ALFA_genesum.ipynb

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   "source": [
    "# Plot population minor allele frequencies (MAFs) for genes\n",
    "\n",
    "Let's compare the MAF for a few populations over the [APOE gene](https://www.ncbi.nlm.nih.gov/variation/view/?cfg=NCID_1_20854257_130.14.22.10_9146_1558129725_3670287406) region.\n",
    "The region is located on chromosome 19 (NC_000019.10): 44,905,384 - 44,909,760\n",
    "\n",
    "## Requirements\n",
    "There are a few things we need to do before starting:\n",
    "\n",
    "NB: This workbook has been tested with Python 3.6 and Python 3.7."
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "%pip install -q requests matplotlib pandas > /dev/null"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
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   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
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   "source": [
    "from requests import get\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import defaultdict\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1651763589386,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "iaPoPETkvQp_"
   },
   "outputs": [],
   "source": [
    "plt.rcParams['figure.figsize'] = [16, 8]  # Make plot bigger"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2HIpQpjlvQp_"
   },
   "source": [
    "## Download data\n",
    "Here is a genomic region we are going to plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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    "id": "4h7_CiaFvQp_"
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   "outputs": [],
   "source": [
    "seq_id = 'NC_000019.10'\n",
    "start = 44905384\n",
    "stop = 44909760"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 1032,
     "status": "ok",
     "timestamp": 1651763590412,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "wgBDlTb2vQqA",
    "outputId": "08e59522-c451-4674-cc92-3d6014da11b6"
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "206\n"
     ]
    }
   ],
   "source": [
    "resp = get(f'https://api.ncbi.nlm.nih.gov/variation/v0/interval/{seq_id}:{start}:{stop - start}/overlapping_frequency_records')\n",
    "print(resp.status_code)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "U35QbPWYvQqA"
   },
   "source": [
    "**Status code**: `206` means that not all the requested variants fit into a single reply.\n",
    "So we need to parse the data, get the latest position and continue requesting data until we get a reply with status code 200."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1651763590414,
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   "source": [
    "def download(seq_id: str, start: int, stop: int):\n",
    "    \"\"\"\n",
    "    Download all frequencies in a specified region\n",
    "    \"\"\"\n",
    "    data = {}\n",
    "    while True:\n",
    "        length = max(1, stop - start + 1)\n",
    "        resp = get(f'https://api.ncbi.nlm.nih.gov/variation/v0/interval/{seq_id}:{start}:{length}/overlapping_frequency_records')\n",
    "        if resp.status_code // 100 != 2:\n",
    "            raise Exception(\"Unable to download data\", resp.status_code, resp.content)\n",
    "        result = resp.json()[\"results\"]\n",
    "        data.update(result)\n",
    "        if resp.status_code == 200:\n",
    "            return data  # We are done\n",
    "\n",
    "        for k,v in result.items():\n",
    "            l, s = k.split('@')\n",
    "            start = max(start, int(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "executionInfo": {
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    "id": "1VsY6TS0vQqC"
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   "outputs": [],
   "source": [
    "data = download(seq_id, start, stop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 12,
     "status": "ok",
     "timestamp": 1651763598127,
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    },
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of variations:  1472\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of variations: \", len(data))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Gn2SNz4_vQqC"
   },
   "source": [
    "Now, for each record, we need to calculate MAF per population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
     "timestamp": 1651763598127,
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      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
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    },
    "id": "1fUXULWuvQqD"
   },
   "outputs": [],
   "source": [
    "def maf(record):\n",
    "    \"\"\"\n",
