the **.download_source_h5ad()** function. This will allow you to retrieve the original low-dimensional embedding (e.g. PCA, scVI, UMAP, tSNE) for visualisation purposes. \n",
+    " \n",
+    " * **Option 2**: If you want data from multiple datasets for specific tissue/disease status/cell types combinations of interest, you can fetch the anndata object with the **.get_anndata()** function. This will enable you to directly combine cells from multiple datasets in the same anndata object but does not retrieve any low-dimensional embedding, so you won't be able to visualise the data. If you want to subset the data of a single dataset you can also do it this way and avoid downloading anything locally. You also lose all the dataset-specific metadata which is not shared across the census.\n",
+    " \n",
+    "For more detailed information about how to access the data hosted on the cellxgene portal have a look at their [Python API](https://chanzuckerberg.github.io/cellxgene-census/python-api.html)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "medieval-subdivision",
+   "metadata": {},
+   "source": [
+    "#### Have a look at what is available on the cellxgene portal"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "drawn-allen",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The \"stable\" release is currently 2023-07-25. Specify 'census_version=\"2023-07-25\"' in future calls to open_soma() to ensure data consistency.\n",
+      "The \"stable\" release is currently 2023-07-25. Specify 'census_version=\"2023-07-25\"' in future calls to open_soma() to ensure data consistency.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>collection_id</th>\n",
+       "      <th>collection_name</th>\n",
+       "      <th>collection_doi</th>\n",
+       "      <th>dataset_id</th>\n",
+       "      <th>dataset_title</th>\n",
+       "      <th>dataset_h5ad_path</th>\n",
+       "      <th>dataset_total_cell_count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>soma_joinid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>e2c257e7-6f79-487c-b81c-39451cd4ab3c</td>\n",
+       "      <td>Spatial multiomics map of trophoblast developm...</td>\n",
+       "      <td>10.1038/s41586-023-05869-0</td>\n",
+       "      <td>f171db61-e57e-4535-a06a-35d8b6ef8f2b</td>\n",
+       "      <td>donor_p13_trophoblasts</td>\n",
+       "      <td>f171db61-e57e-4535-a06a-35d8b6ef8f2b.h5ad</td>\n",
+       "      <td>31497</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>e2c257e7-6f79-487c-b81c-39451cd4ab3c</td>\n",
+       "      <td>Spatial multiomics map of trophoblast developm...</td>\n",
+       "      <td>10.1038/s41586-023-05869-0</td>\n",
+       "      <td>ecf2e08e-2032-4a9e-b466-b65b395f4a02</td>\n",
+       "      <td>All donors trophoblasts</td>\n",
+       "      <td>ecf2e08e-2032-4a9e-b466-b65b395f4a02.h5ad</td>\n",
+       "      <td>67070</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>e2c257e7-6f79-487c-b81c-39451cd4ab3c</td>\n",
+       "      <td>Spatial multiomics map of trophoblast developm...</td>\n",
+       "      <td>10.1038/s41586-023-05869-0</td>\n",
+       "      <td>74cff64f-9da9-4b2a-9b3b-8a04a1598040</td>\n",
+       "      <td>All donors all cell states (in vivo)</td>\n",
+       "      <td>74cff64f-9da9-4b2a-9b3b-8a04a1598040.h5ad</td>\n",
+       "      <td>286326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>f7cecffa-00b4-4560-a29a-8ad626b8ee08</td>\n",
+       "      <td>Mapping single-cell transcriptomes in the intr...</td>\n",
+       "      <td>10.1016/j.ccell.2022.11.001</td>\n",
+       "      <td>5af90777-6760-4003-9dba-8f945fec6fdf</td>\n",
+       "      <td>Single-cell transcriptomic datasets of Renal c...</td>\n",
+       "      <td>5af90777-6760-4003-9dba-8f945fec6fdf.h5ad</td>\n",
+       "      <td>270855</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>3f50314f-bdc9-40c6-8e4a-b0901ebfbe4c</td>\n",
+       "      <td>Single-cell sequencing links multiregional imm...</td>\n",
+       "      <td>10.1016/j.ccell.2021.03.007</td>\n",
+       "      <td>bd65a70f-b274-4133-b9dd-0d1431b6af34</td>\n",
+       "      <td>Single-cell sequencing links multiregional imm...</td>\n",
+       "      <td>bd65a70f-b274-4133-b9dd-0d1431b6af34.h5ad</td>\n",
+       "      <td>167283</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
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+       "      <th>588</th>\n",
+       "      <td>180bff9c-c8a5-4539-b13b-ddbc00d643e6</td>\n",
+       "      <td>Molecular characterization of selectively vuln...</td>\n",
+       "      <td>10.1038/s41593-020-00764-7</td>\n",
+       "      <td>f9ad5649-f372-43e1-a3a8-423383e5a8a2</td>\n",
+       "      <td>Molecular characterization of selectively vuln...</td>\n",
+       "      <td>f9ad5649-f372-43e1-a3a8-423383e5a8a2.h5ad</td>\n",
+       "      <td>8168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>589</th>\n",
+       "      <td>a72afd53-ab92-4511-88da-252fb0e26b9a</td>\n",
+       "      <td>Single-cell atlas of peripheral immune respons...</td>\n",
+       "      <td>10.1038/s41591-020-0944-y</td>\n",
+       "      <td>456e8b9b-f872-488b-871d-94534090a865</td>\n",
+       "      <td>Single-cell atlas of peripheral immune respons...</td>\n",
+       "      <td>456e8b9b-f872-488b-871d-94534090a865.h5ad</td>\n",
+       "      <td>44721</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>590</th>\n",
+       "      <td>38833785-fac5-48fd-944a-0f62a4c23ed1</td>\n",
+       "      <td>Construction of a human cell landscape at sing...</td>\n",
+       "      <td>10.1038/s41586-020-2157-4</td>\n",
+       "      <td>2adb1f8a-a6b1-4909-8ee8-484814e2d4bf</td>\n",
+       "      <td>Construction of a human cell landscape at sing...</td>\n",
+       "      <td>2adb1f8a-a6b1-4909-8ee8-484814e2d4bf.h5ad</td>\n",
+       "      <td>598266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>591</th>\n",
+       "      <td>5d445965-6f1a-4b68-ba3a-b8f765155d3a</td>\n",
+       "      <td>A molecular cell atlas of the human lung from ...</td>\n",
+       "      <td>10.1038/s41586-020-2922-4</td>\n",
+       "      <td>e04daea4-4412-45b5-989e-76a9be070a89</td>\n",
+       "      <td>Krasnow Lab Human Lung Cell Atlas, Smart-seq2</td>\n",
+       "      <td>e04daea4-4412-45b5-989e-76a9be070a89.h5ad</td>\n",
+       "      <td>9409</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>592</th>\n",
+       "      <td>5d445965-6f1a-4b68-ba3a-b8f765155d3a</td>\n",
+       "      <td>A molecular cell atlas of the human lung from ...</td>\n",
+       "      <td>10.1038/s41586-020-2922-4</td>\n",
+       "      <td>8c42cfd0-0b0a-46d5-910c-fc833d83c45e</td>\n",
+       "      <td>Krasnow Lab Human Lung Cell Atlas, 10X</td>\n",
+       "      <td>8c42cfd0-0b0a-46d5-910c-fc833d83c45e.h5ad</td>\n",
