00-day3-notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from io import BytesIO\n",
    "import oxbow as ox\n",
    "import pandas as pd\n",
    "import polars as pl\n",
    "import pyarrow as pa\n",
    "import pysam\n",
    "import numpy as np\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is a proof of concept list of functions that allow us to flatten the INFO column of VCF files into a pandas DataFrame. First we define a set of functions which can parse the VCF file. In this notebook, we use Oxbow to read in the VCF and pysame to parse the header information that contains the mapping of the various key-value pairs in the INFO column. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below is an example VCF header. Note how each element of the INFO field is defined line by line. We need to read this into a mapping to parse that column.\n",
    "\n",
    "```\n",
    "##fileformat=VCFv4.2\n",
    "##FORMAT=<ID=GT,Number=1,Type=String,Description=\"Genotype\">\n",
    "##INFO=<ID=CONFLICT,Number=.,Type=String,Description=\"Sample names for which there are multiple paths in the graph with conflicting alleles\">\n",
    "##INFO=<ID=AC,Number=A,Type=Integer,Description=\"Total number of alternate alleles in called genotypes\">\n",
    "##INFO=<ID=AF,Number=A,Type=Float,Description=\"Estimated allele frequency in the range (0,1]\">\n",
    "##INFO=<ID=NS,Number=1,Type=Integer,Description=\"Number of samples with data\">\n",
    "##INFO=<ID=AN,Number=1,Type=Integer,Description=\"Total number of alleles in called genotypes\">\n",
    "##INFO=<ID=LV,Number=1,Type=Integer,Description=\"Level in the snarl tree (0=top level)\">\n",
    "##INFO=<ID=PS,Number=1,Type=String,Description=\"ID of variant corresponding to parent snarl\">\n",
    "##INFO=<ID=AT,Number=R,Type=String,Description=\"Allele Traversal as path in graph\">\n",
    "##contig=<ID=NC_003197_v2#0#genome,length=4857450>\n",
    "##contig=<ID=NC_003197_v2#genome,length=4857450>\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This dictionary maps the VCF-derived input types to python dtypes\n",
    "TYPE_MAP = {\n",
    "\n",
    "\"\"\"\n",
    "This is a dictionary that maps generic type names used in the VCF INFO column to specific data typr\n",
    "\n",
    "\"\"\"    \"Integer\": \"int32\",\n",
    "    \"\n",
    "Float\": \"float64\",\n",
    "    \"String\": \"object\",\n",
    "    \"Flag\": \"bool\",\n",
    "}\n",
    "\n",
    "\n",
    "def read_vcf_pandas(vcf_path):\n",
    "    ipc = ox.read_vcf(vcf_path)\n",
    "    return pa.ipc.open_file(BytesIO(ipc)).read_pandas()\n",
    "\n",
    "\n",
    "def read_vcf_polars(vcf_path):\n",
    "    ipc = ox.read_vcf(vcf_path)\n",
    "    return pl.read_ipc(ipc)\n",
    "\n",
    "\n",
    "def read_info_schema(vcf_pat\n",
    "    h):\n",
    "    \"\"\" \n",
    "    Read the schema of the INFO column of    We use pysam to do this.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    vcf_path : str\n",
    "        Path to bgzipped vcf, with tabix indexed .tbi file with similar name \n",
    "        in same folder.\n",
    "    \n",
    "    Returns\n",
    "    ----------\n",
    "      pandas DataFrame with columns [\"name\", \"number\", \"type\"\"]\n",
    "    \"\"\"\n",
    "    with pysam.VariantFile(vcf_path) as f:\n",
    "        return pd.DataFrame(\n",
    "            [(obj.name, obj.number, obj.type) for obj in f.header.info.values()],\n",
    "            columns=[\"name\", \"number\", \"type\"],\n",
    "        )\n",
    "    \n",
    "\n",
    "def flatten_info(df: pd.DataFrame, info_schema: pd.DataFrame) -> pd.DataFrame:\n",
    "    \"\"\"\n",
    "    Flattens INFO column of a VCF by one\n",
    "\n",
    "    Parameters\n",
    "    ------\n",
    "    df : pd.DataFrame \n",
    "    info_schema: pd.DataFrame\n",
    "\n",
    "    Returns\n",
    "    ------\n",
    "    pd.DataFrame\n",
    "\n",
    "    \"\"\"\n",
    "    type_series = info_schema.set_index(\"name\")[\"type\"]\n",
    "    arity_series = info_schema.set_index(\"name\")[\"number\"]\n",
    "\n",
