From 4d69eed9c3c3a0a178adac2eb804c87ab39f8801 Mon Sep 17 00:00:00 2001
From: Anna Vathrakokoili Pournara <annavp@ebi.ac.uk>
Date: Tue, 28 Nov 2023 16:16:15 +0000
Subject: [PATCH 1/9] added TF_Activity_notebook

---
 .../TrascriptionFactorActivity.ipynb          | 739 ++++++++++++++++++
 1 file changed, 739 insertions(+)
 create mode 100644 docs/notebooks/TrascriptionFactorActivity.ipynb

diff --git a/docs/notebooks/TrascriptionFactorActivity.ipynb b/docs/notebooks/TrascriptionFactorActivity.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ba83c36d",
+   "metadata": {},
+   "source": [
+    "# Transcription factor activity inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f7a1504",
+   "metadata": {},
+   "source": [
+    "## Load packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "956d287f",
+   "metadata": {},
+   "source": [
+    "We need to load the relevant packages, scanpy to handle scRNA-seq data and decoupler to use statistical methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "566b1a4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scanpy as sc\n",
+    "import decoupler as dc\n",
+    "\n",
+    "\n",
+    "# Only needed for processing\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Needed for some plotting\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "effd4534",
+   "metadata": {},
+   "source": [
+    "### Motivation: \n",
+    "\n",
+    "scRNA-seq yields many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by infering transcription factor (TF) activities from our trascriptomics data from prior knowledge.\n",
+    "\n",
+    "In this notebook we showcase how to use decoupler for transcription factor (TF) and pathway enrichment from a human data-set. The data consists of ~5k Blood myeloid cells from healthy and COVID-19 infected patients available in the Single Cell Expression Atlas [here](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-9221/results/tsne?plotType=umap&plotOption=20).\n",
+    "\n",
+    "\n",
+    "For the TF activity inference analysis we will need a) a gene regulatory network (GRN) and b) a statistical method.\n",
+    "\n",
+    "a) **Gene Regulatory Network(GRN)**: \n",
+    "\n",
+    "[CollecTRI](https://github.com/saezlab/CollecTRI) is a comprehensive resource containing a curated collection of TFs and their transcriptional targets compiled from 12 different resources. This collection provides an increased coverage of transcription factors and a superior performance in identifying perturbed TFs compared to our previous [DoRothEA](https://saezlab.github.io/dorothea/) network and other literature based GRNs. Similar to DoRothEA, interactions are weighted by their mode of regulation (activation or inhibition).\n",
+    "\n",
+    "For this example we will use the human version (mouse and rat are also available). We can use `decoupler` to retrieve it from `omnipath`. The argument `split_complexes` keeps complexes or splits them into subunits, by default we recommend to keep complexes together.\n",
+    "\n",
+    "Note! : In this tutorial we use the network CollecTRI, but we could use any other GRN coming from an inference method such as CellOracle, pySCENIC or SCENIC+.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40314c83",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "**Note**\n",
+    "\n",
+    "In this tutorial we use the network CollecTRI, but we could use any other GRN coming from an inference method such as [CellOracle](https://morris-lab.github.io/CellOracle.documentation/), [pySCENIC](https://pyscenic.readthedocs.io/en/latest/) or [SCENIC+](https://scenicplus.readthedocs.io/en/latest/). </div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bc51ad0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve CollecTRI gene regulatory network\n",
+    "collectri = dc.get_collectri(organism='human', split_complexes=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b1e3afd0",
+   "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>weight</th>\n",
+       "      <th>PMID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MYC</td>\n",
+       "      <td>TERT</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10022128;10491298;10606235;10637317;10723141;1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SPI1</td>\n",
+       "      <td>BGLAP</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10022617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SMAD3</td>\n",
+       "      <td>JUN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10022869;12374795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SMAD4</td>\n",
+       "      <td>JUN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10022869;12374795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>STAT5A</td>\n",
+       "      <td>IL2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10022878;11435608;17182565;17911616;22854263;2...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   source target  weight                                               PMID\n",
+       "0     MYC   TERT       1  10022128;10491298;10606235;10637317;10723141;1...\n",
+       "1    SPI1  BGLAP       1                                           10022617\n",
+       "2   SMAD3    JUN       1                                  10022869;12374795\n",
+       "3   SMAD4    JUN       1                                  10022869;12374795\n",
+       "4  STAT5A    IL2       1  10022878;11435608;17182565;17911616;22854263;2..."
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "collectri.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12725752",
+   "metadata": {},
+   "source": [
+    "**b) Activity inference with Univariate Linear Model (ULM)**\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "To infer TF enrichment scores we will run the Univariate Linear Model (`ulm`) method. For each sample in our dataset (`mat`) and each TF in our network (`net`), it fits a linear model that predicts the observed gene expression based solely on the TF’s TF-Gene interaction weights. Once fitted, the obtained t-value of the slope is the score. If it is positive, we interpret that the TF is active and if it is negative we interpret that it is inactive.\n",
+    "\n",
+    "<img src=\"scverse-tutorials/docs/notebooks/img/ulm.png\"/>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2902d3b8",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "82a1b904",
+   "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>baseMean</th>\n",
+       "      <th>log2FoldChange</th>\n",
+       "      <th>lfcSE</th>\n",
+       "      <th>stat</th>\n",
+       "      <th>pvalue</th>\n",
+       "      <th>padj</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>gene_symbol</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>A1BG</th>\n",
+       "      <td>70.471700</td>\n",
+       "      <td>-0.082879</td>\n",
+       "      <td>0.183383</td>\n",
+       "      <td>-0.876097</td>\n",
+       "      <td>0.380977</td>\n",
+       "      <td>0.719800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A2M</th>\n",
+       "      <td>36.735027</td>\n",
+       "      <td>-1.071370</td>\n",
+       "      <td>0.338611</td>\n",
+       "      <td>-3.848842</td>\n",
+       "      <td>0.000119</td>\n",
+       "      <td>0.006269</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A2MP1</th>\n",
+       "      <td>15.628274</td>\n",
+       "      <td>0.032486</td>\n",
+       "      <td>0.289659</td>\n",
+       "      <td>0.802367</td>\n",
+       "      <td>0.422340</td>\n",
+       "      <td>0.748127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AAAS</th>\n",
+       "      <td>18.305878</td>\n",
+       "      <td>0.040930</td>\n",
+       "      <td>0.244828</td>\n",
+       "      <td>0.593935</td>\n",
+       "      <td>0.552555</td>\n",
+       "      <td>0.827623</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>AACS</th>\n",
+       "      <td>24.630203</td>\n",
+       "      <td>0.060574</td>\n",
+       "      <td>0.231249</td>\n",
+       "      <td>0.754344</td>\n",
+       "      <td>0.450643</td>\n",
+       "      <td>0.766366</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZXDC</th>\n",
+       "      <td>30.181501</td>\n",
+       "      <td>-0.080694</td>\n",
+       "      <td>0.225169</td>\n",
+       "      <td>-0.963323</td>\n",
+       "      <td>0.335385</td>\n",
+       "      <td>0.687688</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZYG11B</th>\n",
+       "      <td>101.884620</td>\n",
+       "      <td>0.065714</td>\n",
+       "      <td>0.231680</td>\n",
+       "      <td>0.827915</td>\n",
+       "      <td>0.407719</td>\n",
+       "      <td>0.738730</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZYX</th>\n",
+       "      <td>81.599910</td>\n",
+       "      <td>0.153035</td>\n",
+       "      <td>0.187370</td>\n",
+       "      <td>1.448655</td>\n",
+       "      <td>0.147434</td>\n",
+       "      <td>0.487976</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZZEF1</th>\n",
+       "      <td>820.790100</td>\n",
+       "      <td>0.011441</td>\n",
+       "      <td>0.179296</td>\n",
+       "      <td>0.122474</td>\n",
+       "      <td>0.902523</td>\n",
+       "      <td>0.971216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZZZ3</th>\n",
+       "      <td>59.928497</td>\n",
+       "      <td>-0.020703</td>\n",
+       "      <td>0.196577</td>\n",
+       "      <td>-0.248011</td>\n",
+       "      <td>0.804126</td>\n",
+       "      <td>0.935499</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10415 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
+       "gene_symbol                                                             \n",
+       "A1BG          70.471700       -0.082879  0.183383 -0.876097  0.380977   \n",
+       "A2M           36.735027       -1.071370  0.338611 -3.848842  0.000119   \n",
+       "A2MP1         15.628274        0.032486  0.289659  0.802367  0.422340   \n",
+       "AAAS          18.305878        0.040930  0.244828  0.593935  0.552555   \n",
+       "AACS          24.630203        0.060574  0.231249  0.754344  0.450643   \n",
+       "...                 ...             ...       ...       ...       ...   \n",
+       "ZXDC          30.181501       -0.080694  0.225169 -0.963323  0.335385   \n",
+       "ZYG11B       101.884620        0.065714  0.231680  0.827915  0.407719   \n",
+       "ZYX           81.599910        0.153035  0.187370  1.448655  0.147434   \n",
+       "ZZEF1        820.790100        0.011441  0.179296  0.122474  0.902523   \n",
+       "ZZZ3          59.928497       -0.020703  0.196577 -0.248011  0.804126   \n",
+       "\n",
+       "                 padj  \n",
+       "gene_symbol            \n",
+       "A1BG         0.719800  \n",
+       "A2M          0.006269  \n",
+       "A2MP1        0.748127  \n",
+       "AAAS         0.827623  \n",
+       "AACS         0.766366  \n",
+       "...               ...  \n",
+       "ZXDC         0.687688  \n",
+       "ZYG11B       0.738730  \n",
+       "ZYX          0.487976  \n",
+       "ZZEF1        0.971216  \n",
+       "ZZZ3         0.935499  \n",
+       "\n",
+       "[10415 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#First import results from previous DEA tutorial with DEseq2\n",
+    "results_df = pd.read_csv(\"T_cell_DEA_pyDeseq2_results_df.csv\",index_col=0)\n",
+    "results_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f18c7033",
+   "metadata": {},
+   "source": [
+    "From the DEA results above, we can use the obtained gene level statistics to perform enrichment analysis. Any statistic can be used, but we recommend using the t-values instead of logFCs since t-values incorporate the significance of change in their value. We will transform the obtained t-values stored in `stats` to a wide matrix so that it can be used by decoupler:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "bdb8292d",
+   "metadata": {
+    "scrolled": false
+   },
+   "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>gene_symbol</th>\n",
+       "      <th>A1BG</th>\n",
+       "      <th>A2M</th>\n",
+       "      <th>A2MP1</th>\n",
+       "      <th>AAAS</th>\n",
+       "      <th>AACS</th>\n",
+       "      <th>AAGAB</th>\n",
+       "      <th>AAK1</th>\n",
+       "      <th>AAMDC</th>\n",
+       "      <th>AAMP</th>\n",
+       "      <th>AAR2</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ZUP1</th>\n",
+       "      <th>ZWILCH</th>\n",
+       "      <th>ZWINT</th>\n",
+       "      <th>ZXDA</th>\n",
+       "      <th>ZXDB</th>\n",
+       "      <th>ZXDC</th>\n",
+       "      <th>ZYG11B</th>\n",
+       "      <th>ZYX</th>\n",
+       "      <th>ZZEF1</th>\n",
+       "      <th>ZZZ3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T cell</th>\n",
+       "      <td>-0.876097</td>\n",
+       "      <td>-3.848842</td>\n",
+       "      <td>0.802367</td>\n",
+       "      <td>0.593935</td>\n",
+       "      <td>0.754344</td>\n",
+       "      <td>-0.227301</td>\n",
+       "      <td>-0.61717</td>\n",
+       "      <td>0.294581</td>\n",
+       "      <td>1.39611</td>\n",
+       "      <td>0.505238</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.56492</td>\n",
+       "      <td>1.396946</td>\n",
+       "      <td>3.544967</td>\n",
+       "      <td>-1.017032</td>\n",
+       "      <td>-2.251387</td>\n",
+       "      <td>-0.963323</td>\n",
+       "      <td>0.827915</td>\n",
+       "      <td>1.448655</td>\n",
+       "      <td>0.122474</td>\n",
+       "      <td>-0.248011</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 10415 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "gene_symbol      A1BG       A2M     A2MP1      AAAS      AACS     AAGAB  \\\n",
+       "T cell      -0.876097 -3.848842  0.802367  0.593935  0.754344 -0.227301   \n",
+       "\n",
+       "gene_symbol     AAK1     AAMDC     AAMP      AAR2  ...     ZUP1    ZWILCH  \\\n",
+       "T cell      -0.61717  0.294581  1.39611  0.505238  ...  1.56492  1.396946   \n",
+       "\n",
+       "gene_symbol     ZWINT      ZXDA      ZXDB      ZXDC    ZYG11B       ZYX  \\\n",
+       "T cell       3.544967 -1.017032 -2.251387 -0.963323  0.827915  1.448655   \n",
+       "\n",
+       "gene_symbol     ZZEF1      ZZZ3  \n",
+       "T cell       0.122474 -0.248011  \n",
+       "\n",
+       "[1 rows x 10415 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#from the DEA results above, we can use the obtained gene level statistics to perform enrichment analysis. Any statistic can be used, but we recommend using the t-values instead of logFCs since t-values incorporate the significance of change in their value. We will transform the obtained t-values stored in stats to a wide matrix so that it can be used by decoupler:\n",
+    "mat = results_df[['stat']].T.rename(index={'stat': 'T cell'})\n",
+    "mat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5db2818f",
+   "metadata": {},
+   "source": [
+    "We can run `ulm` with a one-liner:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "7a618758",
+   "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>ABL1</th>\n",
+       "      <th>AHR</th>\n",
+       "      <th>AIRE</th>\n",
+       "      <th>AP1</th>\n",
+       "      <th>APEX1</th>\n",
+       "      <th>AR</th>\n",
+       "      <th>ARID3B</th>\n",
+       "      <th>ARID4A</th>\n",
+       "      <th>ARID5B</th>\n",
+       "      <th>ARNT</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ZNF350</th>\n",
+       "      <th>ZNF354C</th>\n",
+       "      <th>ZNF362</th>\n",
+       "      <th>ZNF382</th>\n",
+       "      <th>ZNF384</th>\n",
+       "      <th>ZNF395</th>\n",
+       "      <th>ZNF436</th>\n",
+       "      <th>ZNF699</th>\n",
+       "      <th>ZNF76</th>\n",
+       "      <th>ZNF91</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T cell</th>\n",
+       "      <td>0.670022</td>\n",
+       "      <td>0.77803</td>\n",
+       "      <td>-1.94135</td>\n",
+       "      <td>-1.628783</td>\n",
+       "      <td>0.366503</td>\n",
+       "      <td>-0.427226</td>\n",
+       "      <td>-0.325884</td>\n",
+       "      <td>0.013003</td>\n",
+       "      <td>0.410143</td>\n",
+       "      <td>-0.868288</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.321947</td>\n",
+       "      <td>-1.824237</td>\n",
+       "      <td>1.113136</td>\n",
+       "      <td>0.218782</td>\n",
+       "      <td>-1.44051</td>\n",
+       "      <td>0.845993</td>\n",
+       "      <td>0.294943</td>\n",
+       "      <td>-0.835089</td>\n",
+       "      <td>1.839208</td>\n",
+       "      <td>-2.407625</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 548 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            ABL1      AHR     AIRE       AP1     APEX1        AR    ARID3B  \\\n",
+       "T cell  0.670022  0.77803 -1.94135 -1.628783  0.366503 -0.427226 -0.325884   \n",
+       "\n",
+       "          ARID4A    ARID5B      ARNT  ...    ZNF350   ZNF354C    ZNF362  \\\n",
+       "T cell  0.013003  0.410143 -0.868288  ...  0.321947 -1.824237  1.113136   \n",
+       "\n",
+       "          ZNF382   ZNF384    ZNF395    ZNF436    ZNF699     ZNF76     ZNF91  \n",
+       "T cell  0.218782 -1.44051  0.845993  0.294943 -0.835089  1.839208 -2.407625  \n",
+       "\n",
+       "[1 rows x 548 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Infer pathway activities with ulm\n",
+    "tf_acts, tf_pvals = dc.run_ulm(mat=mat, net=collectri)\n",
+    "tf_acts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ede3280b",
+   "metadata": {},
+   "source": [
+    "Let us plot the obtained scores for the top active/inactive transcription factors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "8541c738",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_barplot(tf_acts, 'T cell', top=40, vertical=True,figsize=[10,10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e4c2e5b",
+   "metadata": {},
+   "source": [
+    "In accordance to the previous pathway results, T cells seem to activate for TFs responsible for cell growth (E2F4, TFDP1, E2F1).\n",
+    "\n",
+    "Like with pathways, we can explore how the target genes look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "7241b4f7",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Extract logFCs and pvals\n",
+    "logFCs = results_df[['log2FoldChange']].T.rename(index={'log2FoldChange': 'T cell'})\n",
+    "pvals = results_df[['padj']].T.rename(index={'padj': 'T cell'})\n",
+    "\n",
+    "# Plot\n",
+    "dc.plot_volcano(logFCs, pvals, 'T cell', name='E2F4', net=collectri, top=10, sign_thr=0.05, lFCs_thr=0.5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "Python [conda env:decoupler]",
+   "language": "python",
+   "name": "conda-env-decoupler-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "798.75px",
+    "left": "183px",
+    "top": "208px",
+    "width": "179.492px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From 0063dc18e58623ec0f72d4a8e73187074a868ec3 Mon Sep 17 00:00:00 2001
From: Anna Vathrakokoili Pournara <annavp@ebi.ac.uk>
Date: Tue, 28 Nov 2023 16:19:34 +0000
Subject: [PATCH 2/9] add images from decoupler github for tutorials