+       "      <td>65662</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>593 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    collection_id  \\\n",
+       "soma_joinid                                         \n",
+       "0            e2c257e7-6f79-487c-b81c-39451cd4ab3c   \n",
+       "1            e2c257e7-6f79-487c-b81c-39451cd4ab3c   \n",
+       "2            e2c257e7-6f79-487c-b81c-39451cd4ab3c   \n",
+       "3            f7cecffa-00b4-4560-a29a-8ad626b8ee08   \n",
+       "4            3f50314f-bdc9-40c6-8e4a-b0901ebfbe4c   \n",
+       "...                                           ...   \n",
+       "588          180bff9c-c8a5-4539-b13b-ddbc00d643e6   \n",
+       "589          a72afd53-ab92-4511-88da-252fb0e26b9a   \n",
+       "590          38833785-fac5-48fd-944a-0f62a4c23ed1   \n",
+       "591          5d445965-6f1a-4b68-ba3a-b8f765155d3a   \n",
+       "592          5d445965-6f1a-4b68-ba3a-b8f765155d3a   \n",
+       "\n",
+       "                                               collection_name  \\\n",
+       "soma_joinid                                                      \n",
+       "0            Spatial multiomics map of trophoblast developm...   \n",
+       "1            Spatial multiomics map of trophoblast developm...   \n",
+       "2            Spatial multiomics map of trophoblast developm...   \n",
+       "3            Mapping single-cell transcriptomes in the intr...   \n",
+       "4            Single-cell sequencing links multiregional imm...   \n",
+       "...                                                        ...   \n",
+       "588          Molecular characterization of selectively vuln...   \n",
+       "589          Single-cell atlas of peripheral immune respons...   \n",
+       "590          Construction of a human cell landscape at sing...   \n",
+       "591          A molecular cell atlas of the human lung from ...   \n",
+       "592          A molecular cell atlas of the human lung from ...   \n",
+       "\n",
+       "                          collection_doi  \\\n",
+       "soma_joinid                                \n",
+       "0             10.1038/s41586-023-05869-0   \n",
+       "1             10.1038/s41586-023-05869-0   \n",
+       "2             10.1038/s41586-023-05869-0   \n",
+       "3            10.1016/j.ccell.2022.11.001   \n",
+       "4            10.1016/j.ccell.2021.03.007   \n",
+       "...                                  ...   \n",
+       "588           10.1038/s41593-020-00764-7   \n",
+       "589            10.1038/s41591-020-0944-y   \n",
+       "590            10.1038/s41586-020-2157-4   \n",
+       "591            10.1038/s41586-020-2922-4   \n",
+       "592            10.1038/s41586-020-2922-4   \n",
+       "\n",
+       "                                       dataset_id  \\\n",
+       "soma_joinid                                         \n",
+       "0            f171db61-e57e-4535-a06a-35d8b6ef8f2b   \n",
+       "1            ecf2e08e-2032-4a9e-b466-b65b395f4a02   \n",
+       "2            74cff64f-9da9-4b2a-9b3b-8a04a1598040   \n",
+       "3            5af90777-6760-4003-9dba-8f945fec6fdf   \n",
+       "4            bd65a70f-b274-4133-b9dd-0d1431b6af34   \n",
+       "...                                           ...   \n",
+       "588          f9ad5649-f372-43e1-a3a8-423383e5a8a2   \n",
+       "589          456e8b9b-f872-488b-871d-94534090a865   \n",
+       "590          2adb1f8a-a6b1-4909-8ee8-484814e2d4bf   \n",
+       "591          e04daea4-4412-45b5-989e-76a9be070a89   \n",
+       "592          8c42cfd0-0b0a-46d5-910c-fc833d83c45e   \n",
+       "\n",
+       "                                                 dataset_title  \\\n",
+       "soma_joinid                                                      \n",
+       "0                                       donor_p13_trophoblasts   \n",
+       "1                                      All donors trophoblasts   \n",
+       "2                         All donors all cell states (in vivo)   \n",
+       "3            Single-cell transcriptomic datasets of Renal c...   \n",
+       "4            Single-cell sequencing links multiregional imm...   \n",
+       "...                                                        ...   \n",
+       "588          Molecular characterization of selectively vuln...   \n",
+       "589          Single-cell atlas of peripheral immune respons...   \n",
+       "590          Construction of a human cell landscape at sing...   \n",
+       "591              Krasnow Lab Human Lung Cell Atlas, Smart-seq2   \n",
+       "592                     Krasnow Lab Human Lung Cell Atlas, 10X   \n",
+       "\n",
+       "                                     dataset_h5ad_path  \\\n",
+       "soma_joinid                                              \n",
+       "0            f171db61-e57e-4535-a06a-35d8b6ef8f2b.h5ad   \n",
+       "1            ecf2e08e-2032-4a9e-b466-b65b395f4a02.h5ad   \n",
+       "2            74cff64f-9da9-4b2a-9b3b-8a04a1598040.h5ad   \n",
+       "3            5af90777-6760-4003-9dba-8f945fec6fdf.h5ad   \n",
+       "4            bd65a70f-b274-4133-b9dd-0d1431b6af34.h5ad   \n",
+       "...                                                ...   \n",
+       "588          f9ad5649-f372-43e1-a3a8-423383e5a8a2.h5ad   \n",
+       "589          456e8b9b-f872-488b-871d-94534090a865.h5ad   \n",
+       "590          2adb1f8a-a6b1-4909-8ee8-484814e2d4bf.h5ad   \n",
+       "591          e04daea4-4412-45b5-989e-76a9be070a89.h5ad   \n",
+       "592          8c42cfd0-0b0a-46d5-910c-fc833d83c45e.h5ad   \n",
+       "\n",
+       "             dataset_total_cell_count  \n",
+       "soma_joinid                            \n",
+       "0                               31497  \n",
+       "1                               67070  \n",
+       "2                              286326  \n",
+       "3                              270855  \n",
+       "4                              167283  \n",
+       "...                               ...  \n",
+       "588                              8168  \n",
+       "589                             44721  \n",
+       "590                            598266  \n",
+       "591                              9409  \n",
+       "592                             65662  \n",
+       "\n",
+       "[593 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census = cellxgene_census.open_soma()\n",
+    "census_datasets = census[\"census_info\"][\"datasets\"].read().concat().to_pandas()\n",
+    "\n",
+    "# for convenience, indexing on the soma_joinid which links this to other census data.\n",
+    "census_datasets = census_datasets.set_index(\"soma_joinid\")\n",
+    "\n",
+    "census_datasets"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "athletic-publication",
+   "metadata": {},
+   "source": [
+    "#### Option 1: Download the **.h5ad** object of a dataset of interest locally from cellxgene"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "monthly-giant",
