03_Correlation_and_Visualizing_Data.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMGNm54wVApLmcpfqUbZVyP",
      "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/github/tb-harris/neuroscience-2024/blob/main/03_Correlation_and_Visualizing_Data.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Correlation and Visualizing Data"
      ],
      "metadata": {
        "id": "ig0YqE3aK83R"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Quick Exercise"
      ],
      "metadata": {
        "id": "hgl8b_19LAFE"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Plot:\n",
        "* 10:36 - 2\n",
        "* 10:37 - 1\n",
        "* 10:38 - 8\n",
        "* 10:39 - 1"
      ],
      "metadata": {
        "id": "xC70_ajmI_5_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNwb_nHUI96Z",
        "outputId": "9e7b7c86-8a59-4ad1-e819-3f105a993e06"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='time', ylabel='num_students'>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "df = pd.DataFrame([[\"10:36\", 2], [\"10:37\", 1], [\"10:38\", 8], [\"10:39\", 9]])\n",
        "df.columns = [\"time\", \"num_students\"]\n",
        "sns.set_theme()\n",
        "sns.barplot(data=df, x=\"time\", y=\"num_students\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "qPtamCa2K3he"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gXze9uYHM5yO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "qTgcPvN7LFPr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ncOeeHMoM7sy",
        "outputId": "e09f5d4a-dd9d-48fd-f808-6642174a1ebe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's load in the morphological and transcriptomics data for mouse cells.\n",
        "\n",
        "This is faster thank working w/ the morph and gene files sparately -- Mr. Feng used pandas.join() to combine them into one dataset. The transcriptomic dataset originally had thousands of cells, but this one only has those where we have both morph and gene data."
      ],
      "metadata": {
        "id": "OFGsrtrdN8n9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cells = pd.read_csv(\n",
        "    '/content/drive/Shareddrives/Lisman Laboratory/Lisman 2024/Neuro/Data/processed data/genes_morph.csv',\n",
        "    index_col=\"cell_specimen_id\"\n",
        ")"
      ],
      "metadata": {
        "id": "cpOkuVGqNa2l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Show data frame\n",
        "cells"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "AAE8z7_yN6x-",
        "outputId": "4a474c5a-708c-4ea1-be54-df20e423ba83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  0610005C13Rik  0610006L08Rik  0610007P14Rik  0610009B22Rik  \\\n",
              "cell_specimen_id                                                               \n",
              "601506507                   0.0            0.0       0.000000       0.000000   \n",
              "601790961                   0.0            0.0       0.000000       0.000000   \n",
              "601803754                   0.0            0.0       0.000000      69.658825   \n",
              "601808698                   0.0            0.0     152.239614       0.000000   \n",
              "601810307                   0.0            0.0     165.312294       0.000000   \n",
              "...                         ...            ...            ...            ...   \n",
              "963055521                   0.0            0.0      46.237643      46.237643   \n",
              "963063283                   0.0            0.0      57.018453      15.550487   \n",
              "992386952                   0.0            0.0     131.578123       0.000000   \n",
              "992268582                   0.0            0.0       0.000000      33.372182   \n",
              "992830261                   0.0            0.0       0.000000      27.464750   \n",
              "\n",
              "                  0610009E02Rik  0610009L18Rik  0610009O20Rik  0610010B08Rik  \\\n",
              "cell_specimen_id                                                               \n",
              "601506507              0.000000       0.000000       0.000000       0.000000   \n",
              "601790961              0.000000       0.000000       0.000000       0.000000   \n",
              "601803754              0.000000       0.000000       0.000000       0.000000   \n",
              "601808698              0.000000       0.000000       0.000000       0.000000   \n",
              "601810307              0.000000       0.000000       0.000000       0.000000   \n",
              "...                         ...            ...            ...            ...   \n",
              "963055521             18.495057       0.000000      46.237643       0.000000   \n",
              "963063283              5.183496       0.000000       5.183496       0.000000   \n",
              "992386952              0.000000       0.000000       0.000000       0.000000   \n",
              "992268582              0.000000       0.000000       2.567091       2.567091   \n",
              "992830261              0.000000       4.225346       0.000000       0.000000   \n",
              "\n",
              "                  0610010F05Rik  0610010K14Rik  ...  all_neurites.num_tips  \\\n",
              "cell_specimen_id                                ...                          \n",
              "601506507             15.216454       0.000000  ...                   32.0   \n",
              "601790961              0.000000       0.000000  ...                  169.0   \n",
              "601803754              1.698996       0.000000  ...                  381.0   \n",
