{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Applying CellPhenoX to Ulcerative Colitis Fibroblasts (single-cell transcriptomics)\n",
    "## Goal: Identify subtle, fibroblast-specific phenotypic changes associated with differing levels of inflammation\n",
    "This tutorial explores the utility of CellPhenoX within a specific cell type. Using the single-cell transcriptomics data from [Smillie et al.](https://doi.org/10.1016/j.cell.2019.06.029), we (a) create the latent dimensions for the neighborhood abundance matrix (NAM), (b) train a random forest model to predict inflamed vs. non-inflamed status, and (c) visualize the resulting CellPhenoX Interpretable Score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhanglab/Documents/Python Projects/pyCellPhenoX/pycpx/lib/python3.12/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pyCellPhenoX\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1: Import data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The expression file is expected to be a cells by gene matrix, and the meta file is expected to be a cells by meta data features matrix. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# paths to expression data and meta data files\n",
    "expression_file = \"/example_UC_data/fibroblast_exp.csv\"\n",
    "meta_file = \"/example_UC_data/fibroblast_met.csv\"\n",
    "output_path = \"/output/\"\n",
    "# read in data\n",
    "expression_mat = pd.read_csv(expression_file, index_col=0)\n",
    "meta = pd.read_csv(meta_file, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ADAMDEC1</th>\n",
       "      <th>ACTA2</th>\n",
       "      <th>TAGLN</th>\n",
       "      <th>CCL11</th>\n",
       "      <th>CCL13</th>\n",
       "      <th>APOE</th>\n",
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       "      <th>CCL8</th>\n",
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       "      <th>...</th>\n",
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       "      <th>MEIS1</th>\n",
       "      <th>AP001258.4</th>\n",
       "      <th>FBXO42</th>\n",
       "      <th>ASUN</th>\n",
       "      <th>ELP6</th>\n",
       "      <th>CCDC77</th>\n",
       "      <th>ELK3</th>\n",
       "      <th>INO80E</th>\n",
       "      <th>FHOD3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N7.LPA.ATGTTCACATCGAC</th>\n",
       "      <td>4.591305</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.402383</td>\n",
       "      <td>5.382054</td>\n",
       "      <td>2.619834</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.668685</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.657177</td>\n",
       "      <td>0.0</td>\n",
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       "      <th>N7.LPA.CATTAGCTGAGACG</th>\n",
       "      <td>4.904113</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.694547</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.570602</td>\n",
       "      <td>5.383111</td>\n",
       "      <td>4.751197</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.820224</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.997891</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.AAGGCTTGTGTAGC</th>\n",
       "      <td>4.600380</td>\n",
       "      <td>2.220309</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.243785</td>\n",
       "      <td>4.600380</td>\n",
       "      <td>4.420066</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.220309</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>N7.LPA.TATCAAGATGTGAC</th>\n",
       "      <td>5.900079</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.745390</td>\n",
       "      <td>3.204398</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.970470</td>\n",
       "      <td>3.970470</td>\n",
       "      <td>4.134618</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.055687</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>N7.LPA.GAGTGGGAATGTGC</th>\n",
       "      <td>5.472313</td>\n",
       "      <td>1.715218</td>\n",
       "      <td>1.715218</td>\n",
       "      <td>5.259739</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.623356</td>\n",
       "      <td>4.856868</td>\n",
       "      <td>4.239430</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.169241</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
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       "<p>5 rows × 5494 columns</p>\n",