+   "metadata": {},
+   "source": [
+    "We want to look for the **core healthy integrated lung cell atlas** from [Sikkema et al 2023](https://www.nature.com/articles/s41591-023-02327-2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "capital-antique",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>collection_id</th>\n",
+       "      <th>collection_name</th>\n",
+       "      <th>collection_doi</th>\n",
+       "      <th>dataset_id</th>\n",
+       "      <th>dataset_title</th>\n",
+       "      <th>dataset_h5ad_path</th>\n",
+       "      <th>dataset_total_cell_count</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>soma_joinid</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>6f6d381a-7701-4781-935c-db10d30de293</td>\n",
+       "      <td>The integrated Human Lung Cell Atlas</td>\n",
+       "      <td>10.1038/s41591-023-02327-2</td>\n",
+       "      <td>9f222629-9e39-47d0-b83f-e08d610c7479</td>\n",
+       "      <td>An integrated cell atlas of the human lung in ...</td>\n",
+       "      <td>9f222629-9e39-47d0-b83f-e08d610c7479.h5ad</td>\n",
+       "      <td>2282447</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>425</th>\n",
+       "      <td>6f6d381a-7701-4781-935c-db10d30de293</td>\n",
+       "      <td>The integrated Human Lung Cell Atlas</td>\n",
+       "      <td>10.1038/s41591-023-02327-2</td>\n",
+       "      <td>066943a2-fdac-4b29-b348-40cede398e4e</td>\n",
+       "      <td>An integrated cell atlas of the human lung in ...</td>\n",
+       "      <td>066943a2-fdac-4b29-b348-40cede398e4e.h5ad</td>\n",
+       "      <td>584944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>456</th>\n",
+       "      <td>625f6bf4-2f33-4942-962e-35243d284837</td>\n",
+       "      <td>LungMAP — Human data from a broad age healthy ...</td>\n",
+       "      <td>10.7554/eLife.62522</td>\n",
+       "      <td>3de0ad6d-4378-4f62-b37b-ec0b75a50d94</td>\n",
+       "      <td>Single-cell multiomic profiling of human lungs...</td>\n",
+       "      <td>3de0ad6d-4378-4f62-b37b-ec0b75a50d94.h5ad</td>\n",
+       "      <td>46500</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                    collection_id  \\\n",
+       "soma_joinid                                         \n",
+       "56           6f6d381a-7701-4781-935c-db10d30de293   \n",
+       "425          6f6d381a-7701-4781-935c-db10d30de293   \n",
+       "456          625f6bf4-2f33-4942-962e-35243d284837   \n",
+       "\n",
+       "                                               collection_name  \\\n",
+       "soma_joinid                                                      \n",
+       "56                        The integrated Human Lung Cell Atlas   \n",
+       "425                       The integrated Human Lung Cell Atlas   \n",
+       "456          LungMAP — Human data from a broad age healthy ...   \n",
+       "\n",
+       "                         collection_doi                            dataset_id  \\\n",
+       "soma_joinid                                                                     \n",
+       "56           10.1038/s41591-023-02327-2  9f222629-9e39-47d0-b83f-e08d610c7479   \n",
+       "425          10.1038/s41591-023-02327-2  066943a2-fdac-4b29-b348-40cede398e4e   \n",
+       "456                 10.7554/eLife.62522  3de0ad6d-4378-4f62-b37b-ec0b75a50d94   \n",
+       "\n",
+       "                                                 dataset_title  \\\n",
+       "soma_joinid                                                      \n",
+       "56           An integrated cell atlas of the human lung in ...   \n",
+       "425          An integrated cell atlas of the human lung in ...   \n",
+       "456          Single-cell multiomic profiling of human lungs...   \n",
+       "\n",
+       "                                     dataset_h5ad_path  \\\n",
+       "soma_joinid                                              \n",
+       "56           9f222629-9e39-47d0-b83f-e08d610c7479.h5ad   \n",
+       "425          066943a2-fdac-4b29-b348-40cede398e4e.h5ad   \n",
+       "456          3de0ad6d-4378-4f62-b37b-ec0b75a50d94.h5ad   \n",
+       "\n",
+       "             dataset_total_cell_count  \n",
+       "soma_joinid                            \n",
+       "56                            2282447  \n",
+       "425                            584944  \n",
+       "456                             46500  "
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "census_datasets[census_datasets[\"dataset_title\"].str.contains(\"human lung\")]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dominican-donor",
+   "metadata": {},
+   "source": [
+    "We find the **dataset_id** for the dataset of interest and download the associated .h5ad file. This takes several minutes to download given the size (~600k cells), and it is only put here for illustrative purposes, so **you can skip this step unless you're interested in having the dataset downloaded locally**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "diagnostic-madness",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cellxgene_census.download_source_h5ad(\n",
+    "    \"066943a2-fdac-4b29-b348-40cede398e4e\",\n",
+    "    to_path=\"HLCA_healthy.h5ad\",  # change this to the path and file name that you prefer\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "young-walker",
+   "metadata": {},
+   "source": [
+    "As this dataset contains ~600k cells, we will not read it into memory and we will insted use **Option 2** to fetch only a subset of the data containing the cells from the **lung parenchyma** and from **female** individuals. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "color-grant",
+   "metadata": {},
+   "source": [
+    "#### Option 2: Fetch the anndata object corresponding to a query of interest in cellxgene"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "golden-texas",
+   "metadata": {},
+   "source": [
+    "Let's start by looking at what cell-level metadata are available to query in cellxgene"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "built-draft",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['soma_joinid',\n",
+       " 'dataset_id',\n",
+       " 'assay',\n",
+       " 'assay_ontology_term_id',\n",
+       " 'cell_type',\n",
+       " 'cell_type_ontology_term_id',\n",
+       " 'development_stage',\n",
+       " 'development_stage_ontology_term_id',\n",
+       " 'disease',\n",
+       " 'disease_ontology_term_id',\n",
+       " 'donor_id',\n",
+       " 'is_primary_data',\n",
+       " 'self_reported_ethnicity',\n",
+       " 'self_reported_ethnicity_ontology_term_id',\n",
+       " 'sex',\n",
+       " 'sex_ontology_term_id',\n",
+       " 'suspension_type',\n",
+       " 'tissue',\n",
+       " 'tissue_ontology_term_id',\n",
+       " 'tissue_general',\n",
