templates/generic_cell_clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Generic cell clustering notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "import"
    ]
   },
   "outputs": [],
   "source": [
    "# import required packages\n",
    "import os\n",
    "from datetime import datetime as dt\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from alpineer import load_utils, misc_utils\n",
    "from matplotlib import rc_file_defaults\n",
    "\n",
    "from ark.phenotyping import (cell_cluster_utils, cell_meta_clustering,\n",
    "                             cell_som_clustering)\n",
    "from ark.utils import data_utils, example_dataset, plot_utils\n",
    "from ark.utils.metacluster_remap_gui import (MetaClusterGui,\n",
    "                                             colormap_helper,\n",
    "                                             metaclusterdata_from_files)\n",
    "from ark import settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 0: Set root directory and download the example dataset\n",
    "\n",
    "Here we are using the example data located in `/data/example_dataset/input_data`. To modify this notebook to run using your own data, simply change `base_dir` to point to your own sub-directory within the data folder, rather than `'example_dataset'`. \n",
    "\n",
    "* `base_dir`: the path to all of your imaging data. Should contain a directory for your images, segmentations, and cell table (generated from `1_Segment_Image_Data.ipynb`). This directory will also store all of the directories/files created during pixel clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "base_dir"
    ]
   },
   "outputs": [],
   "source": [
    "# define the home directory containing pixel_output_dir\n",
    "base_dir = \"../data/example_dataset/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you would like to test the features in Ark with an example dataset, run the cell below. It will download a dataset consisting of 11 FOVs with 22 channels. You may find more information about the example dataset in the [README](../README.md#example-dataset).\n",
    "\n",
    "If you are using your own data, skip the cell below.\n",
    "\n",
    "* `overwrite_existing`: If set to `False`, it will not overwrite existing data in the `data/example_dataset`. Recommended always setting to `True` to refresh the dataset at the start of a run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "ex_data_download"
    ]
   },
   "outputs": [],
   "source": [
    "example_dataset.get_example_dataset(dataset=\"cluster_cells\", save_dir = base_dir, overwrite_existing = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1: Set input paths"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the following paths:\n",
    "\n",
    "- `cell_som_input_path`: for generic cell clustering, place your input file in `base_dir` and set the name of the file accordingly. The structure of this file is defined <a href=https://ark-analysis.readthedocs.io/en/latest/_rtd/data_types.html>here</a>. At the end of the pipeline, this file will contain the SOM and meta labels.\n",
    "- `tiff_dir`: the path to the directory with extracted image data\n",
    "- `cell_table_path`: the path to the cell table generated by `1_Segment_Image_Data.ipynb`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "input_set"
    ]
   },
   "outputs": [],
   "source": [
    "cell_som_input_path = os.path.join(base_dir, 'segmentation', 'cell_table', 'generalized_cell_table_input.csv')\n",
    "tiff_dir = os.path.join(base_dir, \"image_data\")\n",
    "cell_table_path = os.path.join(base_dir, 'segmentation', 'cell_table', 'cell_table_size_normalized.csv')\n",
    "\n",
    "# load cell SOM input data\n",
    "cell_som_input_data = pd.read_csv(cell_som_input_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define the list of FOVs and expression columns to use for cell SOM clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "param_set"
    ]
   },
   "outputs": [],
   "source": [
    "# explicitly set the SOM expression columns here\n",
    "cell_som_cluster_cols = [\n",
    "    \"CD3_pred\", \"CD4_pred\", \"CD8_pred\", \"CD14_pred\", \"CD20_pred\",\n",
    "    \"CD31_pred\", \"CD45_pred\", \"CD68_pred\",\n",
    "    \"CD163_pred\", \"CK17_pred\", \"Collagen1_pred\", \"Fibronectin_pred\",\n",
    "    \"ECAD_pred\", \"HLADR_pred\", \"SMA_pred\", \"Vim_pred\"\n",
    "]\n",