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       "                       ADAMDEC1     ACTA2     TAGLN     CCL11  CCL13  \\\n",
       "cell                                                                   \n",
       "N7.LPA.ATGTTCACATCGAC  4.591305  0.000000  0.000000  0.000000    0.0   \n",
       "N7.LPA.CATTAGCTGAGACG  4.904113  0.000000  0.000000  4.694547    0.0   \n",
       "N7.LPA.AAGGCTTGTGTAGC  4.600380  2.220309  0.000000  0.000000    0.0   \n",
       "N7.LPA.TATCAAGATGTGAC  5.900079  0.000000  1.745390  3.204398    0.0   \n",
       "N7.LPA.GAGTGGGAATGTGC  5.472313  1.715218  1.715218  5.259739    0.0   \n",
       "\n",
       "                           APOE    CXCL14       CFD  CCL8      CCL2  ...  \\\n",
       "cell                                                                 ...   \n",
       "N7.LPA.ATGTTCACATCGAC  4.402383  5.382054  2.619834   0.0  3.668685  ...   \n",
       "N7.LPA.CATTAGCTGAGACG  4.570602  5.383111  4.751197   0.0  3.820224  ...   \n",
       "N7.LPA.AAGGCTTGTGTAGC  3.243785  4.600380  4.420066   0.0  2.220309  ...   \n",
       "N7.LPA.TATCAAGATGTGAC  3.970470  3.970470  4.134618   0.0  6.055687  ...   \n",
       "N7.LPA.GAGTGGGAATGTGC  3.623356  4.856868  4.239430   0.0  3.169241  ...   \n",
       "\n",
       "                       CCDC23  MEIS1  AP001258.4  FBXO42  ASUN  ELP6  CCDC77  \\\n",
       "cell                                                                           \n",
       "N7.LPA.ATGTTCACATCGAC     0.0    0.0         0.0     0.0   0.0   0.0     0.0   \n",
       "N7.LPA.CATTAGCTGAGACG     0.0    0.0         0.0     0.0   0.0   0.0     0.0   \n",
       "N7.LPA.AAGGCTTGTGTAGC     0.0    0.0         0.0     0.0   0.0   0.0     0.0   \n",
       "N7.LPA.TATCAAGATGTGAC     0.0    0.0         0.0     0.0   0.0   0.0     0.0   \n",
       "N7.LPA.GAGTGGGAATGTGC     0.0    0.0         0.0     0.0   0.0   0.0     0.0   \n",
       "\n",
       "                           ELK3    INO80E  FHOD3  \n",
       "cell                                              \n",
       "N7.LPA.ATGTTCACATCGAC  0.000000  1.657177    0.0  \n",
       "N7.LPA.CATTAGCTGAGACG  1.997891  0.000000    0.0  \n",
       "N7.LPA.AAGGCTTGTGTAGC  0.000000  0.000000    0.0  \n",
       "N7.LPA.TATCAAGATGTGAC  0.000000  0.000000    0.0  \n",
       "N7.LPA.GAGTGGGAATGTGC  0.000000  0.000000    0.0  \n",
       "\n",
       "[5 rows x 5494 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "expression_mat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cell.1</th>\n",
       "      <th>sample</th>\n",
       "      <th>disease</th>\n",
       "      <th>cell_type</th>\n",
       "      <th>cluster</th>\n",
       "      <th>nGene</th>\n",
       "      <th>nUMI</th>\n",
       "      <th>percent_mito</th>\n",
       "      <th>fibroblast_clusters</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N7.LPA.ATGTTCACATCGAC</th>\n",
       "      <td>N7.LPA.ATGTTCACATCGAC</td>\n",
       "      <td>N7</td>\n",
       "      <td>Non-inflamed</td>\n",
       "      <td>LP</td>\n",
       "      <td>WNT2B+ Fos-lo 1</td>\n",
       "      <td>969.0</td>\n",
       "      <td>2357.0</td>\n",
       "      <td>0.031409</td>\n",
       "      <td>WNT2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.CATTAGCTGAGACG</th>\n",
       "      <td>N7.LPA.CATTAGCTGAGACG</td>\n",
       "      <td>N7</td>\n",
       "      <td>Non-inflamed</td>\n",
       "      <td>LP</td>\n",
       "      <td>WNT2B+ Fos-hi</td>\n",
       "      <td>681.0</td>\n",
       "      <td>1569.0</td>\n",
       "      <td>0.044614</td>\n",
       "      <td>WNT2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.AAGGCTTGTGTAGC</th>\n",
       "      <td>N7.LPA.AAGGCTTGTGTAGC</td>\n",
       "      <td>N7</td>\n",
       "      <td>Non-inflamed</td>\n",
       "      <td>LP</td>\n",
       "      <td>WNT2B+ Fos-lo 2</td>\n",
       "      <td>615.0</td>\n",
       "      <td>1218.0</td>\n",
       "      <td>0.013957</td>\n",
       "      <td>WNT2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.TATCAAGATGTGAC</th>\n",
       "      <td>N7.LPA.TATCAAGATGTGAC</td>\n",
       "      <td>N7</td>\n",
       "      <td>Non-inflamed</td>\n",
       "      <td>LP</td>\n",
       "      <td>WNT2B+ Fos-hi</td>\n",
       "      <td>841.0</td>\n",
       "      <td>2115.0</td>\n",
       "      <td>0.021749</td>\n",
       "      <td>WNT2B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.GAGTGGGAATGTGC</th>\n",
       "      <td>N7.LPA.GAGTGGGAATGTGC</td>\n",