              "601808698              0.000000      20.674515  ...                  265.0   \n",
              "601810307             25.931340      19.448505  ...                  285.0   \n",
              "...                         ...            ...  ...                    ...   \n",
              "963055521            231.188215       0.000000  ...                   56.0   \n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-289241d3-64b6-4d9b-9540-308e7bb93fbf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_7d9fdc0b-fcac-456f-a245-bbc86627363e\">\n",
              "    <style>\n",
              "      .colab-df-generate {\n",
              "        background-color: #E8F0FE;\n",
              "        border: none;\n",
              "        border-radius: 50%;\n",
              "        cursor: pointer;\n",
              "        display: none;\n",
              "        fill: #1967D2;\n",
              "        height: 32px;\n",
              "        padding: 0 0 0 0;\n",
              "        width: 32px;\n",
              "      }\n",
              "\n",
              "      .colab-df-generate:hover {\n",
              "        background-color: #E2EBFA;\n",
              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "        fill: #174EA6;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('cells')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_7d9fdc0b-fcac-456f-a245-bbc86627363e button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('cells');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "cells"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploring the data\n",
        "\n",
        "Let's calculate the surface area to volume ratio of our cells, and add it to the dataframe:"
      ],
      "metadata": {
        "id": "pk_y0fiLOwtp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cells[\"dendrite.sa_to_vol\"] = cells[\"dendrite.total_surface_area\"] / cells[\"dendrite.total_volume\"]"
      ],
      "metadata": {
        "id": "Tre0jBHhO14N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can now access this as we would any other column in the dataframe:"
      ],
      "metadata": {
        "id": "j4VOqswvPyx3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cells[\"dendrite.sa_to_vol\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "collapsed": true,
        "id": "KrxeAIx_PmLR",
        "outputId": "2862c587-f5a5-4514-8ce8-9188042e754e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cell_specimen_id\n",
              "601506507    7.524696\n",
              "601790961    7.041403\n",
              "601803754    5.954947\n",
              "601808698    7.129930\n",
              "601810307    7.687275\n",
              "               ...   \n",
              "963055521    5.537477\n",
              "963063283    4.932737\n",
              "992386952    7.557409\n",
              "992268582    6.415364\n",
              "992830261    7.674917\n",
              "Name: dendrite.sa_to_vol, Length: 573, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's see if we can find any correlation between dendrite SA to vol ratio and num of branches"
      ],
      "metadata": {
        "id": "Q2MvJFsxP7AI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the correlation coefficient between our two columns\n",
        "print(cells[\"dendrite.sa_to_vol\"].corr(cells[\"dendrite.num_branches\"]))\n",
        "\n",
        "# Plot the correlation\n",
        "sns.scatterplot(data=cells, x=\"dendrite.sa_to_vol\", y=\"dendrite.num_branches\")\n",
        "\n",
        "sns.lmplot(data=cells, x=\"dendrite.sa_to_vol\", y=\"dendrite.num_branches\", ci=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 977
        },
        "id": "kLVhywpkQF1r",
        "outputId": "11e40dbf-cce2-4211-c223-be1e0a2202e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.09178796952371189\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7bec43c67f40>"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Correlation Morph and Transcriptomic Data"
      ],
      "metadata": {
        "id": "6nwD1ak5RQe1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cells.columns[-25:] # We can see that the last 25 cols are our morph data, rest are gene\n",
        "\n",
        "# Puts everything from start of data frame up to (but not including) the 25th-to-last\n",
        "# cell into a dataframe called gene_data\n",
        "# .iloc[r, c] -> Gets a slice of the dataframe according to row and col #\n",
        "gene_data = cells.iloc[:, :-25] # all rows, all columns from start until 25th-to-last\n",
        "\n",
        "# Puts the last 25 cols into a morph_data dataframe\n",
        "morph_data = cells.iloc[:, -25:] # all rows, all columns from 25-th-to-last to end"
      ],
      "metadata": {
        "id": "0UVOD0I4RS_W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gene_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 492
        },
        "id": "c3knlPfyTGYG",
        "outputId": "bd11174d-0ce4-459a-e328-df9e974eea10",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  0610005C13Rik  0610006L08Rik  0610007P14Rik  0610009B22Rik  \\\n",
              "cell_specimen_id                                                               \n",
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              "601803754                   0.0            0.0       0.000000      69.658825   \n",
              "601808698                   0.0            0.0     152.239614       0.000000   \n",
              "601810307                   0.0            0.0     165.312294       0.000000   \n",
              "...                         ...            ...            ...            ...   \n",
