05_Transcriptomics_Exploration.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMapdnrc+UpsxVkNOHJF9+g",
      "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/05_Transcriptomics_Exploration.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup\n",
        "Let's load in Google Drive and our morphological + transcriptomics dataset:"
      ],
      "metadata": {
        "id": "XraqT02kvXEL"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YZsXPatrZehv",
        "outputId": "36e755c5-4807-47fd-f896-d8fa044ccfa9"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Mount Google Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "# Read in our morph + genetic data\n",
        "cell_data = pd.read_hdf('/content/drive/Shareddrives/Lisman Laboratory/Lisman 2024/Neuro/Data/processed data/genes_morph.hdf5.lz4')\n",
        "# Create dataframes with just the gene and morph data\n",
        "gene_data = cell_data.iloc[:, :-24] # all rows, all columns from start until 24th-to-last\n",
        "# Puts the last 24 cols into a morph_data dataframe\n",
        "morph_data = cell_data.iloc[:, -24:] # all rows, all columns from 24-th-to-last to end\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Looking at 0 counts in our gene data"
      ],
      "metadata": {
        "id": "I3VKQijCvwCp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cell_data['Slc17a7']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "czs6EjXDZ-zL",
        "outputId": "1b13c6a1-b8e2-44d4-85f5-8121e9198640"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cell_specimen_id\n",
              "601506507      0.000000\n",
              "601790961      0.000000\n",
              "601803754     44.173889\n",
              "601808698      0.000000\n",
              "601810307     32.414175\n",
              "                ...    \n",
              "963055521    832.277574\n",
              "963063283    321.376736\n",
              "992386952      0.000000\n",
              "992268582      0.000000\n",
              "992830261      0.000000\n",
              "Name: Slc17a7, Length: 573, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "YIlYjj5VaX-e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(cell_data['Slc17a7'], bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "imGGjV9paxrI",
        "outputId": "433ac129-de2d-4ef5-9502-d58a199b2ff9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Slc17a7', ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "IkAbK3jJbojL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It looks like we have a lot of values that are 0 or close to 0 -- let's find out the exact proportion"
      ],
      "metadata": {
        "id": "SmzqIzrBbHlw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "(cell_data['Slc17a7'] == 0).value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vDfdipI5a2W5",
        "outputId": "c4945571-c36c-4c19-a72b-04f20e457423"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Slc17a7\n",
              "True     310\n",
              "False    263\n",
              "Name: count, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "310 of our cells have 0 values for this particular gene.\n",
        "\n",
        "It looks like we have a lot of zero values and also a lot of values that are close to 0 -- let's get a closer idea of what the distribution between 0 and 50 looks like:"
      ],
      "metadata": {
        "id": "H3AwoJ4vbqVV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(cell_data['Slc17a7'], binwidth=1).set(xlim=[0, 50], title=\"Distribution of Slc17a7 gene counts (0 to 50 only)\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "khzBgN6rbU82",
        "outputId": "05125d55-2166-47ff-fdf0-13463f1b28d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 50.0),\n",
              " Text(0.5, 1.0, 'Distribution of Slc17a7 gene counts (0 to 50 only)')]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## What % of genes are measured as expressed across cells?"
      ],
      "metadata": {
        "id": "jvFT9amuec4I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Counts how many genes have expression values of greater than 0 in each cell\n",
        "genes_expressed_by_cell = (gene_data > 0).sum(axis='columns')\n",
        "genes_expressed_by_cell"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7dq1rSmHcc2w",
        "outputId": "1fa86839-faeb-4b85-8e11-93f40ea146e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cell_specimen_id\n",
              "601506507     5724\n",
              "601790961     2568\n",
              "601803754     7322\n",
              "601808698     8086\n",
              "601810307     7321\n",
              "             ...  \n",
              "963055521     7948\n",
              "963063283     8259\n",
              "992386952     7892\n",
              "992268582     8171\n",
              "992830261    10468\n",
              "Length: 573, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the proportion of genes that are expressed by dividing by the total number of genes\n",
        "# dividing by number of columns in the dataframe (number of genes)\n",
        "genes_expressed_proportion = genes_expressed_by_cell / gene_data.shape[1]"
      ],
      "metadata": {
        "id": "gMOQ0fUXevCh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "genes_expressed_proportion.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YDdpyXBIfqwY",
        "outputId": "09581877-7808-4e91-dd30-40c154ac8f23"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    573.000000\n",
              "mean       0.199336\n",
              "std        0.032415\n",
              "min        0.056109\n",
              "25%        0.178531\n",
              "50%        0.201342\n",
              "75%        0.222666\n",
              "max        0.289525\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "genes_expressed_by_cell.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WlzubWe2gvvF",
        "outputId": "2e22240d-75a8-40a9-a595-3f3828222c6c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count      573.000000\n",
              "mean      9123.226876\n",
              "std       1483.560748\n",
              "min       2568.000000\n",
              "25%       8171.000000\n",
              "50%       9215.000000\n",
              "75%      10191.000000\n",
              "max      13251.000000\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This tells us that of the genes that are tracked in the dataset, the target cells were measured as having between 6% and 29% of those genes expressed (in any quantity).\n",
        "\n",
        "Questions:\n",
        "* How much of that is because genes aren't being measured vs aren't actually being expressed?\n",
        "* Is every gene being tracked?"
