04_network.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "secure-survivor",
   "metadata": {},
   "source": [
    "---\n",
    "output-file: network.html\n",
    "title: network\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "specialized-integrity",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| include: false\n",
    "#| default_exp network\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "from nbdev.showdoc import show_doc\n",
    "from IPython.display import HTML\n",
    "#%load_ext autoreload\n",
    "#%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "greatest-adaptation",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| export\n",
    "from glycowork.glycan_data.loader import glycomics_data_loader\n",
    "human_skin_O_PMC5871710_BCC = glycomics_data_loader.human_skin_O_PMC5871710_BCC\n",
    "from glycowork.network.biosynthesis import *\n",
    "from glycowork.network.evolution import *"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "preliminary-approval",
   "metadata": {},
   "source": [
    "`network` contains functions to arrange and analyze glycans in the context of networks. In such a network, each node represents a glycan and edges represent, for instance, their connection via a biosynthetic step. It should be noted, since `glycowork` treats glycans as molecular graphs, that these networks represent hierarchical graphs, with the network being one graph and each node within the network also a graph. `network` contains the following modules:\n",
    "\n",
    "- `biosynthesis` contains functions to construct and analyze biosynthetic glycan networks\n",
    "- `evolution` contains functions to compare (taxonomic) groups as to their glycan repertoires"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "random-islam",
   "metadata": {},
   "source": [
    "## biosynthesis\n",
    ">constructing and analyzing biosynthetic glycan networks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "musical-jumping",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### construct_network\n",
       "\n",
       ">      construct_network (glycans:List[str],\n",
       ">                         allowed_ptms:FrozenSet[str]=frozenset({'6S', '3P',\n",
       ">                         'OS', '3S', '6P', '4Ac', 'OP', '1P', 'OAc', '9Ac'}),\n",
       ">                         edge_type:str='monolink',\n",
       ">                         permitted_roots:Optional[FrozenSet[str]]=None,\n",
       ">                         abundances:List[float]=[])\n",
       "\n",
       "*Construct glycan biosynthetic network*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| glycans | List |  | List of glycans |\n",
       "| allowed_ptms | FrozenSet | frozenset({'6S', '3P', 'OS', '3S', '6P', '4Ac', 'OP', '1P', 'OAc', '9Ac'}) | Set of allowed PTMs |\n",
       "| edge_type | str | monolink | Edge label type: monolink/monosaccharide/enzyme |\n",
       "| permitted_roots | Optional | None | Allowed root nodes |\n",
       "| abundances | List | [] | Glycan abundances in the same order as glycans; default:empty |\n",
       "| **Returns** | **DiGraph** |  | **Biosynthetic network** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### construct_network\n",
       "\n",
       ">      construct_network (glycans:List[str],\n",
       ">                         allowed_ptms:FrozenSet[str]=frozenset({'6S', '3P',\n",
       ">                         'OS', '3S', '6P', '4Ac', 'OP', '1P', 'OAc', '9Ac'}),\n",
       ">                         edge_type:str='monolink',\n",
       ">                         permitted_roots:Optional[FrozenSet[str]]=None,\n",
       ">                         abundances:List[float]=[])\n",
       "\n",
       "*Construct glycan biosynthetic network*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| glycans | List |  | List of glycans |\n",
       "| allowed_ptms | FrozenSet | frozenset({'6S', '3P', 'OS', '3S', '6P', '4Ac', 'OP', '1P', 'OAc', '9Ac'}) | Set of allowed PTMs |\n",
       "| edge_type | str | monolink | Edge label type: monolink/monosaccharide/enzyme |\n",
       "| permitted_roots | Optional | None | Allowed root nodes |\n",
       "| abundances | List | [] | Glycan abundances in the same order as glycans; default:empty |\n",
       "| **Returns** | **DiGraph** |  | **Biosynthetic network** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(construct_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "supported-valuable",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView(('Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)[Gal(b1-3)GlcNAc(b1-6)]Gal(b1-4)Glc-ol', 'Gal(b1-4)GlcNAc(b1-3)Gal(b1-4)Glc-ol', 'GlcNAc6S(b1-3)Gal(b1-4)Glc-ol', 'GlcNAc(b1-3)Gal(b1-4)Glc-ol', 'Fuc(a1-2)Gal(b1-4)Glc-ol', 'Gal(b1-4)Glc-ol', 'Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)[GlcNAc(b1-6)]Gal(b1-4)Glc-ol', 'Gal(b1-3)GlcNAc(b1-6)[Gal(b1-4)GlcNAc(b1-3)]Gal(b1-4)Glc-ol', 'Gal(b1-4)GlcNAc(b1-3)[GlcNAc(b1-6)]Gal(b1-4)Glc-ol', 'Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)Gal(b1-4)Glc-ol', 'Gal(b1-3)GlcNAc(b1-6)[GlcNAc(b1-3)]Gal(b1-4)Glc-ol', 'GlcNAc(b1-6)[GlcNAc(b1-3)]Gal(b1-4)Glc-ol'))"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "glycans = [\"Gal(b1-4)Glc-ol\", \"GlcNAc(b1-3)Gal(b1-4)Glc-ol\",\n",
    "           \"GlcNAc6S(b1-3)Gal(b1-4)Glc-ol\",\n",
    "           \"Gal(b1-4)GlcNAc(b1-3)Gal(b1-4)Glc-ol\", \"Fuc(a1-2)Gal(b1-4)Glc-ol\",\n",
    "          \"Neu5Ac(a2-3)Gal(b1-4)GlcNAc(b1-3)[Gal(b1-3)GlcNAc(b1-6)]Gal(b1-4)Glc-ol\"]\n",
    "network = construct_network(glycans)\n",
    "network.nodes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "distant-valley",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### plot_network\n",
       "\n",
       ">      plot_network (network:networkx.classes.graph.Graph,\n",
       ">                    plot_format:str='pydot2', edge_label_draw:bool=True,\n",
       ">                    lfc_dict:Optional[Dict[str,float]]=None)\n",
       "\n",
       "*Visualize biosynthetic network*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| plot_format | str | pydot2 | Layout type: pydot2/kamada_kawai/spring |\n",
       "| edge_label_draw | bool | True | Whether to draw edge labels |\n",
       "| lfc_dict | Optional | None | Enzyme:log2FC mapping for edge width |\n",
