forest_lab_kidney_preservation_comparison.ipynb

forest_lab_kidney_preservation_comparison.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyPZ4n40f31/MGrcro96wzCO",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "187bcc4304ed47c994844b62032c0d39": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_d7a1a857f5ef4cef87412f8c83d3741f",
              "IPY_MODEL_74eff1a1d7ed4a9385d21c348113243c",
              "IPY_MODEL_143e493da90b43a4838784288e898f97"
            ],
            "layout": "IPY_MODEL_21e62c0dcfdd400aaea09fd4240402c9"
          }
        },
        "d7a1a857f5ef4cef87412f8c83d3741f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_13afe54c8cfb4e6d849b729371d2b63e",
            "placeholder": "​",
            "style": "IPY_MODEL_e68d0323b6dd4def9565862004ab71c0",
            "value": "100%"
          }
        },
        "74eff1a1d7ed4a9385d21c348113243c": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_8a82481c76c2458288b5a7aeafb18cd6",
            "max": 2082767463,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_3f9d7d83ec2e4ff58c4f5cc46b68e0e0",
            "value": 2082767463
          }
        },
        "143e493da90b43a4838784288e898f97": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3b99396991bf4bf5b170bd2391c49af5",
            "placeholder": "​",
            "style": "IPY_MODEL_55a67149e9ad4dada8735559e4d872ab",
            "value": " 1.94G/1.94G [00:20<00:00, 189MB/s]"
          }
        },
        "21e62c0dcfdd400aaea09fd4240402c9": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "13afe54c8cfb4e6d849b729371d2b63e": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "e68d0323b6dd4def9565862004ab71c0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "8a82481c76c2458288b5a7aeafb18cd6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3f9d7d83ec2e4ff58c4f5cc46b68e0e0": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "3b99396991bf4bf5b170bd2391c49af5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "55a67149e9ad4dada8735559e4d872ab": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/jacobkimmel/acfe64bedc70c6e288c50efeabaf1bef/forest_lab_kidney_preservation_comparison.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qLhcIWc1-tst",
        "outputId": "345442f5-decf-4824-d825-a913d6cfb4ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting scvi_colab\n",
            "  Downloading scvi_colab-0.12.0-py3-none-any.whl (4.2 kB)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from scvi_colab) (13.3.4)\n",
            "Requirement already satisfied: markdown-it-py<3.0.0,>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->scvi_colab) (2.2.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->scvi_colab) (2.14.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py<3.0.0,>=2.2.0->rich->scvi_colab) (0.1.2)\n",
            "Installing collected packages: scvi_colab\n",
            "Successfully installed scvi_colab-0.12.0\n",
            "\u001b[34mINFO    \u001b[0m scvi-colab: Installing scvi-tools.                                                                        \n",
            "\u001b[34mINFO    \u001b[0m scvi-colab: Install successful. Testing import.                                                           \n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/scvi/_settings.py:63: UserWarning: Since v1.0.0, scvi-tools no longer uses a random seed by default. Run `scvi.settings.seed = 0` to reproduce results from previous versions.\n",
            "  self.seed = seed\n",
            "/usr/local/lib/python3.10/dist-packages/scvi/_settings.py:70: UserWarning: Setting `dl_pin_memory_gpu_training` is deprecated in v1.0 and will be removed in v1.1. Please pass in `pin_memory` to the data loaders instead.\n",
            "  self.dl_pin_memory_gpu_training = (\n"
          ]
        }
      ],
      "source": [
        "!pip install scvi_colab\n",
        "\n",
        "from scvi_colab import install \n",
        "\n",
        "install()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import scanpy as sc\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "qXutr1rx-0_m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url = \"https://storage.googleapis.com/calico-website-scnym-storage/reference_data/mouse_kidney_methods_comparison_log1p_cpm.h5ad\""
