Project_Module2_FMML20210660.ipynb

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  "metadata": {
    "colab": {
      "name": "Project_Module2_FMML20210660 ",
      "provenance": [],
      "collapsed_sections": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SreenadhSingamaneni/IIITH-FMML20210660/blob/main/Project_Module2_FMML20210660.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmWIufUKnVK6"
      },
      "source": [
        "# MODULE 2: Appreciating, Interpreting and Visualizing Data\n",
        "#### Coordinator: Kushagra Agarwal\n",
        "\n",
        "---\n",
        "# Project\n",
        "\n",
        "---"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fzcKLePrnV3U"
      },
      "source": [
        "For the last 2 years we have been severely affected by the COVID-19 pandemic. And if you would know a little about it, you would have heard about **SARS-CoV-2** (Severe Acute Respiratory Syndrome Coronavirus 2) which is the virus responsible for the COVID-19 disease.\n",
        "\n",
        "A little biology might intrigue you further. This SARS-CoV-2 is a RNA virus and when this virus enters your body, it interacts with the ACE-2 receptor using its Spike protein. When this happens, it gets access to be able to multiply inside your body, ultimately resulting in you getting infected with the COVID-19 disease.\n",
        "\n",
        "Before I explain you the details of the project and what you will be doing, let me first introduce you to what a virus genome is. Every living organism in this world has someting called a Nucleus which produces its characteristic genome sequence. Even your body has a genome and it will be very different from that of lets say a cat. Now, this SARS-CoV-2 virus also has its characteristic genome sequence and it is roughly of the length 29000 bases. A little more Class 9 biology lesson: Each genome is built up with just 4 characters called bases (A, T, G and C).\n",
        "\n",
        "For this project, I will provide you with some SARS-CoV-2 genomes collected from infected people in India and you will use just the Visualization techniques you learnt in the module to analyze the genomes and infer the spread of the COVID-19 disease in the country."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xIiHGVQKnY1t"
      },
      "source": [
        "685 genome sequences from India downloaded from GISAID (https://www.gisaid.org/) in early to mid 2020 have been provided to you in the _sequences.fasta_ file and some cleaned metadata regarding these sequences has been given in the _INDIA_685.csv_ file. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A2OHuH6nnRD_"
      },
      "source": [
        "from google.colab import files"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "3FqnXuP7nfL7",
        "outputId": "2d89d441-69fd-422a-fb20-a8bb0f69dd43"
      },
      "source": [
        "# Upload the INDIA_685.csv file here by running this cell\n",
        "\n",
        "uploaded1 = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-de9307a5-40c7-4e90-a67f-49fe3b04aeef\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-de9307a5-40c7-4e90-a67f-49fe3b04aeef\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving INDIA_685.csv to INDIA_685.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "id": "Ojhce7sOnrF3",
        "outputId": "29852049-05a2-4f6e-978d-382e57f942b7"
      },
      "source": [
        "# Upload the sequences.fasta file here by running this cell\n",
        "\n",
        "uploaded2 = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-c3a6ce75-b75d-4e03-a276-c3f150f63d8b\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-c3a6ce75-b75d-4e03-a276-c3f150f63d8b\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving sequences.fasta to sequences.fasta\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dBFPYuhEn9lg",
        "outputId": "f060c7a7-314f-4e1d-b13c-cb2e60a7ad38"
      },
      "source": [
        "!pip3 install biopython"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting biopython\n",
            "  Downloading biopython-1.79-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl (2.3 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.3 MB 8.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from biopython) (1.19.5)\n",
            "Installing collected packages: biopython\n",
            "Successfully installed biopython-1.79\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZqOz_ZDoBBz"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from Bio import SeqIO"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "id": "fb2dcglpoBIs",
        "outputId": "e0709221-af7f-4f25-f7e6-5d34b2ee78ea"
      },
      "source": [
        "data = pd.read_csv(\"INDIA_685.csv\")\n",
        "data.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StrainID</th>\n",
              "      <th>Accession_ID</th>\n",
              "      <th>Location</th>\n",
              "      <th>State</th>\n",
              "      <th>Host</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Patient age</th>\n",
              "      <th>Patient status</th>\n",
              "      <th>Collection_date</th>\n",
              "      <th>Originating lab</th>\n",
              "      <th>Authors</th>\n",
              "      <th>Nuc_muts</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>680</th>\n",
              "      <td>hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|20...</td>\n",
              "      <td>EPI_ISL_436137</td>\n",
              "      <td>Asia / India / Karnataka</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>28</td>\n",
              "      <td>Asymptomatic</td>\n",
              "      <td>4/21/2020</td>\n",
              "      <td>District Surveillance Unit</td>\n",
              "      <td>Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...</td>\n",
              "      <td>['C1281T', 'C4158T', 'G11083T', 'C13730T', 'C2...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|20...</td>\n",
              "      <td>EPI_ISL_436138</td>\n",