       "| **Returns** | **None** |  | **Displays plot** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### plot_network\n",
       "\n",
       ">      plot_network (network:networkx.classes.graph.Graph,\n",
       ">                    plot_format:str='pydot2', edge_label_draw:bool=True,\n",
       ">                    lfc_dict:Optional[Dict[str,float]]=None)\n",
       "\n",
       "*Visualize biosynthetic network*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| plot_format | str | pydot2 | Layout type: pydot2/kamada_kawai/spring |\n",
       "| edge_label_draw | bool | True | Whether to draw edge labels |\n",
       "| lfc_dict | Optional | None | Enzyme:log2FC mapping for edge width |\n",
       "| **Returns** | **None** |  | **Displays plot** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(plot_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "interim-contact",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_network(network, plot_format = 'kamada_kawai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sustained-competition",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### infer_network\n",
       "\n",
       ">      infer_network (network:networkx.classes.graph.Graph, network_species:str,\n",
       ">                     species_list:List[str],\n",
       ">                     network_dic:Dict[str,networkx.classes.graph.Graph])\n",
       "\n",
       "*Replace virtual nodes observed in other species*\n",
       "\n",
       "|    | **Type** | **Details** |\n",
       "| -- | -------- | ----------- |\n",
       "| network | Graph | Network to infer |\n",
       "| network_species | str | Source species |\n",
       "| species_list | List | Species to compare against |\n",
       "| network_dic | Dict | Species:network mapping |\n",
       "| **Returns** | **Graph** | **Network with inferred nodes** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### infer_network\n",
       "\n",
       ">      infer_network (network:networkx.classes.graph.Graph, network_species:str,\n",
       ">                     species_list:List[str],\n",
       ">                     network_dic:Dict[str,networkx.classes.graph.Graph])\n",
       "\n",
       "*Replace virtual nodes observed in other species*\n",
       "\n",
       "|    | **Type** | **Details** |\n",
       "| -- | -------- | ----------- |\n",
       "| network | Graph | Network to infer |\n",
       "| network_species | str | Source species |\n",
       "| species_list | List | Species to compare against |\n",
       "| network_dic | Dict | Species:network mapping |\n",
       "| **Returns** | **Graph** | **Network with inferred nodes** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(infer_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "piano-madness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### retrieve_inferred_nodes\n",
       "\n",
       ">      retrieve_inferred_nodes (network:networkx.classes.graph.Graph,\n",
       ">                               species:Optional[str]=None)\n",
       "\n",
       "*Get inferred virtual nodes from network*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Network with inferred nodes |\n",
       "| species | Optional | None | Source species if multiple |\n",
       "| **Returns** | **Union** |  | **Inferred nodes list or dict** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### retrieve_inferred_nodes\n",
       "\n",
       ">      retrieve_inferred_nodes (network:networkx.classes.graph.Graph,\n",
       ">                               species:Optional[str]=None)\n",
       "\n",
       "*Get inferred virtual nodes from network*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Network with inferred nodes |\n",
       "| species | Optional | None | Source species if multiple |\n",
       "| **Returns** | **Union** |  | **Inferred nodes list or dict** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(retrieve_inferred_nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "modular-absolute",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### update_network\n",
       "\n",
       ">      update_network (network_in:networkx.classes.graph.Graph,\n",
       ">                      edge_list:List[Tuple[str,str]],\n",
       ">                      edge_labels:Optional[List[str]]=None,\n",
       ">                      node_labels:Optional[Dict[str,int]]=None)\n",
       "\n",
       "*Update network with new edges and labels*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network_in | Graph |  | Input network |\n",
       "| edge_list | List |  | List of edges to add |\n",
       "| edge_labels | Optional | None | Labels for new edges |\n",
       "| node_labels | Optional | None | Node virtual status (0: observed, 1: virtual) |\n",
       "| **Returns** | **Graph** |  | **Updated network** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### update_network\n",
       "\n",
       ">      update_network (network_in:networkx.classes.graph.Graph,\n",
       ">                      edge_list:List[Tuple[str,str]],\n",
       ">                      edge_labels:Optional[List[str]]=None,\n",
       ">                      node_labels:Optional[Dict[str,int]]=None)\n",
       "\n",
       "*Update network with new edges and labels*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network_in | Graph |  | Input network |\n",
       "| edge_list | List |  | List of edges to add |\n",
       "| edge_labels | Optional | None | Labels for new edges |\n",
       "| node_labels | Optional | None | Node virtual status (0: observed, 1: virtual) |\n",
       "| **Returns** | **Graph** |  | **Updated network** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(update_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "minute-tiffany",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### trace_diamonds\n",
       "\n",
       ">      trace_diamonds (network:networkx.classes.graph.Graph,\n",
       ">                      species_list:List[str],\n",
       ">                      network_dic:Dict[str,networkx.classes.graph.Graph],\n",
       ">                      threshold:float=0.0, nb_intermediates:int=2,\n",
       ">                      mode:str='presence')\n",
       "\n",
       "*Analyze diamond motif (A->B,A->C,B->D,C->D) path preferences using evolutionary data*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| species_list | List |  | Species to compare against |\n",