    "    Calculate minor allele frequencies (MAFs) for all populations within the given record\n",
    "    \"\"\"\n",
    "    maf = {}  # Population to MAF\n",
    "    for project, counts in record['counts'].items():\n",
    "        for population, ac in counts['allele_counts'].items():\n",
    "            counts = [allele_count for allele, allele_count in ac.items()]\n",
    "            counts.sort(reverse=True)\n",
    "            if len(counts) <= 1:\n",
    "                maf[population] = 0\n",
    "                continue\n",
    "            total = sum(counts)\n",
    "            if total > 0: \n",
    "                maf[population] = counts[1] / total\n",
    "            else:\n",
    "                maf[population] = 0\n",
    "    return maf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "executionInfo": {
     "elapsed": 9,
     "status": "ok",
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   "outputs": [],
   "source": [
    "def translate(data):\n",
    "    \"\"\"\n",
    "    Translate overlapped region endpoint result to data ready to plot.\n",
    "    \"\"\"\n",
    "    position = []\n",
    "    freq = defaultdict(list)\n",
    "    for k, v in data.items():\n",
    "        l, s = k.split('@')\n",
    "        start = int(s)\n",
    "        position.append(start)\n",
    "        mafs = maf(v)\n",
    "        for p, ma in mafs.items():\n",
    "            freq[p].append(ma)\n",
    "    return position, freq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "executionInfo": {
     "elapsed": 222,
     "status": "ok",
     "timestamp": 1651763598341,
     "user": {
      "displayName": "Lon Phan",
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     },
     "user_tz": 240
    },
    "id": "GLM-KmMbvQqD"
   },
   "outputs": [],
   "source": [
    "x, pop = translate(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mMkGb4-MvQqD"
   },
   "source": [
    "## Metadata\n",
    "If we look into this data we will see that all populations were reported using their biosample ids. That is not the best way to use in a visualization.\n",
    "So, we are going to translate it to human readable form: here is where the metadata service is helpful."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
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     "user_tz": 240
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    "id": "i-CMoPjpvQqE"
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   "outputs": [],
   "source": [
    "def build_pop_map():\n",
    "    \"\"\"\n",
    "    Download metadata and parse it.\n",
    "    \"\"\"\n",
    "    meta_r = get('https://api.ncbi.nlm.nih.gov/variation/v0/metadata/frequency')\n",
    "    if meta_r.status_code != 200:\n",
    "        raise Exception(\"Can't download metadata\")\n",
    "    result = {}\n",
    "    def _read(pops):\n",
    "        for pop in pops:\n",
    "            result[pop[\"biosample_id\"]] = pop[\"name\"]\n",
    "            if \"subs\" in pop:\n",
    "                _read(pop[\"subs\"])\n",
    "    _read(meta_r.json()[0][\"populations\"])\n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "executionInfo": {
     "elapsed": 147,
     "status": "ok",
     "timestamp": 1651763598487,
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     "user_tz": 240
    },
    "id": "9WjdmXWivQqE"
   },
   "outputs": [],
   "source": [
    "meta = build_pop_map()  # Now meta is the map of biosample_id to population name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import cycler\n",
    "def show1():\n",
    "    for p, data in pop.items():\n",
    "        # Plot only non-zero MAF\n",
    "        filtered_x = [xx for i,xx in enumerate(x) if data[i] > 0]\n",
    "        filtered_data = [dd for i,dd in enumerate(data) if data[i] > 0]\n",
    "        plt.scatter(filtered_x, filtered_data, label=meta[p])\n",
    "\n",
    "    plt.title('Minor Allele Frequency')\n",
    "    plt.legend(loc=2, bbox_to_anchor=(1.04,1))\n",
    "    plt.yscale('log')\n",
    "    num_colors = len(meta)\n",
    "    colormap = plt.get_cmap('gist_rainbow')\n",
    "    plt.rc('axes', prop_cycle=cycler(color=[colormap(1.*i/num_colors) for i in range(num_colors)]))\n",
    "    plt.show()\n",
    "show1()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "2o85E46NvQqF"
   },
   "source": [
    "# Comparing population frequencies\n",
    "Let's build a scatter plot for allele frequencies for two populations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "executionInfo": {
     "elapsed": 184,
     "status": "ok",
     "timestamp": 1651763600662,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "E0C9qpgPvQqF"