      ],
      "metadata": {
        "id": "jQZ2Sqcn-xKO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "adata = sc.read(\"./adata.h5ad\", backup_url=url)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "187bcc4304ed47c994844b62032c0d39",
            "d7a1a857f5ef4cef87412f8c83d3741f",
            "74eff1a1d7ed4a9385d21c348113243c",
            "143e493da90b43a4838784288e898f97",
            "21e62c0dcfdd400aaea09fd4240402c9",
            "13afe54c8cfb4e6d849b729371d2b63e",
            "e68d0323b6dd4def9565862004ab71c0",
            "8a82481c76c2458288b5a7aeafb18cd6",
            "3f9d7d83ec2e4ff58c4f5cc46b68e0e0",
            "3b99396991bf4bf5b170bd2391c49af5",
            "55a67149e9ad4dada8735559e4d872ab"
          ]
        },
        "id": "XQ3kdOR-_ujd",
        "outputId": "9804a84a-0cb1-47a9-f4ff-160d71d57f6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0.00/1.94G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "187bcc4304ed47c994844b62032c0d39"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "adata.obs[\"n_genes\"] = np.array((adata.X>0).sum(1)).flatten()"
      ],
      "metadata": {
        "id": "QvH5xnZ8_4kG"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "adata.obs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 696
        },
        "id": "K3oiMCyIAYyD",
        "outputId": "a46ebdc6-d2fa-41bc-d9ad-da27bcf2a0f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Cell type Dissociation protocol    Library  \\\n",
              "BG1_BG20C_AAACCTGAGAATTCCC      Podo                  cold  BG1_BG20C   \n",
              "BG1_BG20C_AAACCTGAGTCGTACT        PT                  cold  BG1_BG20C   \n",
              "BG1_BG20C_AAACCTGCACACTGCG       MPH                  cold  BG1_BG20C   \n",
              "BG1_BG20C_AAACCTGGTTGAGTTC        PT                  cold  BG1_BG20C   \n",
              "BG1_BG20C_AAACGGGAGACAAGCC        PT                  cold  BG1_BG20C   \n",
              "...                              ...                   ...        ...   \n",
              "MJ59_SN_O_TTTGTTGCACCCTCTA      aLOH                  warm       MJ59   \n",
              "MJ59_SN_O_TTTGTTGCAGCATTGT        MC                  warm       MJ59   \n",
              "MJ59_SN_O_TTTGTTGGTAGCTGCC        PT                  warm       MJ59   \n",
              "MJ59_SN_O_TTTGTTGGTGTTAACC        PT                  warm       MJ59   \n",
              "MJ59_SN_O_TTTGTTGGTTGCCGCA        PT                  warm       MJ59   \n",
              "\n",
              "                           Preservation                   Protocol  \\\n",
              "BG1_BG20C_AAACCTGAGAATTCCC        Fresh  Single_Cell_v2-Fresh-Cold   \n",
              "BG1_BG20C_AAACCTGAGTCGTACT        Fresh  Single_Cell_v2-Fresh-Cold   \n",
              "BG1_BG20C_AAACCTGCACACTGCG        Fresh  Single_Cell_v2-Fresh-Cold   \n",
              "BG1_BG20C_AAACCTGGTTGAGTTC        Fresh  Single_Cell_v2-Fresh-Cold   \n",
              "BG1_BG20C_AAACGGGAGACAAGCC        Fresh  Single_Cell_v2-Fresh-Cold   \n",
              "...                                 ...                        ...   \n",
              "MJ59_SN_O_TTTGTTGCACCCTCTA        Fresh   Single_Nuclei_v2-Sucrose   \n",
              "MJ59_SN_O_TTTGTTGCAGCATTGT        Fresh   Single_Nuclei_v2-Sucrose   \n",
              "MJ59_SN_O_TTTGTTGGTAGCTGCC        Fresh   Single_Nuclei_v2-Sucrose   \n",
              "MJ59_SN_O_TTTGTTGGTGTTAACC        Fresh   Single_Nuclei_v2-Sucrose   \n",
              "MJ59_SN_O_TTTGTTGGTTGCCGCA        Fresh   Single_Nuclei_v2-Sucrose   \n",
              "\n",
              "                                                  worksheet  \\\n",
              "BG1_BG20C_AAACCTGAGAATTCCC  cold_and_warm_fresh_suspensions   \n",
              "BG1_BG20C_AAACCTGAGTCGTACT  cold_and_warm_fresh_suspensions   \n",
              "BG1_BG20C_AAACCTGCACACTGCG  cold_and_warm_fresh_suspensions   \n",
              "BG1_BG20C_AAACCTGGTTGAGTTC  cold_and_warm_fresh_suspensions   \n",
