{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Proportion analysis using scanpro"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A notebook to test the scanpro tool.\n",
    "\n",
    "The dataset used in this tutorial is a simulated dataset with three conditions and five cell clusters. The proportions of these clusters do not change significantly across conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import anndata as ad\n",
    "import numpy as np\n",
    "\n",
    "from scanpro import scanpro, anova, t_test\n",
    "from scanpro.linear_model import create_design\n",
    "from scanpro.get_transformed_props import get_transformed_props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set seed to make simulations reproducible\n",
    "np.random.seed(1)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load anndata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AnnData object with n_obs × n_vars = 37079 × 10\n",
       "    obs: 'sample', 'cluster', 'group'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata = ad.read_h5ad('scanpro_tutorial.h5ad')\n",
    "adata"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The anndata.obs table has to include 3 columns, for example:\n",
    "  - sample\n",
    "  - biological condition / group\n",
    "  - clusters/celltypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sample</th>\n",
       "      <th>cluster</th>\n",
       "      <th>group</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cells</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>cell_1</th>\n",
       "      <td>S1</td>\n",
       "      <td>c1</td>\n",
       "      <td>cond_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_2</th>\n",
       "      <td>S1</td>\n",
       "      <td>c1</td>\n",
       "      <td>cond_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_3</th>\n",
       "      <td>S1</td>\n",
       "      <td>c1</td>\n",
       "      <td>cond_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_4</th>\n",
       "      <td>S1</td>\n",
       "      <td>c1</td>\n",
       "      <td>cond_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_5</th>\n",
       "      <td>S1</td>\n",
       "      <td>c1</td>\n",
       "      <td>cond_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_37075</th>\n",
       "      <td>S6</td>\n",
       "      <td>c5</td>\n",
       "      <td>cond_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_37076</th>\n",
       "      <td>S6</td>\n",
       "      <td>c5</td>\n",
       "      <td>cond_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_37077</th>\n",
       "      <td>S6</td>\n",
       "      <td>c5</td>\n",
       "      <td>cond_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_37078</th>\n",
       "      <td>S6</td>\n",
       "      <td>c5</td>\n",
       "      <td>cond_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cell_37079</th>\n",
       "      <td>S6</td>\n",
       "      <td>c5</td>\n",
       "      <td>cond_3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>37079 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           sample cluster   group\n",
       "cells                            \n",
       "cell_1         S1      c1  cond_1\n",
       "cell_2         S1      c1  cond_1\n",
       "cell_3         S1      c1  cond_1\n",
       "cell_4         S1      c1  cond_1\n",
       "cell_5         S1      c1  cond_1\n",
       "...           ...     ...     ...\n",
       "cell_37075     S6      c5  cond_3\n",
       "cell_37076     S6      c5  cond_3\n",
       "cell_37077     S6      c5  cond_3\n",
       "cell_37078     S6      c5  cond_3\n",
       "cell_37079     S6      c5  cond_3\n",
       "\n",
       "[37079 rows x 3 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "adata.obs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run scanpro\n",
    "* You can use the wrapper function \"scanpro\". \n",
    "* Parameters are:\n",
    "    - anndata object\n",
    "    - clusters: name of clusters/celltypes column in obs table\n",
    "    - sample: name of sample column in obs table\n",
    "    - cond: name of condition/group column in obs table\n",
    "    - transform: type of transformation; logit or arcsin, default is logit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are more than 2 conditions. ANOVA will be performed...\n",
      "Done!\n"
     ]
    },
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>baseline_props</th>\n",
       "      <th>mean_props_cond_1</th>\n",
       "      <th>mean_props_cond_2</th>\n",
       "      <th>mean_props_cond_3</th>\n",
       "      <th>f_statistics</th>\n",
       "      <th>p_values</th>\n",
       "      <th>adjusted_p_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clusters</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>c1</th>\n",
       "      <td>0.409531</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.010391</td>\n",
       "      <td>1.015703</td>\n",
       "      <td>0.362148</td>\n",
       "      <td>0.452685</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2</th>\n",
       "      <td>0.374093</td>\n",
       "      <td>0.043683</td>\n",
       "      <td>0.075932</td>\n",
       "      <td>0.043750</td>\n",
       "      <td>1.907890</td>\n",
       "      <td>0.148393</td>\n",
       "      <td>0.370983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c3</th>\n",
       "      <td>0.156935</td>\n",
       "      <td>0.137301</td>\n",
       "      <td>0.157528</td>\n",
       "      <td>0.184273</td>\n",