   },
   "outputs": [],
   "source": [
    "def cross_frequency(data, pop_id_x, pop_id_y):\n",
    "    \n",
    "    res_x = defaultdict(list)\n",
    "    res_y = defaultdict(list)\n",
    "    records_missing_pop_data = 0\n",
    "    total_records = 0\n",
    "    for _, record in data.items():\n",
    "        total_records += 1\n",
    "        for _, counts in record['counts'].items():\n",
    "            \n",
    "            px = counts['allele_counts'][pop_id_x]\n",
    "            py = counts['allele_counts'][pop_id_y]\n",
    "            \n",
    "            def _pop_freq(p):\n",
    "                r = {}\n",
    "                total = sum([allele_count for allele, allele_count in p.items()])\n",
    "                if total == 0: # This population had no one with any measurements\n",
    "                    return None\n",
    "                for allele, allele_count in p.items():\n",
    "                    r[allele] = allele_count / total\n",
    "                return r\n",
    "            pfx = _pop_freq(px)\n",
    "            pfy = _pop_freq(py)\n",
    "            if pfx is None or pfy is None:\n",
    "              records_missing_pop_data += 1\n",
    "              continue\n",
    "            for a, fx in pfx.items():\n",
    "                if a in pfy:\n",
    "                    res_x[a].append(fx)\n",
    "                    res_y[a].append(pfy[a])\n",
    "    print(f\"Skipped {records_missing_pop_data} of {total_records} records because they had no measurements for one of the populations\",\n",
    "         file=sys.stderr)\n",
    "    return res_x, res_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1651763600663,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "f6PXLEvovQqF"
   },
   "outputs": [],
   "source": [
    "def show_cross_frequency(seq_id, start, stop, pop_x, pop_y):\n",
    "    data = download(seq_id, start, stop)\n",
    "    cf_x, cf_y = cross_frequency(data, pop_x, pop_y)\n",
    "    for allele, allele_freq_x in cf_x.items():\n",
    "        allele_freq_y = cf_y[allele]\n",
    "        \n",
    "        plt.scatter(allele_freq_x, allele_freq_y, label=allele)\n",
    "        plt.xlabel(meta[pop_x])\n",
    "        plt.ylabel(meta[pop_y])\n",
    "    plt.title(f'Cross Allele frequency {seq_id}, {start}, {stop}')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 2,
     "status": "ok",
     "timestamp": 1651763600663,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "ILstvZxzvQqF"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "executionInfo": {
     "elapsed": 8029,
     "status": "ok",
     "timestamp": 1651763608690,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "Q9dNeQRWvQqG",
    "outputId": "3ed98444-6888-4d80-8af1-dfdd743e7bcf"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Skipped 9 of 1472 records because they had no measurements for one of the populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_cross_frequency('NC_000019.10', 44905384, 44909760, 'SAMN10492695', 'SAMN10492703')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "executionInfo": {
     "elapsed": 30191,
     "status": "ok",
     "timestamp": 1651763638879,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "SeyYl0TzvQqG",
    "outputId": "c7de5225-f5bf-4337-8278-58dec48ac44e"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Skipped 34 of 4639 records because they had no measurements for one of the populations\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_cross_frequency('NC_000011.10',61362000, 61378224, 'SAMN10492695', 'SAMN10492703')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "executionInfo": {
     "elapsed": 3,
     "status": "ok",
     "timestamp": 1651763638879,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "x5QY48_NvQqG"
   },
   "outputs": [],
   "source": [
    "def show_genesum_frequency(df):\n",
    "    \n",
    "    colormap = plt.get_cmap('gist_rainbow')\n",
    "    num_colors=len(df.columns)\n",
    "    plt.rc('axes', prop_cycle=cycler(color=[colormap(1.*i/num_colors) for i in range(num_colors)]))\n",
    "    \n",
    "    meta = build_pop_map()\n",
    "    plt.rcParams[\"figure.figsize\"] = [7.50, 3.50]\n",
    "    plt.rcParams[\"figure.autolayout\"] = True\n",
    "    \n",
    "    common=[]\n",
    "    rare=[]\n",
    "    ultrarare=[]\n",
    "    missing=[]\n",
    "    pop_codes=list(df.columns)\n",
    "    pop_names=[]\n",
    "    for c in pop_codes:\n",
    "        pop_names.append(meta[c])\n",
    "    \n",
    "    for p in df.columns:\n",
    "        common.append(len(df[p][df[p]>=0.01]))\n",
    "        rare.append(len(df[p][(df[p]>=0.001) & (df[p]<=0.01)]))\n",
