notebooks/fastsmc-minimal.ipynb

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  {
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   "source": [
    "# FastSMC minimal working example notebook\n",
    "\n",
    "This notebook demonstrates a minimal working example of the FastSMC python bindings, where sensible default parameters are set automatically.\n",
    "\n",
    "Please make sure you have installed the python bindings by following the instructions in `../README.md` before attempting to run this notebook.\n",
    "\n",
    "The example dataset was simulated using the setup described in the paper, corresponding to SNP data for 150 diploid individuals and a chromosomal region of 30 Mb, with recombination rate from chromosome 2 and under a European demographic model (see https://www.biorxiv.org/content/10.1101/2020.04.20.029819v1 for more details)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1) Import `asmc` which is installed with the Python bindings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from asmc import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2) Specify paths for input (example provided with this repository) and output. Input is expected to have the following files:\n",
    "- `<input_files_root>.decodingQuantities.gz`\n",
    "- `<input_files_root>.hap.gz`\n",
    "- `<input_files_root>.map`\n",
    "- `<input_files_root>.samples`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_files_root = '../FILES/FASTSMC_EXAMPLE/example'\n",
    "output_files_root = '../notebooks/tmp_output_minimal'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3) Create the Python FastSMC object and run it. This should only take a few seconds."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fast_smc = FastSMC(in_dir=input_files_root, out_dir=output_files_root)\n",
    "fast_smc.run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4) Read data, add column names and filter to remove IBD segments with low IBD score. Note that for a large analysis, loading all data into memory is unlikely to be possible. See fastsmc.ipynb for an example that reads the output line-by-line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>ind1_famid</th>\n",
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       "     ind1_famid ind1_id  ind1_hap ind2_famid ind2_id  ind2_hap  chromosome  \\\n",
       "0          1_94    1_94         1      1_104   1_104         1           1   \n",
       "2         1_121   1_121         2      1_124   1_124         1           1   \n",
       "3          1_33    1_33         1      1_127   1_127         2           1   \n",
       "5          1_81    1_81         1      1_105   1_105         1           1   \n",
       "6          1_35    1_35         1       1_99    1_99         2           1   \n",
       "...         ...     ...       ...        ...     ...       ...         ...   \n",
       "1525      1_131   1_131         1      1_135   1_135         2           1   \n",
       "1526       1_28    1_28         1      1_131   1_131         1           1   \n",
       "1530       1_43    1_43         1      1_129   1_129         2           1   \n",
       "1531       1_28    1_28         1      1_135   1_135         2           1   \n",
       "1532       1_28    1_28         1      1_135   1_135         2           1   \n",
       "\n",
       "      ibd_start   ibd_end  length_in_cM  ibd_score     post_est    map_est  \n",
       "0          8740   1660011      1.869621   0.406675   154.388000  24.999930  \n",
       "2          8740   1592010      1.711646   0.310009   260.902900  34.999920  \n",
       "3          8740   1679626      2.185055   0.411068   171.023000  24.999930  \n",
       "5          8740   1608858      1.731873   0.334969   167.744800  34.999920  \n",
       "6          8740   1660011      1.869621   0.406424   150.308700  24.999930  \n",
       "...         ...       ...           ...        ...          ...        ...  \n",
       "1525   21527883  29849472      7.540405   0.988267    26.526950   4.999943  \n",
       "1526   21527883  29849472      7.540405   0.988267    26.526950   4.999943  \n",
       "1530   20052325  29849472      9.803328   0.995904    21.177900   4.999943  \n",
       "1531    5694783   5694783      0.000000   0.327205  6986.108000   4.999943  \n",
       "1532    5695399  29849472     38.517670   0.998743     9.249431   4.999943  \n",
       "\n",
       "[576 rows x 13 columns]"
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     "execution_count": 4,
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   ],
   "source": [
    "%config InlineBackend.figure_formats = ['svg']\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "data = pd.read_csv(output_files_root + '.1.1.FastSMC.ibd.gz', sep='\\t', header=None)\n",
    "\n",
    "data.columns = ['ind1_famid', 'ind1_id', 'ind1_hap', 'ind2_famid', 'ind2_id', 'ind2_hap', 'chromosome',\n",
    "                'ibd_start', 'ibd_end', 'length_in_cM', 'ibd_score', 'post_est', 'map_est']\n",
    "\n",
    "filtered = data[data['ibd_score'] > 0.1]\n",
    "filtered"
   ]
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   "source": [
    "5) Visualise data: here we simply bin the MAP age estimates and the IBD segment length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "source": [
    "plt.xlabel(\"MAP age estimate (in generations)\")\n",
    "filtered['map_est'].hist(range=(0, 100))\n",
    "plt.gca().set_yscale('linear')"
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   "cell_type": "code",
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