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              "963063283                   0.0            0.0      57.018453      15.550487   \n",
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              "992268582                   0.0            0.0       0.000000      33.372182   \n",
              "992830261                   0.0            0.0       0.000000      27.464750   \n",
              "\n",
              "                  0610009E02Rik  0610009L18Rik  0610009O20Rik  0610010B08Rik  \\\n",
              "cell_specimen_id                                                               \n",
              "601506507              0.000000       0.000000       0.000000       0.000000   \n",
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              "...                         ...            ...            ...            ...   \n",
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              "\n",
              "                  0610010F05Rik  0610010K14Rik  ...  n-R5s134  n-R5s136  \\\n",
              "cell_specimen_id                                ...                       \n",
              "601506507             15.216454       0.000000  ...       0.0       0.0   \n",
              "601790961              0.000000       0.000000  ...       0.0       0.0   \n",
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              "601810307             25.931340      19.448505  ...       0.0       0.0   \n",
              "...                         ...            ...  ...       ...       ...   \n",
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              "992268582             77.012728       0.000000  ...       0.0       0.0   \n",
              "992830261             59.154846     118.309693  ...       0.0       0.0   \n",
              "\n",
              "                  n-R5s138  n-R5s139  n-R5s141  n-R5s142  n-R5s143  n-R5s144  \\\n",
              "cell_specimen_id                                                               \n",
              "601506507              0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "601790961              0.0       0.0       0.0       0.0       0.0       0.0   \n",
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              "...                    ...       ...       ...       ...       ...       ...   \n",
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              "992830261              0.0       0.0       0.0       0.0       0.0       0.0   \n",
              "\n",
              "                  n-R5s146  n-R5s149  \n",
              "cell_specimen_id                      \n",
              "601506507              0.0       0.0  \n",
              "601790961              0.0       0.0  \n",
              "601803754              0.0       0.0  \n",
              "601808698              0.0       0.0  \n",
              "601810307              0.0       0.0  \n",
              "...                    ...       ...  \n",
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              "\n",
              "[573 rows x 45768 columns]"
            ],
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              "      <th></th>\n",
              "      <th>0610005C13Rik</th>\n",
              "      <th>0610006L08Rik</th>\n",
              "      <th>0610007P14Rik</th>\n",
              "      <th>0610009B22Rik</th>\n",
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              "      <th>0610009L18Rik</th>\n",
              "      <th>0610009O20Rik</th>\n",
              "      <th>0610010B08Rik</th>\n",
              "      <th>0610010F05Rik</th>\n",
              "      <th>0610010K14Rik</th>\n",
              "      <th>...</th>\n",
              "      <th>n-R5s134</th>\n",
              "      <th>n-R5s136</th>\n",
              "      <th>n-R5s138</th>\n",
              "      <th>n-R5s139</th>\n",
              "      <th>n-R5s141</th>\n",
              "      <th>n-R5s142</th>\n",
              "      <th>n-R5s143</th>\n",
              "      <th>n-R5s144</th>\n",
              "      <th>n-R5s146</th>\n",
              "      <th>n-R5s149</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>cell_specimen_id</th>\n",
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              "      border-top-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      [theme=dark] .colab-df-generate:hover {\n",
              "        background-color: #434B5C;\n",
              "        box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "        filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('gene_data')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8cb0e983-cf69-4385-80c5-1ab494d8c503 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('gene_data');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "gene_data"
            }
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's try getting correlation values and plotting some specific morph feature / gene relationships from last year"
      ],
      "metadata": {
        "id": "RoVEntvNUqxX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gene_data['Sv2b'].corr(morph_data['dendrite.num_tips'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OZgzG49vUuJU",
        "outputId": "e7417a5e-e04b-4343-dd68-c8d7d11b9490"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.4764843800003373"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(x=gene_data['Sv2b'], y=morph_data['dendrite.num_tips'])\n",
        "sns.lmplot(data=cells, x='Sv2b', y='dendrite.num_tips', ci=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 958
        },
        "id": "_5WYCpJfVS07",
