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+{
+    "cells": [
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "# TIGER\n",
+                "\n",
+                "## Introduction\n",
+                "The goal of TIGER is to estimate gene regulatory network and transcription factor \n",
+                "activities using Bayesian matrix factorization.        \n",
+                "![](TIGER.png)<!-- -->              \n",
+                "Please read and cite the following article when you use TIGER:     \n",
+                "[Joint inference of transcription factor activity and context-specific regulatory networks, Chen&Padi 2022](https://www.biorxiv.org/content/10.1101/2022.12.12.520141v1)\n",
+                "\n",
+                "## Installation\n",
+                "\n",
+                "TIGER relies on [cmdstanr](https://mc-stan.org/cmdstanr/) for Beyesian Inference. \n",
+                "You can install the latest beta release of the cmdstanr R package with\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "install.packages(\"cmdstanr\", repos = c(\"https://mc-stan.org/r-packages/\", getOption(\"repos\")))\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": "Then, you can use cmdstanr to install [CmdStan](https://mc-stan.org/users/interfaces/cmdstan.html), the shell interface to [Stan](https://mc-stan.org/) with\n"
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "install_cmdstan()\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "These two steps are usually enough if your C++ toolchain is set up properly. For example, use RTools 4.0 toolchain which contains a g++ 8 compiler and mingw32-make on Windows platform. If you see problems with installation, you can go to cmdstanr [installation](https://mc-stan.org/cmdstanr/articles/cmdstanr.html) for more information.     \n",
+                "\n",
+                "After cmdstan is correctly installed, you can install the development version of TIGER with:\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "devtools::install_github(\"cchen22/TIGER\")\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Quick start\n",
+                "\n",
+                "This is a simple example of TIGER on a small dataset. TIGER requires two inputs -      \n",
+                "1. a normalized expression matrix with rows as genes and column as samples;       \n",
+                "2. a prior network with rows as TFs and column as genes. The network is signed and binarized (e.g., -1,0,1).       \n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "library(TIGER)\n",
+                "\n",
+                "##1. load data\n",
+                "expr = TIGER::expr\n",
+                "prior = TIGER::prior\n",
+                "\n",
+                "##2. run TIGER with default parameters\n",
+                "ss = TIGER(expr,prior)\n",
+                "\n",
+                "##3. print the TFA score in first three samples\n",
+                "tgres = ss$Z\n",
+                "tgres[,1:3]\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "## Wokring with DoRothEA prior\n",
+                "TIGER provides some convenient functions to work with DoRothEA prior database. Firstly, install DoRothEA R package from [Bioconductor](https://bioconductor.org/packages/release/data/experiment/html/dorothea.html)\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "BiocManager::install(\"dorothea\")\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": [
+                "DoRothEA provides regulons for two species - human and mouse. For example,if we have a human cancer expression matrix and want to estimate the TFA in each cancer sample, then we can use the following code to prepare the prior network.\n",
+                "\n"
+            ]
+        },
+        {
+            "cell_type": "code",
+            "execution_count": null,
+            "metadata": {},
+            "outputs": [],
+            "source": [
+                "## load dorothea pancancer database\n",
+                "df = dorothea::dorothea_hs_pancancer\n",
+                "\n",
+                "## convert it to TIGER prior format (e.g., adjacency matrix) \n",
+                "prior = el2adj(df[,-2])\n"
+            ]
+        },
+        {
+            "cell_type": "markdown",
+            "metadata": {},
+            "source": "\n"
+        }
+    ],
+    "metadata": {
+        "anaconda-cloud": "",
+        "kernelspec": {
+            "display_name": "R",
+            "langauge": "R",
+            "name": "ir"
+        },
+        "language_info": {
+            "codemirror_mode": "r",
+            "file_extension": ".r",
+            "mimetype": "text/x-r-source",
+            "name": "R",
+            "pygments_lexer": "r",
+            "version": "3.4.1"
+        }
+    },
+    "nbformat": 4,
+    "nbformat_minor": 1
+}