DotMotif-Search-in-Hemibrain.ipynb

dotmotif-search-in-custom-networkx.ipynb

dotmotif-search-in-hemibrain.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DotMotif-Search-in-Hemibrain.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyO170eGdApzRZtofHs3IiSH",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/j6k4m8/0c878f9fbd0bd691a005110e8043dbcd/dotmotif-search-in-custom-networkx.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using DotMotif to search for motifs in the Janelia Hemibrain Dataset\n",
        "\n",
        "[DotMotif](https://github.com/aplbrain/dotmotif) is a performant, powerful query framework to search for network motifs.\n",
        "\n",
        "In this notebook, we search for subgraph structures in the [Janelia Hemibrain](https://neuprint.janelia.org) dataset."
      ],
      "metadata": {
        "id": "ZjweaPr1LtEk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "# Install dotmotif with one line.\n",
        "# On your own computer, you can run this in the terminal.\n",
        "!pip3 install dotmotif\n",
        "\n",
        "from dotmotif import Motif\n",
        "from dotmotif.executors.NeuPrintExecutor import NeuPrintExecutor"
      ],
      "metadata": {
        "id": "qVIoPW6UK3rG"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# You will need an account on neuPrint in order to search for\n",
        "# motifs using the free online database.\n",
        "# To get a neuprint token, find your \"auth token\" on this page\n",
        "# after creating an account:\n",
        "# https://neuprint.janelia.org/account\n",
        "MY_NEUPRINT_TOKEN = \" ... \""
      ],
      "metadata": {
        "id": "-4U9mEOQK7rG"
      },
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the search engine.\n",
        "E = NeuPrintExecutor(host=\"neuprint.janelia.org\", token=MY_NEUPRINT_TOKEN, dataset=\"hemibrain:v1.2.1\")"
      ],
      "metadata": {
        "id": "ooxAE9DuLSyB"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Build your own motif here!\n",
        "motif = Motif(\"\"\"\n",
        "\n",
        "EPG -> PEG\n",
        "\n",
        "EPG.type = \"EPG\"\n",
        "PEG.type = \"PEG\"\n",
        "\n",
        "\"\"\")"
      ],
      "metadata": {
        "id": "B8_jzbUvLUHd"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = E.find(motif)"
      ],
      "metadata": {
        "id": "kTRYjBSDLWzY"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"There are {len(results)} synapse edges from EPG neurons to PEG neurons.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7paIFMBLYiZ",
        "outputId": "77c21466-e6ca-4fab-d902-06c8d1fbe3b4"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 280 synapse edges from EPG neurons to PEG neurons.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "7tqVQfx8O7cJ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}