      ],
      "metadata": {
        "id": "SmOYN41HgBg6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "gene_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jsdL4mMQfsU6",
        "outputId": "199db7fc-3e5a-4b09-93a7-8e511bb9ca04"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(573, 45768)"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(genes_expressed_by_cell, bins=20)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "p9msyILShAx_",
        "outputId": "ff466292-f42b-4ac6-c33b-4db4d7ec9b74"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: ylabel='Count'>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## A closer look at Slc17a7\n",
        "\n",
        "Let's take a closer look at the relationship between Slc17a7 count and dendrite number of tips"
      ],
      "metadata": {
        "id": "vJ6JTry_hu7i"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "morph_data.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YqbkMRm5ij41",
        "outputId": "f19cb721-0391-4671-af3a-8034159707c6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['axon.total_length', 'all_neurites.total_length',\n",
              "       'basal_dendrite.total_length', 'dendrite.total_length',\n",
              "       'dendrite.total_surface_area', 'axon.total_surface_area',\n",
              "       'all_neurites.total_surface_area', 'basal_dendrite.total_surface_area',\n",
              "       'dendrite.total_volume', 'axon.total_volume',\n",
              "       'all_neurites.total_volume', 'basal_dendrite.total_volume',\n",
              "       'dendrite.num_tips', 'axon.num_tips', 'all_neurites.num_tips',\n",
              "       'basal_dendrite.num_tips', 'dendrite.num_nodes', 'axon.num_nodes',\n",
              "       'all_neurites.num_nodes', 'basal_dendrite.num_nodes',\n",
              "       'dendrite.num_branches', 'axon.num_branches',\n",
              "       'all_neurites.num_branches', 'basal_dendrite.num_branches'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Slc17a7 count is our independent variable, so it goes on x axis by convention\n",
        "sns.scatterplot(data=cell_data, x='Slc17a7', y='dendrite.num_tips')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "wZozQcBVhW2K",
        "outputId": "3047e577-6c4e-4f31-ace9-6cf39fc9a937"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Slc17a7', ylabel='dendrite.num_tips'>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.scatterplot(data=cell_data, x='Arpp21', y='dendrite.num_tips', color='red', alpha=0.2)\n",
        "sns.scatterplot(data=cell_data, x='Slc17a7', y='dendrite.num_tips', color='green', alpha=0.2)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "ha_fM39ujCB1",
        "outputId": "74c7493d-c6c3-4906-913a-e813a8bf078e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Arpp21', ylabel='dendrite.num_tips'>"
            ]
          },
          "metadata": {},
          "execution_count": 55
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Matthew's parts per million discovery"
      ],
      "metadata": {
        "id": "u5zw-NhSv5u5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rna_total_by_cell = gene_data.sum(axis='columns')\n",
        "rna_total_by_cell"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nVCCaa8_iwIL",
        "outputId": "53968886-45f9-4edf-fa8a-b7f1343f427b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cell_specimen_id\n",
              "601506507    1000000.0\n",
              "601790961    1000000.0\n",
              "601803754    1000000.0\n",
              "601808698    1000000.0\n",
              "601810307    1000000.0\n",
              "               ...    \n",
              "963055521    1000000.0\n",
              "963063283    1000000.0\n",
              "992386952    1000000.0\n",
              "992268582    1000000.0\n",
              "992830261    1000000.0\n",
              "Length: 573, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rna_total_by_cell.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6Xh6IuUo0on",
        "outputId": "6be54110-1138-4468-8ef5-83051b819ab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    5.730000e+02\n",
              "mean     1.000000e+06\n",
              "std      2.052215e-08\n",
              "min      1.000000e+06\n",
              "25%      1.000000e+06\n",
              "50%      1.000000e+06\n",
              "75%      1.000000e+06\n",
              "max      1.000000e+06\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Discovery: It really is parts per million -- our expression count is proportional to the total amount of RNA expressed in the cell (normalized to 1 million)\n",
        "\n",
        "(amount of RNA for specific gene / amount of overall RNA)*1,000,000"
      ],
      "metadata": {
        "id": "O8slwV4DpmoS"
      }
    }
  ]
}