       "      <td>N7</td>\n",
       "      <td>Non-inflamed</td>\n",
       "      <td>LP</td>\n",
       "      <td>WNT2B+ Fos-lo 1</td>\n",
       "      <td>923.0</td>\n",
       "      <td>2194.0</td>\n",
       "      <td>0.019599</td>\n",
       "      <td>WNT2B</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      cell.1 sample       disease cell_type  \\\n",
       "cell                                                                          \n",
       "N7.LPA.ATGTTCACATCGAC  N7.LPA.ATGTTCACATCGAC     N7  Non-inflamed        LP   \n",
       "N7.LPA.CATTAGCTGAGACG  N7.LPA.CATTAGCTGAGACG     N7  Non-inflamed        LP   \n",
       "N7.LPA.AAGGCTTGTGTAGC  N7.LPA.AAGGCTTGTGTAGC     N7  Non-inflamed        LP   \n",
       "N7.LPA.TATCAAGATGTGAC  N7.LPA.TATCAAGATGTGAC     N7  Non-inflamed        LP   \n",
       "N7.LPA.GAGTGGGAATGTGC  N7.LPA.GAGTGGGAATGTGC     N7  Non-inflamed        LP   \n",
       "\n",
       "                               cluster  nGene    nUMI  percent_mito  \\\n",
       "cell                                                                  \n",
       "N7.LPA.ATGTTCACATCGAC  WNT2B+ Fos-lo 1  969.0  2357.0      0.031409   \n",
       "N7.LPA.CATTAGCTGAGACG    WNT2B+ Fos-hi  681.0  1569.0      0.044614   \n",
       "N7.LPA.AAGGCTTGTGTAGC  WNT2B+ Fos-lo 2  615.0  1218.0      0.013957   \n",
       "N7.LPA.TATCAAGATGTGAC    WNT2B+ Fos-hi  841.0  2115.0      0.021749   \n",
       "N7.LPA.GAGTGGGAATGTGC  WNT2B+ Fos-lo 1  923.0  2194.0      0.019599   \n",
       "\n",
       "                      fibroblast_clusters  \n",
       "cell                                       \n",
       "N7.LPA.ATGTTCACATCGAC               WNT2B  \n",
       "N7.LPA.CATTAGCTGAGACG               WNT2B  \n",
       "N7.LPA.AAGGCTTGTGTAGC               WNT2B  \n",
       "N7.LPA.TATCAAGATGTGAC               WNT2B  \n",
       "N7.LPA.GAGTGGGAATGTGC               WNT2B  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meta.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2: Preprocess data - generate latent dimensions and configure input for CellPhenoX (include covariates and identify target column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we generate the NAM to summarize the cell co-abundance. CellPhenoX offers a wrapper around the Python [CNA](https://github.com/immunogenomics/cna) package functions to accomplish this, `neighborhoodAbundanceMatrix`. \n",
    "\n",
    "Parameters:\n",
    "- `expression_mat` - the single-cell omics feature matrix (with cells as rows and features as columns)\n",
    "- `meta` - corresponding meta data where columns are categories\n",
    "- `sampleid` - the name of the column in meta that has the individual/subject identification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "nam = neighborhoodAbundanceMatrix(expression_mat=expression_mat, meta_data=meta, sampleid=\"sampleid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CellPhenoX has two functions that will decompose the NAM, `nonnegativeMatrixFactorization` and `principalComponentAnalysis`.\n",
    "\n",
    "_Note_: if using a different dimensionality reduction technique, ensure that the format of the latent embeddings are in the following format for the preprocessing step (cells as rows, latent embeddings as columns)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`nonnegativeMatrixFactorization` uses NMF to generate the ranks from the NAM. \n",
    "\n",
    "Parameters:\n",
    "- `numberOfComponents` - the number of ranks (latent embeddings) to learn. Default = -1; if the number of components is not specified, then we select the number of components by performing NMF on a range of k values and selecting the one with the highest silhouette score.\n",
    "    - `min_k` - minimum for the range\n",
    "    - `max_k` - maximum for the range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhanglab_mac2/Library/CloudStorage/OneDrive-TheUniversityofColoradoDenver/Zhang_Lab/Research/shap/.conda/lib/python3.11/site-packages/sklearn/decomposition/_nmf.py:1710: ConvergenceWarning: Maximum number of iterations 200 reached. Increase it to improve convergence.\n"
     ]
    }
   ],
   "source": [
    "# get the latent dimensions using NMF\n",
    "latent_features = nonnegativeMatrixFactorization(nam, numberOfComponents=4, min_k=3, max_k=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can perform PCA with `principalComponentAnalysis` and use the principal components as the latent embeddings.\n",