       "      <td>0.503052</td>\n",
       "      <td>0.604682</td>\n",
       "      <td>0.604682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c4</th>\n",
       "      <td>0.051161</td>\n",
       "      <td>0.401103</td>\n",
       "      <td>0.270136</td>\n",
       "      <td>0.415870</td>\n",
       "      <td>2.019649</td>\n",
       "      <td>0.132702</td>\n",
       "      <td>0.370983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5</th>\n",
       "      <td>0.008280</td>\n",
       "      <td>0.411515</td>\n",
       "      <td>0.488800</td>\n",
       "      <td>0.345716</td>\n",
       "      <td>1.347237</td>\n",
       "      <td>0.259957</td>\n",
       "      <td>0.433262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          baseline_props  mean_props_cond_1  mean_props_cond_2   \n",
       "clusters                                                         \n",
       "c1              0.409531           0.006399           0.007605  \\\n",
       "c2              0.374093           0.043683           0.075932   \n",
       "c3              0.156935           0.137301           0.157528   \n",
       "c4              0.051161           0.401103           0.270136   \n",
       "c5              0.008280           0.411515           0.488800   \n",
       "\n",
       "          mean_props_cond_3  f_statistics  p_values  adjusted_p_values  \n",
       "clusters                                                                \n",
       "c1                 0.010391      1.015703  0.362148           0.452685  \n",
       "c2                 0.043750      1.907890  0.148393           0.370983  \n",
       "c3                 0.184273      0.503052  0.604682           0.604682  \n",
       "c4                 0.415870      2.019649  0.132702           0.370983  \n",
       "c5                 0.345716      1.347237  0.259957           0.433262  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = scanpro(adata, clusters_col='cluster', samples_col='sample', conds_col='group', transform='logit')\n",
    "\n",
    "out.results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* the `scanpro` function generates a ScanproResult object that has the following methods:\n",
    "    - results: returns the results as a dataframe\n",
    "    - props: returns clusters proportions as a dataframe\n",
    "    - prop_trans: returns transformed proportions as a dataframe\n",
    "    - counts: returns counts as a dataframe\n",
    "    - design: returns the design matrix\n",
    "    - plot and plot_samples: for plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cluster</th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
       "      <th>c5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample</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>S1</th>\n",
       "      <td>0.005436</td>\n",
       "      <td>0.063044</td>\n",
       "      <td>0.150121</td>\n",
       "      <td>0.388767</td>\n",
       "      <td>0.392632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>0.007362</td>\n",
       "      <td>0.024321</td>\n",
       "      <td>0.124481</td>\n",
       "      <td>0.413438</td>\n",
       "      <td>0.430398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3</th>\n",
       "      <td>0.010331</td>\n",
       "      <td>0.055854</td>\n",
       "      <td>0.097744</td>\n",
       "      <td>0.241583</td>\n",
       "      <td>0.594487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4</th>\n",
       "      <td>0.004880</td>\n",
       "      <td>0.096009</td>\n",
       "      <td>0.217312</td>\n",
       "      <td>0.298688</td>\n",
       "      <td>0.383112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S5</th>\n",
       "      <td>0.010221</td>\n",
       "      <td>0.041330</td>\n",
       "      <td>0.202284</td>\n",
       "      <td>0.434774</td>\n",
       "      <td>0.311391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S6</th>\n",
       "      <td>0.010562</td>\n",
       "      <td>0.046169</td>\n",
       "      <td>0.166261</td>\n",
       "      <td>0.396966</td>\n",
       "      <td>0.380042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster        c1        c2        c3        c4        c5\n",
       "sample                                                   \n",
       "S1       0.005436  0.063044  0.150121  0.388767  0.392632\n",
       "S2       0.007362  0.024321  0.124481  0.413438  0.430398\n",
       "S3       0.010331  0.055854  0.097744  0.241583  0.594487\n",
       "S4       0.004880  0.096009  0.217312  0.298688  0.383112\n",
       "S5       0.010221  0.041330  0.202284  0.434774  0.311391\n",
       "S6       0.010562  0.046169  0.166261  0.396966  0.380042"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out.props"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualisation of proportions for each condition in each cluster\n",
    "* You can plot proportion per condition using `out.plot()` function. Parameters are:\n",
    "    - kind: kind of plot: `stripplot`, `barplot` or `boxplot`\n",
    "    - clusters: specify which clusters you want to plot, default is all\n",
    "    - save: path to where you want to save the plot\n",
    "* or `out.plot_samples()` to plot a bar plot of proportions of clusters in samples. Parameters are:\n",
    "    - stacked: True for stacked bar plot\n",
    "    - x: Whether samples or clusters should be on the x axis\n",