+       " 'tissue_general_ontology_term_id']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(census[\"census_data\"][\"homo_sapiens\"].obs.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "modern-barrier",
+   "metadata": {},
+   "source": [
+    "As you can see, for each cell-level metadata field there is the corresponding ontogeny field. Let's go ahead and fetch the data from our dataset of interest corresponding to **lung parenchyma** of **female** individuals. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "moderate-martin",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata = cellxgene_census.get_anndata(\n",
+    "    census,\n",
+    "    organism=\"Homo sapiens\",\n",
+    "    obs_value_filter=\"dataset_id == '066943a2-fdac-4b29-b348-40cede398e4e' and tissue == 'lung parenchyma' and sex == 'female'\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "third-prophet",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "AnnData object with n_obs × n_vars = 123656 × 60664\n",
+       "    obs: 'soma_joinid', 'dataset_id', 'assay', 'assay_ontology_term_id', 'cell_type', 'cell_type_ontology_term_id', 'development_stage', 'development_stage_ontology_term_id', 'disease', 'disease_ontology_term_id', 'donor_id', 'is_primary_data', 'self_reported_ethnicity', 'self_reported_ethnicity_ontology_term_id', 'sex', 'sex_ontology_term_id', 'suspension_type', 'tissue', 'tissue_ontology_term_id', 'tissue_general', 'tissue_general_ontology_term_id'\n",
+       "    var: 'soma_joinid', 'feature_id', 'feature_name', 'feature_length'"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "complicated-syria",
+   "metadata": {},
+   "source": [
+    "Subset the data to keep only the cell types with more than 100 cells"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "attractive-island",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "chubby-greek",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "32"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Find cell types with at least 100 cells\n",
+    "cell_types_with_at_least_100_cells = [\n",
+    "    ct for ct in np.unique(adata.obs[\"cell_type\"]) if adata.obs[adata.obs[\"cell_type\"] == ct].shape[0] > 100\n",
+    "]\n",
+    "len(cell_types_with_at_least_100_cells)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "coated-weather",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Subset the AnnData object to include only the selected cell types\n",
+    "adata = adata[adata.obs[\"cell_type\"].isin(cell_types_with_at_least_100_cells)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "central-electric",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(123208, 60664)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "proved-amount",
+   "metadata": {},
+   "source": [
+    "### Data preprocessing"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "decent-seller",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
+       "       [1., 4., 0., 0., 0., 1., 2., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 1., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
+       "       [0., 0., 2., 0., 0., 0., 0., 0., 0., 0.],\n",
+       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# in the .X slot of the anndata object we have the raw (integer) counts\n",
+    "adata.X[20:30, 20:30].toarray()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "neural-trinity",
+   "metadata": {},
+   "source": [
+    "As LIANA+ works with normalised and log-tranformed data, we need to perform these two steps "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "formed-listening",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jovyan/my-conda-envs/ccc_env/lib/python3.10/site-packages/scanpy/preprocessing/_simple.py:250: ImplicitModificationWarning: Trying to modify attribute `.var` of view, initializing view as actual.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "(123208, 23537)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Filter lowly expressed genes\n",
+    "sc.pp.filter_genes(adata, min_cells=10)\n",
+    "adata.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "advisory-fiber",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata.raw = adata.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "loving-borough",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pp.normalize_total(adata, target_sum=1e4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "naughty-discharge",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sc.pp.log1p(adata)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "severe-cassette",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([[0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.6421145 , 0.        , 0.        , 0.        , 0.        ,\n",
+       "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.57734966, 0.        , 0.        , 0.        , 0.32977524,\n",
+       "        0.32977524, 0.        , 0.57734966, 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "        0.        , 0.        , 0.        , 1.0628667 , 0.        ],\n",
+       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "        1.2565256 , 0.        , 0.        , 0.        , 0.        ],\n",
+       "       [1.4676205 , 0.        , 0.        , 0.        , 0.        ,\n",
+       "        0.        , 0.        , 0.        , 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "        0.        , 0.        , 1.72995   , 0.        , 0.        ],\n",
+       "       [0.        , 0.        , 0.        , 0.        , 0.        ,\n",
+       "        0.        , 0.        , 0.        , 0.        , 0.        ]],\n",
+       "      dtype=float32)"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.X[20:30, 20:30].toarray()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "objective-border",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>soma_joinid</th>\n",
+       "      <th>feature_id</th>\n",
+       "      <th>feature_name</th>\n",
+       "      <th>feature_length</th>\n",
+       "      <th>n_cells</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>ENSG00000121410</td>\n",
+       "      <td>A1BG</td>\n",
+       "      <td>3999</td>\n",
+       "      <td>14962</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>ENSG00000268895</td>\n",
+       "      <td>A1BG-AS1</td>\n",
+       "      <td>3374</td>\n",
+       "      <td>2382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>ENSG00000175899</td>\n",
+       "      <td>A2M</td>\n",
+       "      <td>6318</td>\n",
+       "      <td>16146</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>ENSG00000245105</td>\n",
+       "      <td>A2M-AS1</td>\n",
+       "      <td>2948</td>\n",
+       "      <td>1467</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5</td>\n",
+       "      <td>ENSG00000166535</td>\n",
+       "      <td>A2ML1</td>\n",
