ipynb/Python/Importing_data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Importing-data",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hZYW3_qkx7V0",
        "colab_type": "text"
      },
      "source": [
        "# Importing data\n",
        "\n",
        "This notebook will show you how to import a `DataFrame` of node attributes into Cytoscape as Node Table columns. The same approach works for edge and network attriubutes."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i7b4ULFnX3lY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "458557c1-3fc5-4f1c-9365-f45486a0e8c2"
      },
      "source": [
        "import py4cytoscape as p4c\n",
        "p4c.set_summary_logger(False)\n",
        "p4c.cytoscape_ping()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "You are connected to Cytoscape!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qK3Cr81zywcT",
        "colab_type": "text"
      },
      "source": [
        "## Always Start with a Network\n",
        "\n",
        "When importing data, you are actually performing a merge function of sorts, appending columns to nodes (or edges) that are present in the referenced network. Data that do not match elements in the network are effectively discarded upon import.\n",
        "\n",
        "So, in order to demonstrate data import, we first need to have a network. This command will import network files in any of the supported formats (e.g., SIF, GML, XGMML, etc)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3kDWKvKyx6Zy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 208
        },
        "outputId": "7c99cad2-8f80-4fc3-e718-e610593ecdfa"
      },
      "source": [
        "!wget https://raw.githubusercontent.com/cytoscape/RCy3/master/inst/extdata/galFiltered.sif"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-07-02 18:17:45--  https://raw.githubusercontent.com/cytoscape/RCy3/master/inst/extdata/galFiltered.sif\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 6822 (6.7K) [text/plain]\n",
            "Saving to: ‘galFiltered.sif’\n",
            "\n",
            "\rgalFiltered.sif       0%[                    ]       0  --.-KB/s               \rgalFiltered.sif     100%[===================>]   6.66K  --.-KB/s    in 0s      \n",
            "\n",
            "2020-07-02 18:17:45 (70.3 MB/s) - ‘galFiltered.sif’ saved [6822/6822]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "t9FvU6goYU8Q",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 191
        },
        "outputId": "b07e1e19-98a1-4d57-acc6-0d6874be9e87"
      },
      "source": [
        "!head galFiltered.sif"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "YKR026C pp YGL122C\n",
            "YGR218W pp YGL097W\n",
            "YGL097W pp YOR204W\n",
            "YLR249W pp YPR080W\n",
            "YLR249W pp YBR118W\n",
            "YLR293C pp YGL097W\n",
            "YMR146C pp YDR429C\n",
            "YDR429C pp YFL017C\n",
            "YPR080W pp YAL003W\n",
            "YBR118W pp YAL003W\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlDVbTrqYYaW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d1be085d-fc5b-4e01-952b-2afa3927f656"
      },
      "source": [
        "p4c.import_network_from_file(\"./galFiltered.sif\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'networks': [51], 'views': [750]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ro7WdWdQzsBq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 382
        },
        "outputId": "bc717104-caeb-4753-9f8b-82b19c17a81a"
      },
      "source": [
        "p4c.export_image(filename=\"galFiltered.png\")\n",
        "from IPython.display import Image\n",
        "Image('galFiltered.png')"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "STIa0ZT_0R70",
        "colab_type": "text"
      },
      "source": [
        "You should now see a network with just over 300 nodes. If you look at the Node Table, you’ll see that there are no attributes other than node names. Let’s fix that…"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w6tbPsB60X-d",
        "colab_type": "text"
      },
      "source": [
        "## Import Data\n",
        "You can import data into Cytoscape from any `DataFrame` in Python as long as it contains row.names (or an arbitrary column) that match a Node Table column in Cytoscape. In this example, we are starting with a network with yeast identifiers in the “name” column. We also have a CSV file with gene expression data values keyed by yeast identifiers here:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "stbkwprm06Z2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "url=\"https://raw.githubusercontent.com/cytoscape/RCy3/master/inst/extdata/galExpData.csv\"\n",
        "df=pd.read_csv(url)"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TadDpDx40-zi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 202
        },
        "outputId": "bd27854c-b0ea-4932-9661-72812e26a51e"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>name</th>\n",
              "      <th>COMMON</th>\n",
              "      <th>gal1RGexp</th>\n",
              "      <th>gal1RGsig</th>\n",
              "      <th>gal4RGexp</th>\n",
              "      <th>gal4RGsig</th>\n",
              "      <th>gal80Rexp</th>\n",
              "      <th>gal80Rsig</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>YDL194W</td>\n",
              "      <td>SNF3</td>\n",
              "      <td>0.139</td>\n",
              "      <td>1.804300e-02</td>\n",
              "      <td>0.333</td>\n",
              "      <td>3.396100e-02</td>\n",