---
 docs/notebooks/img/graphical_abstract.png | Bin 0 -> 343847 bytes
 docs/notebooks/img/logo.png               | Bin 0 -> 21495 bytes
 docs/notebooks/img/mlm.png                | Bin 0 -> 219324 bytes
 docs/notebooks/img/net_plot.png           | Bin 0 -> 49386 bytes
 docs/notebooks/img/ora.png                | Bin 0 -> 244931 bytes
 docs/notebooks/img/ulm.png                | Bin 0 -> 193740 bytes
 6 files changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 docs/notebooks/img/graphical_abstract.png
 create mode 100644 docs/notebooks/img/logo.png
 create mode 100644 docs/notebooks/img/mlm.png
 create mode 100644 docs/notebooks/img/net_plot.png
 create mode 100644 docs/notebooks/img/ora.png
 create mode 100644 docs/notebooks/img/ulm.png

diff --git a/docs/notebooks/img/graphical_abstract.png b/docs/notebooks/img/graphical_abstract.png
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From f156a95e6a313b6f18c58988201bd41e9ca2767a Mon Sep 17 00:00:00 2001
From: Adrienne <adrienne.unsworth@gmail.com>
Date: Wed, 29 Nov 2023 09:37:34 +0000
Subject: [PATCH 3/9] add merged notebook

---
 .../pseudobulk_functional_analysis.ipynb      | 1238 +++++++++++++++++
 1 file changed, 1238 insertions(+)
 create mode 100644 docs/notebooks/pseudobulk_functional_analysis.ipynb