    "\n",
    "Paramters:\n",
    "- `var` - the desired proportion of variance explained. This will be used to select the number of principle components."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "proportion_var_explained = 0.9\n",
    "latent_features = principalComponentAnalysis(nam, var=proportion_var_explained)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have the latent dimensions, we can make final preparations to run CellPhenoX. The `preprocessing` function will add desired covariates to the model predictor dataframe, `X`, label encode any non-numerical columns to be used in the classification model, and optionally perform subsampling. \n",
    "\n",
    "Parameters:\n",
    "- `sub_samp` - boolean flag, do you want to subsample your data\n",
    "- `subset_percentage` - if `sub_samp` is True, specify the desired proportion of the dataset\n",
    "- `bal_col` - name of columns to use to balance the subsampling\n",
    "- `target` -  name of the column in the meta data for the target/outcome variable for the classification model\n",
    "- `covariates` - list of column names for covariates that you want to include as features in the classification model "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>0</th>\n",
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       "      <th>3</th>\n",
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       "    <tr>\n",
       "      <th>cell</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N7.LPA.ATGTTCACATCGAC</th>\n",
       "      <td>0.285334</td>\n",
       "      <td>0.120589</td>\n",
       "      <td>0.458974</td>\n",
       "      <td>0.205040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.CATTAGCTGAGACG</th>\n",
       "      <td>0.239124</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.587435</td>\n",
       "      <td>0.094555</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.AAGGCTTGTGTAGC</th>\n",
       "      <td>0.255212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.462912</td>\n",
       "      <td>0.226226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.TATCAAGATGTGAC</th>\n",
       "      <td>0.340951</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.347580</td>\n",
       "      <td>0.300781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.GAGTGGGAATGTGC</th>\n",
       "      <td>0.231140</td>\n",
       "      <td>0.175743</td>\n",
       "      <td>0.420713</td>\n",
       "      <td>0.348904</td>\n",
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      ],
      "text/plain": [
       "                              0         1         2         3\n",
       "cell                                                         \n",
       "N7.LPA.ATGTTCACATCGAC  0.285334  0.120589  0.458974  0.205040\n",
       "N7.LPA.CATTAGCTGAGACG  0.239124  0.000000  0.587435  0.094555\n",
       "N7.LPA.AAGGCTTGTGTAGC  0.255212  0.000000  0.462912  0.226226\n",
       "N7.LPA.TATCAAGATGTGAC  0.340951  0.000000  0.347580  0.300781\n",
       "N7.LPA.GAGTGGGAATGTGC  0.231140  0.175743  0.420713  0.348904"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# then, set up the input data for CellPhenoX\n",
    "X,y = preprocessing(latent_features, meta, sub_samp=False, bal_col=[\"subject_id\", \"cell_type\", \"disease\"], subset_percentage=0.25 , target=\"disease\", covariates=[], interaction_covs=[])\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3698, 4)\n",
      "(3698,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3: Run CellPhenoX - train the classification model and calculate the CellPhenoX Interpretable Score"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We start by instantiating the CellPhenoX object with `CellPhenoX`, specifying the following attributes:\n",
    "- `outer_num_splits` - the number of cross validation folds.\n",
    "- `inner_num_splits` - the number of cross validation folds for the hyperparameter grid search.\n",
    "- `CV_repeats` - the number of cross validation repeats to perform. This ensures that the SHAP values calculated are robust to the randomization of the folds; the final SHAP values for each cell for each feature will be the average across CV repeats.\n",
    "\n",
    "*other CellPhenoX object attributes*\n",
    "\n",
    "The function for running this step is `model_training_shap_val`. You can pass the path to a specific output path for the produced plots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "entering CV loop\n",