              "      <td>0.449</td>\n",
              "      <td>0.011348</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>YDR277C</td>\n",
              "      <td>MTH1</td>\n",
              "      <td>0.243</td>\n",
              "      <td>2.190000e-05</td>\n",
              "      <td>0.192</td>\n",
              "      <td>2.804400e-02</td>\n",
              "      <td>0.448</td>\n",
              "      <td>0.000573</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>YBR043C</td>\n",
              "      <td>YBR043C</td>\n",
              "      <td>0.454</td>\n",
              "      <td>5.370000e-08</td>\n",
              "      <td>0.023</td>\n",
              "      <td>9.417800e-01</td>\n",
              "      <td>0.000</td>\n",
              "      <td>0.999999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>YPR145W</td>\n",
              "      <td>ASN1</td>\n",
              "      <td>-0.195</td>\n",
              "      <td>3.170000e-05</td>\n",
              "      <td>-0.614</td>\n",
              "      <td>1.150000e-07</td>\n",
              "      <td>-0.232</td>\n",
              "      <td>0.001187</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>YER054C</td>\n",
              "      <td>GIP2</td>\n",
              "      <td>0.057</td>\n",
              "      <td>1.695800e-01</td>\n",
              "      <td>0.206</td>\n",
              "      <td>6.200000e-04</td>\n",
              "      <td>0.247</td>\n",
              "      <td>0.004360</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      name   COMMON  gal1RGexp  ...     gal4RGsig  gal80Rexp  gal80Rsig\n",
              "0  YDL194W     SNF3      0.139  ...  3.396100e-02      0.449   0.011348\n",
              "1  YDR277C     MTH1      0.243  ...  2.804400e-02      0.448   0.000573\n",
              "2  YBR043C  YBR043C      0.454  ...  9.417800e-01      0.000   0.999999\n",
              "3  YPR145W     ASN1     -0.195  ...  1.150000e-07     -0.232   0.001187\n",
              "4  YER054C     GIP2      0.057  ...  6.200000e-04      0.247   0.004360\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZDThaYrQrm-1",
        "colab_type": "text"
      },
      "source": [
        "**Note: there may be times where your network and data identifers are of different types. This calls for identifier mapping. py4cytoscape provides a function to perform ID mapping in Cytoscape:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQBec9VzuiVI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 832
        },
        "outputId": "6ebde38b-7a47-4645-ba05-e2dc61537b8d"
      },
      "source": [
        "help(p4c.map_table_column)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on function map_table_column in module py4cytoscape.tables:\n",
            "\n",
            "map_table_column(column, species, map_from, map_to, force_single=True, table='node', namespace='default', network=None, base_url='http://localhost:1234/v1')\n",
            "    Map Table Column.\n",
            "    \n",
            "    Perform identifier mapping using an existing column of supported identifiers to populate a new column with\n",
            "    identifiers mapped to the originals.\n",
            "    \n",
            "    Supported species: Human, Mouse, Rat, Frog, Zebrafish, Fruit fly, Mosquito, Worm, Arabidopsis thaliana, Yeast,\n",
            "    E. coli, Tuberculosis. Supported identifier types (depending on species): Ensembl, Entrez Gene, Uniprot-TrEMBL,\n",
            "    miRBase, UniGene,  HGNC (symbols), MGI, RGD, SGD, ZFIN, FlyBase, WormBase, TAIR.\n",
            "    \n",
            "    Args:\n",
            "        column (str): Name of column containing identifiers of type specified by ``map.from``\n",
            "        species (str): Common name for species associated with identifiers, e.g., Human. See details.\n",
            "        map_from (str): Type of identifier found in specified ``column``. See details.\n",
            "        map.to (str): Type of identifier to populate in new column. See details.\n",
            "        force.single (bool): Whether to return only first result in cases of one-to-many mappings; otherwise\n",
            "            the new column will hold lists of identifiers. Default is TRUE.\n",
            "        table (str): name of Cytoscape table to load data into, e.g., node, edge or network; default is \"node\"\n",
            "        namespace (str): Namespace of table. Default is \"default\".\n",
            "        network (SUID or str or None): Name or SUID of a network. Default is the\n",
            "            \"current\" network active in Cytoscape.\n",
            "        base_url (str): Ignore unless you need to specify a custom domain,\n",
            "            port or version to connect to the CyREST API. Default is http://localhost:1234\n",
            "            and the latest version of the CyREST API supported by this version of py4cytoscape.\n",
            "    \n",
            "    Returns:\n",
            "        dataframe: contains map_from and map_to columns.\n",
            "    \n",
            "    Warnings:\n",
            "        If map_to is not unique, it will be suffixed with an incrementing number in parentheses, e.g.,\n",
            "        if mapIdentifiers is repeated on the same network. However, the original map_to column will be returned regardless.\n",
            "    \n",
            "    Raises:\n",
            "        HTTPError: if table or namespace or table doesn't exist in network\n",