    "        ultrarare.append(len(df[p][(df[p]>0) & (df[p]<=0.001)] ))\n",
    "        missing.append(len(df[p][df[p]==0 ]))\n",
    "    \n",
    "    b1 = plt.barh(pop_names, common, color='green' )\n",
    "    b2 = plt.barh(pop_names, rare, left=common, color='orange' )\n",
    "    b3 = plt.barh(pop_names, ultrarare, left=rare, color='red' )\n",
    "    b4 = plt.barh(pop_names, missing, left=ultrarare, color='lightgrey' )\n",
    "\n",
    "    plt.legend([b1, b2, b3, b4], [\"Common\", \"Rare\", \"Ultra Rare\", \"Missing\"], title=\"Freq Count\", loc=\"upper right\")\n",
    "\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 513
    },
    "executionInfo": {
     "elapsed": 353,
     "status": "ok",
     "timestamp": 1651763639230,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "ay-TaGkcvQqG",
    "outputId": "bf8b474c-62d1-485f-998a-30fd597e876a"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1651763639230,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "JHKeI7YpvQqG"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1651763639231,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "_7FoCxydvnii"
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data=pop)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 540x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_genesum_frequency(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 11,
     "status": "ok",
     "timestamp": 1651763639231,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "qRpdG0llxlIZ",
    "outputId": "8933d3a2-0413-43f0-dea7-0809dfa5d356"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-41938792-46d2-4c06-9bef-cd5a0f7dd3d9\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>SAMN10492695</th>\n",
       "      <th>SAMN10492696</th>\n",
       "      <th>SAMN10492697</th>\n",
       "      <th>SAMN10492698</th>\n",
       "      <th>SAMN10492699</th>\n",
       "      <th>SAMN10492700</th>\n",
       "      <th>SAMN10492701</th>\n",
       "      <th>SAMN10492702</th>\n",
       "      <th>SAMN11605645</th>\n",
       "      <th>SAMN10492703</th>\n",
       "      <th>SAMN10492704</th>\n",
       "      <th>SAMN10492705</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-41938792-46d2-4c06-9bef-cd5a0f7dd3d9')\"\n",
       "              title=\"Convert this dataframe to an interactive table.\"\n",
       "              style=\"display:none;\">\n",
       "        \n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
       "       width=\"24px\">\n",
       "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-41938792-46d2-4c06-9bef-cd5a0f7dd3d9 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-41938792-46d2-4c06-9bef-cd5a0f7dd3d9');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ],
      "text/plain": [
       "   SAMN10492695  SAMN10492696  SAMN10492697  SAMN10492698  SAMN10492699  \\\n",
       "0           0.0           0.0           0.0           0.0           0.0   \n",
       "1           0.0           0.0           0.0           0.0           0.0   \n",
       "2           0.0           0.0           0.0           0.0           0.0   \n",
       "3           0.0           0.0           0.0           0.0           0.0   \n",
       "4           0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "   SAMN10492700  SAMN10492701  SAMN10492702  SAMN11605645  SAMN10492703  \\\n",
       "0           0.0           0.0           0.0           0.0           0.0   \n",
       "1           0.0           0.0           0.0           0.0           0.0   \n",
       "2           0.0           0.0           0.0           0.0           0.0   \n",
       "3           0.0           0.0           0.0           0.0           0.0   \n",
       "4           0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "   SAMN10492704  SAMN10492705  \n",
       "0           0.0           0.0  \n",
       "1           0.0           0.0  \n",
       "2           0.0           0.0  \n",
       "3           0.0           0.0  \n",
       "4           0.0           0.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "def show_colored_frequency(df):\n",
    "    from matplotlib import colors\n",
    "    meta = build_pop_map()\n",
    "    pop_codes=list(meta.keys())\n",
    "    pop_names=list(meta.values())\n",
    "    \n",
    "    plt.rcParams[\"figure.figsize\"] = [7.50, 15]\n",
    "    plt.rcParams[\"figure.autolayout\"] = True\n",
    "    \n",
    "    plot_data=[]\n",
    "    \n",
    "    for i in df.index:\n",
    "        if df['SAMN10492705'][i]==0:\n",
    "            continue\n",
    "        else:\n",
    "            for c in pop_codes:\n",
    "                if df[c][i]>=0.01:\n",