              "      <td>Asia / India / Karnataka</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>38</td>\n",
              "      <td>Asymptomatic</td>\n",
              "      <td>4/23/2020</td>\n",
              "      <td>District Surveillance Unit</td>\n",
              "      <td>Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...</td>\n",
              "      <td>['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>682</th>\n",
              "      <td>hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|20...</td>\n",
              "      <td>EPI_ISL_436139</td>\n",
              "      <td>Asia / India / Karnataka</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Human</td>\n",
              "      <td>Male</td>\n",
              "      <td>20</td>\n",
              "      <td>Symptomatic</td>\n",
              "      <td>4/23/2020</td>\n",
              "      <td>District Surveillance Unit</td>\n",
              "      <td>Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...</td>\n",
              "      <td>['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|20...</td>\n",
              "      <td>EPI_ISL_436140</td>\n",
              "      <td>Asia / India / Karnataka</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>52</td>\n",
              "      <td>Asymptomatic</td>\n",
              "      <td>4/24/2020</td>\n",
              "      <td>District Surveillance Unit</td>\n",
              "      <td>Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...</td>\n",
              "      <td>['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|20...</td>\n",
              "      <td>EPI_ISL_436141</td>\n",
              "      <td>Asia / India / Karnataka</td>\n",
              "      <td>Karnataka</td>\n",
              "      <td>Human</td>\n",
              "      <td>Female</td>\n",
              "      <td>27</td>\n",
              "      <td>Asymptomatic</td>\n",
              "      <td>4/24/2020</td>\n",
              "      <td>District Surveillance Unit</td>\n",
              "      <td>Chitra Pattabiraman, Vijayalakshmi Reddy, Hars...</td>\n",
              "      <td>['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              StrainID  ...                                           Nuc_muts\n",
              "680  hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|20...  ...  ['C1281T', 'C4158T', 'G11083T', 'C13730T', 'C2...\n",
              "681  hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|20...  ...  ['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...\n",
              "682  hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|20...  ...  ['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...\n",
              "683  hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|20...  ...  ['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...\n",
              "684  hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|20...  ...  ['C241T', 'C3037T', 'C3634T', 'A3742G', 'C1440...\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CXBttf4aoBLy",
        "outputId": "cecdb602-ff5e-4ce6-afad-23eb7a166b53"
      },
      "source": [
        "fasta_sequences = SeqIO.parse(open(\"sequences.fasta\"),'fasta')\n",
        "\n",
        "counter = 0\n",
        "for fasta in fasta_sequences:\n",
        "    counter +=1\n",
        "    \n",
        "print(\"Number of sequences is:\", counter)    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of sequences is: 685\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LN32UzxtoJ11"
      },
      "source": [
        "You might have heard in the news that the SARS-CoV-2 is mutating and might have heard the words Double Mutant, Delta Variant and so on. These are nothing but very slight differences in the genomes caused due to Mutations. Some of these mutations end up making the virus deadlier or more infectious.\n",
        "\n",
        "Our goal here is to analyze these genomes and find the ones closer to one another (similar mutations in both) or distant (different profile of mutations). These mutations in turn help us to understand the spread of the Virus and the Demographics of it. Imagine a sequence from Italy has the same set of 10 random mutations as a sequence in India. We can then infer that the source of infection for both the people could be the same. This sort of analysis helps us analyze hotspots and predict if a large outbreak is about to happen.\n",
        "\n",
        "We will try to look at these genome sequences and be able to cluster similar ones together and distant ones furhter away. In this project you will be using two methods to do this:\n",
        "\n",
        "**1. k-mer based methods** <br>\n",
        "**2. Mutation Profile based methods**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KG-Wrc18oNTz"
      },
      "source": [
        "# kmer based methods\n",
        "\n",
        "In bioinformatics, k-mers are substrings of length k contained within a biological sequence. A sequence of length L will have ```L-k+1 k-mers``` and ```4^k total possible k-mers```. Here we, will be using kmers of size 7. We will represent each genome in this kmer space. Basically a genome of size 30000 bases would have a total of 29994 7-mers. \n",
        "\n",
        "**Now how do you use this information to cluster genome sequences?** <br>\n",
        "We would be looking at the counts of different kmers in the genomes and the ones which have similar counts for different kmers should end up closer than the others.\n",
        "\n",
        "For **k=7**, there are **16384** possible kmers. We will represent each genome sequence as a vector of size 16384 with each position scoring the frequency of the kmer corresponding to that position.\n",
        "\n",
        "**But how do we find out the position in the array for a kmer?**<br>\n",
        "We will be using a hash function to map each kmer to a position in the array (0 to 16383). Whenever that kmer will be observed in a sequence, the value at the corresponding position in the array would be increased by 1.\n",