       "| network_dic | Dict |  | Species:network mapping |\n",
       "| threshold | float | 0.0 | Cutoff threshold |\n",
       "| nb_intermediates | int | 2 | Number of intermediate nodes; has to be a multiple of 2 |\n",
       "| mode | str | presence | Analysis mode: presence/abundance |\n",
       "| **Returns** | **DataFrame** |  | **Path analysis results, with proportion (0-1) of how often glycan has been experimentally observed in this path (or average abundance)** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### trace_diamonds\n",
       "\n",
       ">      trace_diamonds (network:networkx.classes.graph.Graph,\n",
       ">                      species_list:List[str],\n",
       ">                      network_dic:Dict[str,networkx.classes.graph.Graph],\n",
       ">                      threshold:float=0.0, nb_intermediates:int=2,\n",
       ">                      mode:str='presence')\n",
       "\n",
       "*Analyze diamond motif (A->B,A->C,B->D,C->D) path preferences using evolutionary data*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| species_list | List |  | Species to compare against |\n",
       "| network_dic | Dict |  | Species:network mapping |\n",
       "| threshold | float | 0.0 | Cutoff threshold |\n",
       "| nb_intermediates | int | 2 | Number of intermediate nodes; has to be a multiple of 2 |\n",
       "| mode | str | presence | Analysis mode: presence/abundance |\n",
       "| **Returns** | **DataFrame** |  | **Path analysis results, with proportion (0-1) of how often glycan has been experimentally observed in this path (or average abundance)** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(trace_diamonds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "subtle-grace",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### evoprune_network\n",
       "\n",
       ">      evoprune_network (network:networkx.classes.graph.Graph,\n",
       ">                        network_dic:Optional[Dict[str,networkx.classes.graph.Gr\n",
       ">                        aph]]=None, species_list:Optional[List[str]]=None,\n",
       ">                        node_attr:str='abundance', threshold:float=0.01,\n",
       ">                        nb_intermediates:int=2, mode:str='presence')\n",
       "\n",
       "*Prune network using evolutionary path preferences*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| network_dic | Optional | None | Species:network mapping |\n",
       "| species_list | Optional | None | Species to compare against |\n",
       "| node_attr | str | abundance | Node attribute to use for pruning |\n",
       "| threshold | float | 0.01 | Cutoff threshold |\n",
       "| nb_intermediates | int | 2 | Number of intermediate nodes; has to be a multiple of 2 |\n",
       "| mode | str | presence | Analysis mode: presence/abundance |\n",
       "| **Returns** | **Graph** |  | **Evolutionarily pruned network (with virtual node probability as a new node attribute)** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### evoprune_network\n",
       "\n",
       ">      evoprune_network (network:networkx.classes.graph.Graph,\n",
       ">                        network_dic:Optional[Dict[str,networkx.classes.graph.Gr\n",
       ">                        aph]]=None, species_list:Optional[List[str]]=None,\n",
       ">                        node_attr:str='abundance', threshold:float=0.01,\n",
       ">                        nb_intermediates:int=2, mode:str='presence')\n",
       "\n",
       "*Prune network using evolutionary path preferences*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| network_dic | Optional | None | Species:network mapping |\n",
       "| species_list | Optional | None | Species to compare against |\n",
       "| node_attr | str | abundance | Node attribute to use for pruning |\n",
       "| threshold | float | 0.01 | Cutoff threshold |\n",
       "| nb_intermediates | int | 2 | Number of intermediate nodes; has to be a multiple of 2 |\n",
       "| mode | str | presence | Analysis mode: presence/abundance |\n",
       "| **Returns** | **Graph** |  | **Evolutionarily pruned network (with virtual node probability as a new node attribute)** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(evoprune_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "under-meditation",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "\n",
       "<style>\n",
       "\n",
       "</style>\n",
       "\n",
       "<div id=\"fig_el2307621374800491525619506521\"></div>\n",
       "<script>\n",
       "function mpld3_load_lib(url, callback){\n",
       "  var s = document.createElement('script');\n",
       "  s.src = url;\n",
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       "  s.onreadystatechange = s.onload = callback;\n",
       "  s.onerror = function(){console.warn(\"failed to load library \" + url);};\n",
       "  document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
       "}\n",
       "\n",
       "if(typeof(mpld3) !== \"undefined\" && mpld3._mpld3IsLoaded){\n",
       "   // already loaded: just create the figure\n",
       "   !function(mpld3){\n",
       "       \n",
       "       mpld3.draw_figure(\"fig_el2307621374800491525619506521\", {\"width\": 1600.0, \"height\": 1600.0, \"axes\": [{\"bbox\": [0.125, 0.10999999999999999, 0.775, 0.77], \"xlim\": [-0.6825996181324383, 0.7020189141187811], \"ylim\": [-1.205593855908122, 1.1636305788426187], \"xdomain\": [-0.6825996181324383, 0.7020189141187811], \"ydomain\": [-1.205593855908122, 1.1636305788426187], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"top\", \"nticks\": 9, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"right\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el230762137479622256\", \"lines\": [], 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       "      });\n",
       "    });\n",
       "}else{\n",
       "    // require.js not available: dynamically load d3 & mpld3\n",
       "    mpld3_load_lib(\"https://d3js.org/d3.v5.js\", function(){\n",
       "         mpld3_load_lib(\"https://mpld3.github.io/js/mpld3.v0.5.10.js\", function(){\n",
       "                 \n",