+       "      <td>7156</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   soma_joinid       feature_id feature_name  feature_length  n_cells\n",
+       "0            0  ENSG00000121410         A1BG            3999    14962\n",
+       "1            1  ENSG00000268895     A1BG-AS1            3374     2382\n",
+       "3            3  ENSG00000175899          A2M            6318    16146\n",
+       "4            4  ENSG00000245105      A2M-AS1            2948     1467\n",
+       "5            5  ENSG00000166535        A2ML1            7156       15"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.var.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "surgical-block",
+   "metadata": {},
+   "source": [
+    "For LIANA+, **.var_names** need to be set to **gene symbols** (not ENSEMBL gene IDs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "oriented-status",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata.var_names = adata.var[\"feature_name\"].astype(str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "biological-stone",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['A1BG', 'A1BG-AS1', 'A2M', 'A2M-AS1', 'A2ML1', 'A4GALT', 'A4GNT',\n",
+       "       'AAAS', 'AACS', 'AADAC',\n",
+       "       ...\n",
+       "       'WFDC10A', 'IGLV5-48', 'IGLC5', 'PLA2G3', 'SCUBE1-AS2', 'NXF2B',\n",
+       "       'TCP11X2', 'RP11-635O16.2', 'RP11-533E23.2', 'RP11-566H16.2'],\n",
+       "      dtype='object', name='feature_name', length=23537)"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.var_names"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "developed-discussion",
+   "metadata": {},
+   "source": [
+    "For computational efficiency, we are going to downsample randomly per cell type label to a maximum of 1000 cells per cell type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "minor-eligibility",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import random\n",
+    "from itertools import chain"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "numerous-trailer",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Random downsampling per cell type\n",
+    "def downsample(adata, labels, n):\n",
+    "    myindex = adata.obs[labels].value_counts().index\n",
+    "    myvalues = adata.obs[labels].value_counts().values\n",
+    "    clusters = pd.Series(myvalues, index=myindex)\n",
+    "\n",
+    "    # Find clusters with > n cells\n",
+    "    cl2downsample = clusters.index[clusters.values > n]\n",
+    "\n",
+    "    # save all barcode ids from small clusters\n",
+    "    holder = []\n",
+    "    holder.append(adata.obs_names[[i not in cl2downsample for i in adata.obs[labels]]])\n",
+    "\n",
+    "    # randomly sample n cells in the cl2downsample\n",
+    "    for cl in cl2downsample:\n",
+    "        print(cl)\n",
+    "        cl_sample = adata[[i == cl for i in adata.obs[labels]]].obs_names\n",
+    "        cl_downsample = random.sample(set(cl_sample), n)\n",
+    "        holder.append(cl_downsample)\n",
+    "\n",
+    "    # samples to include\n",
+    "    samples = list(chain(*holder))\n",
+    "\n",
+    "    # Filter adata_count\n",
+    "    adata = adata[[i in samples for i in adata.obs_names]]\n",
+    "    return adata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "auburn-chuck",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "type II pneumocyte\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_10674/3783639370.py:19: DeprecationWarning: Sampling from a set deprecated\n",
+      "since Python 3.9 and will be removed in a subsequent version.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "alveolar macrophage\n",
+      "natural killer cell\n",
+      "CD4-positive, alpha-beta T cell\n",
+      "CD8-positive, alpha-beta T cell\n",
+      "classical monocyte\n",
+      "elicited macrophage\n",
+      "capillary endothelial cell\n",
+      "mast cell\n",
+      "type I pneumocyte\n",
+      "CD1c-positive myeloid dendritic cell\n",
+      "non-classical monocyte\n",
+      "ciliated columnar cell of tracheobronchial tree\n",
+      "vein endothelial cell\n",
+      "alveolar type 1 fibroblast cell\n",
+      "pulmonary artery endothelial cell\n",
+      "B cell\n",
+      "alveolar type 2 fibroblast cell\n"
+     ]
+    }
+   ],
+   "source": [
+    "adata = downsample(adata, \"cell_type\", 1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "extensive-yorkshire",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(24694, 23537)"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "mathematical-portugal",
+   "metadata": {},
+   "source": [
+    "### Cell-cell communication inference with LIANA+"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "protected-eligibility",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import liana's rank_aggregate\n",
+    "from liana.mt import rank_aggregate"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "hearing-moisture",
+   "metadata": {},
+   "source": [
+    "LIANA+ provides a consensus that integrates the predictions of individual cell-cell communication inference methods. This is done by [ranking and aggregating](https://academic.oup.com/bioinformatics/article/28/4/573/213339?login=true) the ligand-receptor interaction predictions from all methods. The methods that will be run by default in LIANA by calling the function **rank_aggregate()** are: \n",
+    "\n",
+    " 1. [CellPhoneDB](https://cellphonedb.readthedocs.io/en/latest/) with their permutation-based approach ([method 2: statistical_analysis](https://cellphonedb.readthedocs.io/en/latest/RESULTS-DOCUMENTATION.html#method-2-statistical-inference-of-interaction-specificity)) &rarr; returns both a magnitude and specificy score\n",
+    " 2. [Connectome](https://msraredon.github.io/Connectome/) &rarr; returns both a magnitude and specificity score\n",
+    " 3. [log2FC]() &rarr; returns only a specificity score \n",
+    " 4. [NATMI](https://github.com/forrest-lab/NATMI/) &rarr; returns only a specificity score\n",
+    " 5. [SingleCellSignalR](https://www.bioconductor.org/packages/release/bioc/html/SingleCellSignalR.html) &rarr; returns only a magnitude score\n",
+    " 6. [CellChat](https://github.com/jinworks/CellChat) &rarr; returns both a magnitude and specificity score"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "mounted-dublin",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using `.X`!\n",
+      "/home/jovyan/my-conda-envs/ccc_env/lib/python3.10/site-packages/anndata/_core/anndata.py:522: FutureWarning: The dtype argument is deprecated and will be removed in late 2024.\n",
+      "77 features of mat are empty, they will be removed.\n",
+      "Converting `cell_type` to categorical!\n",
+      "/home/jovyan/my-conda-envs/ccc_env/lib/python3.10/site-packages/liana/method/_pipe_utils/_pre.py:265: ImplicitModificationWarning: Trying to modify attribute `.obs` of view, initializing view as actual.\n",