        "\n",
        "#### Hash function:\n",
        "\n",
        "A: 0 <br>\n",
        "G: 1<br>\n",
        "T: 2<br>\n",
        "C: 3<br>\n",
        "\n",
        "Multiply the character value by ```4^(position of the character in the kmer)``` <br>\n",
        "therefore, TAG would equal = (4^0 * 2) + (4^1 * 0) + (4^2 * 1) = 2 + 0 + 16 = 18\n",
        "\n",
        "**Can you find out the hash value for CATCGAG?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DmIdC1-aSq-4"
      },
      "source": [
        "**Answer:**\n",
        "4^0*3 + 4^1*0 + 4^2*2 + 4^3*3 + 4^4*1 + 4^5*0 + 4^6*1\n",
        "\n",
        "\n",
        "=**4579**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sddXsg9QoFIr",
        "outputId": "da866c98-e5b1-442f-bbed-77085ed0df57"
      },
      "source": [
        "def calc_score(examplesequence):\n",
        "\n",
        "    position = 0\n",
        "    score = 0\n",
        "    value = 0\n",
        "    flag = 0\n",
        "    \n",
        "    for base in examplesequence:\n",
        "\n",
        "        if(base == \"A\" or base == \"a\"):\n",
        "            value = 0\n",
        "        elif(base == \"G\" or base == \"g\"):\n",
        "            value = 1\n",
        "        elif(base == \"T\" or base == \"t\"):\n",
        "            value = 2\n",
        "        elif(base == \"C\" or base == \"c\"):\n",
        "            value = 3\n",
        "        else:\n",
        "            flag=1\n",
        "\n",
        "        score += (4 ** position)* value\n",
        "        position+=1\n",
        "\n",
        "    return (flag,score)\n",
        "    \n",
        "calc_score(\"CATCGAG\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, 4579)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IfZYG8IxoFLZ"
      },
      "source": [
        "k=7\n",
        "big_matrix = []\n",
        "sequences_name = []\n",
        "\n",
        "fasta_sequences = SeqIO.parse(open(\"sequences.fasta\"),'fasta')\n",
        "\n",
        "for fasta in fasta_sequences:\n",
        "    \n",
        "    embedding = np.zeros(4**k, dtype=int)\n",
        "    sequence = str(fasta.seq)\n",
        "    sequence_name = fasta.id\n",
        "    sequences_name.append(sequence_name)\n",
        "\n",
        "    for j in range(len(sequence)-k+1):\n",
        "        kmer = sequence[j:j+k]\n",
        "        flag,score = calc_score(kmer)\n",
        "        if(flag!=1):\n",
        "            embedding[score]+=1\n",
        "\n",
        "    embedding = embedding.tolist()\n",
        "    big_matrix.append(embedding)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ny9at6TVoFN6",
        "outputId": "7962da95-66a1-4141-d1af-0ab74e99e2e3"
      },
      "source": [
        "print(\"Number of sequences:\", len(sequences_name))\n",
        "print(\"Number of features for each sequence:\", len(big_matrix[0]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of sequences: 685\n",
            "Number of features for each sequence: 16384\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "clSklu6EofaE"
      },
      "source": [
        "So essentially we reduced a **29000 base sequence** into a **vector of size 16384**. Now we need to further reduce the dimensionality to 2 or 3 dimensions to be able to visualize it. \n",
        "\n",
        "**But how dow we do that??**\n",
        "\n",
        "We can use any of the methods taught to you in the Labs. But here I will explain how to do this using PCA. Then you will use the knowledge from Lab3 on t-SNE to do a similar analysis yourself.\n",
        "\n",
        "### PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k6XZ2uOtodUe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b95e02bf-3362-460a-a2fd-cf1caff62dcc"
      },
      "source": [
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA() # create a PCA object\n",
        "pca.fit(big_matrix) # do the math\n",
        "pca_data = pca.transform(big_matrix) # get PCA coordinates for the matrix\n",
        "pca_data"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-4.32623538e+00, -3.48413897e+00, -1.78632876e+00, ...,\n",
              "        -1.25940924e-15, -8.99020441e-16,  5.19484629e-15],\n",
              "       [-4.23439876e+00, -3.50999582e+00, -1.80426267e+00, ...,\n",
              "        -1.32532874e-15, -1.01004274e-15,  4.12972607e-15],\n",
              "       [-4.18409038e+00, -3.75489585e+00, -1.69668764e+00, ...,\n",
              "        -7.55472074e-16, -5.19116000e-16,  2.45398320e-15],\n",
              "       ...,\n",
              "       [ 1.06599426e+01,  4.14484743e-01,  8.04045178e-01, ...,\n",
              "        -3.27602528e-15, -6.03380193e-15,  2.86229374e-16],\n",
              "       [ 1.20729193e+01,  3.86609582e-01,  1.87214139e+00, ...,\n",
              "        -2.77382284e-15, -4.61262972e-15, -1.14058069e-15],\n",
              "       [ 5.80019420e+00,  4.00379327e-01, -2.64765952e+00, ...,\n",
              "        -2.25427316e-15, -3.53883589e-15, -4.64732419e-15]])"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "UHbpD3-oodXA",
        "outputId": "b80768e6-5f39-4ec3-cc45-bce1f4b518bf"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "per_var = np.round(pca.explained_variance_ratio_* 100, decimals=1)\n",
        "labels_all = ['PC'+str(x) for x in range(1, len(per_var)+1)]\n",
        "per_var=per_var[:10]\n",
        "labels = [str(x) for x in range(1, len(per_var)+1)]\n",
        "\n",
        "with plt.style.context('dark_background'):\n",
        "    plt.figure(figsize = (15, 9))\n",
        "    plt.xlabel(\"Number of Principal Components\")\n",
        "    plt.ylabel(\"Percentage of variance explained\")\n",
        "    plt.bar(range(1,len(per_var)+1), per_var, tick_label=labels, color = \"aqua\")\n",
        "    plt.plot(range(1,len(per_var)+1), np.cumsum(per_var), color = \"red\")\n",
        "    plt.scatter(range(1,len(per_var)+1), np.cumsum(per_var), color = \"yellow\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rHLyNlYaooVO"