        "outputId": "e2a78dd0-4d7e-4594-8a34-28a10be76e18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7bec460d0820>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finding highest correlation coefficients\n"
      ],
      "metadata": {
        "id": "nycDczvGWl0B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Gets all gene cols where the value for every cell is 0.\n",
        "zero_genes = gene_data.columns[(gene_data == 0).all()]\n",
        "# gene_data[zero_genes].sum().sum() # Double check these columns are 0\n",
        "\n",
        "# Removes all of the columns in zero_genes from our dataframe\n",
        "gene_data = gene_data.drop(zero_genes, axis=\"columns\")"
      ],
      "metadata": {
        "id": "tl-b0FVoW5EL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gene_data.sum(axis=\"rows\").min() # Check the minimum expression of each gene across all neurons\n",
        "# Not 0 -> We've successfully deleted the zero columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DR37Em6PYt4a",
        "outputId": "4746a017-e645-408a-9cc1-cfb7ba7d603a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.573763625451695"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So far, we've found the correlation coefficient between pairs of columns (one morph feature vs one gene expression) to help get an idea of how correlated a single morphological feature and gene expression is across cells.\n",
        "\n",
        "It can also be useful to find many correlation values at once to help us find what factors out of the thousands we have are most correlated to help us decide which to plot / investgiate further.\n",
        "\n",
        "We can accomplish with the [corrwith()](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.corrwith.html) method."
      ],
      "metadata": {
        "id": "wo4fRL3AmODW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the correlations between each gene col and the dendrite.sa_to_vol column\n",
        "sa_to_vol_cors = gene_data.corrwith(morph_data['dendrite.sa_to_vol'])"
      ],
      "metadata": {
        "id": "-fB74GW_mt_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "High negative values are also relevant bc they indicate inverse relationship, so for now we can just look at the abs value (magnitude) of the correlation coefficients."
      ],
      "metadata": {
        "id": "D6kisoDqowM1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "correlation_magnitudes = sa_to_vol_cors.abs() # Gets the absolute value (magnitude) of each correlation"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdQo4Ho1oOgE",
        "outputId": "9a4e571f-fba6-4aea-a99c-a5e5e56041b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.39982276275182e-07"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's sort the correlation values and find the highest ones:"
      ],
      "metadata": {
        "id": "5rkrCGmEpLdY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sort in descending order (biggest # first)\n",
        "correlation_magnitudes.sort_values(ascending=False).head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B-U11FN-pONQ",
        "outputId": "b77690de-3960-42ab-d293-360107454e94"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Col25a1          0.186690\n",
              "6330416G13Rik    0.172039\n",
              "Ate1             0.169452\n",
              "8030451O07Rik    0.164726\n",
              "Gcfc2            0.160754\n",
              "Unc80            0.158629\n",
              "Gpatch1          0.158524\n",
              "BC048644         0.157528\n",
              "Gtf3c1           0.156515\n",
              "Chd4             0.153992\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot our highest-magnitude correlation\n",
        "sns.lmplot(data=cells, x='Col25a1', y='dendrite.sa_to_vol', ci=None)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "xRsV_IVXp0CT",
        "outputId": "dd66afd3-0d5b-46b4-c7be-dad725c23d29"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7bec5286b9a0>"
            ]
          },
          "metadata": {},
          "execution_count": 79
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get correlations between total axon length and all genes\n",
        "correlations = gene_data.corrwith(morph_data['axon.total_length'])\n",
        "\n",
        "# Sort the absolute values of the correlations, get top 10\n",
        "correlations.abs().sort_values(ascending=False).head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XPYsnWkBqEZ7",
        "outputId": "710d3097-f84a-4626-bdd7-4ac275ed1186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Arpp21     0.461585\n",
              "Slc17a7    0.395843\n",
              "R3hdm1     0.394971\n",
              "Car10      0.388338\n",
              "Ptprd      0.387511\n",
              "Pcsk2      0.385144\n",
              "Rbfox1     0.382710\n",
              "Coro6      0.374227\n",
              "Sorcs3     0.372476\n",
              "Nell2      0.370651\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.lmplot(data=cells, x='Arpp21', y='axon.total_length', ci=None, aspect=1.8).set(ylim=[-1000, 50000])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "Ph3sY4G9teXK",
        "outputId": "5e72a1be-db4b-43bf-9745-c91916ccc025"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.FacetGrid at 0x7bec5147e650>"
            ]
          },
          "metadata": {},
          "execution_count": 90
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 900x500 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We just used corrwith() to correlate a single column with a lot of columns. If we want to get a matrix of correlations between two sets of columns, we can run corrwith() on each column of our dataframe."