            "        CyError: if network name or SUID doesn't exist, or if mapping parameter is invalid\n",
            "        requests.exceptions.RequestException: if can't connect to Cytoscape or Cytoscape returns an error\n",
            "    \n",
            "    Examples:\n",
            "        >>> map_table_column('name','Yeast','Ensembl','SGD')\n",
            "                  name        SGD\n",
            "        17920  YER145C S000000947\n",
            "        17921  YMR058W S000004662\n",
            "        17922  YJL190C S000003726\n",
            "        ...\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hhXArCtoq0N",
        "colab_type": "text"
      },
      "source": [
        "Check out the *Identifier mapping* tutorial for detailed examples.\n",
        "\n",
        "Now we have a `DataFrame` that includes our identifiers in a column called “name”, plus a bunch of data columns. Knowing our key columns, we can now perform the import:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qOg4GU502TVi",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "9ae095aa-6249-4b44-c3eb-4695bf384e79"
      },
      "source": [
        "p4c.load_table_data(df, data_key_column=\"name\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic": {
              "type": "string"
            },
            "text/plain": [
              "'Success: Data loaded in defaultnode table'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nd038hTy5yBn",
        "colab_type": "text"
      },
      "source": [
        "![](https://cytoscape.github.io/cytoscape-automation/for-scripters/R/notebooks/data/img/galfiltereddata.png)\n",
        "\n",
        "If you look back at the Node Table, you’ll now see that the corresponding rows of our `DataFrame` have been imported as new columns.\n",
        "\n",
        "**Note: we relied on the default values for table (\"node\") and `table_key_column` (\"name\"), but these can be specified as well.**\n",
        "**See help docs for parameter details.**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OoEkjxZD2r-s",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 714
        },
        "outputId": "ef1ab582-cb36-401a-be09-cdbef8a50e23"
      },
      "source": [
        "help(p4c.load_table_data)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Help on function load_table_data in module py4cytoscape.tables:\n",
            "\n",
            "load_table_data(data, data_key_column='row.names', table='node', table_key_column='name', namespace='default', network=None, base_url='http://localhost:1234/v1')\n",
            "    Loads data into Cytoscape tables keyed by row.\n",
            "    \n",
            "    This function loads data into Cytoscape node/edge/network\n",
            "    tables provided a common key, e.g., name. Data.frame column names will be\n",
            "    used to set Cytoscape table column names.\n",
            "    Numeric values will be stored as Doubles in Cytoscape tables.\n",
            "    Integer values will be stored as Integers. Character or mixed values will be\n",
            "    stored as Strings. Logical values will be stored as Boolean. Lists are\n",
            "    stored as Lists by CyREST v3.9+. Existing columns with the same names will\n",
            "    keep original type but values will be overwritten.\n",
            "    \n",
            "    Args:\n",
            "        data (dataframe): each row is a node and columns contain node attributes\n",
            "        data_key_column (str): name of data.frame column to use as key; ' default is \"row.names\"\n",
            "        table (str): name of Cytoscape table to load data into, e.g., node, edge or network; default is \"node\"\n",
            "        namespace (str): Namespace of table. Default is \"default\".\n",
            "        network (SUID or str or None): Name or SUID of a network. Default is the\n",
            "            \"current\" network active in Cytoscape.\n",
            "        base_url (str): Ignore unless you need to specify a custom domain,\n",
            "            port or version to connect to the CyREST API. Default is http://localhost:1234\n",
            "            and the latest version of the CyREST API supported by this version of py4cytoscape.\n",
            "    \n",
            "    Returns:\n",
            "        str: 'Success: Data loaded in <table name> table' or 'Failed to load data: <reason>'\n",
            "    \n",
            "    Raises:\n",
            "        HTTPError: if table or namespace or table doesn't exist in network\n",
            "        CyError: if network name or SUID doesn't exist\n",
            "        requests.exceptions.RequestException: if can't connect to Cytoscape or Cytoscape returns an error\n",
            "    \n",
            "    Examples:\n",
            "        >>> data = df.DataFrame(data={'id':['New1','New2','New3'], 'newcol':[1,2,3]})\n",
            "        >>> load_table_data(data, data_key_column='id', table='node', table_key_column='name')\n",
            "        'Failed to load data: Provided key columns do not contain any matches'\n",
            "        >>> data = df.DataFrame(data={'id':['YDL194W','YDR277C','YBR043C'], 'newcol':[1,2,3]})\n",
            "        >>> load_table_data(data, data_key_column='id', table='node', table_key_column='name', network='galfiltered.sif')\n",
            "        'Success: Data loaded in defaultnode table'\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oZf5QGx47BNO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}