      },
      "source": [
        "As you can see, the first principal component alone explains more than 50% of the variance in the dataset. To put this into context, more than **50% variance in 685 genomes of size 29000 bases could be explained with just one dimension**. This shows the amount of structure present in the dataset.\n",
        "\n",
        "We will be now visualizing the first 2 principal components and plot the genomes according to the State they belonged to."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "NunbP88XodZ0",
        "outputId": "abda7108-f021-4b87-bf48-f154df3cd86a"
      },
      "source": [
        "final_df = pd.DataFrame(columns=[\"StrainID\", \"PC1\", \"PC2\", \"PC3\", \"State\"])\n",
        "pca_df = pd.DataFrame(pca_data, index=[*sequences_name], columns = labels_all)\n",
        "\n",
        "for sample in pca_df.index:\n",
        "    \n",
        "    dicti = dict()\n",
        "    \n",
        "    dicti[\"StrainID\"] = sample\n",
        "    dicti[\"PC1\"] = pca_df.PC1.loc[sample]\n",
        "    dicti[\"PC2\"] = pca_df.PC2.loc[sample]\n",
        "    dicti[\"PC3\"] = pca_df.PC3.loc[sample]\n",
        "    dicti[\"State\"] = data[data[\"StrainID\"]==sample][\"State\"].tolist()[0]\n",
        "    \n",
        "    final_df = final_df.append(dicti, ignore_index = True)\n",
        "\n",
        "final_df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StrainID</th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "      <th>PC3</th>\n",
              "      <th>State</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>hCoV-19/India/763/2020|EPI_ISL_420543|2020-03-03</td>\n",
              "      <td>-4.326235</td>\n",
              "      <td>-3.484139</td>\n",
              "      <td>-1.786329</td>\n",
              "      <td>Stateless</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>hCoV-19/India/770/2020|EPI_ISL_420545|2020-03-03</td>\n",
              "      <td>-4.234399</td>\n",
              "      <td>-3.509996</td>\n",
              "      <td>-1.804263</td>\n",
              "      <td>Stateless</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>hCoV-19/India/772/2020|EPI_ISL_420547|2020-03-03</td>\n",
              "      <td>-4.184090</td>\n",
              "      <td>-3.754896</td>\n",
              "      <td>-1.696688</td>\n",
              "      <td>Stateless</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>hCoV-19/India/773/2020|EPI_ISL_420549|2020-03-03</td>\n",
              "      <td>-4.194559</td>\n",
              "      <td>-3.531680</td>\n",
              "      <td>-1.895380</td>\n",
              "      <td>Stateless</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>hCoV-19/India/777/2020|EPI_ISL_420551|2020-03-03</td>\n",
              "      <td>-4.186462</td>\n",
              "      <td>-3.550434</td>\n",
              "      <td>-1.888697</td>\n",
              "      <td>Stateless</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                           StrainID  ...      State\n",
              "0  hCoV-19/India/763/2020|EPI_ISL_420543|2020-03-03  ...  Stateless\n",
              "1  hCoV-19/India/770/2020|EPI_ISL_420545|2020-03-03  ...  Stateless\n",
              "2  hCoV-19/India/772/2020|EPI_ISL_420547|2020-03-03  ...  Stateless\n",
              "3  hCoV-19/India/773/2020|EPI_ISL_420549|2020-03-03  ...  Stateless\n",
              "4  hCoV-19/India/777/2020|EPI_ISL_420551|2020-03-03  ...  Stateless\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzUxyucCosjg"
      },
      "source": [
        "import plotly.graph_objs as go\n",
        "import plotly.express as px"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "PIwDqixTosm9",
        "outputId": "32012ac4-627b-4812-b653-ed576d4a12f2"
      },
      "source": [
        "for_x = final_df.PC1.tolist()\n",
        "for_y = final_df.PC2.tolist()\n",
        "for_states = final_df.State.tolist()\n",
        "for_hover = final_df.StrainID.tolist()\n",
        "\n",
        "fig = px.scatter(x=for_x, y=for_y, color=for_states, title=\"Principal Component Axis\",\n",
        "                color_discrete_map={\n",
        "                \"Stateless\": \"grey\",\n",
        "                \"Tamil Nadu\": \"green\",\n",
        "                \"Telangana\": \"red\",\n",
        "                \"Maharashtra\": \"orange\",\n",
        "                \"Gujarat\": \"magenta\",\n",
        "                \"Delhi\": \"purple\",\n",
        "                \"West Bengal\": \"aqua\",\n",
        "                \"Haryana\": \"pink\",\n",
        "                \"Odisha\": \"blue\",\n",
        "                \"Madhya Pradesh\": \"brown\",\n",
        "                \"Karnataka\": \"black\"\n",
        "                })\n",
        "\n",
        "fig.show(renderer = \"colab\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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        "Even though the plot looks densely clustered, there are still a lot of obvious observations that you should be able to make.\n",
        "\n",
        "### The first one, is that for some reason the Blue dots (Odisha) are scattered away from the big main cluster.\n",
        "\n",
        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)\n",
        "\n",
        "### Also, we can observe a secondary cluster away from the primary cluster. On inspecting, we can see that the sequences forming this cluster are primarily from Telangana (red), Delhi (magenta), and Tamil Nadu (green).\n",
        "\n",
        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)\n",
        "\n",
        "### The third less obvious observation is that within the main cluster, there is a dense cluster of sequences from Gujarat.\n",
        "\n",
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Z_H5O0epB43"
      },
      "source": [
        "## t-SNE"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZAvrdGToosqU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28dc2f6c-7103-41a4-92d7-ac17b3ce51ca"
      },
      "source": [
