{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "# Identification of High Curl, High Elongation, Low Elongation, High Linearity and Low Linearity cell types - DLPFC 151508"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-DzZ7eYKWfr4",
        "outputId": "1562c047-712b-479c-b22c-e1173a4d79ba"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Building wheels for collected packages: docrep, pims\n",
            "  Building wheel for docrep (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "Successfully built docrep pims\n",
            "Installing collected packages: sortedcontainers, slicerator, rangehttpserver, zict, wrapt, validators, tblib, stdlib_list, session-info2, pyct, pathlib-abc, numcodecs, lightning-utilities, legacy-api-wrap, jmespath, imagecodecs, fast-array-utils, donfig, docrep, array-api-compat, aioitertools, zarr, universal-pathlib, sknw, session-info, scverse-misc, pims, pandas, Deprecated, dask, botocore, torchmetrics, omnipath, matplotlib-scalebar, distributed, anndata, aiobotocore, xarray-spatial, xarray-dataclass, s3fs, pytorch-lightning, datashader, annsel, torchgmm, spatial-image, scanpy, dask-image, ome-zarr, multiscale-spatial-image, spatialdata, spatialdata-plot, squidpy, cellcharter\n",
            "  Attempting uninstall: wrapt\n",
            "    Found existing installation: wrapt 2.1.2\n",
            "    Uninstalling wrapt-2.1.2:\n",
            "      Successfully uninstalled wrapt-2.1.2\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 2.2.2\n",
            "    Uninstalling pandas-2.2.2:\n",
            "      Successfully uninstalled pandas-2.2.2\n",
            "  Attempting uninstall: dask\n",
            "    Found existing installation: dask 2026.3.0\n",
            "    Uninstalling dask-2026.3.0:\n",
            "      Successfully uninstalled dask-2026.3.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.3.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Deprecated-1.3.1 aiobotocore-2.26.0 aioitertools-0.13.0 anndata-0.12.16 annsel-0.1.2 array-api-compat-1.14.0 botocore-1.41.5 cellcharter-0.3.7 dask-2026.1.1 dask-image-2025.11.0 datashader-0.19.1 distributed-2026.1.1 docrep-0.3.2 donfig-0.8.1.post1 fast-array-utils-1.4.1 imagecodecs-2025.11.11 jmespath-1.1.0 legacy-api-wrap-1.5 lightning-utilities-0.15.3 matplotlib-scalebar-0.9.0 multiscale-spatial-image-2.0.3 numcodecs-0.16.5 ome-zarr-0.15.0 omnipath-1.0.12 pandas-2.3.3 pathlib-abc-0.5.2 pims-0.7 pyct-0.6.0 pytorch-lightning-2.6.4 rangehttpserver-1.4.0 s3fs-2025.3.0 scanpy-1.12.1 scverse-misc-0.0.7 session-info-1.0.1 session-info2-0.4.1 sknw-0.15 slicerator-1.1.0 sortedcontainers-2.4.0 spatial-image-1.2.3 spatialdata-0.7.3 spatialdata-plot-0.3.4 squidpy-1.8.1 stdlib_list-0.12.0 tblib-3.2.2 torchgmm-0.1.4 torchmetrics-1.9.0 universal-pathlib-0.3.10 validators-0.35.0 wrapt-1.17.3 xarray-dataclass-3.0.0 xarray-spatial-0.9.9 zarr-3.2.1 zict-3.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install cellcharter\n",
        "!pip install squidpy\n",
        "!pip install scanpy"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FZMdJrze1DrZ"
      },
      "outputs": [],
      "source": [
        "import squidpy as sq\n",
        "import cellcharter as cc\n",
        "import scanpy as sc\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "flag = True\n",
        "df_val = pd.DataFrame()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9DOTptaLXs5Q"
      },
      "source": [
        "Mounting google drive to accessing input data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sszU7MS-XsCl",
        "outputId": "edff9364-240f-4365-dd7e-4c2dc3a158a1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLAaLOfi5Xot"
      },
      "source": [
        "Shape Characterization"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j_hk64PqYaze"
      },
      "outputs": [],
      "source": [
        "def cellcharter_metrics(data_name, category, data_path, ctp_path):\n",
        "\n",
        "    global flag, df_val\n",
        "\n",
        "    adata = sc.read_h5ad(data_path)\n",
        "    ctp = pd.read_csv(ctp_path)\n",
        "    print (\"ctp shape\", ctp.shape, \"and\", ctp.columns)\n",
        "\n",
        "    if (adata.shape[0] != ctp.shape[0]):\n",