    "    # Split the key-value pairs in the info column into dictionaries\n",
    "    records = (\n",
    "        df[\"info\"]\n",
    "            .str\n",
    "            .split(\";\")\n",
    "            .apply(\n",
    "                lambda info_list: dict(x.split(\"=\")                 \n",
    "                if '=' in x else (x, True) \n",
    "                for x in info_list\n",
    "            )\n",
    "        )\n",
    "    )\n",
    "\n",
    "    # Convert to dataframe\n",
    "    info_df = pd.DataFrame.from_records (records)\n",
    "\n",
    "    # Split comma-separated values into lists\n",
    "    for column in info_df.columns:\n",
    "        if arity_series[column] not in {\".\", \"1\", \"0\"}:\n",
    "            info_df[column] = info_df[column].str.split(\",\")\n",
    "\n",
    "    # Convert to correct dtypes\n",
    "    for column in info_df.columns:\n",
    "        data_type = TYPE_MAP[type_series[column]]\n",
    "\n",
    "        if arity_series[column] not in {\".\", \"1\", \"0\"}:\n",
    "            # info_df[column] = info_df[column].apply(\n",
    "            #     lambda x: x if x is pd.NA else [y for y in x]\n",
    "            # )\n",
    "            pass\n",
    "        else:\n",
    "            info_df[column] = info_df[column].astype(data_type)\n",
    "\n",
    "    return info_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example of these functions parsing a VCF's INFO column. A reproduction of the first few data lines of the VCF is below:\n",
    "\n",
    "```\n",
    "#CHROM\tPOS\tID\tREF\tALT\tQUAL\tFILTER\tINFO\tFORMAT\tDRR259112\n",
    "NC_045512.2\t241\t.\tC\tT\t4345.64\tPASS\tAC=1;AF=0.5;AN=2;BaseQRankSum=-0.372;DP=679;ExcessHet=0;FS=0.546;MLEAC=1;MLEAF=0.5;MQ=60;MQRankSum=0;QD=6.43;ReadPosRankSum=-0.585;SOR=0.653;EFF=INTERGENIC(MODIFIER||||||||||T)\tGT:AD:DP:GQ:PL\t0/1:445,231:676:99:4353,0,10881\n",
    "NC_045512.2\t316\t.\tTTT\tTT\t3583.6\tPASS\tAC=1;AF=0.5;AN=2;BaseQRankSum=1.07;DP=408;ExcessHet=0;FS=7.647;MLEAC=1;MLEAF=0.5;MQ=60;MQRankSum=0;QD=8.8;ReadPosRankSum=-2.848;SOR=1.3;EFF=FRAME_SHIFT(HIGH||ttg/|L18|179|leader|protein_coding|CODING|TRANSCRIPT_leader-gene|1|TT|WARNING_TRANSCRIPT_NO_STOP_CODON);LOF=(leader|leader-gene|1|1.00)\tGT:AD:DP:GQ:PL\t0/1:272,135:407:99:3591,0,8449\n",
    "NC_045512.2\t2662\t.\tC\tT\t1702.06\tPASS\tAC=2;AF=1;AN=2;DP=70;ExcessHet=0;FS=0;MLEAC=2;MLEAF=1;MQ=60;QD=24.32;SOR=0.75;EFF=SYNONYMOUS_CODING(LOW|SILENT|taC/taT|Y619|637|nsp2|protein_coding|CODING|TRANSCRIPT_nsp2-gene|1|T|WARNING_TRANSCRIPT_NO_START_CODON)\tGT:AD:DP:GQ:PL\t1/1:0,70:70:99:1716,209,0\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W::hts_idx_load3] The index file is older than the data file: ../DRR259112.ref.snpeff.vcf.gz.tbi\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "'float' object is not iterable",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[28], line 6\u001b[0m\n\u001b[1;32m      3\u001b[0m df \u001b[39m=\u001b[39m read_vcf_pandas(vcf_path)\n\u001b[1;32m      4\u001b[0m info_schema \u001b[39m=\u001b[39m read_info_schema(vcf_path)\n\u001b[0;32m----> 6\u001b[0m info_df \u001b[39m=\u001b[39m flatten_info(df, info_schema)\n\u001b[1;32m      7\u001b[0m info_df\n",
      "Cell \u001b[0;32mIn[27], line 82\u001b[0m, in \u001b[0;36mflatten_info\u001b[0;34m(df, info_schema)\u001b[0m\n\u001b[1;32m     79\u001b[0m data_type \u001b[39m=\u001b[39m TYPE_MAP[type_series[column]]\n\u001b[1;32m     81\u001b[0m \u001b[39mif\u001b[39;00m arity_series[column] \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m {\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m1\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m0\u001b[39m\u001b[39m\"\u001b[39m}:\n\u001b[0;32m---> 82\u001b[0m     info_df[column] \u001b[39m=\u001b[39m info_df[column]\u001b[39m.\u001b[39;49mapply(\n\u001b[1;32m     83\u001b[0m         \u001b[39mlambda\u001b[39;49;00m x: x \u001b[39mif\u001b[39;49;00m x \u001b[39mis\u001b[39;49;00m pd\u001b[39m.\u001b[39;49mNA \u001b[39melse\u001b[39;49;00m [y \u001b[39mfor\u001b[39;49;00m y \u001b[39min\u001b[39;49;00m x]\n\u001b[1;32m     84\u001b[0m     )\n\u001b[1;32m     85\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     86\u001b[0m     info_df[column] \u001b[39m=\u001b[39m info_df[column]\u001b[39m.\u001b[39mastype(data_type)\n",