      "\n",
      "------------ CV Repeat number: 1\n",
      "\n",
      "------ Fold Number: 1\n",
      "--- Accuracy:  0.7023519870235199\n",
      "1\n",
      "--- Validation Accuracy:  0.8275862068965517  - Validation AUROC:  0.8185670261941448  - Val AUPRC:  0.9549581934555448\n",
      "\n",
      "------ Fold Number: 2\n",
      "--- Accuracy:  0.7055961070559611\n",
      "2\n",
      "--- Validation Accuracy:  0.9006085192697769  - Validation AUROC:  0.892869371682931  - Val AUPRC:  0.9770399352399095\n",
      "\n",
      "------ Fold Number: 3\n",
      "--- Accuracy:  0.6801948051948052\n",
      "3\n",
      "--- Validation Accuracy:  0.8765182186234818  - Validation AUROC:  0.8679925048973682  - Val AUPRC:  0.9707836787744281\n",
      "Average AUROC: 0.8598096342581479 | Average AUPRC: 0.9675939358232942\n",
      "best model precision-recall score = 0.9770\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhanglab_mac2/Library/CloudStorage/OneDrive-TheUniversityofColoradoDenver/Zhang_Lab/Research/shap/.conda/lib/python3.11/site-packages/shap/plots/_beeswarm.py:699: UserWarning: No data for colormapping provided via 'c'. Parameters 'vmin', 'vmax' will be ignored\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x310 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# create CellPhenoX object \n",
    "cellpx_obj = CellPhenoX(X, y, CV_repeats=1, outer_num_splits=3, inner_num_splits=2)\n",
    "# and then train the classification model\n",
    "cellpx_obj.model_training_shap_val(outpath = output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `.shap_df` attribute will hold the SHAP values for the individual features in the model (these columns will have the names of the features and \"_shap\"). The `interpretable_score` column holds the CellPhenoX Interpretable Score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>0_shap</th>\n",
       "      <th>1_shap</th>\n",
       "      <th>2_shap</th>\n",
       "      <th>3_shap</th>\n",
       "      <th>interpretable_score</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>N7.LPA.ATGTTCACATCGAC</th>\n",
       "      <td>0.019978</td>\n",
       "      <td>-0.007582</td>\n",
       "      <td>0.129763</td>\n",
       "      <td>0.017164</td>\n",
       "      <td>0.159322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.CATTAGCTGAGACG</th>\n",
       "      <td>0.018379</td>\n",
       "      <td>0.019989</td>\n",
       "      <td>0.234010</td>\n",
       "      <td>0.130304</td>\n",
       "      <td>0.402682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.AAGGCTTGTGTAGC</th>\n",
       "      <td>0.052278</td>\n",
       "      <td>0.037604</td>\n",
       "      <td>0.184861</td>\n",
       "      <td>-0.004939</td>\n",
       "      <td>0.269803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.TATCAAGATGTGAC</th>\n",
       "      <td>0.013674</td>\n",
       "      <td>0.056203</td>\n",
       "      <td>0.068779</td>\n",
       "      <td>-0.102463</td>\n",
       "      <td>0.036193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N7.LPA.GAGTGGGAATGTGC</th>\n",
       "      <td>-0.017015</td>\n",
       "      <td>0.016166</td>\n",
       "      <td>-0.011645</td>\n",
       "      <td>-0.110724</td>\n",
       "      <td>-0.123219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N110.LPB.CCAGCGATCCTCCTAG</th>\n",
       "      <td>-0.025661</td>\n",
       "      <td>-0.084043</td>\n",
       "      <td>-0.027967</td>\n",
       "      <td>0.075934</td>\n",
       "      <td>-0.061737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N110.LPB.CGAATGTAGACTAGGC</th>\n",
       "      <td>0.047468</td>\n",
       "      <td>0.004443</td>\n",
       "      <td>0.219336</td>\n",
       "      <td>-0.048841</td>\n",
       "      <td>0.222407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N110.LPB.TCAACGACAATCCAAC</th>\n",
       "      <td>0.076038</td>\n",
       "      <td>0.083048</td>\n",
       "      <td>0.001184</td>\n",
       "      <td>0.036729</td>\n",
       "      <td>0.196999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N110.LPB.CTGATAGAGCATGGCA</th>\n",
       "      <td>-0.014526</td>\n",
       "      <td>0.030951</td>\n",
       "      <td>-0.008796</td>\n",
       "      <td>0.198806</td>\n",
       "      <td>0.206434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>N110.LPB.CTTCTCTCATCGGTTA</th>\n",
       "      <td>0.012489</td>\n",
       "      <td>0.024917</td>\n",