    "\n",
    "# verify that the columns provided exist in cell_som_input_data\n",
    "misc_utils.verify_in_list(\n",
    "    provided_cell_cols=cell_som_cluster_cols,\n",
    "    cell_som_input_cols=cell_som_input_data.columns.values\n",
    ")\n",
    "\n",
    "# extract the set of FOVs used in the dataset\n",
    "fovs = list(cell_som_input_data['fov'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set a prefix to be applied to all data directories/files created by pixel and cell clustering. If the prefix is not set, a default of the datetime at the start of the run is used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cluster_prefix"
    ]
   },
   "outputs": [],
   "source": [
    "# explicitly set cell_cluster_prefix to override datetime default\n",
    "cell_cluster_prefix = \"example\"\n",
    "\n",
    "if cell_cluster_prefix is None:\n",
    "    cell_cluster_prefix = dt.now().strftime('%Y-%m-%dT%H:%M:%S')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following folders/files will be created with names prefixed by `cell_cluster_prefix`:\n",
    "\n",
    "* `cell_output_dir`: the name of the folder to store the cell clustering directories/files\n",
    "* `cell_som_weights_name`: file name to place the cell SOM weights\n",
    "* `cell_som_expr_col_avg_name`: file name to store the average expression values of each `cell_som_cluster_cols` per cell SOM cluster\n",
    "* `cell_meta_expr_col_avg_name`: same as above except for cell meta clusters\n",
    "* `cell_meta_cluster_remap_name`: for the meta cluster remapping process, the file to store the new SOM to meta mappings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cell_cluster_files"
    ]
   },
   "outputs": [],
   "source": [
    "# define the base output cell folder\n",
    "cell_output_dir = '%s_cell_output_dir_generic' % cell_cluster_prefix\n",
    "if not os.path.exists(os.path.join(base_dir, \"pixie\", cell_output_dir)):\n",
    "    os.mkdir(os.path.join(base_dir, \"pixie\", cell_output_dir))\n",
    "    \n",
    "# define the paths to cell clustering files, explicitly set the variables to use custom names\n",
    "cell_som_weights_name = os.path.join(\"pixie\", cell_output_dir, 'cell_som_weights.feather')\n",
    "cell_som_expr_col_avg_name = os.path.join(\"pixie\", cell_output_dir, 'cell_som_expr_col_avg.csv')\n",
    "cell_meta_expr_col_avg_name = os.path.join(\"pixie\", cell_output_dir, 'cell_meta_expr_col_avg.csv')\n",
    "cell_meta_cluster_remap_name = os.path.join(\"pixie\", cell_output_dir, 'cell_meta_cluster_mapping.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**99.9% Normalization**\n",
    "\n",
    "Whether to normalize each of the `cell_som_cluster_cols` by their 99.9% value prior to training FlowSOM. Set to `False` to skip this normalization step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "normalize_param"
    ]
   },
   "outputs": [],
   "source": [
    "normalize = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2: Cell clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.1: train cell SOM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the cell SOM on the expression values provided per cell (the data stored in `cell_som_input_name`).  Training is done using the `FlowSOM` algorithm.\n",
    "\n",
    "For a full set of parameters you can customize for `train_cell_som`, please consult: <a href=https://ark-analysis.readthedocs.io/en/latest/_markdown/ark.phenotyping.html#ark.phenotyping.cell_cluster_utils.train_cell_som>cell training docs</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "train_cell_som"
    ]
   },
   "outputs": [],
   "source": [
    "# create the cell-level SOM weights\n",
    "cell_pysom = cell_som_clustering.train_cell_som(\n",
    "    fovs,\n",
    "    base_dir,\n",
    "    cell_table_path,\n",
    "    cell_som_cluster_cols,\n",
    "    cell_som_input_data,\n",
    "    som_weights_name=cell_som_weights_name,\n",
    "    num_passes=1,\n",
    "    normalize=normalize\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2: assign cell SOM clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use the weights learned from `train_cell_som` to assign cell clusters to the cell dataset. As with `train_pixel_som`, each of the columns are normalized by their 99.9% value prior to assigning a cell SOM cluster label.\n",
    "\n",