        "from sklearn.manifold import TSNE\n",
        "\n",
        "# Use these parameters\n",
        "random_state = 0\n",
        "n_components = 2\n",
        "perplexity = 30\n",
        "n_iter = 10000\n",
        "\n",
        "####### Enter code below ####### \n",
        "\n",
        "model = TSNE(n_components=2, random_state=random_state, perplexity=perplexity, n_iter=n_iter)\n",
        "tsne_data = model.fit_transform(big_matrix)\n",
        "\n",
        "################################"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning:\n",
            "\n",
            "The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning:\n",
            "\n",
            "The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2LlFxnf4pEZG",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "244f17ec-3512-41b5-e019-16b609e85bc3"
      },
      "source": [
        "final_df = pd.DataFrame(columns=[\"StrainID\", \"X\", \"Y\", \"State\"])\n",
        "pca_df = pd.DataFrame(pca_data, index=[*sequences_name], columns = labels_all)\n",
        "\n",
        "i = 0\n",
        "for sample in pca_df.index:\n",
        "    \n",
        "    dicti = dict()\n",
        "    \n",
        "    dicti[\"StrainID\"] = sample\n",
        "    dicti[\"X\"] = tsne_data[i][0]\n",
        "    dicti[\"Y\"] = tsne_data[i][1]\n",
        "    dicti[\"State\"] = data[data[\"StrainID\"]==sample][\"State\"].tolist()[0]\n",
        "    \n",
        "    final_df = final_df.append(dicti, ignore_index = True)\n",
        "    i+=1\n",
        "\n",
        "final_df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StrainID</th>\n",
              "      <th>X</th>\n",
              "      <th>Y</th>\n",
              "      <th>State</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <td>Delhi</td>\n",
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              "    <tr>\n",
              "      <th>682</th>\n",
              "      <td>hCoV-19/India/MaxCov0048_CSIR-IGIB/2020|EPI_IS...</td>\n",
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              "      <td>1.364001</td>\n",
              "      <td>12.977669</td>\n",
              "      <td>Delhi</td>\n",
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              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>hCoV-19/India/MaxCov0050_CSIR-IGIB/2020|EPI_IS...</td>\n",
              "      <td>2.358655</td>\n",
              "      <td>10.985281</td>\n",
              "      <td>Delhi</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              StrainID  ...  State\n",
              "680  hCoV-19/India/MaxCov0046_CSIR-IGIB/2020|EPI_IS...  ...  Delhi\n",
              "681  hCoV-19/India/MaxCov0047_CSIR-IGIB/2020|EPI_IS...  ...  Delhi\n",
              "682  hCoV-19/India/MaxCov0048_CSIR-IGIB/2020|EPI_IS...  ...  Delhi\n",
              "683  hCoV-19/India/MaxCov0049_CSIR-IGIB/2020|EPI_IS...  ...  Delhi\n",
              "684  hCoV-19/India/MaxCov0050_CSIR-IGIB/2020|EPI_IS...  ...  Delhi\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "us4Joar_pEcB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "outputId": "551df2ba-37bb-4a99-fad2-e5ce60904581"
      },
      "source": [
        "for_x = final_df.X.tolist()\n",
        "for_y = final_df.Y.tolist()\n",
        "for_states = final_df.State.tolist()\n",
        "for_hover = final_df.StrainID.tolist()\n",
        "\n",
        "fig = px.scatter(x=for_x, y=for_y, color=for_states, title=\"t-SNE\", height=600, width=1000,\n",
        "                color_discrete_map={\n",
        "                \"Stateless\": \"grey\",\n",
        "                \"Tamil Nadu\": \"green\",\n",
        "                \"Telangana\": \"red\",\n",
        "                \"Maharashtra\": \"orange\",\n",
        "                \"Gujarat\": \"magenta\",\n",
        "                \"Delhi\": \"purple\",\n",
        "                \"West Bengal\": \"aqua\",\n",
        "                \"Haryana\": \"pink\",\n",
        "                \"Odisha\": \"blue\",\n",
        "                \"Madhya Pradesh\": \"brown\",\n",
        "                \"Karnataka\": \"black\"})\n",
        "\n",
        "fig.show(renderer = 'colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('9c087784-2783-4093-be4f-54d470710246');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
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              "    x.observe(notebookContainer, {childList: true});\n",
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              "\n",
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              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
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              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4bCtiFsOpZaE"
      },
      "source": [
        "You should get a plot similar to this:\n",
        "\n",
        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)\n",
        "\n",
        "**Does making the 3 observations become easier in this plot? \n",
        "Can you make any other new observations from this plot?**\n",
        "\n",
        "Make sure you tweak the parameters to find out if a certain different setting is allowing you to get a more interpretable plot."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ECFR423VpcVd"
      },
      "source": [
        "# Mutation Profile based methods\n",
        "\n",
        "If you would have noticed, there is a column called Nuc_muts (Nucleotide mutations) in the INDIA_685.csv file. These are the specific mutations for each sequence which I have precomputed for you using the first SARS-CoV-2 sequence from Wuhan, China (https://www.ncbi.nlm.nih.gov/nuccore/NC_045512.2?report=fasta) as the reference.\n",
        "\n",
        "A mutation C453T in a sequence means that the original reference sequence from Wuhan had a C at position 453 whereas the sequence has a T at that position. \n",
        "\n",
