tests/examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9c2f0049",
   "metadata": {},
   "outputs": [],
   "source": [
    "import unittest\n",
    "import sys\n",
    "sys.path.append('../funpipe')\n",
    "import os\n",
    "from subprocess import check_call\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import filecmp\n",
    "\n",
    "from utils import run\n",
    "from fasta import fasta\n",
    "from fastq import fastq\n",
    "from gatk import gatk\n",
    "from vcf import vcf\n",
    "from gt_pair import gt_pair\n",
    "from vcfheader import vcfheader\n",
    "from vcfrecord import vcfrecord\n",
    "from plink import plink\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4d21b292",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[W::bcf_hdr_check_sanity] GL should be declared as Number=G\n"
     ]
    }
   ],
   "source": [
    "vcf_complex  = vcf( 'data/phy_test.vcf',prefix='complex',outdir='data' )\n",
    "#vcf_complex.get_plink()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "caa73e1a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - Done: bcftools query -f '[%GT ]\\n' data/phy_test.vcf>data/complex.dos.tsv\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "bcftools query -f '[%GT ]\\n' data/phy_test.vcf>data/complex.dos.tsv\n",
      "[W::bcf_hdr_check_sanity] GL should be declared as Number=G\n",
      "[W::vcf_parse] Contig '1' is not defined in the header. (Quick workaround: index the file with tabix.)\n",
      "[W::vcf_parse] Contig 'X' is not defined in the header. (Quick workaround: index the file with tabix.)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<vcf.vcf at 0x7fee98546190>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#compute dosage matrix\n",
    "vcf_complex.cal_dos()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "7f08deaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "dosage_matrix = vcf_complex.dosage_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a5e9fa30",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_total(l1, l2):\n",
    "    count = 0\n",
    "    for i in range(len(l1)):\n",
    "        if not( l1[i] == '0/0' and l2[i] == '0/0' ):\n",
    "            count = count+1\n",
    "    return count\n",
    "\n",
    "def get_shared(l1, l2):\n",
    "    count = 0\n",
    "    for i in range(len(l1)):\n",
    "        if not( l1[i] == '0/0' and l2[i] == '0/0' ):\n",
    "            if l1[i] == l2[i]:\n",
    "                count = count + 1\n",
    "    return count\n",
    "\n",
    "def get_unique(l1,l2):\n",
    "    return get_total(l1,l2) - get_shared(l1,l2)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c260b2fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "#compute pairwise shared and unique variants\n",
    "pairwise_shared = np.zeros( (vcf_complex.n_samples,vcf_complex.n_samples) )\n",
    "pairwise_unique = np.zeros( (vcf_complex.n_samples,vcf_complex.n_samples) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "afdcd412",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(vcf_complex.n_samples):\n",
    "    for j in range(vcf_complex.n_samples):\n",
    "        pairwise_shared[i][j] = get_shared(list(dosage_matrix[i]),list(dosage_matrix[j]) )\n",
    "        pairwise_unique[i][j] = get_unique( list(dosage_matrix[i]),list(dosage_matrix[j]) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "16b7b7a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "bcftools query -f  '%CHROM\t%POS\t%REF\t%ALT[\t%GT]\n",
      "' data/phy_test.vcf > data/complex.sample_info.tsv\n",
      "[W::bcf_hdr_check_sanity] GL should be declared as Number=G\n",
      "[W::vcf_parse] Contig '1' is not defined in the header. (Quick workaround: index the file with tabix.)\n",
      "[W::vcf_parse] Contig 'X' is not defined in the header. (Quick workaround: index the file with tabix.)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " - Done: bcftools query -f  '%CHROM\t%POS\t%REF\t%ALT[\t%GT]\n",
      "' data/phy_test.vcf > data/complex.sample_info.tsv\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<vcf.vcf at 0x7fee98546190>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vcf_complex.get_sample_info(info=['GT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "8a1c978d",
   "metadata": {},
   "outputs": [],
   "source": [
    "sample_info = vcf_complex._sample_info\n",
    "samples = ['A','B','C','D']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "372d297f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(87.0, 0.5, 'samples')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x1296 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axs = plt.subplots(3,2,figsize=(12,18))\n",
    "for i in range(4):\n",
    "    x_c = int(i/2)\n",
    "    y_c = int(i%2)\n",
    "    axs[x_c][y_c].hist(sample_info['GT_'+samples[i]])\n",
    "    axs[x_c][y_c].set_xlabel('Genotypes')\n",
    "    axs[x_c][y_c].set_ylabel('Count')\n",
    "    axs[x_c][y_c].set_title('Sample '+samples[i])\n",
    "    \n",
    "sns.heatmap(pairwise_shared,annot=True,annot_kws={'fontsize':12},yticklabels=samples,\n",
    "                xticklabels=samples,ax = axs[2][0] )\n",
    "\n",
    "sns.heatmap(pairwise_unique,annot=True,annot_kws={'fontsize':12},yticklabels=samples,\n",
    "                xticklabels=samples,ax=axs[2][1] )\n",
    "axs[2][0].set_title('Shared variants')\n",
    "axs[2][1].set_title('Unique variants')\n",
    "axs[2][1].set_xlabel('samples')\n",
    "axs[2][0].set_xlabel('samples')\n",
    "axs[2][1].set_ylabel('samples')\n",
    "axs[2][0].set_ylabel('samples')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d2d461f8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}