hands-on_tutorial/session-1/before_start.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preliminary work\n",
    "The session showcases how to infer activities of transcription factors from single-cell RNA-sequencing (scRNA-seq) data and spatial transcriptomics (ST) data using three methods:\n",
    "- decoupleR (scRNA-seq based)\n",
    "- pySCENIC (scRNA-seq based)\n",
    "- STAN (ST-based)\n",
    "\n",
    "Please follow this notebook after you have [set up the environment](https://github.com/osmanbeyoglulab/Tutorials-on-ISMB-2024?tab=readme-ov-file#environment-set-up)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scanpy as sc\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Downloading datasets\n",
    "The following scRNA-seq data consists in 3k peripheral blood mononuclear cells (PBMCs) from a Healthy Donor and is freely available from [10x Genomics](https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.1.0/pbmc3k). This command downloads 5.9 MB of data upon the first call and stores it in `data/pbmc3k_raw.h5ad`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 2700 × 32738\n",
       "    var: 'gene_ids'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.datasets.pbmc3k()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following scRNA-seq data are processed 3k PBMCs of the above using [the basic tutorial](https://scanpy.readthedocs.io/en/stable/tutorials/basics/clustering-2017.html). This command downloads 24.7 MB of data upon the first call and stores it in `data/pbmc3k_processed.h5ad`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 2638 × 1838\n",
       "    obs: 'n_genes', 'percent_mito', 'n_counts', 'louvain'\n",
       "    var: 'n_cells'\n",
       "    uns: 'draw_graph', 'louvain', 'louvain_colors', 'neighbors', 'pca', 'rank_genes_groups'\n",
       "    obsm: 'X_pca', 'X_tsne', 'X_umap', 'X_draw_graph_fr'\n",
       "    varm: 'PCs'\n",
       "    obsp: 'distances', 'connectivities'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.datasets.pbmc3k_processed()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will download a Visium spatial transcriptomics dataset of a human lymph node freely available from [10X Genomics](https://support.10xgenomics.com/spatial-gene-expression/datasets/1.0.0/V1_Human_Lymph_Node). This command downloads 47.4 MB of dataset upon the first call and stores it in `data/V1_Human_Lymph_Node/`, including the high-resolution tissue image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 4035 × 36601\n",
       "    obs: 'in_tissue', 'array_row', 'array_col'\n",
       "    var: 'gene_ids', 'feature_types', 'genome'\n",
       "    uns: 'spatial'\n",
       "    obsm: 'spatial'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sc.datasets.visium_sge(sample_id=\"V1_Human_Lymph_Node\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## decoupleR (scRNA-seq based)\n",
    "[decoupler](https://doi.org/10.1093/bioadv/vbac016) [1] is a package containing different statistical methods to extract biological activities from omics data within a unified framework, including pathway activity inference and transcription factor activity inference. We follow [the instruction](https://decoupler-py.readthedocs.io/en/latest/installation.html) to install decoupler.\n",
    "\n",
    "[1] Badia-i-Mompel, Pau, et al. \"decoupleR: ensemble of computational methods to infer biological activities from omics data.\" _Bioinformatics Advances_ 2.1 (2022): vbac016."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install decoupler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import decoupler\n",
    "decoupler.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pySCENIC (scRNA-seq based)\n",
    "[pySCENIC](https://www.nature.com/articles/s41596-020-0336-2) [2] is a package containing different statistical methods to extract biological activities from single-cell RNA-seq data within a unified framework, including gene regulatory network inference and transcription factor activity inference. We follow [the instruction](https://pyscenic.readthedocs.io/en/latest/installation.html) to install pySCENIC. pySCENIC depends on packages e.g. arboreto and ctxcore. \n",
    "\n",
    "**IMPORTANT:** To install and use pySCENIC, we recommend creating a new conda environment as instructed.\n",
    "\n",