diff --git a/docs/notebooks/pseudobulk_functional_analysis.ipynb b/docs/notebooks/pseudobulk_functional_analysis.ipynb
new file mode 100644
index 00000000..271644da
--- /dev/null
+++ b/docs/notebooks/pseudobulk_functional_analysis.ipynb
@@ -0,0 +1,1238 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "ba83c36d",
+   "metadata": {},
+   "source": [
+    "# Transcription factor activity inference"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f7a1504",
+   "metadata": {},
+   "source": [
+    "## Load packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "956d287f",
+   "metadata": {},
+   "source": [
+    "We need to load the relevant packages, scanpy to handle scRNA-seq data and decoupler to use statistical methods."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "566b1a4d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import scanpy as sc\n",
+    "import decoupler as dc\n",
+    "\n",
+    "\n",
+    "# Only needed for processing\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "\n",
+    "# Needed for some plotting\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "effd4534",
+   "metadata": {},
+   "source": [
+    "### Motivation: \n",
+    "\n",
+    "scRNA-seq yields many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by infering transcription factor (TF) activities from our trascriptomics data from prior knowledge.\n",
+    "\n",
+    "In this notebook we showcase how to use decoupler for transcription factor (TF) and pathway enrichment from a human data-set. The data consists of ~5k Blood myeloid cells from healthy and COVID-19 infected patients available in the Single Cell Expression Atlas [here](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-9221/results/tsne?plotType=umap&plotOption=20).\n",
+    "\n",
+    "\n",
+    "For the TF activity inference analysis we will need a) a gene regulatory network (GRN) and b) a statistical method.\n",
+    "\n",
+    "a) **Gene Regulatory Network(GRN)**: \n",
+    "\n",
+    "[CollecTRI](https://github.com/saezlab/CollecTRI) is a comprehensive resource containing a curated collection of TFs and their transcriptional targets compiled from 12 different resources. This collection provides an increased coverage of transcription factors and a superior performance in identifying perturbed TFs compared to our previous [DoRothEA](https://saezlab.github.io/dorothea/) network and other literature based GRNs. Similar to DoRothEA, interactions are weighted by their mode of regulation (activation or inhibition).\n",
+    "\n",
+    "For this example we will use the human version (mouse and rat are also available). We can use `decoupler` to retrieve it from `omnipath`. The argument `split_complexes` keeps complexes or splits them into subunits, by default we recommend to keep complexes together.\n",
+    "\n",
+    "Note! : In this tutorial we use the network CollecTRI, but we could use any other GRN coming from an inference method such as CellOracle, pySCENIC or SCENIC+.\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40314c83",
+   "metadata": {},
+   "source": [
+    "<div class=\"alert alert-info\">\n",
+    "\n",
+    "**Note**\n",
+    "\n",
+    "In this tutorial we use the network CollecTRI, but we could use any other GRN coming from an inference method such as [CellOracle](https://morris-lab.github.io/CellOracle.documentation/), [pySCENIC](https://pyscenic.readthedocs.io/en/latest/) or [SCENIC+](https://scenicplus.readthedocs.io/en/latest/). </div>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "bc51ad0e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve CollecTRI gene regulatory network\n",
+    "collectri = dc.get_collectri(organism='human', split_complexes=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b1e3afd0",
+   "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>weight</th>\n",
+       "      <th>PMID</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>MYC</td>\n",
+       "      <td>TERT</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10022128;10491298;10606235;10637317;10723141;1...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>SPI1</td>\n",
+       "      <td>BGLAP</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10022617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>SMAD3</td>\n",
+       "      <td>JUN</td>\n",
+       "      <td>1</td>\n",
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+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>SMAD4</td>\n",
+       "      <td>JUN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10022869;12374795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>STAT5A</td>\n",
+       "      <td>IL2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>10022878;11435608;17182565;17911616;22854263;2...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   source target  weight                                               PMID\n",
+       "0     MYC   TERT       1  10022128;10491298;10606235;10637317;10723141;1...\n",
+       "1    SPI1  BGLAP       1                                           10022617\n",
+       "2   SMAD3    JUN       1                                  10022869;12374795\n",
+       "3   SMAD4    JUN       1                                  10022869;12374795\n",
+       "4  STAT5A    IL2       1  10022878;11435608;17182565;17911616;22854263;2..."
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "collectri.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "12725752",
+   "metadata": {},
+   "source": [
+    "**b) Activity inference with Univariate Linear Model (ULM)**\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "To infer TF enrichment scores we will run the Univariate Linear Model (`ulm`) method. For each sample in our dataset (`mat`) and each TF in our network (`net`), it fits a linear model that predicts the observed gene expression based solely on the TF’s TF-Gene interaction weights. Once fitted, the obtained t-value of the slope is the score. If it is positive, we interpret that the TF is active and if it is negative we interpret that it is inactive.\n",
+    "\n",
+    "<img src=\"scverse-tutorials/docs/notebooks/img/ulm.png\"/>\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2902d3b8",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "82a1b904",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+       "<div>\n",
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+       "\n",
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+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>baseMean</th>\n",
+       "      <th>log2FoldChange</th>\n",
+       "      <th>lfcSE</th>\n",
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+       "    <tr>\n",
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+       "      <td>-0.876097</td>\n",
+       "      <td>0.380977</td>\n",
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+       "      <td>36.735027</td>\n",
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+       "      <td>0.338611</td>\n",
+       "      <td>-3.848842</td>\n",
+       "      <td>0.000119</td>\n",
+       "      <td>0.006269</td>\n",
+       "    </tr>\n",
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+       "    </tr>\n",
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+       "      <th>AAAS</th>\n",
+       "      <td>18.305878</td>\n",
+       "      <td>0.040930</td>\n",
+       "      <td>0.244828</td>\n",
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+       "      <td>81.599910</td>\n",
+       "      <td>0.153035</td>\n",
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+       "      <td>0.179296</td>\n",
+       "      <td>0.122474</td>\n",
+       "      <td>0.902523</td>\n",
+       "      <td>0.971216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ZZZ3</th>\n",
+       "      <td>59.928497</td>\n",
+       "      <td>-0.020703</td>\n",
+       "      <td>0.196577</td>\n",
+       "      <td>-0.248011</td>\n",
+       "      <td>0.804126</td>\n",
+       "      <td>0.935499</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>10415 rows × 6 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
+       "gene_symbol                                                             \n",
+       "A1BG          70.471700       -0.082879  0.183383 -0.876097  0.380977   \n",
+       "A2M           36.735027       -1.071370  0.338611 -3.848842  0.000119   \n",
+       "A2MP1         15.628274        0.032486  0.289659  0.802367  0.422340   \n",
+       "AAAS          18.305878        0.040930  0.244828  0.593935  0.552555   \n",
+       "AACS          24.630203        0.060574  0.231249  0.754344  0.450643   \n",
+       "...                 ...             ...       ...       ...       ...   \n",
+       "ZXDC          30.181501       -0.080694  0.225169 -0.963323  0.335385   \n",
+       "ZYG11B       101.884620        0.065714  0.231680  0.827915  0.407719   \n",
+       "ZYX           81.599910        0.153035  0.187370  1.448655  0.147434   \n",
+       "ZZEF1        820.790100        0.011441  0.179296  0.122474  0.902523   \n",
+       "ZZZ3          59.928497       -0.020703  0.196577 -0.248011  0.804126   \n",
+       "\n",
+       "                 padj  \n",
+       "gene_symbol            \n",
+       "A1BG         0.719800  \n",
+       "A2M          0.006269  \n",
+       "A2MP1        0.748127  \n",
+       "AAAS         0.827623  \n",
+       "AACS         0.766366  \n",
+       "...               ...  \n",
+       "ZXDC         0.687688  \n",
+       "ZYG11B       0.738730  \n",
+       "ZYX          0.487976  \n",
+       "ZZEF1        0.971216  \n",
+       "ZZZ3         0.935499  \n",
+       "\n",
+       "[10415 rows x 6 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#First import results from previous DEA tutorial with DEseq2\n",
+    "results_df = pd.read_csv(\"T_cell_DEA_pyDeseq2_results_df.csv\",index_col=0)\n",
+    "results_df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f18c7033",
+   "metadata": {},
+   "source": [
+    "From the DEA results above, we can use the obtained gene level statistics to perform enrichment analysis. Any statistic can be used, but we recommend using the t-values instead of logFCs since t-values incorporate the significance of change in their value. We will transform the obtained t-values stored in `stats` to a wide matrix so that it can be used by decoupler:\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "bdb8292d",
+   "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>gene_symbol</th>\n",
+       "      <th>A1BG</th>\n",
+       "      <th>A2M</th>\n",
+       "      <th>A2MP1</th>\n",
+       "      <th>AAAS</th>\n",
+       "      <th>AACS</th>\n",
+       "      <th>AAGAB</th>\n",
+       "      <th>AAK1</th>\n",
+       "      <th>AAMDC</th>\n",
+       "      <th>AAMP</th>\n",
+       "      <th>AAR2</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ZUP1</th>\n",
+       "      <th>ZWILCH</th>\n",
+       "      <th>ZWINT</th>\n",
+       "      <th>ZXDA</th>\n",
+       "      <th>ZXDB</th>\n",
+       "      <th>ZXDC</th>\n",
+       "      <th>ZYG11B</th>\n",
+       "      <th>ZYX</th>\n",
+       "      <th>ZZEF1</th>\n",
+       "      <th>ZZZ3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T cell</th>\n",
+       "      <td>-0.876097</td>\n",
+       "      <td>-3.848842</td>\n",
+       "      <td>0.802367</td>\n",
+       "      <td>0.593935</td>\n",
+       "      <td>0.754344</td>\n",
+       "      <td>-0.227301</td>\n",
+       "      <td>-0.61717</td>\n",
+       "      <td>0.294581</td>\n",
+       "      <td>1.39611</td>\n",
+       "      <td>0.505238</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.56492</td>\n",
+       "      <td>1.396946</td>\n",
+       "      <td>3.544967</td>\n",
+       "      <td>-1.017032</td>\n",
+       "      <td>-2.251387</td>\n",
+       "      <td>-0.963323</td>\n",
+       "      <td>0.827915</td>\n",
+       "      <td>1.448655</td>\n",
+       "      <td>0.122474</td>\n",
+       "      <td>-0.248011</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 10415 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "gene_symbol      A1BG       A2M     A2MP1      AAAS      AACS     AAGAB  \\\n",
+       "T cell      -0.876097 -3.848842  0.802367  0.593935  0.754344 -0.227301   \n",
+       "\n",
+       "gene_symbol     AAK1     AAMDC     AAMP      AAR2  ...     ZUP1    ZWILCH  \\\n",
+       "T cell      -0.61717  0.294581  1.39611  0.505238  ...  1.56492  1.396946   \n",
+       "\n",
+       "gene_symbol     ZWINT      ZXDA      ZXDB      ZXDC    ZYG11B       ZYX  \\\n",
+       "T cell       3.544967 -1.017032 -2.251387 -0.963323  0.827915  1.448655   \n",
+       "\n",
+       "gene_symbol     ZZEF1      ZZZ3  \n",
+       "T cell       0.122474 -0.248011  \n",
+       "\n",
+       "[1 rows x 10415 columns]"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#from the DEA results above, we can use the obtained gene level statistics to perform enrichment analysis. Any statistic can be used, but we recommend using the t-values instead of logFCs since t-values incorporate the significance of change in their value. We will transform the obtained t-values stored in stats to a wide matrix so that it can be used by decoupler:\n",
+    "mat = results_df[['stat']].T.rename(index={'stat': 'T cell'})\n",
+    "mat"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5db2818f",
+   "metadata": {},
+   "source": [
+    "We can run `ulm` with a one-liner:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "7a618758",
+   "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>ABL1</th>\n",
+       "      <th>AHR</th>\n",
+       "      <th>AIRE</th>\n",
+       "      <th>AP1</th>\n",
+       "      <th>APEX1</th>\n",
+       "      <th>AR</th>\n",
+       "      <th>ARID3B</th>\n",
+       "      <th>ARID4A</th>\n",
+       "      <th>ARID5B</th>\n",
+       "      <th>ARNT</th>\n",
+       "      <th>...</th>\n",
+       "      <th>ZNF350</th>\n",
+       "      <th>ZNF354C</th>\n",
+       "      <th>ZNF362</th>\n",
+       "      <th>ZNF382</th>\n",
+       "      <th>ZNF384</th>\n",
+       "      <th>ZNF395</th>\n",
+       "      <th>ZNF436</th>\n",
+       "      <th>ZNF699</th>\n",
+       "      <th>ZNF76</th>\n",
+       "      <th>ZNF91</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T cell</th>\n",
+       "      <td>0.670022</td>\n",
+       "      <td>0.77803</td>\n",
+       "      <td>-1.94135</td>\n",
+       "      <td>-1.628783</td>\n",
+       "      <td>0.366503</td>\n",
+       "      <td>-0.427226</td>\n",
+       "      <td>-0.325884</td>\n",
+       "      <td>0.013003</td>\n",
+       "      <td>0.410143</td>\n",
+       "      <td>-0.868288</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.321947</td>\n",
+       "      <td>-1.824237</td>\n",
+       "      <td>1.113136</td>\n",
+       "      <td>0.218782</td>\n",
+       "      <td>-1.44051</td>\n",
+       "      <td>0.845993</td>\n",
+       "      <td>0.294943</td>\n",
+       "      <td>-0.835089</td>\n",
+       "      <td>1.839208</td>\n",
+       "      <td>-2.407625</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 548 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            ABL1      AHR     AIRE       AP1     APEX1        AR    ARID3B  \\\n",
+       "T cell  0.670022  0.77803 -1.94135 -1.628783  0.366503 -0.427226 -0.325884   \n",
+       "\n",
+       "          ARID4A    ARID5B      ARNT  ...    ZNF350   ZNF354C    ZNF362  \\\n",
+       "T cell  0.013003  0.410143 -0.868288  ...  0.321947 -1.824237  1.113136   \n",
+       "\n",
+       "          ZNF382   ZNF384    ZNF395    ZNF436    ZNF699     ZNF76     ZNF91  \n",
+       "T cell  0.218782 -1.44051  0.845993  0.294943 -0.835089  1.839208 -2.407625  \n",
+       "\n",
+       "[1 rows x 548 columns]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Infer pathway activities with ulm\n",
+    "tf_acts, tf_pvals = dc.run_ulm(mat=mat, net=collectri)\n",
+    "tf_acts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ede3280b",
+   "metadata": {},
+   "source": [
+    "Let us plot the obtained scores for the top active/inactive transcription factors:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "8541c738",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_barplot(tf_acts, 'T cell', top=40, vertical=True,figsize=[10,10])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3e4c2e5b",