      ],
      "metadata": {
        "id": "cFzKVm-3vLrr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Take each column in morph_data and run gene_data.corrwith(col) for that col\n",
        "morph_gene_corrs = morph_data.apply(lambda col: gene_data.corrwith(col))"
      ],
      "metadata": {
        "id": "iFMMewxavcHF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can get the maximum correlation value and corresponding gene for each morphological feature:"
      ],
      "metadata": {
        "id": "mgO0Bgfe1Vt-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "corr_magnitudes = morph_gene_corrs.abs()"
      ],
      "metadata": {
        "id": "k1nw6Hfi1v-G"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(corr_magnitudes.max()) # Highest correlation value (to a gene) for each morph feature\n",
        "print(corr_magnitudes.idxmax()) # Get the name of the column that each max"
      ],
      "metadata": {
        "id": "1Z4IejUwzSXV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can also show these values side-by-side using pd.concat() (which mashes two dataframes together)"
      ],
      "metadata": {
        "id": "b9qYjGr11cxW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pd.concat([corr_magnitudes.max(), corr_magnitudes.idxmax()], axis='columns')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
        },
        "id": "6zrLxSsQ0NK9",
        "outputId": "8da3a98d-d19e-4860-cec7-4759ec76d05a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                          0        1\n",
              "axon.total_length                  0.461585   Arpp21\n",
              "all_neurites.total_length          0.407031   Arpp21\n",
              "basal_dendrite.total_length        0.313626    Tmtc2\n",
              "dendrite.total_length              0.486738    Celf2\n",
              "dendrite.total_surface_area        0.442387  Slc17a7\n",
              "axon.total_surface_area            0.420118   Arpp21\n",
              "all_neurites.total_surface_area    0.359054   Rbfox1\n",
              "basal_dendrite.total_surface_area  0.294983     Pclo\n",
              "dendrite.total_volume              0.369273  Slc17a7\n",
              "axon.total_volume                  0.519513  Gm18673\n",
              "all_neurites.total_volume          0.351915      Vip\n",
              "basal_dendrite.total_volume        0.295724     Pclo\n",
              "dendrite.num_tips                  0.521981  Slc17a7\n",
              "axon.num_tips                      0.472054   Arpp21\n",
              "all_neurites.num_tips              0.413472   Arpp21\n",
              "basal_dendrite.num_tips            0.346541   Ptchd2\n",
              "dendrite.num_nodes                 0.487821    Celf2\n",
              "axon.num_nodes                     0.456677   Arpp21\n",
              "all_neurites.num_nodes             0.394783   Arpp21\n",
              "basal_dendrite.num_nodes           0.319156   Brinp3\n",
              "dendrite.num_branches              0.512190   Arpp21\n",
              "axon.num_branches                  0.472046   Arpp21\n",
              "all_neurites.num_branches          0.413464   Arpp21\n",
              "basal_dendrite.num_branches        0.355941   Ptchd2\n",
              "dendrite.sa_to_vol                 0.186690  Col25a1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ebe4b169-e6ee-4b26-8aa0-8fdac5812c86\" class=\"colab-df-container\">\n",
              "    <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>0</th>\n",
              "      <th>1</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>axon.total_length</th>\n",
              "      <td>0.461585</td>\n",
              "      <td>Arpp21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all_neurites.total_length</th>\n",
              "      <td>0.407031</td>\n",
              "      <td>Arpp21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>basal_dendrite.total_length</th>\n",
              "      <td>0.313626</td>\n",
              "      <td>Tmtc2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dendrite.total_length</th>\n",
              "      <td>0.486738</td>\n",
              "      <td>Celf2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dendrite.total_surface_area</th>\n",
              "      <td>0.442387</td>\n",
              "      <td>Slc17a7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>axon.total_surface_area</th>\n",
              "      <td>0.420118</td>\n",
              "      <td>Arpp21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all_neurites.total_surface_area</th>\n",
              "      <td>0.359054</td>\n",
              "      <td>Rbfox1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>basal_dendrite.total_surface_area</th>\n",
              "      <td>0.294983</td>\n",
              "      <td>Pclo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dendrite.total_volume</th>\n",
              "      <td>0.369273</td>\n",
              "      <td>Slc17a7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>axon.total_volume</th>\n",
              "      <td>0.519513</td>\n",
              "      <td>Gm18673</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all_neurites.total_volume</th>\n",
              "      <td>0.351915</td>\n",
