{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Compiled on: July, 23 2017.*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# My example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "My example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Running Scanpy version 0.1+139.g2c509d7.dirty on 2017-07-23 11:49.\n"
     ]
    }
   ],
   "source": [
    "%config InlineBackend.figure_formats = ['png2x']  # for vector graphics quality, set to svg\n",
    "import numpy as np\n",
    "import scanpy.api as sc\n",
    "\n",
    "sc.settings.verbosity = 3                # show some output\n",
    "sc.settings.set_dpi(80)                  # low pixel number yields small inline figures\n",
    "sc.logging.print_version_and_date()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get help."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# help(sc.read)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read and annotate data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# read data from any path on your system                                                                                                                                         \n",
    "path_to_data = './data/myexample/'\n",
    "adata = sc.read(path_to_data + 'myexample.csv')\n",
    "\n",
    "# other data reading examples\n",
    "# adata = sc.read(path_to_data + 'myexample.csv')\n",
    "# adata = sc.read(path_to_data + 'myexample.h5', sheet='countmatrix')\n",
    "# adata = sc.read(path_to_data + 'myexample.xlsx', sheet='countmatrix')\n",
    "# adata = sc.read(path_to_data + 'myexample.txt')\n",
    "\n",
    "# in the data matrix adata.X, rows should correspond to samples and columns to genes                                                                                                             \n",
    "# to match this convention, transpose your data if necessary                                                                                                                     \n",
    "# adata = adata.transpose()\n",
    "\n",
    "# set group names (as strings)\n",
    "adata.smp['my_groups'] = np.genfromtxt(path_to_data + 'mygroups.csv', dtype=str)\n",
    "# set root cell\n",
    "adata.add['iroot'] = 336"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For single-cell RNA-seq, consider running a preprocessing recipe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sc.pp.recipe_zheng17(adata)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run and plot a tool."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sc.tl.dpt(adata)\n",
    "sc.pl.dpt(adata)"
   ]
  }
 ],
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