+      "/home/jovyan/my-conda-envs/ccc_env/lib/python3.10/site-packages/liana/method/_pipe_utils/_pre.py:142: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n",
+      "['DNAJC9-AS1_ENSG00000227540', 'GOLGA8M_ENSG00000261480', 'PRICKLE2-AS1_ENSG00000241111', 'MIR1539_ENSG00000265496', 'MIR3179-3_ENSG00000257527', 'MIR3180-4_ENSG00000257391', 'CLCA4-AS1_ENSG00000261737', 'LINC00484_ENSG00000229694', 'RPPH1_ENSG00000259001', 'RAET1E-AS1_ENSG00000268592', 'SCARNA2_ENSG00000270066', 'RNU11_ENSG00000270103', 'SPATA13_ENSG00000228741', 'RP11-99J16__A.2', 'RP11-1157N2__B.2', 'XXyac-YX65C7_A.2', 'CCDC39_ENSG00000145075', 'CYB561D2_ENSG00000114395', 'CYB561D2_ENSG00000271858', 'DNAJC9-AS1_ENSG00000236756', 'ELFN2_ENSG00000166897', 'FAM153B_ENSG00000182230', 'RP4-633O19__A.1', 'GOLGA8M_ENSG00000188626', 'ITFG2-AS1_ENSG00000258325', 'KBTBD11-OT1_ENSG00000253696', 'LINC00484_ENSG00000235641', 'LINC00941_ENSG00000235884', 'LINC01115_ENSG00000237667', 'LINC01238_ENSG00000237940', 'LINC01238_ENSG00000261186', 'LINC01605_ENSG00000253161', 'MAFIP_ENSG00000274847', 'MATR3_ENSG00000280987', 'MATR3_ENSG00000015479', 'PDE11A_ENSG00000128655', 'PINX1_ENSG00000254093', 'POLR2J3_ENSG00000168255', 'RGS5_ENSG00000143248', 'RGS5_ENSG00000232995', 'RMRP_ENSG00000269900', 'SFTA3_ENSG00000229415', 'SIGLEC5_ENSG00000105501', 'SLFN12L_ENSG00000205045', 'SPATA13_ENSG00000182957', 'TMSB15B_ENSG00000158427', 'TMSB15B_ENSG00000269226', 'RNU12_ENSG00000270022', 'MIR3180-1_ENSG00000258354', 'RAET1E-AS1_ENSG00000223701', 'DGCR5_ENSG00000237517'] contain `_`. Consider replacing those!\n",
+      "Using resource `consensus`.\n",
+      "0.11 of entities in the resource are missing from the data.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Generating ligand-receptor stats for 24694 samples and 23460 features\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/jovyan/my-conda-envs/ccc_env/lib/python3.10/site-packages/liana/method/sc/_liana_pipe.py:246: ImplicitModificationWarning: Setting element `.layers['scaled']` of view, initializing view as actual.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Assuming that counts were `natural` log-normalized!\n",
+      "Running CellPhoneDB\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [01:15<00:00, 13.32it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running Connectome\n",
+      "Running log2FC\n",
+      "Running NATMI\n",
+      "Running SingleCellSignalR\n",
+      "Running CellChat\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [10:31<00:00,  1.58it/s]\n",
+      "/home/jovyan/my-conda-envs/ccc_env/lib/python3.10/site-packages/liana/method/sc/_rank_aggregate.py:144: ImplicitModificationWarning: Trying to modify attribute `._uns` of view, initializing view as actual.\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Run rank_aggregate --> this takes approximately 20 minutes\n",
+    "li.mt.rank_aggregate(\n",
+    "    adata,\n",
+    "    groupby=\"cell_type\",  # .obs column with cell type annotations\n",
+    "    use_raw=False,\n",
+    "    expr_prop=0.1,  # minimum percentage of cells expressing ligand / receptor\n",
+    "    verbose=True,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "vocal-piece",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>target</th>\n",
+       "      <th>ligand_complex</th>\n",
+       "      <th>receptor_complex</th>\n",
+       "      <th>lr_means</th>\n",
+       "      <th>cellphone_pvals</th>\n",
+       "      <th>expr_prod</th>\n",
+       "      <th>scaled_weight</th>\n",
+       "      <th>lr_logfc</th>\n",
+       "      <th>spec_weight</th>\n",
+       "      <th>lrscore</th>\n",
+       "      <th>lr_probs</th>\n",
+       "      <th>cellchat_pvals</th>\n",
+       "      <th>specificity_rank</th>\n",
+       "      <th>magnitude_rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>172690</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>type I pneumocyte</td>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>AGER</td>\n",
+       "      <td>4.104800</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>16.746553</td>\n",
+       "      <td>3.599371</td>\n",
+       "      <td>5.558186</td>\n",
+       "      <td>0.147037</td>\n",
+       "      <td>0.977557</td>\n",
+       "      <td>0.316083</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.516201e-08</td>\n",
+       "      <td>6.357762e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172689</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>type I pneumocyte</td>\n",
+       "      <td>S100A8</td>\n",
+       "      <td>AGER</td>\n",
+       "      <td>4.071708</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>16.496103</td>\n",
+       "      <td>3.731564</td>\n",
+       "      <td>5.800996</td>\n",
+       "      <td>0.197601</td>\n",
+       "      <td>0.977392</td>\n",
+       "      <td>0.312289</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.505477e-09</td>\n",
+       "      <td>1.507019e-13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27806</th>\n",
+       "      <td>club cell</td>\n",
+       "      <td>alveolar macrophage</td>\n",
+       "      <td>SCGB3A1</td>\n",
+       "      <td>MARCO</td>\n",
+       "      <td>4.475138</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.800630</td>\n",
+       "      <td>3.340693</td>\n",
+       "      <td>4.021232</td>\n",
+       "      <td>0.083267</td>\n",
+       "      <td>0.975334</td>\n",
+       "      <td>0.273894</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.591774e-06</td>\n",
+       "      <td>2.309044e-13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130941</th>\n",
+       "      <td>capillary endothelial cell</td>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>B2M</td>\n",
+       "      <td>KLRD1</td>\n",
+       "      <td>3.929645</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.739497</td>\n",
+       "      <td>2.159858</td>\n",
+       "      <td>2.412535</td>\n",
+       "      <td>0.015208</td>\n",
+       "      <td>0.974353</td>\n",
+       "      <td>0.264795</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.819353e-04</td>\n",
+       "      <td>5.473256e-13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132640</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>B2M</td>\n",
+       "      <td>KLRD1</td>\n",
+       "      <td>3.831153</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.289257</td>\n",
+       "      <td>2.061522</td>\n",
+       "      <td>2.190946</td>\n",
+       "      <td>0.014671</td>\n",
+       "      <td>0.973900</td>\n",
+       "      <td>0.255173</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.373616e-03</td>\n",
+       "      <td>1.847201e-12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            source               target ligand_complex  \\\n",