    "`generate_som_avg_files` will then compute the average values of all columns in `cell_som_cluster_cols` per cell SOM cluster as well as the number of cells in each cell SOM cluster (the data placed in `cell_som_expr_col_avg_name`). This is needed for cell consensus clustering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cluster_cell_data"
    ]
   },
   "outputs": [],
   "source": [
    "# use cell SOM weights to assign cell clusters\n",
    "cell_som_input_data = cell_som_clustering.cluster_cells(\n",
    "    base_dir,\n",
    "    cell_pysom,\n",
    "    cell_som_cluster_cols=cell_som_cluster_cols\n",
    ")\n",
    "\n",
    "# generate the SOM cluster summary files\n",
    "cell_som_clustering.generate_som_avg_files(\n",
    "    base_dir,\n",
    "    cell_som_input_data,\n",
    "    cell_som_cluster_cols=cell_som_cluster_cols,\n",
    "    cell_som_expr_col_avg_name=cell_som_expr_col_avg_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3: run cell consensus clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the SOM cluster labels assigned to the cell data, assign consensus cluster labels. The consensus clusters are trained on the average values of all columns in `cell_som_cluster_cols` per cell SOM cluster (the data stored in `cell_som_expr_col_avg_name`). These values are z-scored and capped at the value specified in the `cap` argument prior to training: this helps improve the meta clustering process.\n",
    "\n",
    "After consensus clustering, the following are also computed by `generate_meta_avg_files`:\n",
    "\n",
    "* The average values of all columns in `cell_som_cluster_cols` per cell meta cluster, and the number of cells per meta cluster (the data placed in `cell_meta_expr_col_avg_name`)\n",
    "* The meta cluster mapping for each cell SOM cluster in `cell_som_expr_col_avg_name` (data is resaved, same data except with an associated meta cluster column)\n",
    "\n",
    "For a full set of parameters you can customize for `cell_consensus_cluster`, please consult: <a href=https://ark-analysis.readthedocs.io/en/latest/_markdown/ark.phenotyping.html#ark.phenotyping.cell_cluster_utils.cell_consensus_cluster>cell consensus clustering docs</a>. Do note that weighted cell channel computations are unsupported as it is outside the scope of generic cell clustering."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* `max_k`: the number of consensus clusters desired\n",
    "* `cap`: used to clip z-scored values prior to consensus clustering (in the range `[-cap, cap]`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cell_consensus_cluster"
    ]
   },
   "outputs": [],
   "source": [
    "max_k = 20\n",
    "cap = 3\n",
    "\n",
    "# run hierarchical clustering based on cell SOM cluster assignments\n",
    "cell_cc, cell_som_input_data = cell_meta_clustering.cell_consensus_cluster(\n",
    "    base_dir,\n",
    "    cell_som_cluster_cols=cell_som_cluster_cols,\n",
    "    cell_som_input_data=cell_som_input_data,\n",
    "    cell_som_expr_col_avg_name=cell_som_expr_col_avg_name,\n",
    "    max_k=max_k,\n",
    "    cap=cap,\n",
    ")\n",
    "\n",
    "# generate the meta cluster summary files\n",
    "cell_meta_clustering.generate_meta_avg_files(\n",
    "    base_dir,\n",
    "    cell_cc,\n",
    "    cell_som_cluster_cols=cell_som_cluster_cols,\n",
    "    cell_som_input_data=cell_som_input_data,\n",
    "    cell_som_expr_col_avg_name=cell_som_expr_col_avg_name,\n",
    "    cell_meta_expr_col_avg_name=cell_meta_expr_col_avg_name\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3: visualize results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1: use the interactive reclustering results to relabel cell meta clusters"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The visualization shows the z-scored average `cell_som_cluster_cols` expression per cell SOM and meta cluster. The heatmaps are faceted by cell SOM clusters on the left and cell meta clusters on the right.\n",
    "\n",
    "## Usage\n",
    "\n",
    "### Quickstart\n",
    "- **Select**: Left Click\n",
    "- **Remap**: **New metacluster button** or Right Click\n",
    "- **Edit Metacluster Name**: Textbox at bottom right of the heatmaps.\n",
    "\n",
    "### Selection and Remapping details\n",
    "- To select a SOM cluster, click on its respective position in the **selected** bar. Click on it again to deselect.\n",