    "[2] Van de Sande, Bram, et al. \"A scalable SCENIC workflow for single-cell gene regulatory network analysis.\" _Nature Protocols_ 15.7 (2020): 2247-2276.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pip install pyscenic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyscenic\n",
    "pyscenic.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading resources and databases\n",
    "\n",
    "Download:\n",
    "- the list of transcription factors of human;\n",
    "- the motif annotations of human;\n",
    "- the gene-based [cisTarget databases](https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc_v10_clust/gene_based/) generated using the 2022 motif collection.\n",
    "\n",
    "**IMPORTANT:** The cisTarget database files are quite big (unit: GB). Alternatively, to avoid corrupt or incomplete downloads, files can be downloaded with [zsync_curl](https://resources.aertslab.org/cistarget/). It allows resuming already partially downloaded databases and only will download missing or redownload corrupted chunks.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir resources_pyscenic\n",
    "!curl https://resources.aertslab.org/cistarget/tf_lists/allTFs_hg38.txt \\\n",
    "    -o resources_pyscenic/allTFs_hg38.txt\n",
    "!curl https://resources.aertslab.org/cistarget/motif2tf/motifs-v10nr_clust-nr.hgnc-m0.001-o0.0.tbl \\\n",
    "    -o resources_pyscenic/motifs-v10nr_clust-nr.hgnc-m0.001-o0.0.tbl\n",
    "!curl https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc_v10_clust/gene_based/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather \\\n",
    "    -o resources_pyscenic/hg38_10kbp_up_10kbp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather\n",
    "!curl https://resources.aertslab.org/cistarget/databases/homo_sapiens/hg38/refseq_r80/mc_v10_clust/gene_based/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather \\\n",
    "    -o resources_pyscenic/hg38_500bp_up_100bp_down_full_tx_v10_clust.genes_vs_motifs.rankings.feather"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## STAN (ST-based)\n",
    "[STAN](https://www.biorxiv.org/content/10.1101/2024.06.26.600782v1) [3] is a computational framework for inferring **spatially informed** transcription factor activity across cellular contexts. Specifically, STAN is a linear mixed-effects computational method that predicts spot-specific, spatially informed TF activities by integrating curated TF-target gene priors, mRNA expression, spatial coordinates, and morphological features from corresponding imaging data. \n",
    "\n",
    "[3] Zhang, Linan, et al. \"STAN, a computational framework for inferring spatially informed transcription factor activity across cellular contexts.\" _bioRxiv_ (2024): 2024-06."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Downloading supporting files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!mkdir resources_stan\n",
    "!curl https://raw.githubusercontent.com/vitkl/cell2location_paper/1c645a0519f8f27ecef18468cf339d35d99f42e7/notebooks/selected_results/lymph_nodes_analysis/CoLocationModelNB4V2_34clusters_4039locations_10241genes_input_inferred_V4_batch1024_l2_0001_n_comb50_5_cps5_fpc3_alpha001/W_cell_density.csv \\\n",
    "    -o resources_stan/W_cell_density.csv\n",
    "!curl https://raw.githubusercontent.com/vitkl/cell2location_paper/1c645a0519f8f27ecef18468cf339d35d99f42e7/notebooks/selected_results/lymph_nodes_analysis/CoLocationModelNB4V2_34clusters_4039locations_10241genes_input_inferred_V4_batch1024_l2_0001_n_comb50_5_cps5_fpc3_alpha001/manual_GC_annot.csv \\\n",
    "    -o resources_stan/manual_GC_annot.csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "<pre>\n",
       "-----\n",
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       "-----\n",
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       "<details>\n",
       "<summary>Click to view modules imported as dependencies</summary>\n",
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       "</details> <!-- seems like this ends pre, so might as well be explicit -->\n",
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       "-----\n",
       "Session information updated at 2024-07-09 12:38\n",
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     "execution_count": 6,
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   ],
   "source": [
    "import session_info\n",
    "session_info.show()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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