{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "604d7f61-33fe-4905-b86f-052b6d503b36",
   "metadata": {},
   "source": [
    "# Day 5 - Using SNPs to infer population structure\n",
    "\n",
    "## Population structure\n",
    "\n",
    "## Download the complete VCF with 30 samples\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0a4dca34-e846-4854-810c-36c7473a030e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Go into the correct directory to download notebook - cd command\n",
    "mkdir -p /home/jovyan/SV_DATA/RESULTS/POP\n",
    "cd /home/jovyan/SV_DATA/RESULTS/POP/\n",
    "wget https://itrop.ird.fr/trainingts/all_30samples.vcf.gz --no-check-certificate"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84dd7af7-4fdb-46b4-9f31-ecce30a9931a",
   "metadata": {},
   "source": [
    "#### Reduce the dataset to be able to run sNMF in a suitable time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "201024f2-e014-44bf-bdf1-2a12fd4d6b03",
   "metadata": {},
   "outputs": [],
   "source": [
    "gzip -d all_30samples.vcf.gz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b464ab75-d60b-4b26-b0d5-618352a0901a",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls -lt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "72eb51db-8dcc-47e2-96f5-519a5ca10d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract vcf header and print it into the new \"smaller\" vcf\n",
    "grep \"#\" all_30samples.vcf > subSample30.vcf\n",
    "\n",
    "# Extract 10000 lines randomly \n",
    "shuf -n 10000 all_30samples.vcf | grep -v \"#\" >> subSample30.vcf\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e578171-dd7b-4008-bd22-afecd6e5793d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count the SNPs number in the \"smaller\" vcf\n",
    "grep -v -c \"#\" *vcf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "745be4c6-435f-4a17-9981-7d15f1974627",
   "metadata": {},
   "source": [
    "#### Estimate of ancestry coefficient with sNMF\n",
    "\n",
    "sNMF is available as a function of the R package LEA (recommended use):\n",
    "* [sNMF webpage](http://membres-timc.imag.fr/Olivier.Francois/snmf/software.htm)\n",
    "* [sNMF Doc](http://membres-timc.imag.fr/Olivier.Francois/snmf/files/note.pdf)\n",
    "* [LEA](http://membres-timc.imag.fr/Olivier.Francois/LEA/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "559a537d-cef4-4396-a28b-8cf1ea21f7af",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\t- number of detected individuals:\t30\n",
      "\t- number of detected loci:\t\t8964\n",
      "\n",
      "For SNP info, please check ./subSample30.vcfsnp.\n",
      "\n",
      "1036 line(s) were removed because these are not SNPs.\n",
      "Please, check ./subSample30.removed file, for more informations.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(LEA)\n",
    "output = vcf2geno(\"subSample30.vcf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "61e28633-284a-41b4-a23e-07d2a33b0d7f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "project = snmf(\"subSample30.geno\",\n",
    "                K = 1:10, \n",
    "                CPU = 4,\n",
    "                entropy = TRUE, \n",
    "                repetitions = 10,\n",
    "                project = \"new\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "81576d7b-635c-4e82-8056-6d6cbd91d82f",
   "metadata": {},
   "outputs": [
    {
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UmSdPvtt1++fNn/nq1bt+bm5g5wbgAA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       "</svg>\n"
      ],
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "application/pdf": {
       "height": 420,
       "width": 420
      },
      "image/jpeg": {
       "height": 420,
       "width": 420
      },
      "image/png": {
       "height": 420,
       "width": 420
      },
      "image/svg+xml": {
       "height": 420,
       "isolated": true,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(project, cex = 1.2, col = \"lightblue\", pch = 19)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a9632395-8822-43b4-8dbc-620ddd407b57",
   "metadata": {},
   "outputs": [],
   "source": [
    "summary(project)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ea569026-ec63-4be1-88b8-cbea526cf488",
   "metadata": {},
   "outputs": [],
   "source": [
    "ce = cross.entropy(project, K = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "112e83a5-421c-4902-93a9-ea3f08fd54e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "best = which.min(ce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "52935bf2-2d38-4495-b2a5-e88b7fee0b73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Use CreatePalette() to define color palettes.\n"
     ]
    },
    {
     "data": {
