ready to publish

docs/.ipynb_checkpoints/README-checkpoint.ipynb

@@ -0,0 +1,57 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# LISA: Landscape In-Silico deletion Analysis\n",
+    "<hr>\n",
+    "\n",
+    "## About\n",
+    "\n",
+    "LISA predicts which TFs regulate a set of genes using integrative modeling of chromatin accessiblity and ChIP-seq binding. Particularly, LISA models the effects of deleting the influence of a TF on the expression of the genes-of-interest. For more information, see <a href=https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1934-6>https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1934-6</a>. This implementation extends the original, running 30x faster, reducing dependencies, and adding useful CLI functions for pipeline integration. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Requirements\n",
+    "\n",
+    "* python >= 3.6\n",
+    "* numpy, scipy, scikit-learn, and h5py\n",
+    "* ~15GB of storage space"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Installation\n",
+    "\n",
+    "LISA is available for install from PyPI. It is recommended that you set up a virtual environment using ```venv```, then run:"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "lisa_test",
+   "language": "python",
+   "name": "lisa_test"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

setup.py

@@ -23,4 +23,5 @@
         'scikit-learn'
     ],
     include_package_data=True,
+    python_requires='>=3.6',
 )