{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Register the training data\n",
    "\n",
    "After preparing the single-cell dataset, this tutorial shows how to register and load the data into UNAGI tool"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load UNAGI tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from UNAGI import UNAGI\n",
    "unagi = UNAGI()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up the training data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Users can register the data using the `UNAGI.setup_data` function. You can specify the `.h5ad` format dataset or the directory of your dataset folder. `UNAGI.setup_data` funciton also requires users to specify the column name (stage_key) to store the stage information in the anndata.obs dataframe and the total number of time-points (stage). Besides, to run the graph convolution neural network (GCN), it's mandatory to construct the cell neighbor graphs for each stage of data. Users can use `neighbors` and `threads` parameters to specify the number of neighbors of cell neighbor graphs and the number of threads to build graphs using `sklearn.neighbors.kneighbors_graph`. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_directory = 'PATH_TO_DATA.h5ad' # path to the data or the folder containing the data\n",
    "stage_column_name = 'column_name_of_stage_information_in_your_data' # key in the obsm of the data that contains the stage information\n",
    "total_time_points = 4 # change it to the total number of time-points in your data\n",
    "gcn_neighbors = 30 # number of neighbors to be used in the knn graph\n",
    "cpu_threads= 5 # number of threads to be used in the knn graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "unagi.setup_data(data_path = data_directory, stage_key=stage_column_name, total_stage=total_time_points, neighbors=gcn_neighbors, threads=cpu_threads)"
   ]
  }
 ],
 "metadata": {
  "language_info": {
   "name": "python"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}