{
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    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
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      "source": [
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n# Normalizing a cancer contact count matrix with\u00a0CAIC\n\n\nCAIC is a normalization method to remove copy-number biases present in a\nmatrix. This example showcases how to perform such a normalization on\nsimulated data with `iced`.\n\n\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Loading the data and normalizing\n--------------------------------\n\nThe normalization is done in three step:\n\n  1. Normalize the data using LOIC, to remove GC, mappability, and other\n     biases\n  2. Estimate the block biases due to copy number.\n  3. Remove the block biases from the LOIC-normalized contact counts\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "from iced import datasets\nfrom iced import normalization\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors\n\n\ncounts, lengths, cnv = datasets.load_sample_cancer()\n\nloic_normed = normalization.ICE_normalization(counts, counts_profile=cnv)\nblock_biases = normalization.estimate_block_biases(counts, lengths, cnv)\ncaic_normed = loic_normed / block_biases"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Visualizing the results using Matplotlib\n----------------------------------------\n\nThe following code visualizes the raw original data, the estimated block\nbiases, and the normalized matrix using the CAIC method.\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "chromosomes = [\"I\", \"II\", \"III\", \"IV\", \"V\", \"VI\"]\n\nfig, axes = plt.subplots(ncols=3, figsize=(14, 3))\n\naxes[0].imshow(counts, cmap=\"RdBu_r\", norm=colors.SymLogNorm(1),\n               origin=\"bottom\",\n               extent=(0, len(counts), 0, len(counts)))\n\n[axes[0].axhline(i, linewidth=1, color=\"#000000\") for i in lengths.cumsum()]\n[axes[0].axvline(i, linewidth=1, color=\"#000000\") for i in lengths.cumsum()]\naxes[0].set_title(\"Raw contact counts\", fontweight=\"bold\")\n\nm = axes[1].imshow(block_biases, cmap=\"RdBu_r\", norm=colors.SymLogNorm(1),\n                   origin=\"bottom\",\n                   extent=(0, len(counts), 0, len(counts)))\n[axes[1].axhline(i, linewidth=1, color=\"#000000\") for i in lengths.cumsum()]\n[axes[1].axvline(i, linewidth=1, color=\"#000000\") for i in lengths.cumsum()]\naxes[1].set_title(\"Estimated block biases\", fontweight=\"bold\")\n\nm = axes[2].imshow(caic_normed,\n                   cmap=\"RdBu_r\", norm=colors.SymLogNorm(1),\n                   origin=\"bottom\",\n                   extent=(0, len(counts), 0, len(counts)))\n[axes[2].axhline(i, linewidth=1, color=\"#000000\") for i in lengths.cumsum()]\n[axes[2].axvline(i, linewidth=1, color=\"#000000\") for i in lengths.cumsum()]\ncb = fig.colorbar(m)\naxes[2].set_title(\"Normalized contact counts with CAIC\", fontweight=\"bold\")"
      ]
    }
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