+       "172690          classical monocyte    type I pneumocyte         S100A9   \n",
+       "172689          classical monocyte    type I pneumocyte         S100A8   \n",
+       "27806                    club cell  alveolar macrophage        SCGB3A1   \n",
+       "130941  capillary endothelial cell  natural killer cell            B2M   \n",
+       "132640         natural killer cell  natural killer cell            B2M   \n",
+       "\n",
+       "       receptor_complex  lr_means  cellphone_pvals  expr_prod  scaled_weight  \\\n",
+       "172690             AGER  4.104800              0.0  16.746553       3.599371   \n",
+       "172689             AGER  4.071708              0.0  16.496103       3.731564   \n",
+       "27806             MARCO  4.475138              0.0  13.800630       3.340693   \n",
+       "130941            KLRD1  3.929645              0.0  12.739497       2.159858   \n",
+       "132640            KLRD1  3.831153              0.0  12.289257       2.061522   \n",
+       "\n",
+       "        lr_logfc  spec_weight   lrscore  lr_probs  cellchat_pvals  \\\n",
+       "172690  5.558186     0.147037  0.977557  0.316083             0.0   \n",
+       "172689  5.800996     0.197601  0.977392  0.312289             0.0   \n",
+       "27806   4.021232     0.083267  0.975334  0.273894             0.0   \n",
+       "130941  2.412535     0.015208  0.974353  0.264795             0.0   \n",
+       "132640  2.190946     0.014671  0.973900  0.255173             0.0   \n",
+       "\n",
+       "        specificity_rank  magnitude_rank  \n",
+       "172690      6.516201e-08    6.357762e-14  \n",
+       "172689      6.505477e-09    1.507019e-13  \n",
+       "27806       3.591774e-06    2.309044e-13  \n",
+       "130941      7.819353e-04    5.473256e-13  \n",
+       "132640      1.373616e-03    1.847201e-12  "
+      ]
+     },
+     "execution_count": 34,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.uns[\"liana_res\"].head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "manual-newton",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(189405, 15)"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.uns[\"liana_res\"].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "imperial-compiler",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Reformat the output so that it's easier to understand which column corresponds to which method\n",
+    "def reformat_liana_output(output):\n",
+    "    # Give more intuitive names to columns\n",
+    "    column_mapping = {\n",
+    "        \"source\": \"source\",\n",
+    "        \"target\": \"target\",\n",
+    "        \"ligand_complex\": \"ligand_complex\",\n",
+    "        \"receptor_complex\": \"receptor_complex\",\n",
+    "        \"lr_means\": \"CellPhoneDB_lr_means\",\n",
+    "        \"cellphone_pvals\": \"CellPhoneDB_pvals\",\n",
+    "        \"expr_prod\": \"Connectome_expr_prod\",\n",
+    "        \"scaled_weight\": \"Connectome_scaled_weight\",\n",
+    "        \"lr_logfc\": \"log2FC_lr\",\n",
+    "        \"spec_weight\": \"NATMI_spec_weight\",\n",
+    "        \"lrscore\": \"SingleCellSignalR_lr_score\",\n",
+    "        \"lr_probs\": \"CellChat_lr_probs\",\n",
+    "        \"cellchat_pvals\": \"CellChat_pvals\",\n",
+    "        \"specificity_rank\": \"RRA_specificity_rank\",\n",
+    "        \"magnitude_rank\": \"RRA_magnitude_rank\",\n",
+    "    }\n",
+    "    # Use the rename method with the columns parameter and the dictionary\n",
+    "    output.rename(columns=column_mapping, inplace=True)\n",
+    "    return output"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "orange-queue",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "adata.uns[\"liana_res\"] = reformat_liana_output(adata.uns[\"liana_res\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "primary-cookbook",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>source</th>\n",
+       "      <th>target</th>\n",
+       "      <th>ligand_complex</th>\n",
+       "      <th>receptor_complex</th>\n",
+       "      <th>CellPhoneDB_lr_means</th>\n",
+       "      <th>CellPhoneDB_pvals</th>\n",
+       "      <th>Connectome_expr_prod</th>\n",
+       "      <th>Connectome_scaled_weight</th>\n",
+       "      <th>log2FC_lr</th>\n",
+       "      <th>NATMI_spec_weight</th>\n",
+       "      <th>SingleCellSignalR_lr_score</th>\n",
+       "      <th>CellChat_lr_probs</th>\n",
+       "      <th>CellChat_pvals</th>\n",
+       "      <th>RRA_specificity_rank</th>\n",
+       "      <th>RRA_magnitude_rank</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>172690</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>type I pneumocyte</td>\n",
+       "      <td>S100A9</td>\n",
+       "      <td>AGER</td>\n",
+       "      <td>4.104800</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>16.746553</td>\n",
+       "      <td>3.599371</td>\n",
+       "      <td>5.558186</td>\n",
+       "      <td>0.147037</td>\n",
+       "      <td>0.977557</td>\n",
+       "      <td>0.316083</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.516201e-08</td>\n",
+       "      <td>6.357762e-14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>172689</th>\n",
+       "      <td>classical monocyte</td>\n",
+       "      <td>type I pneumocyte</td>\n",
+       "      <td>S100A8</td>\n",
+       "      <td>AGER</td>\n",
+       "      <td>4.071708</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>16.496103</td>\n",
+       "      <td>3.731564</td>\n",
+       "      <td>5.800996</td>\n",
+       "      <td>0.197601</td>\n",
+       "      <td>0.977392</td>\n",
+       "      <td>0.312289</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.505477e-09</td>\n",
+       "      <td>1.507019e-13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27806</th>\n",
+       "      <td>club cell</td>\n",
+       "      <td>alveolar macrophage</td>\n",
+       "      <td>SCGB3A1</td>\n",
+       "      <td>MARCO</td>\n",
+       "      <td>4.475138</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>13.800630</td>\n",
+       "      <td>3.340693</td>\n",
+       "      <td>4.021232</td>\n",
+       "      <td>0.083267</td>\n",
+       "      <td>0.975334</td>\n",
+       "      <td>0.273894</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>3.591774e-06</td>\n",
+       "      <td>2.309044e-13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>130941</th>\n",
+       "      <td>capillary endothelial cell</td>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>B2M</td>\n",
+       "      <td>KLRD1</td>\n",
+       "      <td>3.929645</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.739497</td>\n",
+       "      <td>2.159858</td>\n",
+       "      <td>2.412535</td>\n",
+       "      <td>0.015208</td>\n",
+       "      <td>0.974353</td>\n",
+       "      <td>0.264795</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>7.819353e-04</td>\n",