       "      <td>0.012230</td>\n",
       "      <td>0.262411</td>\n",
       "      <td>0.312047</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3698 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                             0_shap    1_shap    2_shap    3_shap  \\\n",
       "cell                                                                \n",
       "N7.LPA.ATGTTCACATCGAC      0.019978 -0.007582  0.129763  0.017164   \n",
       "N7.LPA.CATTAGCTGAGACG      0.018379  0.019989  0.234010  0.130304   \n",
       "N7.LPA.AAGGCTTGTGTAGC      0.052278  0.037604  0.184861 -0.004939   \n",
       "N7.LPA.TATCAAGATGTGAC      0.013674  0.056203  0.068779 -0.102463   \n",
       "N7.LPA.GAGTGGGAATGTGC     -0.017015  0.016166 -0.011645 -0.110724   \n",
       "...                             ...       ...       ...       ...   \n",
       "N110.LPB.CCAGCGATCCTCCTAG -0.025661 -0.084043 -0.027967  0.075934   \n",
       "N110.LPB.CGAATGTAGACTAGGC  0.047468  0.004443  0.219336 -0.048841   \n",
       "N110.LPB.TCAACGACAATCCAAC  0.076038  0.083048  0.001184  0.036729   \n",
       "N110.LPB.CTGATAGAGCATGGCA -0.014526  0.030951 -0.008796  0.198806   \n",
       "N110.LPB.CTTCTCTCATCGGTTA  0.012489  0.024917  0.012230  0.262411   \n",
       "\n",
       "                           interpretable_score  \n",
       "cell                                            \n",
       "N7.LPA.ATGTTCACATCGAC                 0.159322  \n",
       "N7.LPA.CATTAGCTGAGACG                 0.402682  \n",
       "N7.LPA.AAGGCTTGTGTAGC                 0.269803  \n",
       "N7.LPA.TATCAAGATGTGAC                 0.036193  \n",
       "N7.LPA.GAGTGGGAATGTGC                -0.123219  \n",
       "...                                        ...  \n",
       "N110.LPB.CCAGCGATCCTCCTAG            -0.061737  \n",
       "N110.LPB.CGAATGTAGACTAGGC             0.222407  \n",
       "N110.LPB.TCAACGACAATCCAAC             0.196999  \n",
       "N110.LPB.CTGATAGAGCATGGCA             0.206434  \n",
       "N110.LPB.CTTCTCTCATCGGTTA             0.312047  \n",
       "\n",
       "[3698 rows x 5 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cellpx_obj.shap_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4: Visualization - generate manuscript plots"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SHAP Summary plot\n",
    "\n",
    "(inlcude option to keep the covariates in the plot)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boxplots with CellPhenoX Interpretable Score on the y-axis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "CellPhenoX Interpretable Score on the UMAP & Celltype labeled UMAP"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We include the code for correlating the Interpretable Score with the gene expression in (this notebook). In general, the steps are as follows...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fitting model\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/zhanglab_mac2/Library/CloudStorage/OneDrive-TheUniversityofColoradoDenver/Zhang_Lab/Research/shap/.conda/lib/python3.11/site-packages/statsmodels/regression/linear_model.py:1794: RuntimeWarning: divide by zero encountered in divide\n",
      "/Users/zhanglab_mac2/Library/CloudStorage/OneDrive-TheUniversityofColoradoDenver/Zhang_Lab/Research/shap/.conda/lib/python3.11/site-packages/statsmodels/regression/linear_model.py:1794: RuntimeWarning: invalid value encountered in scalar multiply\n",
      "/Users/zhanglab_mac2/Library/CloudStorage/OneDrive-TheUniversityofColoradoDenver/Zhang_Lab/Research/shap/.conda/lib/python3.11/site-packages/statsmodels/regression/linear_model.py:1716: RuntimeWarning: divide by zero encountered in scalar divide\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "results sorted by p vlaue: \n",
      "              Beta  P_Value  Adjusted_P_Value      gene\n",
      "const     0.007220      NaN               NaN     const\n",
      "ADAMDEC1  0.009512      NaN               NaN  ADAMDEC1\n",
      "ACTA2    -0.004977      NaN               NaN     ACTA2\n",
      "TAGLN     0.002539      NaN               NaN     TAGLN\n",
      "CCL11    -0.005101      NaN               NaN     CCL11\n",
      "Significant Markers\n",
      "Empty DataFrame\n",
      "Columns: [Beta, P_Value, Adjusted_P_Value, gene]\n",
      "Index: []\n"
     ]
    }
   ],
   "source": [
    "marker_discovery(cellpx_obj.shap_df, expression_mat)"
   ]
  }
 ],
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