    "    - save: path to where you want to save the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* You can use `kind` to set different styles for the plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out.plot(kind=\"boxplot\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out.plot(kind=\"barplot\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* You can use `clusters` to limit the clusters shown:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 300x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out.plot(clusters=[\"c1\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Proportions of cells per sample can be plotted with `plot_samples`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out.plot_samples(stacked=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out.plot_samples(x=\"clusters\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test specific conditions\n",
    "* by using the parameter `conditions` you can specify conditions of interests you want to compare instead of comparing all the conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 2 conditions. T-Test will be performed...\n",
      "Done!\n"
     ]
    },
    {
     "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>baseline_props</th>\n",
       "      <th>mean_props_cond_1</th>\n",
       "      <th>mean_props_cond_2</th>\n",
       "      <th>prop_ratio</th>\n",
       "      <th>t_statistics</th>\n",
       "      <th>p_values</th>\n",
       "      <th>adjusted_p_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clusters</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>c1</th>\n",
       "      <td>0.409531</td>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.841368</td>\n",
       "      <td>-0.346112</td>\n",
       "      <td>0.734065</td>\n",
       "      <td>0.806602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2</th>\n",
       "      <td>0.374093</td>\n",
       "      <td>0.043683</td>\n",
       "      <td>0.075932</td>\n",
       "      <td>0.575292</td>\n",
       "      <td>-1.821747</td>\n",
       "      <td>0.088493</td>\n",
       "      <td>0.299088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c3</th>\n",
       "      <td>0.156935</td>\n",
       "      <td>0.137301</td>\n",
       "      <td>0.157528</td>\n",
       "      <td>0.871598</td>\n",
       "      <td>-0.249180</td>\n",
       "      <td>0.806602</td>\n",
       "      <td>0.806602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c4</th>\n",
       "      <td>0.051161</td>\n",
       "      <td>0.401103</td>\n",
       "      <td>0.270136</td>\n",
       "      <td>1.484819</td>\n",
       "      <td>1.650414</td>\n",
       "      <td>0.119635</td>\n",
       "      <td>0.299088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5</th>\n",
       "      <td>0.008280</td>\n",
       "      <td>0.411515</td>\n",
       "      <td>0.488800</td>\n",
       "      <td>0.841889</td>\n",
       "      <td>-0.858832</td>\n",
       "      <td>0.403949</td>\n",
       "      <td>0.673249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          baseline_props  mean_props_cond_1  mean_props_cond_2  prop_ratio   \n",
       "clusters                                                                     \n",
       "c1              0.409531           0.006399           0.007605    0.841368  \\\n",
       "c2              0.374093           0.043683           0.075932    0.575292   \n",
       "c3              0.156935           0.137301           0.157528    0.871598   \n",
       "c4              0.051161           0.401103           0.270136    1.484819   \n",
       "c5              0.008280           0.411515           0.488800    0.841889   \n",
       "\n",
       "          t_statistics  p_values  adjusted_p_values  \n",
       "clusters                                             \n",
       "c1           -0.346112  0.734065           0.806602  \n",
       "c2           -1.821747  0.088493           0.299088  \n",
       "c3           -0.249180  0.806602           0.806602  \n",
       "c4            1.650414  0.119635           0.299088  \n",
       "c5           -0.858832  0.403949           0.673249  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out = scanpro(adata, clusters_col='cluster', samples_col='sample',\n",
    "              conds_col='group', transform='logit', conditions=['cond_1', 'cond_2'])\n",
    "\n",
    "out.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x800 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---------------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Bootstrapping method\n",
    "* The bootstrapping method splits the original samples into `n_reps` replicated and run the analysis on the simulated samples.\n",
    "* The bootstrapping method will run for non-replicated datasets in the following scenarios:\n",
    "    - If the number of samples in samples_col is equal to the number of conditions in conds_col\n",
    "    - If samples_col is not given or set to None, the dataset is assumed to be not replicated \n",
    "    and scanpro will run the bootstrapping method by setting conds_col temporarily as samples_col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your data doesn't have replicates! Artificial replicates will be simulated to run scanpro.\n",
      "Simulation may take some minutes...\n",
      "Generating 8 replicates and running 100 simulations...\n",
      "Finished 100 simulations in 10.57 seconds\n"
     ]
    },
    {
     "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>baseline_props</th>\n",
       "      <th>mean_props_cond_1</th>\n",
       "      <th>mean_props_cond_2</th>\n",