        "Now instead of using kmers for sequences, this time we will use the **presence/absence of all mutations** found in our dataset to represent the genomes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zrbWFyL2pEed",
        "outputId": "8fc84f02-3b5f-4dfc-b301-9d215352ed0b"
      },
      "source": [
        "## First we need to get the list of all unique mutations observed in our dataset.\n",
        "\n",
        "mutations = []\n",
        "for i in range(len(data)):\n",
        "\n",
        "    sample_muts = data[\"Nuc_muts\"][i][1:][:-1].split(\",\")\n",
        "\n",
        "    for j in sample_muts:\n",
        "        mut = j.strip()[1:][:-1]\n",
        "        if(len(mut)>=2):\n",
        "            if(mut not in mutations):\n",
        "                mutations.append(mut)\n",
        "\n",
        "print(len(mutations))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1279\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wHS8f3jCphLc"
      },
      "source": [
        "So there are 1279 unique mutations in our sequences. We can now represent each genome as a ```vector of size 1279 where each position indicates the presence (1) or absence (0) of a specific mutation```."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "uR3nnmgMpEhu",
        "outputId": "4f1b82a4-aada-4664-8a66-50487453d99c"
      },
      "source": [
        "samplelist = data[\"StrainID\"].tolist()\n",
        "df = pd.DataFrame(columns = [*samplelist])\n",
        "df = df.replace(np.nan,0)\n",
        "\n",
        "for mutation in mutations:\n",
        "\n",
        "    dicti = dict(zip(samplelist, np.zeros(len(samplelist), dtype=int)))\n",
        "\n",
        "    for i in range(len(data)):\n",
        "        flag=0\n",
        "        strainid = data[\"StrainID\"][i]\n",
        "        sample_muts = data[\"Nuc_muts\"][i][1:][:-1].split(\",\")\n",
        "\n",
        "        for j in sample_muts:\n",
        "            mut = j.strip()[1:][:-1]\n",
        "            if(len(mut)>=2):\n",
        "                if(mut == mutation):\n",
        "                    flag = 1\n",
        "                    break\n",
        "\n",
        "        dicti[strainid]=flag  \n",
        "\n",
        "    df = df.append(dicti, ignore_index = True)\n",
        "    \n",
        "df = df.T\n",
        "\n",
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1279 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                   0    1     ... 1277 1278\n",
              "hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|202...    0    0  ...    0    0\n",
              "hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|202...    0    0  ...    1    0\n",
              "hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|202...    0    0  ...    0    1\n",
              "hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|202...    0    0  ...    0    0\n",
              "hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|202...    0    0  ...    0    0\n",
              "\n",
              "[5 rows x 1279 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V9CIPpHvpmc2"
      },
      "source": [
        "## PCA\n",
        "\n",
        "Performing PCA on this dataframe, we want to reduce the dimensionality of our genome sequences from 1279 to 2."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "64svk1wEpjYC"
      },
      "source": [
        "pca = PCA()\n",
        "pca.fit(df)\n",
        "pca_data = pca.transform(df)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wTEZQIDGpjbB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "outputId": "8223665f-e2ed-48dc-aebe-ffbe5c15171a"
      },
      "source": [
        "per_var = np.round(pca.explained_variance_ratio_* 100, decimals=1)\n",
        "labels_all = ['PC'+str(x) for x in range(1, len(per_var)+1)]\n",
        "per_var=per_var[:10]\n",
        "labels = [str(x) for x in range(1, len(per_var)+1)]\n",
        "\n",
        "with plt.style.context('dark_background'):\n",
        "    plt.figure(figsize = (15, 9))\n",
        "    plt.xlabel(\"Number of Principal Components\")\n",
        "    plt.ylabel(\"Percentage of variance explained\")\n",
        "    plt.bar(range(1,len(per_var)+1), per_var, tick_label=labels, color = \"aqua\")\n",
        "    plt.plot(range(1,len(per_var)+1), np.cumsum(per_var), color = \"red\")\n",
        "    plt.scatter(range(1,len(per_var)+1), np.cumsum(per_var), color = \"yellow\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x648 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W_w644b-ptLV"
      },
      "source": [
        "The first principal component should be able to capture close to 30% of the variance in the dataset."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edpEBZXSpjd2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "35cac672-7480-4415-e404-5f8ad6941401"
      },
      "source": [
        "final_df = pd.DataFrame(columns=[\"StrainID\", \"PC1\", \"PC2\", \"State\"])\n",
        "pca_df = pd.DataFrame(pca_data, index=[*samplelist], columns = labels_all)\n",
        "\n",
        "for sample in pca_df.index:\n",
        "    \n",
        "    dicti = dict()\n",
        "    \n",
        "    dicti[\"StrainID\"] = sample\n",
        "    dicti[\"PC1\"] = pca_df.PC1.loc[sample]\n",
        "    dicti[\"PC2\"] = pca_df.PC2.loc[sample]\n",
        "    dicti[\"State\"] = data[data[\"StrainID\"]==sample][\"State\"].tolist()[0]\n",
        "    \n",
        "    final_df = final_df.append(dicti, ignore_index = True)\n",
        "\n",
        "final_df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>StrainID</th>\n",
              "      <th>PC1</th>\n",
              "      <th>PC2</th>\n",
              "      <th>State</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>680</th>\n",
              "      <td>hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|20...</td>\n",
              "      <td>1.530221</td>\n",
              "      <td>0.079550</td>\n",
              "      <td>Karnataka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>681</th>\n",
              "      <td>hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|20...</td>\n",