+   "metadata": {},
+   "source": [
+    "In accordance to the previous pathway results, T cells seem to activate for TFs responsible for cell growth (E2F4, TFDP1, E2F1).\n",
+    "\n",
+    "Like with pathways, we can explore how the target genes look like:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "7241b4f7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Extract logFCs and pvals\n",
+    "logFCs = results_df[['log2FoldChange']].T.rename(index={'log2FoldChange': 'T cell'})\n",
+    "pvals = results_df[['padj']].T.rename(index={'padj': 'T cell'})\n",
+    "\n",
+    "# Plot\n",
+    "dc.plot_volcano(logFCs, pvals, 'T cell', name='E2F4', net=collectri, top=10, sign_thr=0.05, lFCs_thr=0.5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1e72cbc5-8edb-47b0-83fd-e8fb07801e2d",
+   "metadata": {},
+   "source": [
+    "# Load packages"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69cfa682-ad3a-4568-b5c8-38cbba68007d",
+   "metadata": {},
+   "source": [
+    "First, we need to load the relevant packages, scanpy to handle scRNA-seq data and decoupler to use statistical methods. We also need to load `mat` from earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "58d7b49f-0f36-4ba4-8ca3-bc02711cdfea",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "results_df = pd.read_csv(\"./data/T_cell_DEA_pyDeseq2_results_df.csv\", index_col=\"gene_symbol\")\n",
+    "mat = results_df[['stat']].T.rename(index={'stat': 'T cell'})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8157b21d-cd31-43f7-b8b7-a2e029b9c40c",
+   "metadata": {},
+   "source": [
+    "## Pathway"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d34c69d7-025f-4190-a016-c138e8770eb2",
+   "metadata": {},
+   "source": [
+    "Another analysis we can perform is to infer pathway activities from our transcriptomics data.\n",
+    "\n",
+    "## PROGENy model\n",
+    "\n",
+    "[PROGENy](https://saezlab.github.io/progeny/) is a comprehensive resource containing a curated collection of pathways and their target genes, with weights for each interaction. For this example we will use the human weights (other organisms are available) and we will use the top 500 responsive genes ranked by p-value. Here is a brief description of each pathway:\n",
+    "\n",
+    "* **Androgen**: involved in the growth and development of the male reproductive organs.\n",
+    "\n",
+    "* **EGFR**: regulates growth, survival, migration, apoptosis, proliferation, and differentiation in mammalian cells\n",
+    "\n",
+    "* **Estrogen**: promotes the growth and development of the female reproductive organs.\n",
+    "\n",
+    "* **Hypoxia**: promotes angiogenesis and metabolic reprogramming when O2 levels are low.\n",
+    "\n",
+    "* **JAK-STAT**: involved in immunity, cell division, cell death, and tumor formation.\n",
+    "\n",
+    "* **MAPK**: integrates external signals and promotes cell growth and proliferation.\n",
+    "\n",
+    "* **NFkB**: regulates immune response, cytokine production and cell survival.\n",
+    "\n",
+    "* **p53**: regulates cell cycle, apoptosis, DNA repair and tumor suppression.\n",
+    "\n",
+    "* **PI3K**: promotes growth and proliferation.\n",
+    "\n",
+    "* **TGFb**: involved in development, homeostasis, and repair of most tissues.\n",
+    "\n",
+    "* **TNFa**: mediates haematopoiesis, immune surveillance, tumour regression and protection from infection.\n",
+    "\n",
+    "* **Trail**: induces apoptosis.\n",
+    "\n",
+    "* **VEGF**: mediates angiogenesis, vascular permeability, and cell migration.\n",
+    "\n",
+    "* **WNT**: regulates organ morphogenesis during development and tissue repair.\n",
+    "\n",
+    "To access it we can use `decoupler`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "e0faf08b-cf32-4f0d-9477-5091d4d2b9b1",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "WARNING:root:Failed to download from `https://omnipathdb.org/`.\n",
+      "WARNING:root:Traceback (most recent call last):\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
+      "    res = self._download(req)\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
+      "    resp.raise_for_status()\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
+      "    raise HTTPError(http_error_msg, response=self)\n",
+      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://omnipathdb.org/queries/enzsub?format=json\n",
+      "\n",
+      "WARNING:root:Failed to download from `http://no-tls.omnipathdb.org/`.\n",
+      "WARNING:root:Traceback (most recent call last):\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
+      "    res = self._download(req)\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
+      "    resp.raise_for_status()\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
+      "    raise HTTPError(http_error_msg, response=self)\n",
+      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: http://no-tls.omnipathdb.org/queries/enzsub?format=json\n",
+      "\n",
+      "WARNING:root:Failed to download from `https://omnipathdb.org/`.\n",
+      "WARNING:root:Traceback (most recent call last):\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
+      "    res = self._download(req)\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
+      "    resp.raise_for_status()\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
+      "    raise HTTPError(http_error_msg, response=self)\n",
+      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://omnipathdb.org/queries/interactions?format=json\n",
+      "\n",
+      "WARNING:root:Failed to download from `http://no-tls.omnipathdb.org/`.\n",
+      "WARNING:root:Traceback (most recent call last):\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
+      "    res = self._download(req)\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
+      "    resp.raise_for_status()\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
+      "    raise HTTPError(http_error_msg, response=self)\n",
+      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: http://no-tls.omnipathdb.org/queries/interactions?format=json\n",
+      "\n",
+      "WARNING:root:Failed to download from `https://omnipathdb.org/`.\n",
+      "WARNING:root:Traceback (most recent call last):\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
+      "    res = self._download(req)\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
+      "    resp.raise_for_status()\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
+      "    raise HTTPError(http_error_msg, response=self)\n",
+      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://omnipathdb.org/queries/annotations?format=json\n",
+      "\n",
+      "WARNING:root:Failed to download from `http://no-tls.omnipathdb.org/`.\n",
+      "WARNING:root:Traceback (most recent call last):\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
+      "    res = self._download(req)\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
+      "    resp.raise_for_status()\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
+      "    raise HTTPError(http_error_msg, response=self)\n",
+      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: http://no-tls.omnipathdb.org/queries/annotations?format=json\n",
+      "\n",
+      "WARNING:root:Failed to download from `https://omnipathdb.org/`.\n",
+      "WARNING:root:Traceback (most recent call last):\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
+      "    res = self._download(req)\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
+      "    resp.raise_for_status()\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
+      "    raise HTTPError(http_error_msg, response=self)\n",
+      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://omnipathdb.org/queries/intercell?format=json\n",
+      "\n",
+      "WARNING:root:Failed to download from `http://no-tls.omnipathdb.org/`.\n",
+      "WARNING:root:Traceback (most recent call last):\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
+      "    res = self._download(req)\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
+      "    resp.raise_for_status()\n",
+      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
+      "    raise HTTPError(http_error_msg, response=self)\n",
+      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: http://no-tls.omnipathdb.org/queries/intercell?format=json\n",
+      "\n"
+     ]
+    },
+    {
+     "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>weight</th>\n",
+       "      <th>p_value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Androgen</td>\n",
+       "      <td>TMPRSS2</td>\n",
+       "      <td>11.490631</td>\n",
+       "      <td>0.000000e+00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Androgen</td>\n",
+       "      <td>NKX3-1</td>\n",
+       "      <td>10.622551</td>\n",
+       "      <td>2.242078e-44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Androgen</td>\n",
+       "      <td>MBOAT2</td>\n",
+       "      <td>10.472733</td>\n",
+       "      <td>4.624285e-44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Androgen</td>\n",
+       "      <td>KLK2</td>\n",
+       "      <td>10.176186</td>\n",
+       "      <td>1.944414e-40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Androgen</td>\n",
+       "      <td>SARG</td>\n",
+       "      <td>11.386852</td>\n",
+       "      <td>2.790209e-40</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6995</th>\n",
+       "      <td>p53</td>\n",
+       "      <td>ZMYM4</td>\n",
+       "      <td>-2.325752</td>\n",
+       "      <td>1.522388e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6996</th>\n",
+       "      <td>p53</td>\n",
+       "      <td>CFDP1</td>\n",
+       "      <td>-1.628168</td>\n",
+       "      <td>1.526045e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6997</th>\n",
+       "      <td>p53</td>\n",
+       "      <td>VPS37D</td>\n",
+       "      <td>2.309503</td>\n",
+       "      <td>1.537098e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6998</th>\n",
+       "      <td>p53</td>\n",
+       "      <td>TEDC1</td>\n",
+       "      <td>-2.274823</td>\n",
+       "      <td>1.547037e-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6999</th>\n",
+       "      <td>p53</td>\n",
+       "      <td>CCDC138</td>\n",
+       "      <td>-3.205113</td>\n",
+       "      <td>1.568160e-06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>7000 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        source   target     weight       p_value\n",
+       "0     Androgen  TMPRSS2  11.490631  0.000000e+00\n",
+       "1     Androgen   NKX3-1  10.622551  2.242078e-44\n",
+       "2     Androgen   MBOAT2  10.472733  4.624285e-44\n",
+       "3     Androgen     KLK2  10.176186  1.944414e-40\n",
+       "4     Androgen     SARG  11.386852  2.790209e-40\n",
+       "...        ...      ...        ...           ...\n",
+       "6995       p53    ZMYM4  -2.325752  1.522388e-06\n",
+       "6996       p53    CFDP1  -1.628168  1.526045e-06\n",
+       "6997       p53   VPS37D   2.309503  1.537098e-06\n",
+       "6998       p53    TEDC1  -2.274823  1.547037e-06\n",
+       "6999       p53  CCDC138  -3.205113  1.568160e-06\n",
+       "\n",
+       "[7000 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Retrieve PROGENy model weights\n",
+    "progeny = dc.get_progeny(top=500)\n",
+    "progeny"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "adb9ac21-6d43-4234-bf3e-a4b0646f792f",
+   "metadata": {},
+   "source": [
+    "## Activity inference with Multivariate Linear Model (MLM)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90cb5e28-fc08-4650-ae4c-a1c3747ce572",
+   "metadata": {},
+   "source": [
+    "To infer pathway enrichment scores we will run the Multivariate Linear Model (`mlm`) method. For each sample in our dataset (`adata`), it fits a linear model that predicts the observed gene expression based on all pathways’ Pathway-Gene interactions weights. Once fitted, the obtained t-values of the slopes are the scores. If it is positive, we interpret that the pathway is active and if it is negative we interpret that it is inactive."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5068566-9020-4606-a743-9951ada071c1",
+   "metadata": {},
+   "source": [
+    "<img src=\"img/mlm.png\"/>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "69d81f4d-86de-4cd5-a8c2-7fd209a1a151",
+   "metadata": {},
+   "source": [
+    "We can run `mlm` with a one-liner:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "c5a7d276-31d4-47c4-be14-e8c0e76071c9",
+   "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>Androgen</th>\n",
+       "      <th>EGFR</th>\n",
+       "      <th>Estrogen</th>\n",
+       "      <th>Hypoxia</th>\n",
+       "      <th>JAK-STAT</th>\n",
+       "      <th>MAPK</th>\n",
+       "      <th>NFkB</th>\n",
+       "      <th>PI3K</th>\n",
+       "      <th>TGFb</th>\n",
+       "      <th>TNFa</th>\n",
+       "      <th>Trail</th>\n",
+       "      <th>VEGF</th>\n",
+       "      <th>WNT</th>\n",
+       "      <th>p53</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>T cell</th>\n",
+       "      <td>-0.561211</td>\n",
+       "      <td>1.116455</td>\n",
+       "      <td>1.701393</td>\n",
+       "      <td>-5.912141</td>\n",
+       "      <td>8.3318</td>\n",
+       "      <td>-0.631677</td>\n",
+       "      <td>-5.484974</td>\n",
+       "      <td>-0.074071</td>\n",
+       "      <td>-1.95181</td>\n",
+       "      <td>1.2157</td>\n",
+       "      <td>0.075457</td>\n",
+       "      <td>0.338886</td>\n",
+       "      <td>0.880702</td>\n",
+       "      <td>-1.817807</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        Androgen      EGFR  Estrogen   Hypoxia  JAK-STAT      MAPK      NFkB  \\\n",
+       "T cell -0.561211  1.116455  1.701393 -5.912141    8.3318 -0.631677 -5.484974   \n",
+       "\n",
+       "            PI3K     TGFb    TNFa     Trail      VEGF       WNT       p53  \n",
+       "T cell -0.074071 -1.95181  1.2157  0.075457  0.338886  0.880702 -1.817807  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Infer pathway activities with mlm\n",
+    "pathway_acts, pathway_pvals = dc.run_mlm(mat=mat, net=progeny)\n",
+    "pathway_acts"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3ce3196e-b2e4-4c31-9bd1-277a07c01812",
+   "metadata": {},
+   "source": [
+    "Let us plot the obtained scores:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "80c19b79-43a4-44cb-8c35-83bbd272bece",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n",
+      "/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
+      "  if pd.api.types.is_categorical_dtype(vector):\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_barplot(pathway_acts, 'T cell', top=25, vertical=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e0f15468-4220-4694-9653-3e6a3b947148",
+   "metadata": {},
+   "source": [
+    "It looks like JAK-STAT, a known immunity pathway is more active in T cells from COVID-19 patients than in controls. To further explore how the target genes of a pathway of interest behave, we can plot them in scatter plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "189128ec-4e02-483e-ad38-8c5b32e2c1d4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "dc.plot_targets(results_df, stat='stat', source_name='JAK-STAT', net=progeny, top=15)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f1db8ac0-f6bd-4703-b7cd-af1b0792c5fb",
+   "metadata": {
+    "scrolled": true
+   },
+   "source": [
+    "The observed activation of JAK-STAT is due to the fact that majority of its target genes with positive weights have positive t-values (1st quadrant), and the majority of the ones with negative weights have negative t-values (3d quadrant)."
+   ]
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Raw Cell Format",
+  "kernelspec": {
+   "display_name": "decoupler",
+   "language": "python",
+   "name": "decoupler"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.18"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {
+    "height": "798.75px",
+    "left": "183px",
+    "top": "208px",
+    "width": "179.492px"
+   },
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