       "                 mpld3.draw_figure(\"fig_el2307621374800491525619506521\", {\"width\": 1600.0, \"height\": 1600.0, \"axes\": [{\"bbox\": [0.125, 0.10999999999999999, 0.775, 0.77], \"xlim\": [-0.6825996181324383, 0.7020189141187811], \"ylim\": [-1.205593855908122, 1.1636305788426187], \"xdomain\": [-0.6825996181324383, 0.7020189141187811], \"ydomain\": [-1.205593855908122, 1.1636305788426187], \"xscale\": \"linear\", \"yscale\": \"linear\", \"axes\": [{\"position\": \"top\", \"nticks\": 9, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}, {\"position\": \"right\", \"nticks\": 7, \"tickvalues\": null, \"tickformat_formatter\": \"\", \"tickformat\": \"\", \"scale\": \"linear\", \"fontsize\": null, \"grid\": {\"gridOn\": false}, \"visible\": true}], \"axesbg\": \"#FFFFFF\", \"axesbgalpha\": null, \"zoomable\": true, \"id\": \"el230762137479622256\", \"lines\": [], 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       "            })\n",
       "         });\n",
       "}\n",
       "</script>"
      ],
      "text/plain": [
       "<Figure size 1600x1600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_network(evoprune_network(network), plot_format = 'kamada_kawai')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "magnetic-feature",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### highlight_network\n",
       "\n",
       ">      highlight_network (network:networkx.classes.graph.Graph, highlight:str,\n",
       ">                         motif:Optional[str]=None, abundance_df:Optional[pandas\n",
       ">                         .core.frame.DataFrame]=None, glycan_col:str='glycan',\n",
       ">                         intensity_col:str='rel_intensity', conservation_df:Opt\n",
       ">                         ional[pandas.core.frame.DataFrame]=None, network_dic:O\n",
       ">                         ptional[Dict[str,networkx.classes.graph.Graph]]=None,\n",
       ">                         species:Optional[str]=None)\n",
       "\n",
       "*Add visual highlighting to network nodes, to be used in plot_network*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| highlight | str |  | What to highlight: motif/species/abundance/conservation |\n",
       "| motif | Optional | None | Motif to highlight; highlight=motif |\n",
       "| abundance_df | Optional | None | Glycan abundance data; highlight=abundance |\n",
       "| glycan_col | str | glycan | Glycan column name; highlight=abundance |\n",
       "| intensity_col | str | rel_intensity | Intensity column name; highlight=abundance |\n",
       "| conservation_df | Optional | None | Species-glycan data; highlight=conservation |\n",
       "| network_dic | Optional | None | Species:network mapping; highlight=conservation/species |\n",
       "| species | Optional | None | Species to highlight; highlight=species |\n",
       "| **Returns** | **Graph** |  | **Network with highlight attributes ('origin' (motif/species) or 'abundance' (abundance/conservation) node attribute)** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### highlight_network\n",
       "\n",
       ">      highlight_network (network:networkx.classes.graph.Graph, highlight:str,\n",
       ">                         motif:Optional[str]=None, abundance_df:Optional[pandas\n",
       ">                         .core.frame.DataFrame]=None, glycan_col:str='glycan',\n",
       ">                         intensity_col:str='rel_intensity', conservation_df:Opt\n",
       ">                         ional[pandas.core.frame.DataFrame]=None, network_dic:O\n",
       ">                         ptional[Dict[str,networkx.classes.graph.Graph]]=None,\n",
       ">                         species:Optional[str]=None)\n",
       "\n",
       "*Add visual highlighting to network nodes, to be used in plot_network*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| highlight | str |  | What to highlight: motif/species/abundance/conservation |\n",
       "| motif | Optional | None | Motif to highlight; highlight=motif |\n",
       "| abundance_df | Optional | None | Glycan abundance data; highlight=abundance |\n",
       "| glycan_col | str | glycan | Glycan column name; highlight=abundance |\n",
       "| intensity_col | str | rel_intensity | Intensity column name; highlight=abundance |\n",
       "| conservation_df | Optional | None | Species-glycan data; highlight=conservation |\n",
       "| network_dic | Optional | None | Species:network mapping; highlight=conservation/species |\n",
       "| species | Optional | None | Species to highlight; highlight=species |\n",
       "| **Returns** | **Graph** |  | **Network with highlight attributes ('origin' (motif/species) or 'abundance' (abundance/conservation) node attribute)** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(highlight_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "moral-planet",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### export_network\n",
       "\n",
       ">      export_network (network:networkx.classes.graph.Graph, filepath:str,\n",
       ">                      other_node_attributes:Optional[List[str]]=None)\n",
       "\n",
       "*Export network to Cytoscape/Gephi compatible files*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| filepath | str |  | Output path prefix, will be appended by file description and type |\n",
       "| other_node_attributes | Optional | None | Additional attributes for extraction |\n",
       "| **Returns** | **None** |  | **Saves network files (edge list/labels + node IDs and labels)** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### export_network\n",
       "\n",
       ">      export_network (network:networkx.classes.graph.Graph, filepath:str,\n",
       ">                      other_node_attributes:Optional[List[str]]=None)\n",
       "\n",
       "*Export network to Cytoscape/Gephi compatible files*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| filepath | str |  | Output path prefix, will be appended by file description and type |\n",
       "| other_node_attributes | Optional | None | Additional attributes for extraction |\n",
       "| **Returns** | **None** |  | **Saves network files (edge list/labels + node IDs and labels)** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(export_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bded7c37-ff97-497f-b9df-6b19412d36c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### get_maximum_flow\n",