              "      <td>-1.063943</td>\n",
              "      <td>-0.405022</td>\n",
              "      <td>Karnataka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>682</th>\n",
              "      <td>hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|20...</td>\n",
              "      <td>-1.058232</td>\n",
              "      <td>-0.410670</td>\n",
              "      <td>Karnataka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683</th>\n",
              "      <td>hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|20...</td>\n",
              "      <td>-1.057465</td>\n",
              "      <td>-0.409620</td>\n",
              "      <td>Karnataka</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>684</th>\n",
              "      <td>hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|20...</td>\n",
              "      <td>-1.057465</td>\n",
              "      <td>-0.409620</td>\n",
              "      <td>Karnataka</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                              StrainID  ...      State\n",
              "680  hCoV-19/India/nimh-2873/2020|EPI_ISL_436137|20...  ...  Karnataka\n",
              "681  hCoV-19/India/nimh-3952/2020|EPI_ISL_436138|20...  ...  Karnataka\n",
              "682  hCoV-19/India/nimh-3970/2020|EPI_ISL_436139|20...  ...  Karnataka\n",
              "683  hCoV-19/India/nimh-4376/2020|EPI_ISL_436140|20...  ...  Karnataka\n",
              "684  hCoV-19/India/nimh-4378/2020|EPI_ISL_436141|20...  ...  Karnataka\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1TTC9hCppjgp",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637
        },
        "outputId": "5350ef3b-7f95-4dc5-a153-87195e4fedec"
      },
      "source": [
        "for_x = final_df.PC1.tolist()\n",
        "for_y = final_df.PC2.tolist()\n",
        "for_states = final_df.State.tolist()\n",
        "for_hover = final_df.StrainID.tolist()\n",
        "\n",
        "fig = px.scatter(x=for_x, y=for_y, color=for_states, height=600, width=1200,\n",
        "                 title=\"Principal Component Axis\",\n",
        "                color_discrete_map={\n",
        "                \"Stateless\": \"grey\",\n",
        "                \"Tamil Nadu\": \"green\",\n",
        "                \"Telangana\": \"red\",\n",
        "                \"Maharashtra\": \"orange\",\n",
        "                \"Gujarat\": \"magenta\",\n",
        "                \"Delhi\": \"purple\",\n",
        "                \"West Bengal\": \"aqua\",\n",
        "                \"Haryana\": \"pink\",\n",
        "                \"Odisha\": \"blue\",\n",
        "                \"Madhya Pradesh\": \"brown\",\n",
        "                \"Karnataka\": \"black\"})\n",
        "fig.show(renderer = 'colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"title\": {\"text\": \"Principal Component Axis\"}, \"width\": 1200, \"xaxis\": {\"anchor\": \"y\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"x\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0], \"title\": {\"text\": \"y\"}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('4401e2df-6247-4649-a127-acb3a0b2032a');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K5Gr0uSxp58y"
      },
      "source": [
        "You should get a plot like this:\n",
        "\n",
        "![image.png](data:image/png;base64,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)\n",
        "\n",
        "**This makes the above observations so much easier. Can you list them with respect to this plot?**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fK7u44_Wp9_k"
      },
      "source": [
        "# t-SNE\n",
        "\n",
        "Now this time you will be expected to write more than just 3 lines of the code to get the t-SNE plot for this data. Make sure you play with the **perplexity** values enough to appreciate the algorithm. I hope you are able to retrieve the 3 observations and make some new ones from the plot.\n",
        "\n",
        "---\n",
        "\n",
        "You are encouraged to also implement **ISOMAP (from Lab-4)** and try to get more insights from the plots."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sVJkY3_16ViB"
      },
      "source": [
        "**ANSWERS**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AU8V1X9hcH82",
        "outputId": "d4dc782c-3c1f-4ae7-b326-756909ef59b3"
      },
      "source": [
        "#t-SNE \n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "# Use these parameters\n",
        "random_state = 0\n",
        "n_components = 2\n",
        "perplexity = 60\n",
        "n_iter = 8000\n",
        "\n",
        "\n",
        "model = TSNE(n_components=2, random_state=random_state, perplexity=perplexity, n_iter=n_iter)\n",
        "tsne_data = model.fit_transform(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:783: FutureWarning:\n",
            "\n",
            "The default initialization in TSNE will change from 'random' to 'pca' in 1.2.\n",
            "\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/manifold/_t_sne.py:793: FutureWarning:\n",
            "\n",
            "The default learning rate in TSNE will change from 200.0 to 'auto' in 1.2.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "-EhIE6mAIr30",
        "outputId": "e7b3066d-357c-41e3-e34f-ce7221df1f86"
      },
      "source": [
        "final_df = pd.DataFrame(columns=[\"StrainID\", \"X\", \"Y\", \"State\"])\n",
        "pca_df = pd.DataFrame(pca_data, index=[*sequences_name], columns = labels_all)\n",
        "\n",
        "i = 0\n",
        "for sample in pca_df.index:\n",
        "    \n",
        "    dicti = dict()\n",
        "    \n",
        "    dicti[\"StrainID\"] = sample\n",
        "    dicti[\"X\"] = tsne_data[i][0]\n",
        "    dicti[\"Y\"] = tsne_data[i][1]\n",
        "    dicti[\"State\"] = data[data[\"StrainID\"]==sample][\"State\"].tolist()[0]\n",
        "    \n",
        "    final_df = final_df.append(dicti, ignore_index = True)\n",
        "    i+=1\n",
        "\n",