From ba507fe73cfffb741f8975ecb4c28305633b4713 Mon Sep 17 00:00:00 2001
From: Adrienne <adrienne.unsworth@gmail.com>
Date: Wed, 29 Nov 2023 09:43:55 +0000
Subject: [PATCH 4/9] changed image file path

---
 docs/notebooks/pseudobulk_functional_analysis.ipynb | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/notebooks/pseudobulk_functional_analysis.ipynb b/docs/notebooks/pseudobulk_functional_analysis.ipynb
index 271644da..6b2c1786 100644
--- a/docs/notebooks/pseudobulk_functional_analysis.ipynb
+++ b/docs/notebooks/pseudobulk_functional_analysis.ipynb
@@ -194,7 +194,7 @@
     "\n",
     "To infer TF enrichment scores we will run the Univariate Linear Model (`ulm`) method. For each sample in our dataset (`mat`) and each TF in our network (`net`), it fits a linear model that predicts the observed gene expression based solely on the TF’s TF-Gene interaction weights. Once fitted, the obtained t-value of the slope is the score. If it is positive, we interpret that the TF is active and if it is negative we interpret that it is inactive.\n",
     "\n",
-    "<img src=\"scverse-tutorials/docs/notebooks/img/ulm.png\"/>\n",
+    "<img src=\"img/ulm.png\"/>\n",
     "\n",
     "\n"
    ]

From c94b4539858d52e927ae06eed26e23d44a9f7036 Mon Sep 17 00:00:00 2001
From: Anna Vathrakokoili Pournara <annavp@ebi.ac.uk>
Date: Wed, 29 Nov 2023 10:45:02 +0000
Subject: [PATCH 5/9] add file_updated notebook

---
 ... Functional_analysis_with_decoupler.ipynb} |   286 +-
 .../data/T_cell_DEA_pyDeseq2_results_df.csv   | 10416 ++++++++++++++++
 2 files changed, 10528 insertions(+), 174 deletions(-)
 rename docs/notebooks/{pseudobulk_functional_analysis.ipynb => Functional_analysis_with_decoupler.ipynb} (96%)
 create mode 100644 docs/notebooks/data/T_cell_DEA_pyDeseq2_results_df.csv