    "- To select a meta cluster, click on its corresponding color in the **metacluster** bar. Click on it again to deselect.\n",
    "- To remap the selected clusters, click the **New metacluster** button (alternatively, right click anywhere). Note that remapping an entire metacluster deletes it.\n",
    "- To clear the selected SOM/meta clusters, use the **Clear Selection** button.\n",
    "- **After remapping a meta cluster, make sure to deselect the newly created one to prevent unwanted combinations.**\n",
    "\n",
    "### Other features and notes\n",
    "- You will likely need to zoom out to see the entire visualization. To toggle Zoom, use Ctrl -/Ctrl + on Windows or ⌘ +/⌘ - on Mac.\n",
    "- The bars at the top show the number of cells in each SOM cluster.\n",
    "- The text box at the bottom right allows you to rename a particular meta cluster. This can be useful as remapping may cause inconsistent numbering.\n",
    "- Adjust the z-score limit using the slider on the bottom left to adjust your dynamic range.\n",
    "- When meta clusters are combined or a meta cluster is renamed, the change is immediately saved to `cell_meta_cluster_remap_name`.\n",
    "- You won't be able to advance until you've clicked `New metacluster` or renamed a meta cluster at least once. If you do not want to make changes, just click `New metacluster` to trigger a save before continuing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cell_interactive"
    ]
   },
   "outputs": [],
   "source": [
    "%matplotlib widget\n",
    "rc_file_defaults()\n",
    "plt.ion()\n",
    "\n",
    "cell_mcd = metaclusterdata_from_files(\n",
    "    os.path.join(base_dir, cell_som_expr_col_avg_name),\n",
    "    cluster_type='cell'\n",
    ")\n",
    "cell_mcd.output_mapping_filename = os.path.join(base_dir, cell_meta_cluster_remap_name)\n",
    "cell_mcg = MetaClusterGui(cell_mcd, width=17)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Relabel the cell meta clusters using the mapping."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cell_apply_remap"
    ]
   },
   "outputs": [],
   "source": [
    "# rename the meta cluster values in the cell dataset\n",
    "cell_som_input_data = cell_meta_clustering.apply_cell_meta_cluster_remapping(\n",
    "    base_dir,\n",
    "    cell_som_input_data,\n",
    "    cell_meta_cluster_remap_name\n",
    ")\n",
    "\n",
    "# recompute the mean column expression per meta cluster and apply these new names to the SOM cluster average data\n",
    "cell_meta_clustering.generate_remap_avg_count_files(\n",
    "    base_dir,\n",
    "    cell_som_input_data,\n",
    "    cell_meta_cluster_remap_name,\n",
    "    cell_som_cluster_cols,\n",
    "    cell_som_expr_col_avg_name,\n",
    "    cell_meta_expr_col_avg_name,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate the color scheme returned by the interactive reclustering process. This will be for visualizing the weighted channel average heatmaps and the cell cluster overlay."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cell_cmap_gen"
    ]
   },
   "outputs": [],
   "source": [
    "raw_cmap, renamed_cmap = colormap_helper.generate_meta_cluster_colormap_dict(\n",
    "    cell_mcd.output_mapping_filename,\n",
    "    cell_mcg.im_cl.cmap,\n",
    "    cluster_type='cell'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2: cell cluster overlay (cell meta clusters only)\n",
    "\n",
    "This will take your FOVs and generate cell cluster images in batches. Run this cell if you wish to create a significant amount of cell cluster mask images for downstream analysis.\n",
    "\n",
    "Generating overlays will require you to define the following:\n",
    "\n",
    "- `segmentation_dir`: the path to the directory with Mesmer segmented images\n",
    "- `seg_suffix`: the suffix used for the files in `segmentation_dir`\n",
    "- `subset_cell_fovs`: the list of FOVs you want to visualize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cell_overlay_fovs"
    ]
   },
   "outputs": [],
   "source": [
    "segmentation_dir = os.path.join(\"segmentation\", \"deepcell_output\")\n",
    "seg_suffix = '_whole_cell.tiff'\n",
    "subset_cell_fovs = ['fov0', 'fov1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cell_mask_gen_save"
    ]
   },
   "outputs": [],
   "source": [
    "# generate and save the cell cluster masks for each fov in subset_cell_fovs\n",
    "\n",
    "\n",