        "        adata = adata[adata.obs_names.isin(ctp[ctp.columns[0]])]\n",
        "\n",
        "    print (\"adata shape\", adata.shape)\n",
        "\n",
        "    for c in ctp.columns[1:]:\n",
        "        threshold = np.median(ctp[c].values)\n",
        "        adata.obs[c + \"_spatial_cluster\"] = (ctp[c] > threshold).astype(int).values\n",
        "\n",
        "    sq.gr.spatial_neighbors(adata, coord_type = 'generic', delaunay = True) # library_key='sample',\n",
        "\n",
        "    for c in ctp.columns[1:]:\n",
        "\n",
        "        cc.gr.connected_components(adata, cluster_key = c + '_spatial_cluster')\n",
        "        cc.tl.boundaries(adata, min_hole_area_ratio = 0.1)\n",
        "        cc.tl.linearity(adata)\n",
        "        cc.tl.curl(adata)\n",
        "        cc.tl.elongation(adata)\n",
        "\n",
        "        adata.obs.rename(columns = {'component': c +'_component'}, inplace = True)\n",
        "        #print (adata)\n",
        "        mapping = pd.DataFrame({\"Act\" : adata.obs[c + \"_spatial_cluster\"].values, \"Pred\" : adata.obs[c + \"_component\"].values})\n",
        "        mapping = mapping.drop_duplicates()\n",
        "\n",
        "        #adata.uns[c + '_shape_component'] = adata.uns.pop('shape_component')\n",
        "\n",
        "        t = mapping.drop_duplicates()\n",
        "        t = t[t[\"Act\"] == 1]\n",
        "        t.dropna(inplace = True)\n",
        "\n",
        "        comp_itr = 1\n",
        "        for comp in t[\"Pred\"].values:\n",
        "\n",
        "            linearity = adata.uns[\"shape_component\"][\"linearity\"][comp]\n",
        "            curl = adata.uns[\"shape_component\"][\"curl\"][comp]\n",
        "            elongation = adata.uns[\"shape_component\"][\"elongation\"][comp]\n",
        "\n",
        "            if (flag):\n",
        "                df_val[\"Category\"] = [category]\n",
        "                df_val[\"Dataset\"] = [data_name]\n",
        "                df_val[\"cell_type\"] = [c + \"_\" + str(comp_itr)]\n",
        "                df_val[\"linearity\"] = [linearity]\n",
        "                df_val[\"curl\"] = [curl]\n",
        "                df_val[\"elongation\"] = [elongation]\n",
        "\n",
        "                #print (df_val)\n",
        "                comp_itr += 1\n",
        "                flag = False\n",
        "            else:\n",
        "                new_row = {\"Category\" : category, \"Dataset\" : data_name, \"cell_type\": c + \"_\" + str(comp_itr), \"linearity\" : linearity, \"curl\" : curl, \"elongation\" : elongation}\n",
        "                comp_itr += 1\n",
        "                #df_val = df_val.append(new_row, ignore_index = True)\n",
        "                df_val = pd.concat([df_val, pd.DataFrame([new_row])], ignore_index=True)\n",
        "                #print (df_val)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NNA8JFAzXsJW",
        "outputId": "8628962e-61f9-474e-f767-c70d46636d6e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "ctp shape (4382, 18) and Index(['Unnamed: 0', 'AST_FB', 'AST_PP', 'Endothelial', 'IN_PV', 'IN_SST',\n",
            "       'IN_SV2C', 'IN_VIP', 'L2_3', 'L4', 'L5_6', 'L5_6_CC', 'Microglia',\n",
            "       'Neu_NRGN_I', 'Neu_NRGN_II', 'Neu_mat', 'OPC', 'Oligodendrocytes'],\n",
            "      dtype='object')\n",
            "adata shape (4382, 11403)\n",
            "\u001b[34mINFO    \u001b[0m Creating graph using `generic` coordinates and `\u001b[3;35mNone\u001b[0m` transform and `\u001b[1;36m1\u001b[0m` libraries.                        \n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:24: FutureWarning: linearity is deprecated and will be removed in the next release. Please use `linearity_metric` instead.\n",
            "  cc.tl.linearity(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:25: FutureWarning: curl is deprecated and will be removed in the next release. Please use `curl_metric` instead.\n",
            "  cc.tl.curl(adata)\n",
            "/tmp/ipykernel_3161/929752537.py:26: FutureWarning: elongation is deprecated and will be removed in the next release. Please use `elongation_metric` instead.\n",
            "  cc.tl.elongation(adata)\n"