diff --git a/docs/notebooks/pseudobulk_functional_analysis.ipynb b/docs/notebooks/Functional_analysis_with_decoupler.ipynb
similarity index 96%
rename from docs/notebooks/pseudobulk_functional_analysis.ipynb
rename to docs/notebooks/Functional_analysis_with_decoupler.ipynb
index 6b2c1786..5f3dd3e8 100644
--- a/docs/notebooks/pseudobulk_functional_analysis.ipynb
+++ b/docs/notebooks/Functional_analysis_with_decoupler.ipynb
@@ -5,29 +5,36 @@
    "id": "ba83c36d",
    "metadata": {},
    "source": [
-    "# Transcription factor activity inference"
+    "# Functional Analysis\n",
+    "## Transcription factor activity inference"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "4f7a1504",
+   "id": "effd4534",
    "metadata": {},
    "source": [
-    "## Load packages"
+    "### Motivation: \n",
+    "\n",
+    "scRNA-seq yields many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by infering transcription factor (TF) activities from our trascriptomics data from prior knowledge.\n",
+    "\n",
+    "In this notebook we showcase how to use decoupler for transcription factor (TF) and pathway enrichment from a human data-set. The data consists of ~5k Blood myeloid cells from healthy and COVID-19 infected patients available in the Single Cell Expression Atlas [here](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-9221/results/tsne?plotType=umap&plotOption=20).\n",
+    "\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "956d287f",
+   "id": "adc8ab07",
    "metadata": {},
    "source": [
+    "### Load packages\n",
     "We need to load the relevant packages, scanpy to handle scRNA-seq data and decoupler to use statistical methods."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 65,
-   "id": "566b1a4d",
+   "execution_count": 10,
+   "id": "556a518d",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -45,16 +52,9 @@
   },
   {
    "cell_type": "markdown",
-   "id": "effd4534",
+   "id": "31e0c59e",
    "metadata": {},
    "source": [
-    "### Motivation: \n",
-    "\n",
-    "scRNA-seq yields many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by infering transcription factor (TF) activities from our trascriptomics data from prior knowledge.\n",
-    "\n",
-    "In this notebook we showcase how to use decoupler for transcription factor (TF) and pathway enrichment from a human data-set. The data consists of ~5k Blood myeloid cells from healthy and COVID-19 infected patients available in the Single Cell Expression Atlas [here](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-9221/results/tsne?plotType=umap&plotOption=20).\n",
-    "\n",
-    "\n",
     "For the TF activity inference analysis we will need a) a gene regulatory network (GRN) and b) a statistical method.\n",
     "\n",
     "a) **Gene Regulatory Network(GRN)**: \n",
@@ -63,14 +63,12 @@
     "\n",
     "For this example we will use the human version (mouse and rat are also available). We can use `decoupler` to retrieve it from `omnipath`. The argument `split_complexes` keeps complexes or splits them into subunits, by default we recommend to keep complexes together.\n",
     "\n",
-    "Note! : In this tutorial we use the network CollecTRI, but we could use any other GRN coming from an inference method such as CellOracle, pySCENIC or SCENIC+.\n",
-    "\n",
-    "\n"
+    "Note! : In this tutorial we use the network CollecTRI, but we could use any other GRN coming from an inference method such as CellOracle, pySCENIC or SCENIC+.\n"
    ]
   },
   {
    "cell_type": "markdown",
-   "id": "40314c83",
+   "id": "b131a2f2",
    "metadata": {},
    "source": [
     "<div class=\"alert alert-info\">\n",
@@ -82,7 +80,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 11,
    "id": "bc51ad0e",
    "metadata": {},
    "outputs": [],
@@ -93,7 +91,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 12,
    "id": "b1e3afd0",
    "metadata": {},
    "outputs": [
@@ -173,7 +171,7 @@
        "4  STAT5A    IL2       1  10022878;11435608;17182565;17911616;22854263;2..."
       ]
      },
-     "execution_count": 16,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -204,12 +202,12 @@
    "id": "2902d3b8",
    "metadata": {},
    "source": [
-    "## Load data"
+    "### Load data "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 15,
    "id": "82a1b904",
    "metadata": {},
    "outputs": [
@@ -388,14 +386,16 @@
        "[10415 rows x 6 columns]"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
     "#First import results from previous DEA tutorial with DEseq2\n",
-    "results_df = pd.read_csv(\"T_cell_DEA_pyDeseq2_results_df.csv\",index_col=0)\n",
+    "\n",
+    "\n",
+    "results_df = pd.read_csv(\"data/T_cell_DEA_pyDeseq2_results_df.csv\",index_col=0)\n",
     "results_df"
    ]
   },
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 16,
    "id": "bdb8292d",
    "metadata": {},
    "outputs": [
@@ -503,7 +503,7 @@
        "[1 rows x 10415 columns]"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -524,7 +524,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 17,
    "id": "7a618758",
    "metadata": {},
    "outputs": [
@@ -615,7 +615,7 @@
        "[1 rows x 548 columns]"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -636,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 18,
    "id": "8541c738",
    "metadata": {},
    "outputs": [
@@ -667,7 +667,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 20,
    "id": "7241b4f7",
    "metadata": {},
    "outputs": [
@@ -696,7 +696,7 @@
    "id": "1e72cbc5-8edb-47b0-83fd-e8fb07801e2d",
    "metadata": {},
    "source": [
-    "# Load packages"
+    "## Pathway activity inference"
    ]
   },
   {
@@ -704,26 +704,7 @@
    "id": "69cfa682-ad3a-4568-b5c8-38cbba68007d",
    "metadata": {},
    "source": [
-    "First, we need to load the relevant packages, scanpy to handle scRNA-seq data and decoupler to use statistical methods. We also need to load `mat` from earlier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "58d7b49f-0f36-4ba4-8ca3-bc02711cdfea",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "results_df = pd.read_csv(\"./data/T_cell_DEA_pyDeseq2_results_df.csv\", index_col=\"gene_symbol\")\n",
-    "mat = results_df[['stat']].T.rename(index={'stat': 'T cell'})"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8157b21d-cd31-43f7-b8b7-a2e029b9c40c",
-   "metadata": {},
-   "source": [
-    "## Pathway"
+    "First, we need to load the relevant packages(please return to #Load Packages if not done already). Also load `results_df` and `mat` from earlier if not done already"
    ]
   },
   {
@@ -731,7 +712,7 @@
    "id": "d34c69d7-025f-4190-a016-c138e8770eb2",
    "metadata": {},
    "source": [
-    "Another analysis we can perform is to infer pathway activities from our transcriptomics data.\n",
+    "Pathway analysis is useful for infering pathway activities from our trascriptomic data  \n",
     "\n",
     "## PROGENy model\n",
     "\n",
@@ -770,96 +751,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 21,
    "id": "e0faf08b-cf32-4f0d-9477-5091d4d2b9b1",
    "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Retrieve PROGENy model weights\n",
+    "progeny = dc.get_progeny(top=500)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "a8e0c1a6",
+   "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "WARNING:root:Failed to download from `https://omnipathdb.org/`.\n",
-      "WARNING:root:Traceback (most recent call last):\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
-      "    res = self._download(req)\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
-      "    resp.raise_for_status()\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
-      "    raise HTTPError(http_error_msg, response=self)\n",
-      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://omnipathdb.org/queries/enzsub?format=json\n",
-      "\n",
-      "WARNING:root:Failed to download from `http://no-tls.omnipathdb.org/`.\n",
-      "WARNING:root:Traceback (most recent call last):\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
-      "    res = self._download(req)\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
-      "    resp.raise_for_status()\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
-      "    raise HTTPError(http_error_msg, response=self)\n",
-      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: http://no-tls.omnipathdb.org/queries/enzsub?format=json\n",
-      "\n",
-      "WARNING:root:Failed to download from `https://omnipathdb.org/`.\n",
-      "WARNING:root:Traceback (most recent call last):\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
-      "    res = self._download(req)\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
-      "    resp.raise_for_status()\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
-      "    raise HTTPError(http_error_msg, response=self)\n",
-      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://omnipathdb.org/queries/interactions?format=json\n",
-      "\n",
-      "WARNING:root:Failed to download from `http://no-tls.omnipathdb.org/`.\n",
-      "WARNING:root:Traceback (most recent call last):\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
-      "    res = self._download(req)\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
-      "    resp.raise_for_status()\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
-      "    raise HTTPError(http_error_msg, response=self)\n",
-      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: http://no-tls.omnipathdb.org/queries/interactions?format=json\n",
-      "\n",
-      "WARNING:root:Failed to download from `https://omnipathdb.org/`.\n",
-      "WARNING:root:Traceback (most recent call last):\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
-      "    res = self._download(req)\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
-      "    resp.raise_for_status()\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
-      "    raise HTTPError(http_error_msg, response=self)\n",
-      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://omnipathdb.org/queries/annotations?format=json\n",
-      "\n",
-      "WARNING:root:Failed to download from `http://no-tls.omnipathdb.org/`.\n",
-      "WARNING:root:Traceback (most recent call last):\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
-      "    res = self._download(req)\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
-      "    resp.raise_for_status()\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
-      "    raise HTTPError(http_error_msg, response=self)\n",
-      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: http://no-tls.omnipathdb.org/queries/annotations?format=json\n",
-      "\n",
-      "WARNING:root:Failed to download from `https://omnipathdb.org/`.\n",
-      "WARNING:root:Traceback (most recent call last):\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
-      "    res = self._download(req)\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
-      "    resp.raise_for_status()\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
-      "    raise HTTPError(http_error_msg, response=self)\n",
-      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: https://omnipathdb.org/queries/intercell?format=json\n",
-      "\n",
-      "WARNING:root:Failed to download from `http://no-tls.omnipathdb.org/`.\n",
-      "WARNING:root:Traceback (most recent call last):\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 143, in maybe_download\n",
-      "    res = self._download(req)\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/omnipath/_core/downloader/_downloader.py\", line 181, in _download\n",
-      "    resp.raise_for_status()\n",
-      "  File \"/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/requests/models.py\", line 1021, in raise_for_status\n",
-      "    raise HTTPError(http_error_msg, response=self)\n",
-      "requests.exceptions.HTTPError: 500 Server Error: Internal Server Error for url: http://no-tls.omnipathdb.org/queries/intercell?format=json\n",
-      "\n"
-     ]
-    },
     {
      "data": {
       "text/html": [
@@ -987,14 +893,12 @@
        "[7000 rows x 4 columns]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "# Retrieve PROGENy model weights\n",
-    "progeny = dc.get_progeny(top=500)\n",
     "progeny"
    ]
   },
@@ -1032,7 +936,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 23,
    "id": "c5a7d276-31d4-47c4-be14-e8c0e76071c9",
    "metadata": {},
    "outputs": [
@@ -1076,20 +980,20 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>T cell</th>\n",
-       "      <td>-0.561211</td>\n",
-       "      <td>1.116455</td>\n",
-       "      <td>1.701393</td>\n",
-       "      <td>-5.912141</td>\n",
-       "      <td>8.3318</td>\n",
-       "      <td>-0.631677</td>\n",
-       "      <td>-5.484974</td>\n",
-       "      <td>-0.074071</td>\n",
-       "      <td>-1.95181</td>\n",
+       "      <td>-0.561214</td>\n",
+       "      <td>1.116457</td>\n",
+       "      <td>1.701397</td>\n",
+       "      <td>-5.912148</td>\n",
+       "      <td>8.331803</td>\n",
+       "      <td>-0.631678</td>\n",
+       "      <td>-5.484973</td>\n",
+       "      <td>-0.074073</td>\n",
+       "      <td>-1.951811</td>\n",
        "      <td>1.2157</td>\n",
        "      <td>0.075457</td>\n",
        "      <td>0.338886</td>\n",
-       "      <td>0.880702</td>\n",
-       "      <td>-1.817807</td>\n",
+       "      <td>0.880703</td>\n",
+       "      <td>-1.817809</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1097,13 +1001,13 @@
       ],
       "text/plain": [
        "        Androgen      EGFR  Estrogen   Hypoxia  JAK-STAT      MAPK      NFkB  \\\n",
-       "T cell -0.561211  1.116455  1.701393 -5.912141    8.3318 -0.631677 -5.484974   \n",
+       "T cell -0.561214  1.116457  1.701397 -5.912148  8.331803 -0.631678 -5.484973   \n",
        "\n",
-       "            PI3K     TGFb    TNFa     Trail      VEGF       WNT       p53  \n",
-       "T cell -0.074071 -1.95181  1.2157  0.075457  0.338886  0.880702 -1.817807  "
+       "            PI3K      TGFb    TNFa     Trail      VEGF       WNT       p53  \n",
+       "T cell -0.074073 -1.951811  1.2157  0.075457  0.338886  0.880703 -1.817809  "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1124,22 +1028,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 24,
    "id": "80c19b79-43a4-44cb-8c35-83bbd272bece",
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n",
-      "/home/b9003384/mambaforge/envs/decoupler/lib/python3.9/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
-      "  if pd.api.types.is_categorical_dtype(vector):\n"
-     ]
-    },
     {
      "data": {
       "image/png": 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",
@@ -1165,7 +1057,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 25,
    "id": "189128ec-4e02-483e-ad38-8c5b32e2c1d4",
    "metadata": {},
    "outputs": [
@@ -1193,6 +1085,52 @@
    "source": [
     "The observed activation of JAK-STAT is due to the fact that majority of its target genes with positive weights have positive t-values (1st quadrant), and the majority of the ones with negative weights have negative t-values (3d quadrant)."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "db599576",
+   "metadata": {},
+   "source": [
+    "### Important Links\n",
+    "\n",
+    "1) [Decoupler package docs](https://decoupler-py.readthedocs.io/en/latest/notebooks/pseudobulk.html#Transcription-factor-activity-inference)\n",
+    "\n",
+    "\n",
+    "2) [sc-best-practices Gene set enrichment and pathway analysis tutorial](https://www.sc-best-practices.org/conditions/gsea_pathway.html#)\n",
+    "\n",
+    "\n",
+    "### References\n",
+    "\n",
+    "1. [decoupleR](https://doi.org/10.1093/bioadv/vbac016)   Pau Badia-i-Mompel, Jesús Vélez Santiago, Jana Braunger, Celina Geiss, Daniel Dimitrov, Sophia Müller-Dott, Petr Taus, Aurelien Dugourd, Christian H Holland, Ricardo O Ramirez Flores, Julio Saez-Rodriguez, decoupleR: ensemble of computational methods to infer biological activities from omics data, Bioinformatics Advances, Volume 2, Issue 1, 2022, vbac016, https://doi.org/10.1093/bioadv/vbac016\n",
+    "\n",
+    "2. [Colletri](https://doi.org/10.1093/nar/gkad841)\n",
+    "Müller-Dott, S., Tsirvouli, E., Vazquez, M., Ramirez Flores, R. O., Badia-I-Mompel, P., Fallegger, R., Türei, D., Lægreid, A., & Saez-Rodriguez, J. (2023). Expanding the coverage of regulons from high-confidence prior knowledge for accurate estimation of transcription factor activities. Nucleic Acids Research. https://doi.org/10.1093/nar/gkad841\n",
+    "\n",
+    "\n",
+    "\n",
+    "3. [DoRothEA](10.1101/gr.240663.118.) Garcia-Alonso L, Holland CH, Ibrahim MM, Turei D, Saez-Rodriguez J. Benchmark and integration of resources for the estimation of human transcription factor activities. Genome Research. 2019. DOI: 10.1101/gr.240663.118.\n",
+    "\n",
+    "\n",
+    "4. [CellOracle](https://www.nature.com/articles/s41586-022-05688-9) Kamimoto, K., Stringa, B., Hoffmann, C.M. et al. Dissecting cell identity via network inference and in silico gene perturbation. Nature 614, 742–751 (2023). https://doi.org/10.1038/s41586-022-05688-9\n",
+    "\n",
+    "5. [pySCENIC](doi:10.1038/nmeth.4463)Aibar, S. et al. SCENIC: single-cell regulatory network inference and clustering. Nat Meth 14, 1083–1086 (2017). doi:10.1038/nmeth.4463)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "451832e7",
+   "metadata": {},
+   "source": [
+    "\n",
+    "### Acknowledgements\n",
+    "\n",
+    "Content of this tutorial was pulled from decoupler package docs. \n",
+    "\n",
+    "Thank you to: \n",
+    "\n",
+    "\n",
+    "Pau Badia-i-Mompel, Jesús Vélez Santiago, Jana Braunger, Celina Geiss, Daniel Dimitrov, Sophia Müller-Dott, Petr Taus, Aurelien Dugourd, Christian H Holland, Ricardo O Ramirez Flores, Julio Saez-Rodriguez"
+   ]
   }
  ],
  "metadata": {
@@ -1212,7 +1150,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.18"
+   "version": "3.11.6"
   },
   "toc": {
    "base_numbering": 1,
diff --git a/docs/notebooks/data/T_cell_DEA_pyDeseq2_results_df.csv b/docs/notebooks/data/T_cell_DEA_pyDeseq2_results_df.csv
new file mode 100644
index 00000000..99716b05
--- /dev/null
+++ b/docs/notebooks/data/T_cell_DEA_pyDeseq2_results_df.csv
@@ -0,0 +1,10416 @@
+gene_symbol,baseMean,log2FoldChange,lfcSE,stat,pvalue,padj
+A1BG,70.4717,-0.08287856840036469,0.18338310891348233,-0.876097230277243,0.38097717835997635,0.7198000426535458
+A2M,36.735027,-1.0713701088425682,0.3386106099509754,-3.848842431575221,0.00011867731153325404,0.006268812709721023
+A2MP1,15.628274,0.03248586355123763,0.2896592750347295,0.8023674721262315,0.4223404232332857,0.7481272790972971
+AAAS,18.305878,0.0409302190212397,0.2448281559929168,0.5939352055906724,0.5525554509468502,0.8276227906315118
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From 92ab5bc69688fa73248f94406357774760043edd Mon Sep 17 00:00:00 2001
From: Anna Vathrakokoili Pournara <annavp@ebi.ac.uk>
Date: Wed, 29 Nov 2023 10:45:54 +0000
Subject: [PATCH 6/9] removed_old_notebook

