DotMotif-Search-in-Pinky100.ipynb

dotmotif-search-in-pinky100.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DotMotif-Search-in-Pinky100.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyM4Y1OB2B61m8qzOVdBfAHR",
      "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/j6k4m8/7c5cf55e7feb24685bd13a217cedda1d/dotmotif-search-in-pinky100.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 MICrONS Pinky100 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 triangular motifs (with edge attributes) in the Pinky100 dataset from the [IARPA MICrONS project](https://bossdb.org/project/microns-pinky).\n",
        "\n",
        "For more information about this dataset, see [here](https://www.microns-explorer.org/)."
      ],
      "metadata": {
        "id": "ZjweaPr1LtEk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install dotmotif with one line.\n",
        "# On your own computer, you can run this in the terminal.\n",
        "!pip3 install dotmotif"
      ],
      "metadata": {
        "id": "qVIoPW6UK3rG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CSV_EDGELIST = \"https://github.com/aplbrain/dotmotif/files/8603540/soma_subgraph_synapses_spines_v185.csv\"\n",
        "# You can also use the Zenodo link, but you will likely find that it is considerably slower, and may time out.\n",
        "# CSV_EDGELIST = \"https://zenodo.org/record/3710459/files/soma_subgraph_synapses_spines_v185.csv?download=1\""
      ],
      "metadata": {
        "id": "-4U9mEOQK7rG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from dotmotif import Motif, GrandIsoExecutor\n",
        "from dotmotif.ingest import CSVEdgelistConverter"
      ],
      "metadata": {
        "id": "cvnqgM_2LNQI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "graph = CSVEdgelistConverter(\n",
        "    CSV_EDGELIST,\n",
        "    # Tell DotMotif which columns represent the \"source\" and \"target\"\n",
        "    # of the edgelist:\n",
        "    \"pre_root_id\",\n",
        "    \"post_root_id\",\n",
        ").to_graph()"
      ],
      "metadata": {
        "id": "BXbv8k09LPiP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create the search engine.\n",
        "E = GrandIsoExecutor(graph=graph)"
      ],
      "metadata": {
        "id": "ooxAE9DuLSyB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "motif = Motif(\"\"\"\n",
        "A -> B as AB\n",
        "B -> C\n",
        "C -> A\n",
        "\n",
        "AB.spine_vol_um3 > 0.25\n",
        "\"\"\")"
      ],
      "metadata": {
        "id": "B8_jzbUvLUHd"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "results = E.find(motif)"
      ],
      "metadata": {
        "id": "kTRYjBSDLWzY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(results))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b7paIFMBLYiZ",
        "outputId": "623b41a8-1cb2-44bb-97b3-975de6c6fc47"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "13\n"
          ]
        }
      ]
    }
  ]
}