       "      <th>p_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clusters</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c1</th>\n",
       "      <td>0.409531</td>\n",
       "      <td>0.006574</td>\n",
       "      <td>0.013976</td>\n",
       "      <td>0.657169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2</th>\n",
       "      <td>0.374093</td>\n",
       "      <td>0.045318</td>\n",
       "      <td>0.073735</td>\n",
       "      <td>0.429444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c3</th>\n",
       "      <td>0.156935</td>\n",
       "      <td>0.138966</td>\n",
       "      <td>0.143901</td>\n",
       "      <td>0.596598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c4</th>\n",
       "      <td>0.051161</td>\n",
       "      <td>0.402073</td>\n",
       "      <td>0.256700</td>\n",
       "      <td>0.110951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5</th>\n",
       "      <td>0.008280</td>\n",
       "      <td>0.407068</td>\n",
       "      <td>0.511688</td>\n",
       "      <td>0.218473</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          baseline_props  mean_props_cond_1  mean_props_cond_2  p_values\n",
       "clusters                                                                \n",
       "c1              0.409531           0.006574           0.013976  0.657169\n",
       "c2              0.374093           0.045318           0.073735  0.429444\n",
       "c3              0.156935           0.138966           0.143901  0.596598\n",
       "c4              0.051161           0.402073           0.256700  0.110951\n",
       "c5              0.008280           0.407068           0.511688  0.218473"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_boot = scanpro(adata,\n",
    "                   clusters_col='cluster',\n",
    "                   conds_col='group',\n",
    "                   # no sample column given,\n",
    "                   transform='arcsin',\n",
    "                   n_reps=8,  # number of replicates generated by bootstrapping\n",
    "                   conditions=['cond_1', 'cond_2']  # optional: you can also specify conditions, or test all of them\n",
    "                   )\n",
    "\n",
    "out_boot.results"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* To plot the bootstrapping results, use the `plot` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 900x800 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_boot.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "------------------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Partially replicated data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now imagine that the cond_1 dataset was not replicated. This will trigger a partial bootstrapping method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "adata_partial = adata.copy()\n",
    "adata_partial.obs.loc[adata_partial.obs[\"group\"] == \"cond_1\", \"sample\"] = \"S1\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following conditions don't have replicates: cond_1\n",
      "Both normal scanpro and sim_scanpro will be performed.\n",
      "Running scanpro with original replicates...\n",
      "There are 2 conditions. T-Test will be performed...\n",
      "Done!\n",
      "Running scanpro with simulated replicates...\n",
      "Generating 8 replicates and running 100 simulations...\n",
      "Finished 100 simulations in 10.59 seconds\n",
      "To access results for original replicates, run <out.results>, and <out.sim_results> for simulated results\n"
     ]
    },
    {
     "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>baseline_props</th>\n",
       "      <th>mean_props_cond_1</th>\n",
       "      <th>mean_props_cond_2</th>\n",
       "      <th>prop_ratio</th>\n",
       "      <th>t_statistics</th>\n",
       "      <th>p_values</th>\n",
       "      <th>adjusted_p_values</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clusters</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>c1</th>\n",
       "      <td>0.409531</td>\n",
       "      <td>0.006264</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.823593</td>\n",
       "      <td>-0.123120</td>\n",
       "      <td>0.906333</td>\n",
       "      <td>0.906333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2</th>\n",
       "      <td>0.374093</td>\n",
       "      <td>0.046387</td>\n",
       "      <td>0.075932</td>\n",
       "      <td>0.610909</td>\n",
       "      <td>-0.999106</td>\n",
       "      <td>0.359144</td>\n",
       "      <td>0.743984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c3</th>\n",
       "      <td>0.156935</td>\n",
       "      <td>0.139092</td>\n",
       "      <td>0.157528</td>\n",
       "      <td>0.882967</td>\n",
       "      <td>-0.233504</td>\n",
       "      <td>0.823712</td>\n",
       "      <td>0.906333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c4</th>\n",
       "      <td>0.051161</td>\n",
       "      <td>0.399380</td>\n",
       "      <td>0.270136</td>\n",
       "      <td>1.478442</td>\n",
       "      <td>2.325340</td>\n",
       "      <td>0.062309</td>\n",
       "      <td>0.311547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5</th>\n",
       "      <td>0.008280</td>\n",
       "      <td>0.408877</td>\n",
       "      <td>0.488800</td>\n",
       "      <td>0.836493</td>\n",
       "      <td>-0.819015</td>\n",
       "      <td>0.446390</td>\n",
       "      <td>0.743984</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          baseline_props  mean_props_cond_1  mean_props_cond_2  prop_ratio   \n",