              "BG1_BG20C_AAACGGGAGACAAGCC  cold_and_warm_fresh_suspensions   \n",
              "...                                                     ...   \n",
              "MJ59_SN_O_TTTGTTGCACCCTCTA    cells_and_nuclei_v3_chemistry   \n",
              "MJ59_SN_O_TTTGTTGCAGCATTGT    cells_and_nuclei_v3_chemistry   \n",
              "MJ59_SN_O_TTTGTTGGTAGCTGCC    cells_and_nuclei_v3_chemistry   \n",
              "MJ59_SN_O_TTTGTTGGTGTTAACC    cells_and_nuclei_v3_chemistry   \n",
              "MJ59_SN_O_TTTGTTGGTTGCCGCA    cells_and_nuclei_v3_chemistry   \n",
              "\n",
              "                                                      method  \\\n",
              "BG1_BG20C_AAACCTGAGAATTCCC  Single_Cell_v2-Fresh-Cold | cold   \n",
              "BG1_BG20C_AAACCTGAGTCGTACT  Single_Cell_v2-Fresh-Cold | cold   \n",
              "BG1_BG20C_AAACCTGCACACTGCG  Single_Cell_v2-Fresh-Cold | cold   \n",
              "BG1_BG20C_AAACCTGGTTGAGTTC  Single_Cell_v2-Fresh-Cold | cold   \n",
              "BG1_BG20C_AAACGGGAGACAAGCC  Single_Cell_v2-Fresh-Cold | cold   \n",
              "...                                                      ...   \n",
              "MJ59_SN_O_TTTGTTGCACCCTCTA   Single_Nuclei_v2-Sucrose | warm   \n",
              "MJ59_SN_O_TTTGTTGCAGCATTGT   Single_Nuclei_v2-Sucrose | warm   \n",
              "MJ59_SN_O_TTTGTTGGTAGCTGCC   Single_Nuclei_v2-Sucrose | warm   \n",
              "MJ59_SN_O_TTTGTTGGTGTTAACC   Single_Nuclei_v2-Sucrose | warm   \n",
              "MJ59_SN_O_TTTGTTGGTTGCCGCA   Single_Nuclei_v2-Sucrose | warm   \n",
              "\n",
              "                                               domain  \\\n",
              "BG1_BG20C_AAACCTGAGAATTCCC  Single_Cell_v2-Fresh-Cold   \n",
              "BG1_BG20C_AAACCTGAGTCGTACT  Single_Cell_v2-Fresh-Cold   \n",
              "BG1_BG20C_AAACCTGCACACTGCG  Single_Cell_v2-Fresh-Cold   \n",
              "BG1_BG20C_AAACCTGGTTGAGTTC  Single_Cell_v2-Fresh-Cold   \n",
              "BG1_BG20C_AAACGGGAGACAAGCC  Single_Cell_v2-Fresh-Cold   \n",
              "...                                               ...   \n",
              "MJ59_SN_O_TTTGTTGCACCCTCTA   Single_Nuclei_v2-Sucrose   \n",
              "MJ59_SN_O_TTTGTTGCAGCATTGT   Single_Nuclei_v2-Sucrose   \n",
              "MJ59_SN_O_TTTGTTGGTAGCTGCC   Single_Nuclei_v2-Sucrose   \n",
              "MJ59_SN_O_TTTGTTGGTGTTAACC   Single_Nuclei_v2-Sucrose   \n",
              "MJ59_SN_O_TTTGTTGGTTGCCGCA   Single_Nuclei_v2-Sucrose   \n",
              "\n",
              "                                                          cell_ontology_class  \\\n",
              "BG1_BG20C_AAACCTGAGAATTCCC                glomerular visceral epithelial cell   \n",
              "BG1_BG20C_AAACCTGAGTCGTACT    kidney proximal straight tubule epithelial cell   \n",
              "BG1_BG20C_AAACCTGCACACTGCG                                         macrophage   \n",
              "BG1_BG20C_AAACCTGGTTGAGTTC    kidney proximal straight tubule epithelial cell   \n",
              "BG1_BG20C_AAACGGGAGACAAGCC    kidney proximal straight tubule epithelial cell   \n",
              "...                                                                       ...   \n",
              "MJ59_SN_O_TTTGTTGCACCCTCTA  kidney loop of Henle ascending limb epithelial...   \n",
              "MJ59_SN_O_TTTGTTGCAGCATTGT                                     mesangial cell   \n",
              "MJ59_SN_O_TTTGTTGGTAGCTGCC    kidney proximal straight tubule epithelial cell   \n",
              "MJ59_SN_O_TTTGTTGGTGTTAACC    kidney proximal straight tubule epithelial cell   \n",
              "MJ59_SN_O_TTTGTTGGTTGCCGCA    kidney proximal straight tubule epithelial cell   \n",
              "\n",
              "                            n_genes  \n",
              "BG1_BG20C_AAACCTGAGAATTCCC      571  \n",
              "BG1_BG20C_AAACCTGAGTCGTACT     2025  \n",
              "BG1_BG20C_AAACCTGCACACTGCG     1418  \n",
              "BG1_BG20C_AAACCTGGTTGAGTTC      619  \n",
              "BG1_BG20C_AAACGGGAGACAAGCC      244  \n",