---
 .../TrascriptionFactorActivity.ipynb          | 739 ------------------
 1 file changed, 739 deletions(-)
 delete mode 100644 docs/notebooks/TrascriptionFactorActivity.ipynb

diff --git a/docs/notebooks/TrascriptionFactorActivity.ipynb b/docs/notebooks/TrascriptionFactorActivity.ipynb
deleted file mode 100644
index b9efcec0..00000000
--- a/docs/notebooks/TrascriptionFactorActivity.ipynb
+++ /dev/null
@@ -1,739 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "ba83c36d",
-   "metadata": {},
-   "source": [
-    "# Transcription factor activity inference"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4f7a1504",
-   "metadata": {},
-   "source": [
-    "## Load packages"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "956d287f",
-   "metadata": {},
-   "source": [
-    "We need to load the relevant packages, scanpy to handle scRNA-seq data and decoupler to use statistical methods."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 65,
-   "id": "566b1a4d",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import scanpy as sc\n",
-    "import decoupler as dc\n",
-    "\n",
-    "\n",
-    "# Only needed for processing\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "\n",
-    "# Needed for some plotting\n",
-    "import matplotlib.pyplot as plt"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "effd4534",
-   "metadata": {},
-   "source": [
-    "### Motivation: \n",
-    "\n",
-    "scRNA-seq yields many molecular readouts that are hard to interpret by themselves. One way of summarizing this information is by infering transcription factor (TF) activities from our trascriptomics data from prior knowledge.\n",
-    "\n",
-    "In this notebook we showcase how to use decoupler for transcription factor (TF) and pathway enrichment from a human data-set. The data consists of ~5k Blood myeloid cells from healthy and COVID-19 infected patients available in the Single Cell Expression Atlas [here](https://www.ebi.ac.uk/gxa/sc/experiments/E-MTAB-9221/results/tsne?plotType=umap&plotOption=20).\n",
-    "\n",
-    "\n",
-    "For the TF activity inference analysis we will need a) a gene regulatory network (GRN) and b) a statistical method.\n",
-    "\n",
-    "a) **Gene Regulatory Network(GRN)**: \n",
-    "\n",
-    "[CollecTRI](https://github.com/saezlab/CollecTRI) is a comprehensive resource containing a curated collection of TFs and their transcriptional targets compiled from 12 different resources. This collection provides an increased coverage of transcription factors and a superior performance in identifying perturbed TFs compared to our previous [DoRothEA](https://saezlab.github.io/dorothea/) network and other literature based GRNs. Similar to DoRothEA, interactions are weighted by their mode of regulation (activation or inhibition).\n",
-    "\n",
-    "For this example we will use the human version (mouse and rat are also available). We can use `decoupler` to retrieve it from `omnipath`. The argument `split_complexes` keeps complexes or splits them into subunits, by default we recommend to keep complexes together.\n",
-    "\n",
-    "Note! : In this tutorial we use the network CollecTRI, but we could use any other GRN coming from an inference method such as CellOracle, pySCENIC or SCENIC+.\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40314c83",
-   "metadata": {},
-   "source": [
-    "<div class=\"alert alert-info\">\n",
-    "\n",
-    "**Note**\n",
-    "\n",
-    "In this tutorial we use the network CollecTRI, but we could use any other GRN coming from an inference method such as [CellOracle](https://morris-lab.github.io/CellOracle.documentation/), [pySCENIC](https://pyscenic.readthedocs.io/en/latest/) or [SCENIC+](https://scenicplus.readthedocs.io/en/latest/). </div>"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "id": "bc51ad0e",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Retrieve CollecTRI gene regulatory network\n",
-    "collectri = dc.get_collectri(organism='human', split_complexes=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "id": "b1e3afd0",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
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-       "   source target  weight                                               PMID\n",
-       "0     MYC   TERT       1  10022128;10491298;10606235;10637317;10723141;1...\n",
-       "1    SPI1  BGLAP       1                                           10022617\n",
-       "2   SMAD3    JUN       1                                  10022869;12374795\n",
-       "3   SMAD4    JUN       1                                  10022869;12374795\n",
-       "4  STAT5A    IL2       1  10022878;11435608;17182565;17911616;22854263;2..."
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "collectri.head()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "12725752",
-   "metadata": {},
-   "source": [
-    "**b) Activity inference with Univariate Linear Model (ULM)**\n",
-    "\n",
-    "\n",
-    "\n",
-    "\n",
-    "To infer TF enrichment scores we will run the Univariate Linear Model (`ulm`) method. For each sample in our dataset (`mat`) and each TF in our network (`net`), it fits a linear model that predicts the observed gene expression based solely on the TF’s TF-Gene interaction weights. Once fitted, the obtained t-value of the slope is the score. If it is positive, we interpret that the TF is active and if it is negative we interpret that it is inactive.\n",
-    "\n",
-    "<img src=\"scverse-tutorials/docs/notebooks/img/ulm.png\"/>\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "2902d3b8",
-   "metadata": {},
-   "source": [
-    "## Load data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 63,
-   "id": "82a1b904",
-   "metadata": {},
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-    {
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-       "      <th>baseMean</th>\n",
-       "      <th>log2FoldChange</th>\n",
-       "      <th>lfcSE</th>\n",
-       "      <th>stat</th>\n",
-       "      <th>pvalue</th>\n",
-       "      <th>padj</th>\n",
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-       "      <th>gene_symbol</th>\n",
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-       "      <th>A1BG</th>\n",
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-       "      <td>-0.876097</td>\n",
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-       "      <td>0.593935</td>\n",
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-       "               baseMean  log2FoldChange     lfcSE      stat    pvalue  \\\n",
-       "gene_symbol                                                             \n",
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-       "\n",
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-       "[10415 rows x 6 columns]"
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-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#First import results from previous DEA tutorial with DEseq2\n",
-    "results_df = pd.read_csv(\"T_cell_DEA_pyDeseq2_results_df.csv\",index_col=0)\n",
-    "results_df"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "f18c7033",
-   "metadata": {},
-   "source": [
-    "From the DEA results above, we can use the obtained gene level statistics to perform enrichment analysis. Any statistic can be used, but we recommend using the t-values instead of logFCs since t-values incorporate the significance of change in their value. We will transform the obtained t-values stored in `stats` to a wide matrix so that it can be used by decoupler:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "id": "bdb8292d",
-   "metadata": {
-    "scrolled": false
-   },
-   "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>gene_symbol</th>\n",
-       "      <th>A1BG</th>\n",
-       "      <th>A2M</th>\n",
-       "      <th>A2MP1</th>\n",
-       "      <th>AAAS</th>\n",
-       "      <th>AACS</th>\n",
-       "      <th>AAGAB</th>\n",
-       "      <th>AAK1</th>\n",
-       "      <th>AAMDC</th>\n",
-       "      <th>AAMP</th>\n",
-       "      <th>AAR2</th>\n",
-       "      <th>...</th>\n",
-       "      <th>ZUP1</th>\n",
-       "      <th>ZWILCH</th>\n",
-       "      <th>ZWINT</th>\n",
-       "      <th>ZXDA</th>\n",
-       "      <th>ZXDB</th>\n",
-       "      <th>ZXDC</th>\n",
-       "      <th>ZYG11B</th>\n",
-       "      <th>ZYX</th>\n",
-       "      <th>ZZEF1</th>\n",
-       "      <th>ZZZ3</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>T cell</th>\n",
-       "      <td>-0.876097</td>\n",
-       "      <td>-3.848842</td>\n",
-       "      <td>0.802367</td>\n",
-       "      <td>0.593935</td>\n",
-       "      <td>0.754344</td>\n",
-       "      <td>-0.227301</td>\n",
-       "      <td>-0.61717</td>\n",
-       "      <td>0.294581</td>\n",
-       "      <td>1.39611</td>\n",
-       "      <td>0.505238</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.56492</td>\n",
-       "      <td>1.396946</td>\n",
-       "      <td>3.544967</td>\n",
-       "      <td>-1.017032</td>\n",
-       "      <td>-2.251387</td>\n",
-       "      <td>-0.963323</td>\n",
-       "      <td>0.827915</td>\n",
-       "      <td>1.448655</td>\n",
-       "      <td>0.122474</td>\n",
-       "      <td>-0.248011</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 10415 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "gene_symbol      A1BG       A2M     A2MP1      AAAS      AACS     AAGAB  \\\n",
-       "T cell      -0.876097 -3.848842  0.802367  0.593935  0.754344 -0.227301   \n",
-       "\n",
-       "gene_symbol     AAK1     AAMDC     AAMP      AAR2  ...     ZUP1    ZWILCH  \\\n",
-       "T cell      -0.61717  0.294581  1.39611  0.505238  ...  1.56492  1.396946   \n",
-       "\n",
-       "gene_symbol     ZWINT      ZXDA      ZXDB      ZXDC    ZYG11B       ZYX  \\\n",
-       "T cell       3.544967 -1.017032 -2.251387 -0.963323  0.827915  1.448655   \n",
-       "\n",
-       "gene_symbol     ZZEF1      ZZZ3  \n",
-       "T cell       0.122474 -0.248011  \n",
-       "\n",
-       "[1 rows x 10415 columns]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#from the DEA results above, we can use the obtained gene level statistics to perform enrichment analysis. Any statistic can be used, but we recommend using the t-values instead of logFCs since t-values incorporate the significance of change in their value. We will transform the obtained t-values stored in stats to a wide matrix so that it can be used by decoupler:\n",
-    "mat = results_df[['stat']].T.rename(index={'stat': 'T cell'})\n",
-    "mat"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5db2818f",
-   "metadata": {},
-   "source": [
-    "We can run `ulm` with a one-liner:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "7a618758",
-   "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>ABL1</th>\n",
-       "      <th>AHR</th>\n",
-       "      <th>AIRE</th>\n",
-       "      <th>AP1</th>\n",
-       "      <th>APEX1</th>\n",
-       "      <th>AR</th>\n",
-       "      <th>ARID3B</th>\n",
-       "      <th>ARID4A</th>\n",
-       "      <th>ARID5B</th>\n",
-       "      <th>ARNT</th>\n",
-       "      <th>...</th>\n",
-       "      <th>ZNF350</th>\n",
-       "      <th>ZNF354C</th>\n",
-       "      <th>ZNF362</th>\n",
-       "      <th>ZNF382</th>\n",
-       "      <th>ZNF384</th>\n",
-       "      <th>ZNF395</th>\n",
-       "      <th>ZNF436</th>\n",
-       "      <th>ZNF699</th>\n",
-       "      <th>ZNF76</th>\n",
-       "      <th>ZNF91</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>T cell</th>\n",
-       "      <td>0.670022</td>\n",
-       "      <td>0.77803</td>\n",
-       "      <td>-1.94135</td>\n",
-       "      <td>-1.628783</td>\n",
-       "      <td>0.366503</td>\n",
-       "      <td>-0.427226</td>\n",
-       "      <td>-0.325884</td>\n",
-       "      <td>0.013003</td>\n",
-       "      <td>0.410143</td>\n",
-       "      <td>-0.868288</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.321947</td>\n",
-       "      <td>-1.824237</td>\n",
-       "      <td>1.113136</td>\n",
-       "      <td>0.218782</td>\n",
-       "      <td>-1.44051</td>\n",
-       "      <td>0.845993</td>\n",
-       "      <td>0.294943</td>\n",
-       "      <td>-0.835089</td>\n",
-       "      <td>1.839208</td>\n",
-       "      <td>-2.407625</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>1 rows × 548 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "            ABL1      AHR     AIRE       AP1     APEX1        AR    ARID3B  \\\n",
-       "T cell  0.670022  0.77803 -1.94135 -1.628783  0.366503 -0.427226 -0.325884   \n",
-       "\n",
-       "          ARID4A    ARID5B      ARNT  ...    ZNF350   ZNF354C    ZNF362  \\\n",
-       "T cell  0.013003  0.410143 -0.868288  ...  0.321947 -1.824237  1.113136   \n",
-       "\n",
-       "          ZNF382   ZNF384    ZNF395    ZNF436    ZNF699     ZNF76     ZNF91  \n",
-       "T cell  0.218782 -1.44051  0.845993  0.294943 -0.835089  1.839208 -2.407625  \n",
-       "\n",
-       "[1 rows x 548 columns]"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Infer pathway activities with ulm\n",
-    "tf_acts, tf_pvals = dc.run_ulm(mat=mat, net=collectri)\n",
-    "tf_acts"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "ede3280b",
-   "metadata": {},
-   "source": [
-    "Let us plot the obtained scores for the top active/inactive transcription factors:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 61,
-   "id": "8541c738",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x1000 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "dc.plot_barplot(tf_acts, 'T cell', top=40, vertical=True,figsize=[10,10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "3e4c2e5b",
-   "metadata": {},
-   "source": [
-    "In accordance to the previous pathway results, T cells seem to activate for TFs responsible for cell growth (E2F4, TFDP1, E2F1).\n",
-    "\n",
-    "Like with pathways, we can explore how the target genes look like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "7241b4f7",
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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-      "text/plain": [
-       "<Figure size 700x500 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Extract logFCs and pvals\n",
-    "logFCs = results_df[['log2FoldChange']].T.rename(index={'log2FoldChange': 'T cell'})\n",
-    "pvals = results_df[['padj']].T.rename(index={'padj': 'T cell'})\n",
-    "\n",
-    "# Plot\n",
-    "dc.plot_volcano(logFCs, pvals, 'T cell', name='E2F4', net=collectri, top=10, sign_thr=0.05, lFCs_thr=0.5)"
-   ]
-  }
- ],
- "metadata": {
-  "celltoolbar": "Raw Cell Format",
-  "kernelspec": {
-   "display_name": "Python [conda env:decoupler]",
-   "language": "python",
-   "name": "conda-env-decoupler-py"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.11.6"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {
-    "height": "798.75px",
-    "left": "183px",
-    "top": "208px",
-    "width": "179.492px"
-   },
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}

