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    "# Contribute a BioJupies Plugin | Python 3\n",
    "## Overview\n",
    "This notebook contains instructions for the submission of BioJupies RNA-seq data analysis plugins in Python 3.\n",
    "\n",
    "To submit your plugin, complete the following three steps:\n",
    "1. Add code to the **`analyze`** function.\n",
    "2. Add code to the **`plot`** function.\n",
    "3. **Test the code** using our example datasets, or use your own data.\n",
    "\n",
    "<div style=\"text-align: left;\">\n",
    "<img src=\"data/workflow.png\">\n",
    "</div>\n",
    "\n",
    "\n",
    "## 1. `analyze`\n",
    "The goal of the `analyze` function is to analyze an **RNA-seq dataset or signature** using one or more computational tools or techniques, and return the results of such analysis in a Python data structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "# Analysis function\n",
    "def analyze(dataset):\n",
    "    print('Analyzing the dataset...')\n",
    "    return ''"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. `plot`\n",
    "The goal of the `plot` function is to **visualize the results of the `analyze` function** in the Jupyter Notebook by using a plot, interactive visualization, or embedding downloadable results in the notebook itself."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "# Plot function\n",
    "def plot(analysis_results):\n",
    "    print('Displaying analysis results...')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Test the Plugin\n",
    "Once the `analyze` and `plot` functions have been completed, you can test them using the cells below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import modules\n",
    "import pandas as pd\n",
    "\n",
    "# Read the dataset\n",
    "example_dataset = pd.read_table('data/example_dataset.txt', index_col='gene_symbol')\n",
    "\n",
    "# Read the metadata\n",
    "example_metadata = pd.read_table('data/example_metadata.txt', index_col='Sample_geo_accession')\n",
    "\n",
    "# Read the signature\n",
    "example_signature = pd.read_table('data/example_signature.txt', index_col='gene_symbol')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Analyzing the dataset...\n"
     ]
    }
   ],
   "source": [
    "# Analyze the data\n",
    "results = analyze(dataset=example_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Displaying analysis results...\n"
     ]
    }
   ],
   "source": [
    "# Plot the results\n",
    "plot(results)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
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