kegg_heatmap_tree3.0.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "import sys\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.cluster.hierarchy import dendrogram as original_dendrogram\n",
    "\n",
    "# KEGG API base URL\n",
    "KEGG_API_BASE = \"http://rest.kegg.jp/\"\n",
    "def get_species_list(phylum):\n",
    "    organism_url = KEGG_API_BASE + \"list/organism\"\n",
    "    response = requests.get(organism_url)\n",
    "    lines = response.text.split(\"\\n\")\n",
    "    species_list = []\n",
    "\n",
    "    for line in lines:\n",
    "        if line and phylum in line:\n",
    "            parts = line.split(\"\\t\")\n",
    "            species_code = parts[1]\n",
    "            species_name = parts[2]\n",
    "            species_list.append((species_code, species_name))\n",
    "\n",
    "    return species_list\n",
    "def get_metabolic_pathways(species_code):\n",
    "    pathways_url = KEGG_API_BASE + f\"list/pathway/{species_code}\"\n",
    "    response = requests.get(pathways_url)\n",
    "    lines = response.text.split(\"\\n\")\n",
    "\n",
    "    pathways = []\n",
    "    for line in lines:\n",
    "        if line:\n",
    "            parts = line.split(\"\\t\")\n",
    "            pathway_id = parts[0]\n",
    "            pathways.append(pathway_id)\n",
    "    return pathways\n",
    "\n",
    "\n",
    "def pathways_lists(species_code):\n",
    "    pathways_url = KEGG_API_BASE + f\"list/pathway/{species_code}\"\n",
    "    response = requests.get(pathways_url)\n",
    "    lines = response.text.split(\"\\n\")\n",
    "    pathways = {}\n",
    "    for line in lines:\n",
    "        if line:\n",
    "            parts = line.split(\"\\t\")\n",
    "            pathway_id = parts[0].replace(species_code, \"\")\n",
    "            pathway_name = parts[1].split(\" - \")[0]\n",
    "            pathways[pathway_id] = pathway_name\n",
    "    return pathways\n",
    "\n",
    "\n",
    "def pathways_to_matrix(pathway_list, species_list):\n",
    "    flattened_list = [item for sublist in pathway_list for item in sublist]\n",
    "    prefixes = sorted(set([item[:item.find(\"0\")] for item in flattened_list]))\n",
    "    pathway_ids = sorted(set([item[item.find(\"0\"):] for item in flattened_list]))\n",
    "\n",
    "    matrix = np.zeros((len(prefixes), len(pathway_ids)))\n",
    "\n",
    "    for pathway in flattened_list:\n",
    "        prefix = pathway[:pathway.find(\"0\")]\n",
    "        pathway_id = pathway[pathway.find(\"0\"):]\n",
    "        row_idx = prefixes.index(prefix)\n",
    "        col_idx = pathway_ids.index(pathway_id)\n",
    "        matrix[row_idx][col_idx] = 1\n",
    "\n",
    "    species_fullnames = [species_name for _, species_name in species_list]\n",
    "    df = pd.DataFrame(matrix, index=species_fullnames, columns=pathway_ids)\n",
    "    return df\n",
    "\n",
    "\n",
    "def plot_heatmap(matrix, pathways_dict, output_pdf):\n",
    "    # 根据通路数量动态调整图形的高度\n",
    "    pathway_count = len(matrix.columns)\n",
    "    height_factor = 0.4\n",
    "    heatmap_height= pathway_count * height_factor\n",
    "    # heatmap_height = bacteria_count * height_factor\n",
    "    # 根据细菌数量动态调整图形的宽度\n",
    "    bacteria_count = len(matrix.index)\n",
    "    width_factor = 0.4\n",
    "    # heatmap_width = pathway_count * width_factor\n",
    "    heatmap_width = bacteria_count * width_factor + 15\n",
    "    fig, ax = plt.subplots(figsize=(heatmap_width, heatmap_height))\n",
    "    cmap = sns.color_palette(\"coolwarm\", as_cmap=True)\n",
    "    sns.heatmap(matrix.T, cmap=cmap, annot=False, linewidths=.5, ax=ax, cbar=False,square=True)\n",
    "    ax.invert_yaxis()\n",
    "\n",
    "    ax.set_ylabel(\"Pathway ID\", fontsize=24)\n",
    "    ax.set_xlabel(\" \", fontsize=24)\n",
    "    ax.set_title(\"Metabolic Pathway Presence Heatmap\", fontsize=32)\n",
    "\n",
    "    ax.xaxis.tick_top()\n",
    "    # ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='left')\n",
    "    ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='left', fontstyle='italic')\n",
    "\n",
    "\n",
    "    ax.tick_params(axis='y', which='both', length=0)\n",
    "\n",