    "data_utils.generate_and_save_cell_cluster_masks(\n",
    "    fovs=subset_cell_fovs,\n",
    "    save_dir=os.path.join(base_dir, \"pixie\", cell_output_dir),\n",
    "    seg_dir=os.path.join(base_dir, segmentation_dir),\n",
    "    cell_data=cell_som_input_data,\n",
    "    cluster_id_to_name_path=os.path.join(base_dir, cell_meta_cluster_remap_name),\n",
    "    fov_col = settings.FOV_ID,\n",
    "    label_col = settings.CELL_LABEL,\n",
    "    cell_cluster_col=settings.CELL_TYPE,\n",
    "    seg_suffix=seg_suffix,\n",
    "    sub_dir='cell_masks',\n",
    "    name_suffix='_cell_mask'\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the colored cell masks for each FOV in `subset_cell_fovs`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "save_cell_masks"
    ]
   },
   "outputs": [],
   "source": [
    "plot_utils.save_colored_masks(\n",
    "    fovs=subset_cell_fovs,\n",
    "    mask_dir=os.path.join(base_dir, \"pixie\",cell_output_dir, \"cell_masks\"),\n",
    "    save_dir=os.path.join(base_dir, \"pixie\",cell_output_dir, \"cell_mask_colored\"),\n",
    "    cluster_id_to_name_path=os.path.join(base_dir, cell_meta_cluster_remap_name),\n",
    "    metacluster_colors=raw_cmap,\n",
    "    cluster_type=\"cell\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load a subset of the cell cluster masks that you would like to preview. If the dimensions or text size of the plot need to be adjusted for optimal viewing, change the `figsize` and `dpi` parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cell_overlay_gen"
    ]
   },
   "outputs": [],
   "source": [
    "for cell_fov in subset_cell_fovs:\n",
    "    cell_cluster_mask = load_utils.load_imgs_from_dir(\n",
    "        data_dir = os.path.join(base_dir, \"pixie\", cell_output_dir, \"cell_masks\"),\n",
    "        files=[cell_fov + \"_cell_mask.tiff\"],\n",
    "        trim_suffix=\"_cell_mask\",\n",
    "        match_substring=\"_cell_mask\",\n",
    "        xr_dim_name=\"cell_mask\",\n",
    "        xr_channel_names=None,\n",
    "    )\n",
    "\n",
    "    plot_utils.plot_pixel_cell_cluster(\n",
    "        cell_cluster_mask,\n",
    "        [cell_fov],\n",
    "        os.path.join(base_dir, cell_meta_cluster_remap_name),\n",
    "        metacluster_colors=raw_cmap,\n",
    "        cluster_type='cell',\n",
    "        erode=True,\n",
    "        figsize=(5,5),\n",
    "        dpi=150\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3: save consensus cluster labels to cell table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The cell table with consensus cluster labels is saved to `{cell_table_path}_cell_labels.csv`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cell_append_meta"
    ]
   },
   "outputs": [],
   "source": [
    "cell_cluster_utils.add_consensus_labels_cell_table(\n",
    "    base_dir, cell_table_path, cell_som_input_data\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4.6: save the full results of Pixie cell clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`cell_som_input_data` with the SOM, meta, and renamed meta cluster labels, is resaved to `cell_som_input_path` as a `.csv` file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "pixie_cell_save"
    ]
   },
   "outputs": [],
   "source": [
    "cell_som_input_data.to_csv(\n",
    "    cell_som_input_path,\n",
    "    index=False\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4: Save Images a Mantis Viewer Project\n",
    "\n",
    "Mantis Viewer is a visualization tool for multi-dimensional imaging in pathology. Learn more about Mantis Viewer in the [README](../README.md#mantis-viewer)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": [
     "cell_mantis_project"
    ]
   },
   "outputs": [],
   "source": [
    "plot_utils.create_mantis_dir(\n",
    "    fovs=subset_cell_fovs,\n",
    "    mantis_project_path=os.path.join(base_dir, \"mantis\"),\n",
    "    img_data_path=tiff_dir,\n",
    "    mask_output_dir=os.path.join(base_dir, \"pixie\", cell_output_dir, \"cell_masks\"),\n",
    "    mapping = os.path.join(base_dir, cell_meta_cluster_remap_name),\n",
    "    seg_dir=os.path.join(base_dir, segmentation_dir),\n",
    "    cluster_type='cell',\n",
    "    mask_suffix=\"_cell_mask\",\n",
    "    seg_suffix_name=seg_suffix,\n",
    "    new_mask_suffix='_generic_cell_mask'\n",
    ")"
   ]
  }
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