From 12da4dd767e12c491eb447ed3d42946e5c3e66e1 Mon Sep 17 00:00:00 2001
From: Anna Vathrakokoili Pournara <annavp@ebi.ac.uk>
Date: Wed, 29 Nov 2023 10:59:30 +0000
Subject: [PATCH 7/9] directory_for_tutorials

---
 .../decoupler-functional-analysis/icon.png      | Bin 0 -> 21495 bytes
 .../decoupler-functional-analysis/meta.yaml     |  16 ++++++++++++++++
 2 files changed, 16 insertions(+)
 create mode 100644 tutorial-registry/tutorials/decoupler-functional-analysis/icon.png
 create mode 100644 tutorial-registry/tutorials/decoupler-functional-analysis/meta.yaml

diff --git a/tutorial-registry/tutorials/decoupler-functional-analysis/icon.png b/tutorial-registry/tutorials/decoupler-functional-analysis/icon.png
new file mode 100644
index 0000000000000000000000000000000000000000..259c87c4f2564e25acf1b3c95a89f05fccacd0a0
GIT binary patch
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HcmV?d00001

diff --git a/tutorial-registry/tutorials/decoupler-functional-analysis/meta.yaml b/tutorial-registry/tutorials/decoupler-functional-analysis/meta.yaml
new file mode 100644
index 00000000..552fbc0d
--- /dev/null
+++ b/tutorial-registry/tutorials/decoupler-functional-analysis/meta.yaml
@@ -0,0 +1,16 @@
+name: Functional analysis with decoupler
+description: |
+  This notebook showcases decoupler for pathway and TF enrichment on ~5k
+  Blood myeloid cells from healthy and COVID-19 infected patients.
+link: https://github.com/scverse/scverse-tutorials/tree/main/docs/notebooks/Functional_analysis_with_decoupler.ipynb
+image: icon.png
+primary_category: scRNA-seq
+tags:
+  - functional analysis
+  - transcription factor activity inference
+  - pathway activity inference
+packages:
+  - decoupler
+  - scanpy
+authors:
+  - PauBadiaM

From 4c9cc8643d5252ac3d554757b6306cef3a2858a4 Mon Sep 17 00:00:00 2001
From: "pre-commit-ci[bot]"
 <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Date: Wed, 29 Nov 2023 11:03:15 +0000
Subject: [PATCH 8/9] [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci
---
 .../Functional_analysis_with_decoupler.ipynb  | 22 +++++++++----------
 1 file changed, 11 insertions(+), 11 deletions(-)

diff --git a/docs/notebooks/Functional_analysis_with_decoupler.ipynb b/docs/notebooks/Functional_analysis_with_decoupler.ipynb
index 5f3dd3e8..a086651f 100644
--- a/docs/notebooks/Functional_analysis_with_decoupler.ipynb
+++ b/docs/notebooks/Functional_analysis_with_decoupler.ipynb
@@ -86,7 +86,7 @@
    "outputs": [],
    "source": [
     "# Retrieve CollecTRI gene regulatory network\n",
-    "collectri = dc.get_collectri(organism='human', split_complexes=False)"
+    "collectri = dc.get_collectri(organism=\"human\", split_complexes=False)"
    ]
   },
   {
@@ -392,10 +392,10 @@
     }
    ],
    "source": [
-    "#First import results from previous DEA tutorial with DEseq2\n",
+    "# First import results from previous DEA tutorial with DEseq2\n",
     "\n",
     "\n",
-    "results_df = pd.read_csv(\"data/T_cell_DEA_pyDeseq2_results_df.csv\",index_col=0)\n",
+    "results_df = pd.read_csv(\"data/T_cell_DEA_pyDeseq2_results_df.csv\", index_col=0)\n",
     "results_df"
    ]
   },
@@ -509,8 +509,8 @@
     }
    ],
    "source": [
-    "#from the DEA results above, we can use the obtained gene level statistics to perform enrichment analysis. Any statistic can be used, but we recommend using the t-values instead of logFCs since t-values incorporate the significance of change in their value. We will transform the obtained t-values stored in stats to a wide matrix so that it can be used by decoupler:\n",
-    "mat = results_df[['stat']].T.rename(index={'stat': 'T cell'})\n",
+    "# from the DEA results above, we can use the obtained gene level statistics to perform enrichment analysis. Any statistic can be used, but we recommend using the t-values instead of logFCs since t-values incorporate the significance of change in their value. We will transform the obtained t-values stored in stats to a wide matrix so that it can be used by decoupler:\n",
+    "mat = results_df[[\"stat\"]].T.rename(index={\"stat\": \"T cell\"})\n",
     "mat"
    ]
   },
@@ -652,7 +652,7 @@
     }
    ],
    "source": [
-    "dc.plot_barplot(tf_acts, 'T cell', top=40, vertical=True,figsize=[10,10])"
+    "dc.plot_barplot(tf_acts, \"T cell\", top=40, vertical=True, figsize=[10, 10])"
    ]
   },
   {
@@ -684,11 +684,11 @@
    ],
    "source": [
     "# Extract logFCs and pvals\n",
-    "logFCs = results_df[['log2FoldChange']].T.rename(index={'log2FoldChange': 'T cell'})\n",
-    "pvals = results_df[['padj']].T.rename(index={'padj': 'T cell'})\n",
+    "logFCs = results_df[[\"log2FoldChange\"]].T.rename(index={\"log2FoldChange\": \"T cell\"})\n",
+    "pvals = results_df[[\"padj\"]].T.rename(index={\"padj\": \"T cell\"})\n",
     "\n",
     "# Plot\n",
-    "dc.plot_volcano(logFCs, pvals, 'T cell', name='E2F4', net=collectri, top=10, sign_thr=0.05, lFCs_thr=0.5)"
+    "dc.plot_volcano(logFCs, pvals, \"T cell\", name=\"E2F4\", net=collectri, top=10, sign_thr=0.05, lFCs_thr=0.5)"
    ]
   },
   {
@@ -1044,7 +1044,7 @@
     }
    ],
    "source": [
-    "dc.plot_barplot(pathway_acts, 'T cell', top=25, vertical=False)"
+    "dc.plot_barplot(pathway_acts, \"T cell\", top=25, vertical=False)"
    ]
   },
   {
@@ -1073,7 +1073,7 @@
     }
    ],
    "source": [
-    "dc.plot_targets(results_df, stat='stat', source_name='JAK-STAT', net=progeny, top=15)"
+    "dc.plot_targets(results_df, stat=\"stat\", source_name=\"JAK-STAT\", net=progeny, top=15)"
    ]
   },
   {

From 93981a8a6ccab22d35710ea18206651e9c06373c Mon Sep 17 00:00:00 2001
From: Anna Vathrakokoili Pournara <annavp@ebi.ac.uk>
Date: Wed, 29 Nov 2023 11:10:23 +0000
Subject: [PATCH 9/9] fix meta.yaml

---
 .../tutorials/decoupler-functional-analysis/meta.yaml           | 2 --
 1 file changed, 2 deletions(-)

diff --git a/tutorial-registry/tutorials/decoupler-functional-analysis/meta.yaml b/tutorial-registry/tutorials/decoupler-functional-analysis/meta.yaml
index 552fbc0d..cc7f67e9 100644
--- a/tutorial-registry/tutorials/decoupler-functional-analysis/meta.yaml
+++ b/tutorial-registry/tutorials/decoupler-functional-analysis/meta.yaml
@@ -7,8 +7,6 @@ image: icon.png
 primary_category: scRNA-seq
 tags:
   - functional analysis
-  - transcription factor activity inference
-  - pathway activity inference
 packages:
   - decoupler
   - scanpy