          ]
        }
      ],
      "source": [
        "lst = {\n",
        "        \"Brain\" : [\"151508\"], # \"151673\", \"151508\", \"151509\", \"151669\", \"151670\"\n",
        "      \"Cancer\": [],\n",
        "      \"Development\" : []\n",
        "        }\n",
        "\n",
        "path = \"/content/drive/MyDrive/Major_project/Benchmarking_Shared/spDDB_tutorials/\"\n",
        "\n",
        "for l in lst[\"Brain\"]:\n",
        "\n",
        "    data_path = path + \"1_data/synthetic_spatial_gene_exp/simulated_spatial_gene_expression.h5ad\"\n",
        "    ctp_path = path + \"4_data/Simulated_cell_type_proportion_DLPFC_151508.csv\"\n",
        "\n",
        "    cellcharter_metrics(l, \"Brain\", data_path, ctp_path)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ifpHHj4M5sBt"
      },
      "source": [
        "Specify metrics"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ggR2qJlBYr9q"
      },
      "outputs": [],
      "source": [
        "metric = \"linearity\" # \"elongation\" # \"curl\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zv5MmCoXYucB"
      },
      "outputs": [],
      "source": [
        "def consider_all_comps(ct_lst):\n",
        "\n",
        "    new_ct_lst = []\n",
        "\n",
        "    for b in ct_lst:\n",
        "        b_clip = b[:-1]\n",
        "        new_ct_lst += [b]\n",
        "\n",
        "        last_char = b[-1]\n",
        "\n",
        "        new_ct_lst += [b_clip + str(d) for d in range(1, int(last_char))]\n",
        "\n",
        "    return new_ct_lst\n",
        "\n",
        "def get_top_bottom_25(df_subset, per, m):\n",
        "\n",
        "    df_subset = df_subset[[\"cell_type\", m]]\n",
        "    df_subset = df_subset.groupby('cell_type')[m].median().reset_index()\n",
        "\n",
        "    # Rename the columns for clarity (optional)\n",
        "    df_subset.columns = ['cell_type', m]\n",
        "\n",
        "    sorted_df = df_subset.sort_values(by = m).reset_index(drop = True)\n",
        "    bottom_cts = sorted_df[: round(per*len(sorted_df))][[\"cell_type\", m]]\n",
        "\n",
        "    per_neg = 1 - per\n",
        "    top_cts = sorted_df[round(per_neg*len(sorted_df)) : ][[\"cell_type\", m]]\n",
        "    #print (bottom_cts, top_cts)\n",
        "\n",
        "    new_bottom_cts = consider_all_comps(bottom_cts[\"cell_type\"].values)\n",
        "    new_top_cts = consider_all_comps(top_cts[\"cell_type\"].values)\n",
        "    return new_bottom_cts, new_top_cts"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "woDgzjlN58lT"
      },
      "source": [
        "Top and Bottom cell types with chosen shape metrics for Brain datasets"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "03BqC2NWZEbc",
        "outputId": "0c98d87e-5093-474d-ccb9-bb8d35c6d201"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   Category Dataset           cell_type  linearity      curl  elongation\n",
            "0     Brain  151508            AST_FB_1   0.763348  0.110810    0.110016\n",
            "1     Brain  151508            AST_FB_1   0.763348  0.110810    0.110016\n",
            "2     Brain  151508            AST_PP_1   1.000000  0.000000    0.345660\n",
            "3     Brain  151508            AST_PP_2   1.000000  0.101009    0.653668\n",
            "4     Brain  151508       Endothelial_1   0.941555  0.556208    0.052118\n",
            "5     Brain  151508             IN_PV_1   0.576744  0.343922    0.000154\n",
            "6     Brain  151508            IN_SST_1   0.726808  0.000000    0.052081\n",
            "7     Brain  151508           IN_SV2C_1   0.913603  0.000000    0.345682\n",
            "8     Brain  151508            IN_VIP_1   1.000000  0.000000    0.331758\n",
            "9     Brain  151508              L2_3_1   0.798326  0.032302    0.150539\n",
            "10    Brain  151508                L4_1   1.000000  0.000000    0.414979\n",
            "11    Brain  151508              L5_6_1   1.000000  0.000000    0.471710\n",
            "12    Brain  151508           L5_6_CC_1   1.000000  0.000000    0.492478\n",
            "13    Brain  151508         Microglia_1   0.795943  0.000000    0.396660\n",