              "...                             ...  \n",
              "MJ59_SN_O_TTTGTTGCACCCTCTA     1914  \n",
              "MJ59_SN_O_TTTGTTGCAGCATTGT      952  \n",
              "MJ59_SN_O_TTTGTTGGTAGCTGCC     2113  \n",
              "MJ59_SN_O_TTTGTTGGTGTTAACC     2180  \n",
              "MJ59_SN_O_TTTGTTGGTTGCCGCA     2399  \n",
              "\n",
              "[173649 rows x 10 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-07b848be-7f2b-4093-a82f-e9a133e2731d\">\n",
              "    <div 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>Cell type</th>\n",
              "      <th>Dissociation protocol</th>\n",
              "      <th>Library</th>\n",
              "      <th>Preservation</th>\n",
              "      <th>Protocol</th>\n",
              "      <th>worksheet</th>\n",
              "      <th>method</th>\n",
              "      <th>domain</th>\n",
              "      <th>cell_ontology_class</th>\n",
              "      <th>n_genes</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BG1_BG20C_AAACCTGAGAATTCCC</th>\n",
              "      <td>Podo</td>\n",
              "      <td>cold</td>\n",
              "      <td>BG1_BG20C</td>\n",
              "      <td>Fresh</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold</td>\n",
              "      <td>cold_and_warm_fresh_suspensions</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold | cold</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold</td>\n",
              "      <td>glomerular visceral epithelial cell</td>\n",
              "      <td>571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BG1_BG20C_AAACCTGAGTCGTACT</th>\n",
              "      <td>PT</td>\n",
              "      <td>cold</td>\n",
              "      <td>BG1_BG20C</td>\n",
              "      <td>Fresh</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold</td>\n",
              "      <td>cold_and_warm_fresh_suspensions</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold | cold</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold</td>\n",
              "      <td>kidney proximal straight tubule epithelial cell</td>\n",
              "      <td>2025</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BG1_BG20C_AAACCTGCACACTGCG</th>\n",
              "      <td>MPH</td>\n",
              "      <td>cold</td>\n",
              "      <td>BG1_BG20C</td>\n",
              "      <td>Fresh</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold</td>\n",
              "      <td>cold_and_warm_fresh_suspensions</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold | cold</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold</td>\n",
              "      <td>macrophage</td>\n",
              "      <td>1418</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BG1_BG20C_AAACCTGGTTGAGTTC</th>\n",
              "      <td>PT</td>\n",
              "      <td>cold</td>\n",
              "      <td>BG1_BG20C</td>\n",
              "      <td>Fresh</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold</td>\n",
              "      <td>cold_and_warm_fresh_suspensions</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold | cold</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold</td>\n",
              "      <td>kidney proximal straight tubule epithelial cell</td>\n",
              "      <td>619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BG1_BG20C_AAACGGGAGACAAGCC</th>\n",
              "      <td>PT</td>\n",
              "      <td>cold</td>\n",
              "      <td>BG1_BG20C</td>\n",
              "      <td>Fresh</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold</td>\n",
              "      <td>cold_and_warm_fresh_suspensions</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold | cold</td>\n",
              "      <td>Single_Cell_v2-Fresh-Cold</td>\n",
              "      <td>kidney proximal straight tubule epithelial cell</td>\n",
              "      <td>244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MJ59_SN_O_TTTGTTGCACCCTCTA</th>\n",
              "      <td>aLOH</td>\n",
              "      <td>warm</td>\n",