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DgHhygTE95IWwZRLFXECs4wODU905zqcv8LgYbk0AplbmRzdHJlYW0KZW5kb2Jq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\nu2jvc3tje5nt2zJdgW2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tkcPXq0FvEEAPWOPXYAUBsRERFK1fXu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\n2AEAAAiCsAMAABAEYQc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       "</svg>\n"
      ],
      "text/plain": [
       "Plot with title “Ancestry matrix”"
      ]
     },
     "metadata": {
      "application/pdf": {
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       "width": 420
      },
      "image/jpeg": {
       "height": 420,
       "width": 420
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      "image/png": {
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       "width": 420
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     },
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   ],
   "source": [
    "# devtools::install_github(\"bcm-uga/TESS3_encho_sen\")\n",
    "library(tess3r)\n",
    "Q.matrix <- as.qmatrix(Q(project, K = 4, run = best))\n",
    "barplot(Q.matrix, \n",
    "        border = NA, \n",
    "        space = 0, \n",
    "        xlab = \"Individuals\",\n",
    "        ylab = \"Ancestry proportions\", \n",
    "        main = \"Ancestry matrix\") -> bp\n",
    "        \n",
    "axis(1, at = 1:nrow(Q.matrix), labels = bp$order, las = 3, cex.axis = .4)\n",
    "#G.matrix = G(project, K = 3, run = 2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "961d05cf-9575-4005-b945-4153faccb620",
   "metadata": {},
   "source": [
    "## Annotation of SNP - `snpEff`\n",
    "\n",
    "snpEff annotates and predicts the effects of genetic variants on genes and proteins (such as amino acid changes). \n",
    "\n",
    "http://pcingola.github.io/SnpEff/\n",
    "\n",
    "##### List the precompiled databases\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d486830-e7b7-4c23-9ec2-5c1a530a4c58",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Print only the 10st dtabases\n",
    "java -jar /opt/snpEff/snpEff.jar databases | head"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a1a753c8-4a38-47e7-86f1-308cd392ab64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count the number of databases\n",
    "java -jar /opt/snpEff/snpEff.jar databases | wc -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b13c2e1d-8670-49c0-b434-37c604843079",
   "metadata": {},
   "outputs": [],
   "source": [
    "java -jar /opt/snpEff/snpEff.jar databases | grep -i \"oryza_sativa\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fd9b33f-258a-4d6b-97e3-7f0fd71c54f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "java -jar /opt/snpEff/snpEff.jar databases | grep -i \"bathycoccus\""
   ]
  },
  {
   "cell_type": "markdown",
   "id": "942ed3d2-c781-4574-b88f-234d4d17c4bd",
   "metadata": {},
   "source": [
    "#### Import genome database for `bathycoccus` genome"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a770a3d7-dc0d-46b0-bc44-80b9f909e6ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "wget https://itrop.ird.fr/trainingts/snpEffBathy.zip --no-check-certificate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b45d0ca8-72ed-4578-a82f-c759fb062887",
   "metadata": {},
   "outputs": [],
   "source": [
    "unzip snpEffBathy.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6983bf79-baeb-4ff6-8ea4-708f6dee82d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ls -R "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d12cf62c-8e38-4a07-b0be-ca8477598233",
   "metadata": {},
   "source": [
    "#### Run snpeff and save the result into the file `all_30samples.snpeff.vcf`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be538304-e367-485f-a51a-1484b2e7aad9",
   "metadata": {},
   "outputs": [],
   "source": [
    "java -jar /opt/snpEff/snpEff.jar ann -c ./snpEff.config -dataDir ./data bathycoccus all_30samples.vcf > all_30samples.snpeff.vcf"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3628f57b-25e3-45b9-aa67-0f0a6aaa068d",
   "metadata": {},
   "source": [
    "#### Have a look at the annotated vcf "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee835eab-4b62-48a9-8938-f110de28cab5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f5d09787-0ec3-488f-9ff4-7e7576119fd3",
   "metadata": {},
   "source": [
    "# Download additional notebook about SNP filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7642544-4c1f-47ce-b857-16389d72b5cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "cd /home/jovyan/NOTEBOOK/DAY5-6\n",
    "wget https://itrop.ird.fr/trainingts/notebook/DAY5-6/Day6_SNP_Filtering_Exercice1.ipynb --no-check-certificate"
   ]
  }
 ],
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