+       "      <td>5.473256e-13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>132640</th>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>natural killer cell</td>\n",
+       "      <td>B2M</td>\n",
+       "      <td>KLRD1</td>\n",
+       "      <td>3.831153</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>12.289257</td>\n",
+       "      <td>2.061522</td>\n",
+       "      <td>2.190946</td>\n",
+       "      <td>0.014671</td>\n",
+       "      <td>0.973900</td>\n",
+       "      <td>0.255173</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.373616e-03</td>\n",
+       "      <td>1.847201e-12</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                            source               target ligand_complex  \\\n",
+       "172690          classical monocyte    type I pneumocyte         S100A9   \n",
+       "172689          classical monocyte    type I pneumocyte         S100A8   \n",
+       "27806                    club cell  alveolar macrophage        SCGB3A1   \n",
+       "130941  capillary endothelial cell  natural killer cell            B2M   \n",
+       "132640         natural killer cell  natural killer cell            B2M   \n",
+       "\n",
+       "       receptor_complex  CellPhoneDB_lr_means  CellPhoneDB_pvals  \\\n",
+       "172690             AGER              4.104800                0.0   \n",
+       "172689             AGER              4.071708                0.0   \n",
+       "27806             MARCO              4.475138                0.0   \n",
+       "130941            KLRD1              3.929645                0.0   \n",
+       "132640            KLRD1              3.831153                0.0   \n",
+       "\n",
+       "        Connectome_expr_prod  Connectome_scaled_weight  log2FC_lr  \\\n",
+       "172690             16.746553                  3.599371   5.558186   \n",
+       "172689             16.496103                  3.731564   5.800996   \n",
+       "27806              13.800630                  3.340693   4.021232   \n",
+       "130941             12.739497                  2.159858   2.412535   \n",
+       "132640             12.289257                  2.061522   2.190946   \n",
+       "\n",
+       "        NATMI_spec_weight  SingleCellSignalR_lr_score  CellChat_lr_probs  \\\n",
+       "172690           0.147037                    0.977557           0.316083   \n",
+       "172689           0.197601                    0.977392           0.312289   \n",
+       "27806            0.083267                    0.975334           0.273894   \n",
+       "130941           0.015208                    0.974353           0.264795   \n",
+       "132640           0.014671                    0.973900           0.255173   \n",
+       "\n",
+       "        CellChat_pvals  RRA_specificity_rank  RRA_magnitude_rank  \n",
+       "172690             0.0          6.516201e-08        6.357762e-14  \n",
+       "172689             0.0          6.505477e-09        1.507019e-13  \n",
+       "27806              0.0          3.591774e-06        2.309044e-13  \n",
+       "130941             0.0          7.819353e-04        5.473256e-13  \n",
+       "132640             0.0          1.373616e-03        1.847201e-12  "
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "adata.uns[\"liana_res\"].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "absent-above",
+   "metadata": {},
+   "source": [
+    "### Plotting interactions"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "velvet-violin",
+   "metadata": {},
+   "source": [
+    "We can now explore the interactions that were found most significant (based on RRA specificity and magnitude scores) across methods"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "capital-taylor",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fontsize 0.00 < 1.0 pt not allowed by FreeType. Setting fontsize = 1 pt\n"
+     ]
+    },
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1000x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 800,
+       "width": 1000
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (1000 x 800)>"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "li.pl.dotplot(\n",
+    "    adata=adata,\n",
+    "    colour=\"RRA_magnitude_rank\",\n",
+    "    inverse_colour=True,\n",
+    "    size=\"RRA_specificity_rank\",\n",
+    "    inverse_size=True,\n",
+    "    source_labels=[\"type I pneumocyte\", \"type II pneumocyte\"],\n",
+    "    target_labels=[\"alveolar macrophage\"],\n",
+    "    filterby=\"RRA_specificity_rank\",\n",
+    "    filter_lambda=lambda x: x <= 0.01,\n",
+    "    figure_size=(10, 8),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "unlikely-regulation",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Fontsize 0.00 < 1.0 pt not allowed by FreeType. Setting fontsize = 1 pt\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 800x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 1200,
+       "width": 800
+      }
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<Figure Size: (800 x 1200)>"
+      ]
+     },
+     "execution_count": 51,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "li.pl.dotplot(\n",
+    "    adata=adata,\n",
+    "    colour=\"RRA_magnitude_rank\",\n",
+    "    inverse_colour=True,\n",
+    "    size=\"RRA_specificity_rank\",\n",
+    "    inverse_size=True,\n",
+    "    source_labels=[\"classical monocyte\"],\n",
+    "    target_labels=[\"capillary endothelial cell\", \"vein endothelial cell\", \"pulmonary artery endothelial cell\"],\n",
+    "    filterby=\"RRA_specificity_rank\",\n",
+    "    filter_lambda=lambda x: x <= 0.01,\n",
+    "    figure_size=(8, 12),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "senior-richards",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "ccc_env",
+   "language": "python",
+   "name": "ccc_env"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/tutorial-registry/tutorials/ccc-liana/icon.png b/tutorial-registry/tutorials/ccc-liana/icon.png
new file mode 100644
index 0000000..6ebf189
Binary files /dev/null and b/tutorial-registry/tutorials/ccc-liana/icon.png differ
diff --git a/tutorial-registry/tutorials/ccc-liana/meta.yaml b/tutorial-registry/tutorials/ccc-liana/meta.yaml
new file mode 100644
index 0000000..a4cc50b
--- /dev/null
+++ b/tutorial-registry/tutorials/ccc-liana/meta.yaml
@@ -0,0 +1,15 @@
+name: Cell-cell communication analysis with LIANA+
+description: |
+  This notebook showcases how to fetch publicly available datasets from the cellxgene portal and how to run cell-cell communication with LIANA+ in steady state conditions.
+link: CCC_Liana_1.ipynb
+image: icon.png
+primary_category: scRNA-seq
+tags:
+  - fetch data from cellxgene census
+  - cell-cell communication analysis
+packages:
+  - cellxgene_census
+  - liana
+  - scanpy
+authors:
+  - dbdimitrov