              "      <td>MJ59</td>\n",
              "      <td>Fresh</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose</td>\n",
              "      <td>cells_and_nuclei_v3_chemistry</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose | warm</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose</td>\n",
              "      <td>kidney loop of Henle ascending limb epithelial...</td>\n",
              "      <td>1914</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MJ59_SN_O_TTTGTTGCAGCATTGT</th>\n",
              "      <td>MC</td>\n",
              "      <td>warm</td>\n",
              "      <td>MJ59</td>\n",
              "      <td>Fresh</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose</td>\n",
              "      <td>cells_and_nuclei_v3_chemistry</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose | warm</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose</td>\n",
              "      <td>mesangial cell</td>\n",
              "      <td>952</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MJ59_SN_O_TTTGTTGGTAGCTGCC</th>\n",
              "      <td>PT</td>\n",
              "      <td>warm</td>\n",
              "      <td>MJ59</td>\n",
              "      <td>Fresh</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose</td>\n",
              "      <td>cells_and_nuclei_v3_chemistry</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose | warm</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose</td>\n",
              "      <td>kidney proximal straight tubule epithelial cell</td>\n",
              "      <td>2113</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MJ59_SN_O_TTTGTTGGTGTTAACC</th>\n",
              "      <td>PT</td>\n",
              "      <td>warm</td>\n",
              "      <td>MJ59</td>\n",
              "      <td>Fresh</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose</td>\n",
              "      <td>cells_and_nuclei_v3_chemistry</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose | warm</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose</td>\n",
              "      <td>kidney proximal straight tubule epithelial cell</td>\n",
              "      <td>2180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MJ59_SN_O_TTTGTTGGTTGCCGCA</th>\n",
              "      <td>PT</td>\n",
              "      <td>warm</td>\n",
              "      <td>MJ59</td>\n",
              "      <td>Fresh</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose</td>\n",
              "      <td>cells_and_nuclei_v3_chemistry</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose | warm</td>\n",
              "      <td>Single_Nuclei_v2-Sucrose</td>\n",
              "      <td>kidney proximal straight tubule epithelial cell</td>\n",
              "      <td>2399</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>173649 rows × 10 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-07b848be-7f2b-4093-a82f-e9a133e2731d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\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=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\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-convert: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-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert: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",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-07b848be-7f2b-4093-a82f-e9a133e2731d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-07b848be-7f2b-4093-a82f-e9a133e2731d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(1,1,figsize=(4, 6))\n",
        "sns.boxplot(\n",
        "    data=adata.obs,\n",
        "    x=\"n_genes\",\n",
        "    hue=\"Preservation\",\n",
        "    y=\"Cell type\",\n",
        ")\n",
        "ax.legend(bbox_to_anchor=(1., 1.))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "cXHL_JCoAAcG",
        "outputId": "643d1e14-aec8-4aba-a3b5-dbc886d32acd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 400x600 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7shvSIt_AcVx"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}