              "      <td>Vip</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>basal_dendrite.total_volume</th>\n",
              "      <td>0.295724</td>\n",
              "      <td>Pclo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dendrite.num_tips</th>\n",
              "      <td>0.521981</td>\n",
              "      <td>Slc17a7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>axon.num_tips</th>\n",
              "      <td>0.472054</td>\n",
              "      <td>Arpp21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all_neurites.num_tips</th>\n",
              "      <td>0.413472</td>\n",
              "      <td>Arpp21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>basal_dendrite.num_tips</th>\n",
              "      <td>0.346541</td>\n",
              "      <td>Ptchd2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dendrite.num_nodes</th>\n",
              "      <td>0.487821</td>\n",
              "      <td>Celf2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>axon.num_nodes</th>\n",
              "      <td>0.456677</td>\n",
              "      <td>Arpp21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all_neurites.num_nodes</th>\n",
              "      <td>0.394783</td>\n",
              "      <td>Arpp21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>basal_dendrite.num_nodes</th>\n",
              "      <td>0.319156</td>\n",
              "      <td>Brinp3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dendrite.num_branches</th>\n",
              "      <td>0.512190</td>\n",
              "      <td>Arpp21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>axon.num_branches</th>\n",
              "      <td>0.472046</td>\n",
              "      <td>Arpp21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>all_neurites.num_branches</th>\n",
              "      <td>0.413464</td>\n",
              "      <td>Arpp21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>basal_dendrite.num_branches</th>\n",
              "      <td>0.355941</td>\n",
              "      <td>Ptchd2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dendrite.sa_to_vol</th>\n",
              "      <td>0.186690</td>\n",
              "      <td>Col25a1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    fill: var(--fill-color);\n",
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              "    width: 32px;\n",
              "  }\n",
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              "\n",
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              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "        document.querySelector('#df-e54b20db-b700-4836-a404-a07ac9c08638 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 25,\n  \"fields\": [\n    {\n      \"column\": 0,\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0836464961652939,\n        \"min\": 0.18668952195976246,\n        \"max\": 0.5219805829668844,\n        \"num_unique_values\": 25,\n        \"samples\": [\n          0.36927307359554506,\n          0.48782142552088803,\n          0.4615845289718583\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": 1,\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 11,\n        \"samples\": [\n          \"Pclo\",\n          \"Arpp21\",\n          \"Brinp3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# dendrite.num_branches\thas a really high max correlation - so let's look at more genes\n",
        "abs_corrs = gene_data.corrwith(morph_data['dendrite.num_branches']).abs()"
      ],
      "metadata": {
        "id": "cqxKoWmZ0-5w"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "abs_corrs.sort_values(ascending=False).head(30)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LrH9TyT94DcE",
        "outputId": "967c6e13-9f13-4ffb-ebbf-6caaef9b0da1",
        "collapsed": true
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Arpp21           0.512190\n",
              "Slc17a7          0.508691\n",
              "Cacna2d1         0.499428\n",
              "Ptprd            0.491913\n",
              "R3hdm1           0.487666\n",
              "Phactr1          0.482130\n",
              "Nell2            0.480631\n",
              "Celf2            0.479085\n",
              "Nrgn             0.475427\n",
              "Nlgn1            0.461476\n",
              "Sv2b             0.458883\n",
              "Ptk2b            0.458349\n",
              "Pdzrn3           0.458225\n",
              "Slc24a4          0.455782\n",
              "Fam19a1          0.453304\n",
              "Itpka            0.446871\n",
              "Cobl             0.439213\n",
              "2010300C02Rik    0.435725\n",
              "Mapk4            0.429970\n",
              "LOC101056001     0.429898\n",
              "Meis2            0.427351\n",
              "Arhgap10         0.417411\n",
              "5031426D15Rik    0.416868\n",
              "Hpca             0.414519\n",
              "Gm11549          0.410882\n",
              "Ldlrad4          0.409695\n",
              "Kcnv1            0.409074\n",
              "Bdnf             0.408751\n",
              "Igfn1            0.408157\n",
              "Arhgap32         0.407985\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    }
  ]
}