       "\n",
       ">      get_maximum_flow (network:networkx.classes.graph.Graph,\n",
       ">                        source:str='Gal(b1-4)Glc-ol',\n",
       ">                        sinks:Optional[List[str]]=None)\n",
       "\n",
       "*Estimate maximum flow and flow paths between source and sinks*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| source | str | Gal(b1-4)Glc-ol | Source node |\n",
       "| sinks | Optional | None | Target nodes; default:all terminal nodes |\n",
       "| **Returns** | **Dict** |  | **Flow results; sink: {maximum flow value, flow path dictionary}** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### get_maximum_flow\n",
       "\n",
       ">      get_maximum_flow (network:networkx.classes.graph.Graph,\n",
       ">                        source:str='Gal(b1-4)Glc-ol',\n",
       ">                        sinks:Optional[List[str]]=None)\n",
       "\n",
       "*Estimate maximum flow and flow paths between source and sinks*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| source | str | Gal(b1-4)Glc-ol | Source node |\n",
       "| sinks | Optional | None | Target nodes; default:all terminal nodes |\n",
       "| **Returns** | **Dict** |  | **Flow results; sink: {maximum flow value, flow path dictionary}** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(get_maximum_flow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8d66873-a764-4572-b63a-43084fbd11aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### get_max_flow_path\n",
       "\n",
       ">      get_max_flow_path (network:networkx.classes.graph.Graph,\n",
       ">                         flow_dict:Dict[str,Dict[str,float]], sink:str,\n",
       ">                         source:str='Gal(b1-4)Glc-ol')\n",
       "\n",
       "*Get path giving maximum flow value*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| flow_dict | Dict |  | Flow dictionary as returned by get_maximum_flow |\n",
       "| sink | str |  | Target node |\n",
       "| source | str | Gal(b1-4)Glc-ol | Source node |\n",
       "| **Returns** | **List** |  | **Path edge list** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### get_max_flow_path\n",
       "\n",
       ">      get_max_flow_path (network:networkx.classes.graph.Graph,\n",
       ">                         flow_dict:Dict[str,Dict[str,float]], sink:str,\n",
       ">                         source:str='Gal(b1-4)Glc-ol')\n",
       "\n",
       "*Get path giving maximum flow value*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| flow_dict | Dict |  | Flow dictionary as returned by get_maximum_flow |\n",
       "| sink | str |  | Target node |\n",
       "| source | str | Gal(b1-4)Glc-ol | Source node |\n",
       "| **Returns** | **List** |  | **Path edge list** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(get_max_flow_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5d3acc42-cfc2-4473-a51e-5dabbf6cb907",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### get_reaction_flow\n",
       "\n",
       ">      get_reaction_flow (network:networkx.classes.graph.Graph,\n",
       ">                         res:Dict[str,Dict[str,float]],\n",
       ">                         aggregate:Optional[str]=None)\n",
       "\n",
       "*Get aggregated flows by reaction type*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| res | Dict |  | Flow results as returned by get_maximum_flow |\n",
       "| aggregate | Optional | None | Aggregation: sum/mean/None |\n",
       "| **Returns** | **Union** |  | **Reaction flows (reaction: flow)** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### get_reaction_flow\n",
       "\n",
       ">      get_reaction_flow (network:networkx.classes.graph.Graph,\n",
       ">                         res:Dict[str,Dict[str,float]],\n",
       ">                         aggregate:Optional[str]=None)\n",
       "\n",
       "*Get aggregated flows by reaction type*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| res | Dict |  | Flow results as returned by get_maximum_flow |\n",
       "| aggregate | Optional | None | Aggregation: sum/mean/None |\n",
       "| **Returns** | **Union** |  | **Reaction flows (reaction: flow)** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(get_reaction_flow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69572140-718f-4bb1-9814-f6afc6412bd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### get_differential_biosynthesis\n",
       "\n",
       ">      get_differential_biosynthesis (df:Union[pandas.core.frame.DataFrame,str],\n",
       ">                                     group1:List[Union[str,int]], group2:Option\n",
       ">                                     al[List[Union[str,int]]]=None,\n",
       ">                                     analysis:str='reaction',\n",
       ">                                     paired:bool=False,\n",
       ">                                     longitudinal:bool=False,\n",
       ">                                     id_column:str='ID')\n",
       "\n",
       "*Compare biosynthetic patterns between conditions/timepoints*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| df | Union |  | Glycan abundance data (first column: glycan sequences) |\n",
       "| group1 | List |  | First group column indices/names (or time points in longitudinal analysis) |\n",
       "| group2 | Optional | None | Second group column indices/names (or time points in longitudinal analysis) |\n",
       "| analysis | str | reaction | Type: reaction/flow |\n",
       "| paired | bool | False | Whether samples are paired |\n",
       "| longitudinal | bool | False | Whether to do perform longitudinal analysis |\n",
       "| id_column | str | ID | Sample ID column for longitudinal analysis in the ID-style of participant_time_replicate |\n",
       "| **Returns** | **DataFrame** |  | **Differential analysis results (differential flow features and statistics OR reaction changes over time** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### get_differential_biosynthesis\n",
       "\n",
       ">      get_differential_biosynthesis (df:Union[pandas.core.frame.DataFrame,str],\n",
       ">                                     group1:List[Union[str,int]], group2:Option\n",
       ">                                     al[List[Union[str,int]]]=None,\n",