    "    divider = make_axes_locatable(ax)\n",
    "    legend_ax = divider.append_axes(\"right\", size=\"5%\", pad=0.5)\n",
    "\n",
    "    n_lines = len(pathways_dict)\n",
    "    for idx, (pathway_id, pathway_name) in enumerate(pathways_dict.items()):\n",
    "        legend_ax.text(0, 1 - idx / n_lines, f'{pathway_id}: {pathway_name}', fontsize=8, ha='left', va='center')\n",
    "\n",
    "    legend_ax.set_axis_off()\n",
    "\n",
    "    plt.savefig(output_pdf, format='pdf')\n",
    "    # plt.savefig(output_pdf, format='png')\n",
    "\n",
    "\n",
    "def main(genus, output_csv, output_pdf):\n",
    "    species_list = get_species_list(genus)\n",
    "    pathways_list = []\n",
    "    pathways_dict = {}\n",
    "    for code, _ in species_list:\n",
    "        pathways_list.append(get_metabolic_pathways(code))\n",
    "        pathways_dict.update(pathways_lists(code))\n",
    "\n",
    "    pathway_matrix = pathways_to_matrix(pathways_list, species_list)\n",
    "    pathway_matrix.to_csv(output_csv, index=True, header=True, index_label='row_name', float_format='%.0f')\n",
    "    plot_heatmap(pathway_matrix, pathways_dict, output_pdf)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tree(x):\n",
    "    pathway_matrix = pd.read_csv(x+'.csv', index_col=0)\n",
    "    linked = linkage(pathway_matrix, method='average', metric='euclidean')\n",
    "\n",
    "    # Determine the number of bacteria\n",
    "    num_bacteria = len(pathway_matrix.index)\n",
    "    \n",
    "    # Calculate the figure height based on the number of bacteria\n",
    "    figure_height = max(7, num_bacteria * 0.3) # 0.2 is the scaling factor; adjust it as needed\n",
    "\n",
    "    # Visualize the dendrogram\n",
    "    plt.figure(figsize=(10, figure_height))\n",
    "   \n",
    "    dendrogram(linked, labels=pathway_matrix.index, orientation='right', leaf_font_size=10)\n",
    "    plt.title(\"Bacterial Phylogenetic Tree using UPGMA Method\")\n",
    "    plt.xlabel(\"Euclidean Distance\")\n",
    "    plt.savefig(x+'_tree.pdf', format='pdf', bbox_inches='tight')\n",
    "    plt.savefig(x+'_tree.png', format='png', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import font_manager\n",
    "from matplotlib.font_manager import FontProperties\n",
    "\n",
    "\n",
    "def apply_fontstyle(ax, fontstyle, pathway_matrix):\n",
    "    for txt in ax.get_children():\n",
    "        if isinstance(txt, plt.Text) and txt.get_text() in pathway_matrix.index:\n",
    "            txt.set_fontproperties(font_manager.FontProperties(style=fontstyle))\n",
    "\n",
    "def tree(x):\n",
    "    pathway_matrix = pd.read_csv(x+'.csv', index_col=0)\n",
    "    linked = linkage(pathway_matrix, method='average', metric='euclidean')\n",
    "\n",
    "    # Determine the number of bacteria\n",
    "    num_bacteria = len(pathway_matrix.index)\n",
    "\n",
    "    # Calculate the figure height based on the number of bacteria\n",
    "    figure_height = max(7, num_bacteria * 0.3) # 0.2 is the scaling factor; adjust it as needed\n",
    "\n",
    "    # Visualize the dendrogram\n",
    "    plt.figure(figsize=(10, figure_height))\n",
    "    font = FontProperties()\n",
    "    font.set_style('italic')\n",
    "    plt.yticks(fontproperties=font)\n",
    "\n",
    "    ax = plt.gca()\n",
    "    dendrogram(linked, labels=pathway_matrix.index, orientation='right', leaf_font_size=10)\n",
    "    apply_fontstyle(ax, 'italic', pathway_matrix)\n",
    "    plt.title(\"Bacterial Phylogenetic Tree using UPGMA Method\")\n",
    "    plt.xlabel(\"Euclidean Distance\")\n",
    "    plt.savefig(x+'_tree.pdf', format='pdf', bbox_inches='tight')\n",
    "    # plt.savefig(x+'_tree.png', format='png', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The test code to run the program"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# main(\"Delftia\", \"Delftia.csv\", \"Delftia.png\")\n",
    "# main(\"Rhodococcus\", \"Rhodococcus.csv\", \"Rhodococcus.png\")\n",
    "# main(\"Vibrio\", \"Vibrio.csv\", \"Vibrio.png\")\n",
    "# tree(\"Delftia\")\n",
    "# tree(\"Rhodococcus\")\n",
    "# tree(\"Vibrio\")"
   ]
  }
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