      "File \u001b[0;32m~/Documents/GitHub/oxbow_project/oxbow/py-oxbow/.venv/oxbow/lib/python3.11/site-packages/pandas/core/series.py:4630\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, **kwargs)\u001b[0m\n\u001b[1;32m   4520\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mapply\u001b[39m(\n\u001b[1;32m   4521\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m   4522\u001b[0m     func: AggFuncType,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4525\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m   4526\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m DataFrame \u001b[39m|\u001b[39m Series:\n\u001b[1;32m   4527\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   4528\u001b[0m \u001b[39m    Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m   4529\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4628\u001b[0m \u001b[39m    dtype: float64\u001b[39;00m\n\u001b[1;32m   4629\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4630\u001b[0m     \u001b[39mreturn\u001b[39;00m SeriesApply(\u001b[39mself\u001b[39;49m, func, convert_dtype, args, kwargs)\u001b[39m.\u001b[39;49mapply()\n",
      "File \u001b[0;32m~/Documents/GitHub/oxbow_project/oxbow/py-oxbow/.venv/oxbow/lib/python3.11/site-packages/pandas/core/apply.py:1025\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1022\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_str()\n\u001b[1;32m   1024\u001b[0m \u001b[39m# self.f is Callable\u001b[39;00m\n\u001b[0;32m-> 1025\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply_standard()\n",
      "File \u001b[0;32m~/Documents/GitHub/oxbow_project/oxbow/py-oxbow/.venv/oxbow/lib/python3.11/site-packages/pandas/core/apply.py:1076\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1074\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1075\u001b[0m         values \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39mastype(\u001b[39mobject\u001b[39m)\u001b[39m.\u001b[39m_values\n\u001b[0;32m-> 1076\u001b[0m         mapped \u001b[39m=\u001b[39m lib\u001b[39m.\u001b[39;49mmap_infer(\n\u001b[1;32m   1077\u001b[0m             values,\n\u001b[1;32m   1078\u001b[0m             f,\n\u001b[1;32m   1079\u001b[0m             convert\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconvert_dtype,\n\u001b[1;32m   1080\u001b[0m         )\n\u001b[1;32m   1082\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(mapped) \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(mapped[\u001b[39m0\u001b[39m], ABCSeries):\n\u001b[1;32m   1083\u001b[0m     \u001b[39m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m   1084\u001b[0m     \u001b[39m#  See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m   1085\u001b[0m     \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39m_constructor_expanddim(\u001b[39mlist\u001b[39m(mapped), index\u001b[39m=\u001b[39mobj\u001b[39m.\u001b[39mindex)\n",
      "File \u001b[0;32m~/Documents/GitHub/oxbow_project/oxbow/py-oxbow/.venv/oxbow/lib/python3.11/site-packages/pandas/_libs/lib.pyx:2834\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "Cell \u001b[0;32mIn[27], line 83\u001b[0m, in \u001b[0;36mflatten_info.<locals>.<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m     79\u001b[0m data_type \u001b[39m=\u001b[39m TYPE_MAP[type_series[column]]\n\u001b[1;32m     81\u001b[0m \u001b[39mif\u001b[39;00m arity_series[column] \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m {\u001b[39m\"\u001b[39m\u001b[39m.\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m1\u001b[39m\u001b[39m\"\u001b[39m, \u001b[39m\"\u001b[39m\u001b[39m0\u001b[39m\u001b[39m\"\u001b[39m}:\n\u001b[1;32m     82\u001b[0m     info_df[column] \u001b[39m=\u001b[39m info_df[column]\u001b[39m.\u001b[39mapply(\n\u001b[0;32m---> 83\u001b[0m         \u001b[39mlambda\u001b[39;00m x: x \u001b[39mif\u001b[39;00m x \u001b[39mis\u001b[39;00m pd\u001b[39m.\u001b[39mNA \u001b[39melse\u001b[39;00m [y \u001b[39mfor\u001b[39;00m y \u001b[39min\u001b[39;00m x]\n\u001b[1;32m     84\u001b[0m     )\n\u001b[1;32m     85\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     86\u001b[0m     info_df[column] \u001b[39m=\u001b[39m info_df[column]\u001b[39m.\u001b[39mastype(data_type)\n",