       ">                                     analysis:str='reaction',\n",
       ">                                     paired:bool=False,\n",
       ">                                     longitudinal:bool=False,\n",
       ">                                     id_column:str='ID')\n",
       "\n",
       "*Compare biosynthetic patterns between conditions/timepoints*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| df | Union |  | Glycan abundance data (first column: glycan sequences) |\n",
       "| group1 | List |  | First group column indices/names (or time points in longitudinal analysis) |\n",
       "| group2 | Optional | None | Second group column indices/names (or time points in longitudinal analysis) |\n",
       "| analysis | str | reaction | Type: reaction/flow |\n",
       "| paired | bool | False | Whether samples are paired |\n",
       "| longitudinal | bool | False | Whether to do perform longitudinal analysis |\n",
       "| id_column | str | ID | Sample ID column for longitudinal analysis in the ID-style of participant_time_replicate |\n",
       "| **Returns** | **DataFrame** |  | **Differential analysis results (differential flow features and statistics OR reaction changes over time** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(get_differential_biosynthesis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "69ed83c6-422e-4334-953d-cd5e2e52da73",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You're working with an alpha of 0.044390023979542614 that has been adjusted for your sample size of 40.\n"
     ]
    },
    {
     "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>Mean abundance</th>\n",
       "      <th>Log2FC</th>\n",
       "      <th>p-val</th>\n",
       "      <th>corr p-val</th>\n",
       "      <th>significant</th>\n",
       "      <th>Effect size</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Neu5Ac(a2-3)</th>\n",
       "      <td>4.542537</td>\n",
       "      <td>-0.391611</td>\n",
       "      <td>0.001702</td>\n",
       "      <td>0.003147</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.816234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gal(b1-?)</th>\n",
       "      <td>3.395793</td>\n",
       "      <td>-0.435594</td>\n",
       "      <td>0.002206</td>\n",
       "      <td>0.003147</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.790748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gal(b1-3)</th>\n",
       "      <td>5.967389</td>\n",
       "      <td>-0.460385</td>\n",
       "      <td>0.003713</td>\n",
       "      <td>0.003147</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.739291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neu5Ac(a2-?)</th>\n",
       "      <td>4.193290</td>\n",
       "      <td>-0.449044</td>\n",
       "      <td>0.004196</td>\n",
       "      <td>0.003147</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.727147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neu5Ac(a2-6)</th>\n",
       "      <td>5.143191</td>\n",
       "      <td>-0.496132</td>\n",
       "      <td>0.007521</td>\n",
       "      <td>0.004513</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.668654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neu5Ac(a2-8)</th>\n",
       "      <td>2.894141</td>\n",
       "      <td>-0.464140</td>\n",
       "      <td>0.013887</td>\n",
       "      <td>0.006944</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.605967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OS</th>\n",
       "      <td>2.249050</td>\n",
       "      <td>-0.521844</td>\n",
       "      <td>0.019236</td>\n",
       "      <td>0.008244</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.571950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6S</th>\n",
       "      <td>0.824198</td>\n",
       "      <td>-0.270074</td>\n",
       "      <td>0.050454</td>\n",
       "      <td>0.015136</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.466992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GlcNAc(b1-6)</th>\n",
       "      <td>0.824198</td>\n",
       "      <td>-0.270074</td>\n",
       "      <td>0.050454</td>\n",
       "      <td>0.015136</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.466992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Gal(b1-4)</th>\n",
       "      <td>0.824198</td>\n",
       "      <td>-0.270074</td>\n",
       "      <td>0.050454</td>\n",
       "      <td>0.015136</td>\n",
       "      <td>True</td>\n",
       "      <td>-0.466992</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Mean abundance    Log2FC     p-val  corr p-val  significant  \\\n",
       "Feature                                                                     \n",
       "Neu5Ac(a2-3)        4.542537 -0.391611  0.001702    0.003147         True   \n",
       "Gal(b1-?)           3.395793 -0.435594  0.002206    0.003147         True   \n",
       "Gal(b1-3)           5.967389 -0.460385  0.003713    0.003147         True   \n",
       "Neu5Ac(a2-?)        4.193290 -0.449044  0.004196    0.003147         True   \n",
       "Neu5Ac(a2-6)        5.143191 -0.496132  0.007521    0.004513         True   \n",
       "Neu5Ac(a2-8)        2.894141 -0.464140  0.013887    0.006944         True   \n",
       "OS                  2.249050 -0.521844  0.019236    0.008244         True   \n",
       "6S                  0.824198 -0.270074  0.050454    0.015136         True   \n",
       "GlcNAc(b1-6)        0.824198 -0.270074  0.050454    0.015136         True   \n",
       "Gal(b1-4)           0.824198 -0.270074  0.050454    0.015136         True   \n",
       "\n",
       "              Effect size  \n",
       "Feature                    \n",
       "Neu5Ac(a2-3)    -0.816234  \n",
       "Gal(b1-?)       -0.790748  \n",
       "Gal(b1-3)       -0.739291  \n",
       "Neu5Ac(a2-?)    -0.727147  \n",
       "Neu5Ac(a2-6)    -0.668654  \n",
       "Neu5Ac(a2-8)    -0.605967  \n",
       "OS              -0.571950  \n",
       "6S              -0.466992  \n",
       "GlcNAc(b1-6)    -0.466992  \n",
       "Gal(b1-4)       -0.466992  "
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_differential_biosynthesis(human_skin_O_PMC5871710_BCC, [1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39],\n",