        "final_df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>StrainID</th>\n",
              "      <th>X</th>\n",
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              "      <th>680</th>\n",
              "      <td>hCoV-19/India/MaxCov0046_CSIR-IGIB/2020|EPI_IS...</td>\n",
              "      <td>3.014042</td>\n",
              "      <td>21.180193</td>\n",
              "      <td>Delhi</td>\n",
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              "      <td>0.944139</td>\n",
              "      <td>-6.759534</td>\n",
              "      <td>Delhi</td>\n",
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              "      <td>0.944148</td>\n",
              "      <td>-6.759574</td>\n",
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              "                                              StrainID  ...  State\n",
              "680  hCoV-19/India/MaxCov0046_CSIR-IGIB/2020|EPI_IS...  ...  Delhi\n",
              "681  hCoV-19/India/MaxCov0047_CSIR-IGIB/2020|EPI_IS...  ...  Delhi\n",
              "682  hCoV-19/India/MaxCov0048_CSIR-IGIB/2020|EPI_IS...  ...  Delhi\n",
              "683  hCoV-19/India/MaxCov0049_CSIR-IGIB/2020|EPI_IS...  ...  Delhi\n",
              "684  hCoV-19/India/MaxCov0050_CSIR-IGIB/2020|EPI_IS...  ...  Delhi\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 116
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 617
        },
        "id": "81dhN_cixZ6O",
        "outputId": "0c85a2d2-a970-40df-ed1c-2d075b61bfc8"
      },
      "source": [
        "for_x = final_df.X.tolist()\n",
        "for_y = final_df.Y.tolist()\n",
        "for_states = final_df.State.tolist()\n",
        "for_hover = final_df.StrainID.tolist()\n",
        "\n",
        "fig = px.scatter(x=for_x, y=for_y, color=for_states, title=\"t-SNE\", height=600, width=1000,\n",
        "                color_discrete_map={\n",
        "                \"Stateless\": \"grey\",\n",
        "                \"Tamil Nadu\": \"green\",\n",
        "                \"Telangana\": \"red\",\n",
        "                \"Maharashtra\": \"orange\",\n",
        "                \"Gujarat\": \"magenta\",\n",
        "                \"Delhi\": \"purple\",\n",
        "                \"West Bengal\": \"aqua\",\n",
        "                \"Haryana\": \"pink\",\n",
        "                \"Odisha\": \"blue\",\n",
        "                \"Madhya Pradesh\": \"brown\",\n",
        "                \"Karnataka\": \"black\"})\n",
        "\n",
        "fig.show(renderer = 'colab')"
      ],
      "execution_count": null,
      "outputs": [
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        "# Conclusion\n",
        "\n",
        "You might be wondering how these 3 observations help in understanding the demographic spread of the virus in the country. \n",
        "\n",
        "### This is not a Bioinformatics course, so you may choose to ignore the details mentioned below\n",
        "But the idea was to motivate you to understand the importance of visualization techniques even with no domain knowlege.\n",
        "\n",
        "---\n",
        "---\n",
        "\n",
        "On performing genetic analysis on the sequences to understand why exactly these observations could have been happening I found the following:\n",
        "\n",
        "#### Observation 1: \n",
        "Samples from Odisha are different than the rest of the country, hence they were scattered away from the clusters in the plots.\n",
        "\n",
        "#### Reason: \n",
        "Genomic sequences of isolates from Odisha were shorter in length with missing bases (deletions); 1 - 29 bases in 5' UTR (38 isolates), 23842 - 24400 bases in S gene, 26306-26524 bases in E gene, 27527 – 28033 bases in ORF7b and ORF8 (8 samples), 28462 - 28680 bases in the N gene (10 samples), 29000 – 29685 in ORF10 gene (15 samples), and 29686 - 29903 bases in 3' UTR (39 isolates). These deletions were unique to the state and werent observed in samples from the rest of the country. \n",
        "\n",
        "#### Observation 2: \n",
        "A secondary cluster away from the primary cluster with sequences from Telangana, Delhi, and Tamil Nadu.\n",
        "\n",
        "#### Reason: \n",
        "About one-third of the Indian isolates (219/685) are part of a subclade I/A3i defined by the co-occurring mutations C6312A, C13730T, C23929T, and C28311T. The subclade's predominance is observed in Telangana (69/97, 71%), Delhi (52/76, 68%), and Tamil Nadu (16/34). Analysis of this subclade is important both scientifically and epidemiologically as the mutations defining it are found in 32% of the Indian samples sequenced, while outside India its distribution is very low (3.5%). This clearly hints at early community transmission due to some super spreader event during March-April, as it is highly unlikely that around one-third of the samples sharing the same set of mutations could have arisen by multiple independent entries with international travel history, especially when its presence globally is negligibly small.\n",
        "\n",
        "#### Observation 3:\n",
        "A dense cluster of sequences from Gujarat.\n",
        "\n",
        "#### Reason:\n",
        "Isolates from Gujarat are a part of a subclade, I/GJ-20A defined by a shared set of mutations C18877T, G25563T (ORF3a: Q57H), and C26735T. This set of mutations are not well represented in the rest of the country. Mutations present in Gujarat have also been found in samples from Europe and that could have been the possible route of transmission into India. Also, Gujarat has the highest COVID-19 death rate (5.12%) in the country (Avg: 2.67%), and this death rate in Gujarat is comparable to that in Europe. This high fatality could be due to these characteristic mutations increasing the deleteriousness of the virus.\n",
        "\n",
        "---\n",
        "\n",
        "**So I hope you appreciate the valuable insights visualization techniques provide us with.** \n",
        "\n",
        "Best of luck for the next Module......\n",
        "---"
      ]
    }
  ]
}