    "                    plot_data.append(1)\n",
    "                elif df[c][i]>=0.001 and df[c][i]<0.01:\n",
    "                    plot_data.append(2)\n",
    "                elif df[c][i]>0 and df[c][i]<0.001:\n",
    "                    plot_data.append(3)\n",
    "                else:\n",
    "                    plot_data.append(0)\n",
    "    array=np.reshape(plot_data,(-1, len(pop_codes)))\n",
    "    cmap = colors.ListedColormap(['lightgrey', 'green','orange', 'red'])\n",
    "    fig, ax = plt.subplots(figsize=(2, 5))\n",
    "    ax.imshow(array,cmap=cmap)\n",
    "    ax.set_aspect('auto')\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 144x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "show_colored_frequency(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x864 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X=np.random.randint(256, size=(10, 10))\n",
    "\n",
    "fig = plt.figure(figsize=(8,12))\n",
    "plt.imshow(X)\n",
    "plt.title(\"Plot 2D array\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 152,
     "status": "ok",
     "timestamp": 1651764038802,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "RmWDinq1yZ8d",
    "outputId": "366aacb5-a262-4208-9459-83b18538214b"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_values(['Total', 'European', 'African', 'African Others', 'African American', 'Asian', 'East Asian', 'Other Asian', 'Latin American 1', 'Latin American 2', 'South Asian', 'Other'])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "executionInfo": {
     "elapsed": 137,
     "status": "ok",
     "timestamp": 1651764165480,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "wuP6tmM70IDB"
   },
   "outputs": [],
   "source": [
    "df.columns = meta.values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "executionInfo": {
     "elapsed": 194,
     "status": "ok",
     "timestamp": 1651764181476,
     "user": {
      "displayName": "Lon Phan",
      "userId": "15301104000330314929"
     },
     "user_tz": 240
    },
    "id": "itBUOlleyZVS",
    "outputId": "3beea4aa-237e-49b1-dafb-cf9e44f5269a"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "  <div id=\"df-a4fbf96f-dd4f-465a-8487-e5f52028ec1e\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <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>Total</th>\n",
       "      <th>European</th>\n",
       "      <th>African</th>\n",
       "      <th>African Others</th>\n",
       "      <th>African American</th>\n",
       "      <th>Asian</th>\n",
       "      <th>East Asian</th>\n",
       "      <th>Other Asian</th>\n",
       "      <th>Latin American 1</th>\n",
       "      <th>Latin American 2</th>\n",
       "      <th>South Asian</th>\n",
       "      <th>Other</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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      ],
      "text/plain": [
       "   Total  European  African  African Others  African American  Asian  \\\n",
       "0    0.0       0.0      0.0             0.0               0.0    0.0   \n",
       "1    0.0       0.0      0.0             0.0               0.0    0.0   \n",
       "2    0.0       0.0      0.0             0.0               0.0    0.0   \n",
       "3    0.0       0.0      0.0             0.0               0.0    0.0   \n",
       "4    0.0       0.0      0.0             0.0               0.0    0.0   \n",
       "\n",
       "   East Asian  Other Asian  Latin American 1  Latin American 2  South Asian  \\\n",
       "0         0.0          0.0               0.0               0.0          0.0   \n",
       "1         0.0          0.0               0.0               0.0          0.0   \n",
       "2         0.0          0.0               0.0               0.0          0.0   \n",
       "3         0.0          0.0               0.0               0.0          0.0   \n",
       "4         0.0          0.0               0.0               0.0          0.0   \n",
       "\n",
       "   Other  \n",
       "0    0.0  \n",
       "1    0.0  \n",
       "2    0.0  \n",
       "3    0.0  \n",
       "4    0.0  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "5c0zIr6FyX6B"
   },
   "outputs": [],
   "source": [
    "#@title\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "WTldgcKY0qhC"
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
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   "collapsed_sections": [],
   "name": "ALFA_genesummary.ipynb",
   "provenance": [
    {
     "file_id": "https://github.com/BioITHackathons/Visualization-of-NCBI-ALFA-Variants/blob/main/ALFA_genesum.ipynb",
     "timestamp": 1651763530916
    }
   ]
  },
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