    "                              [2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40], paired = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "22a6eb5b-3b53-446a-8c34-5b81d7910876",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### extend_network\n",
       "\n",
       ">      extend_network (network:networkx.classes.graph.Graph, steps:int=1,\n",
       ">                      to_extend:Union[str,Dict[str,int],List[str]]='all',\n",
       ">                      strict_context:bool=False)\n",
       "\n",
       "*Extend biosynthetic network physiologically*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| steps | int | 1 | Number of extension steps; default:1 |\n",
       "| to_extend | Union | all | Nodes to extend (all, specific leaf node, target composition) |\n",
       "| strict_context | bool | False | Whether to use network only to derive allowed reaction products; default:False |\n",
       "| **Returns** | **Tuple** |  | **(Extended network, New glycans)** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### extend_network\n",
       "\n",
       ">      extend_network (network:networkx.classes.graph.Graph, steps:int=1,\n",
       ">                      to_extend:Union[str,Dict[str,int],List[str]]='all',\n",
       ">                      strict_context:bool=False)\n",
       "\n",
       "*Extend biosynthetic network physiologically*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network | Graph |  | Biosynthetic network |\n",
       "| steps | int | 1 | Number of extension steps; default:1 |\n",
       "| to_extend | Union | all | Nodes to extend (all, specific leaf node, target composition) |\n",
       "| strict_context | bool | False | Whether to use network only to derive allowed reaction products; default:False |\n",
       "| **Returns** | **Tuple** |  | **(Extended network, New glycans)** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(extend_network)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a8e8226-58b5-4134-9638-8308bd8c770d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_network, new_glycans = extend_network(network, strict_context = True)\n",
    "len(new_glycans)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "distant-graphics",
   "metadata": {},
   "source": [
    "## evolution\n",
    ">investigating evolutionary relationships of glycans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "conceptual-checklist",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### distance_from_embeddings\n",
       "\n",
       ">      distance_from_embeddings (df:pandas.core.frame.DataFrame,\n",
       ">                                embeddings:pandas.core.frame.DataFrame,\n",
       ">                                cut_off:int=10, rank:str='Species',\n",
       ">                                averaging:str='median')\n",
       "\n",
       "*Calculate cosine distance matrix from learned embeddings*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| df | DataFrame |  | DataFrame with glycans (rows) and taxonomic info (columns) |\n",
       "| embeddings | DataFrame |  | DataFrame with glycans (rows) and embeddings (columns) (e.g., from glycans_to_emb) |\n",
       "| cut_off | int | 10 | Minimum glycans per rank to be included; default:10 |\n",
       "| rank | str | Species | Taxonomic rank for grouping; default:Species |\n",
       "| averaging | str | median | How to average embeddings: median/mean |\n",
       "| **Returns** | **DataFrame** |  | **Rank x rank distance matrix** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### distance_from_embeddings\n",
       "\n",
       ">      distance_from_embeddings (df:pandas.core.frame.DataFrame,\n",
       ">                                embeddings:pandas.core.frame.DataFrame,\n",
       ">                                cut_off:int=10, rank:str='Species',\n",
       ">                                averaging:str='median')\n",
       "\n",
       "*Calculate cosine distance matrix from learned embeddings*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| df | DataFrame |  | DataFrame with glycans (rows) and taxonomic info (columns) |\n",
       "| embeddings | DataFrame |  | DataFrame with glycans (rows) and embeddings (columns) (e.g., from glycans_to_emb) |\n",
       "| cut_off | int | 10 | Minimum glycans per rank to be included; default:10 |\n",
       "| rank | str | Species | Taxonomic rank for grouping; default:Species |\n",
       "| averaging | str | median | How to average embeddings: median/mean |\n",
       "| **Returns** | **DataFrame** |  | **Rank x rank distance matrix** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(distance_from_embeddings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "numerous-newport",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### distance_from_metric\n",
       "\n",
       ">      distance_from_metric (df:pandas.core.frame.DataFrame,\n",
       ">                            networks:List[networkx.classes.graph.Graph],\n",
       ">                            metric:str='Jaccard', cut_off:int=10,\n",
       ">                            rank:str='Species')\n",
       "\n",
       "*Calculate distance matrix between networks using provided metric*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| df | DataFrame |  | DataFrame with glycans (rows) and taxonomic info (columns) |\n",
       "| networks | List |  | List of networkx networks |\n",
       "| metric | str | Jaccard | Distance metric to use |\n",
       "| cut_off | int | 10 | Minimum glycans per rank to be included; default:10 |\n",
       "| rank | str | Species | Taxonomic rank for grouping; default:Species |\n",
       "| **Returns** | **DataFrame** |  | **Rank x rank distance matrix** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### distance_from_metric\n",
       "\n",
       ">      distance_from_metric (df:pandas.core.frame.DataFrame,\n",
       ">                            networks:List[networkx.classes.graph.Graph],\n",
       ">                            metric:str='Jaccard', cut_off:int=10,\n",
       ">                            rank:str='Species')\n",
       "\n",
       "*Calculate distance matrix between networks using provided metric*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| df | DataFrame |  | DataFrame with glycans (rows) and taxonomic info (columns) |\n",
       "| networks | List |  | List of networkx networks |\n",