            "14    Brain  151508         Microglia_2   0.927239  0.000000    0.658984\n",
            "15    Brain  151508        Neu_NRGN_I_1   0.816390  0.326698    0.052081\n",
            "16    Brain  151508       Neu_NRGN_II_1   1.000000  0.000000    0.462463\n",
            "17    Brain  151508           Neu_mat_1   0.747737  0.000000    0.144467\n",
            "18    Brain  151508               OPC_1   1.000000  0.170473    0.365133\n",
            "19    Brain  151508               OPC_2   0.875739  0.000000    0.655748\n",
            "20    Brain  151508  Oligodendrocytes_1   1.000000  0.006502    0.427947\n"
          ]
        }
      ],
      "source": [
        "print (df_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvV8_06c54_C",
        "outputId": "a58693ed-f881-44ba-f393-d3493b1459c6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Bottom ['IN_PV_1', 'IN_SST_1', 'Neu_mat_1', 'AST_FB_1', 'Microglia_1'] \n",
            " Top ['AST_PP_2', 'AST_PP_1', 'Neu_NRGN_II_1', 'L4_1', 'OPC_1', 'Oligodendrocytes_1'] \n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ],
      "source": [
        "### Linearity\n",
        "df_brain = df_val[df_val[\"Category\"] == \"Brain\"]\n",
        "dfs = []\n",
        "\n",
        "Organs = {\n",
        "    \"DLPFC\" : ['151508'] # '151674', '151508', '151509', '151669', '151670'\n",
        "    #\"Mouse_brain\" : ['Mouse_brain_ST48', 'Mouse_brain_ST52']\n",
        "}\n",
        "\n",
        "for key in Organs.keys():\n",
        "\n",
        "    data = Organs[key]\n",
        "    #print (data)\n",
        "    df_subset = df_brain[df_brain[\"Dataset\"].isin(data)].reset_index(drop = True)\n",
        "\n",
        "    #print (df_subset.shape)\n",
        "    bottom_cts, top_cts = get_top_bottom_25(df_subset, 0.25, metric)\n",
        "    print (\"Bottom\", bottom_cts, \"\\n Top\", top_cts, \"\\n\\n\\n\")\n",
        "\n",
        "    df_bottom = df_subset[df_subset[\"cell_type\"].isin(bottom_cts)].reset_index(drop = True)\n",
        "    df_bottom[\"type\"] = \"bottom\"\n",
        "    dfs += [df_bottom]\n",
        "\n",
        "    df_top = df_subset[df_subset[\"cell_type\"].isin(top_cts)].reset_index(drop = True)\n",
        "    df_top[\"type\"] = \"top\"\n",
        "    dfs += [df_top]\n",
        "\n",
        "df_shape_brain = pd.concat(dfs, axis = 0).reset_index(drop = True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cfwD9xNqZWFg",
        "outputId": "41f516e7-e702-4253-bee3-c06b476be651"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "   Category Dataset           cell_type  linearity      curl  elongation  \\\n",
            "0     Brain  151508            AST_FB_1   0.763348  0.110810    0.110016   \n",
            "1     Brain  151508            AST_FB_1   0.763348  0.110810    0.110016   \n",
            "2     Brain  151508             IN_PV_1   0.576744  0.343922    0.000154   \n",
            "3     Brain  151508            IN_SST_1   0.726808  0.000000    0.052081   \n",
            "4     Brain  151508         Microglia_1   0.795943  0.000000    0.396660   \n",
            "5     Brain  151508           Neu_mat_1   0.747737  0.000000    0.144467   \n",
            "6     Brain  151508            AST_PP_1   1.000000  0.000000    0.345660   \n",
            "7     Brain  151508            AST_PP_2   1.000000  0.101009    0.653668   \n",
            "8     Brain  151508                L4_1   1.000000  0.000000    0.414979   \n",
            "9     Brain  151508       Neu_NRGN_II_1   1.000000  0.000000    0.462463   \n",
            "10    Brain  151508               OPC_1   1.000000  0.170473    0.365133   \n",
            "11    Brain  151508  Oligodendrocytes_1   1.000000  0.006502    0.427947   \n",
            "\n",
            "      type  \n",
            "0   bottom  \n",
            "1   bottom  \n",
            "2   bottom  \n",
            "3   bottom  \n",
            "4   bottom  \n",
            "5   bottom  \n",
            "6      top  \n",
            "7      top  \n",
            "8      top  \n",
            "9      top  \n",
            "10     top  \n",
            "11     top  \n"
          ]
        }
      ],
      "source": [
        "print (df_shape_brain)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}