      "\u001b[0;31mTypeError\u001b[0m: 'float' object is not iterable"
     ]
    }
   ],
   "source": [
    "\n",
    "vcf_path = \"../DRR259112.ref.snpeff.vcf.gz\"\n",
    "\n",
    "df = read_vcf_pandas(vcf_path)\n",
    "info_schema = read_info_schema(vcf_path)\n",
    "\n",
    "info_df = flatten_info(df, info_schema)\n",
    "info_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W::hts_idx_load3] The index file is older than the data file: ../DRR.vcf.gz.tbi\n"
     ]
    },
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       "      <td>[100]</td>\n",
       "      <td>[&gt;2561&gt;2562&gt;2564, &gt;2561&lt;2563&gt;2564]</td>\n",
       "      <td>[100]</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>[2]</td>\n",
       "      <td>[0.02]</td>\n",
       "      <td>[100]</td>\n",
       "      <td>[&gt;2566&gt;2567&gt;2569, &gt;2566&lt;2568&gt;2569]</td>\n",
       "      <td>[100]</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>[20]</td>\n",
       "      <td>[0.2]</td>\n",
       "      <td>[100]</td>\n",
       "      <td>[&gt;2569&gt;2570&gt;2572, &gt;2569&lt;2571&gt;2572]</td>\n",
       "      <td>[100]</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>[10]</td>\n",
       "      <td>[0.1]</td>\n",
       "      <td>[100]</td>\n",
       "      <td>[&gt;2574&gt;2575&gt;2577, &gt;2574&lt;2576&gt;2577]</td>\n",
       "      <td>[100]</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>[83]</td>\n",
       "      <td>[0.83]</td>\n",
       "      <td>[100]</td>\n",
       "      <td>[&gt;2581&gt;2583&gt;2584, &gt;2581&lt;2582&gt;2584]</td>\n",
       "      <td>[100]</td>\n",
       "      <td>[0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>487 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         AC                     AF     AN  \\\n",
       "0      [12]             [0.121212]   [99]   \n",
       "1    [2, 5]  [0.020202, 0.0505051]   [99]   \n",
       "2       [6]            [0.0606061]   [99]   \n",
       "3      [54]             [0.545455]   [99]   \n",
       "4    [2, 4]           [0.02, 0.04]  [100]   \n",
       "..      ...                    ...    ...   \n",
       "482     [2]                 [0.02]  [100]   \n",
       "483     [2]                 [0.02]  [100]   \n",
       "484    [20]                  [0.2]  [100]   \n",
       "485    [10]                  [0.1]  [100]   \n",
       "486    [83]                 [0.83]  [100]   \n",
       "\n",
       "                                             AT     NS   LV  \n",
       "0                           [>8>10>11, >8<9>11]   [99]  [0]  \n",
       "1             [>12>13>16, >12<15>16, >12<14>16]   [99]  [0]  \n",
       "2                        [>16>17>19, >16<18>19]   [99]  [0]  \n",
       "3                        [>19>21>22, >19<20>22]   [99]  [0]  \n",
       "4    [>22>23>25>27, >22<24>25>27, >22>23<26>27]  [100]  [0]  \n",
       "..                                          ...    ...  ...  \n",
       "482          [>2561>2562>2564, >2561<2563>2564]  [100]  [0]  \n",
       "483          [>2566>2567>2569, >2566<2568>2569]  [100]  [0]  \n",
       "484          [>2569>2570>2572, >2569<2571>2572]  [100]  [0]  \n",
       "485          [>2574>2575>2577, >2574<2576>2577]  [100]  [0]  \n",
       "486          [>2581>2583>2584, >2581<2582>2584]  [100]  [0]  \n",
       "\n",
       "[487 rows x 6 columns]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "vcf_path = \"../DRR.vcf.gz\"\n",
    "\n",
    "df = read_vcf_pandas(vcf_path)\n",
    "info_schema = read_info_schema(vcf_path)\n",
    "\n",
    "info_df = flatten_info(df, info_schema)\n",
    "info_df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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