       "| metric | str | Jaccard | Distance metric to use |\n",
       "| cut_off | int | 10 | Minimum glycans per rank to be included; default:10 |\n",
       "| rank | str | Species | Taxonomic rank for grouping; default:Species |\n",
       "| **Returns** | **DataFrame** |  | **Rank x rank distance matrix** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(distance_from_metric)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "honest-minnesota",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### dendrogram_from_distance\n",
       "\n",
       ">      dendrogram_from_distance (dm:pandas.core.frame.DataFrame,\n",
       ">                                ylabel:str='Mammalia', filepath:str='')\n",
       "\n",
       "*Plot dendrogram from distance matrix*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| dm | DataFrame |  | Rank x rank distance matrix (e.g., from distance_from_embeddings) |\n",
       "| ylabel | str | Mammalia | Y-axis label |\n",
       "| filepath | str |  | Path to save plot including filename |\n",
       "| **Returns** | **None** |  | **Displays or saves dendrogram plot** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### dendrogram_from_distance\n",
       "\n",
       ">      dendrogram_from_distance (dm:pandas.core.frame.DataFrame,\n",
       ">                                ylabel:str='Mammalia', filepath:str='')\n",
       "\n",
       "*Plot dendrogram from distance matrix*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| dm | DataFrame |  | Rank x rank distance matrix (e.g., from distance_from_embeddings) |\n",
       "| ylabel | str | Mammalia | Y-axis label |\n",
       "| filepath | str |  | Path to save plot including filename |\n",
       "| **Returns** | **None** |  | **Displays or saves dendrogram plot** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(dendrogram_from_distance)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "exciting-lloyd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### check_conservation\n",
       "\n",
       ">      check_conservation (glycan:str, df:pandas.core.frame.DataFrame,\n",
       ">                          network_dic:Optional[Dict[str,networkx.classes.graph.\n",
       ">                          Graph]]=None, rank:str='Order', threshold:int=5,\n",
       ">                          motif:bool=False)\n",
       "\n",
       "*Estimate evolutionary conservation of glycans via biosynthetic networks*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| glycan | str |  | Glycan or motif in IUPAC-condensed format |\n",
       "| df | DataFrame |  | DataFrame with glycans (rows) and taxonomic levels (columns) |\n",
       "| network_dic | Optional | None | Species:biosynthetic network mapping |\n",
       "| rank | str | Order | Taxonomic level to assess |\n",
       "| threshold | int | 5 | Minimum glycans per species to be included |\n",
       "| motif | bool | False | Whether glycan is a motif vs sequence |\n",
       "| **Returns** | **Dict** |  | **Taxonomic group-to-conservation mapping** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### check_conservation\n",
       "\n",
       ">      check_conservation (glycan:str, df:pandas.core.frame.DataFrame,\n",
       ">                          network_dic:Optional[Dict[str,networkx.classes.graph.\n",
       ">                          Graph]]=None, rank:str='Order', threshold:int=5,\n",
       ">                          motif:bool=False)\n",
       "\n",
       "*Estimate evolutionary conservation of glycans via biosynthetic networks*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| glycan | str |  | Glycan or motif in IUPAC-condensed format |\n",
       "| df | DataFrame |  | DataFrame with glycans (rows) and taxonomic levels (columns) |\n",
       "| network_dic | Optional | None | Species:biosynthetic network mapping |\n",
       "| rank | str | Order | Taxonomic level to assess |\n",
       "| threshold | int | 5 | Minimum glycans per species to be included |\n",
       "| motif | bool | False | Whether glycan is a motif vs sequence |\n",
       "| **Returns** | **Dict** |  | **Taxonomic group-to-conservation mapping** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(check_conservation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "canadian-consultancy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/markdown": [
       "---\n",
       "\n",
       "### get_communities\n",
       "\n",
       ">      get_communities (network_list:List[networkx.classes.graph.Graph],\n",
       ">                       label_list:Optional[List[str]]=None)\n",
       "\n",
       "*Find communities for each graph in list of graphs*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network_list | List |  | List of undirected biosynthetic networks |\n",
       "| label_list | Optional | None | Labels for community names, running_number + _ + label_list[k]  for network_list[k]; default:range(len(graph_list)) |\n",
       "| **Returns** | **Dict** |  | **Community-to-glycan list mapping** |"
      ],
      "text/plain": [
       "---\n",
       "\n",
       "### get_communities\n",
       "\n",
       ">      get_communities (network_list:List[networkx.classes.graph.Graph],\n",
       ">                       label_list:Optional[List[str]]=None)\n",
       "\n",
       "*Find communities for each graph in list of graphs*\n",
       "\n",
       "|    | **Type** | **Default** | **Details** |\n",
       "| -- | -------- | ----------- | ----------- |\n",
       "| network_list | List |  | List of undirected biosynthetic networks |\n",
       "| label_list | Optional | None | Labels for community names, running_number + _ + label_list[k]  for network_list[k]; default:range(len(graph_list)) |\n",
       "| **Returns** | **Dict** |  | **Community-to-glycan list mapping** |"
      ]
     },
     "execution_count": null,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show_doc(get_communities)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "sweet-agriculture",
   "metadata": {},
   "outputs": [],
   "source": [
    "#| hide\n",
    "import nbdev; nbdev.nbdev_export()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "tribal-thread",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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