       "clusters                                                                     \n",
       "c1              0.409531           0.006264           0.007605    0.823593  \\\n",
       "c2              0.374093           0.046387           0.075932    0.610909   \n",
       "c3              0.156935           0.139092           0.157528    0.882967   \n",
       "c4              0.051161           0.399380           0.270136    1.478442   \n",
       "c5              0.008280           0.408877           0.488800    0.836493   \n",
       "\n",
       "          t_statistics  p_values  adjusted_p_values  \n",
       "clusters                                             \n",
       "c1           -0.123120  0.906333           0.906333  \n",
       "c2           -0.999106  0.359144           0.743984  \n",
       "c3           -0.233504  0.823712           0.906333  \n",
       "c4            2.325340  0.062309           0.311547  \n",
       "c5           -0.819015  0.446390           0.743984  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out_boot = scanpro(adata_partial,\n",
    "                   clusters_col='cluster',\n",
    "                   conds_col='group',\n",
    "                   samples_col='sample',\n",
    "                   transform='arcsin',\n",
    "                   n_reps=8,\n",
    "                   conditions=['cond_1', 'cond_2']\n",
    "                   )\n",
    "\n",
    "out_boot.results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No artists with labels found to put in legend.  Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 900x800 with 11 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "out_boot.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scanpro workflow"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The scanpro function is a wrapper function, to have more controls you can perform the steps below and see how the scanpro\n",
    "function works"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Get proportions and transformed proportions using `get_transformed_props` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts, props, prop_trans = get_transformed_props(adata, sample_col='sample', cluster_col='cluster', transform='logit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>cluster</th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
       "      <th>c5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample</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>S1</th>\n",
       "      <td>45</td>\n",
       "      <td>522</td>\n",
       "      <td>1243</td>\n",
       "      <td>3219</td>\n",
       "      <td>3251</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>46</td>\n",
       "      <td>152</td>\n",
       "      <td>778</td>\n",
       "      <td>2584</td>\n",
       "      <td>2690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3</th>\n",
       "      <td>54</td>\n",
       "      <td>292</td>\n",
       "      <td>511</td>\n",
       "      <td>1263</td>\n",
       "      <td>3108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4</th>\n",
       "      <td>16</td>\n",
       "      <td>315</td>\n",
       "      <td>713</td>\n",
       "      <td>980</td>\n",
       "      <td>1257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S5</th>\n",
       "      <td>68</td>\n",
       "      <td>275</td>\n",
       "      <td>1346</td>\n",
       "      <td>2893</td>\n",
       "      <td>2072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S6</th>\n",
       "      <td>78</td>\n",
       "      <td>341</td>\n",
       "      <td>1228</td>\n",
       "      <td>2932</td>\n",
       "      <td>2807</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster  c1   c2    c3    c4    c5\n",
       "sample                            \n",
       "S1       45  522  1243  3219  3251\n",
       "S2       46  152   778  2584  2690\n",
       "S3       54  292   511  1263  3108\n",
       "S4       16  315   713   980  1257\n",
       "S5       68  275  1346  2893  2072\n",
       "S6       78  341  1228  2932  2807"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>cluster</th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
       "      <th>c5</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S1</th>\n",
       "      <td>0.005436</td>\n",
       "      <td>0.063044</td>\n",
       "      <td>0.150121</td>\n",
       "      <td>0.388767</td>\n",
       "      <td>0.392632</td>\n",
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       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>0.007362</td>\n",
       "      <td>0.024321</td>\n",
       "      <td>0.124481</td>\n",
       "      <td>0.413438</td>\n",
       "      <td>0.430398</td>\n",
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       "    <tr>\n",
       "      <th>S3</th>\n",
       "      <td>0.010331</td>\n",
       "      <td>0.055854</td>\n",
       "      <td>0.097744</td>\n",
       "      <td>0.241583</td>\n",
       "      <td>0.594487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4</th>\n",
       "      <td>0.004880</td>\n",
       "      <td>0.096009</td>\n",
       "      <td>0.217312</td>\n",
       "      <td>0.298688</td>\n",
       "      <td>0.383112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S5</th>\n",
       "      <td>0.010221</td>\n",
       "      <td>0.041330</td>\n",
       "      <td>0.202284</td>\n",
       "      <td>0.434774</td>\n",
       "      <td>0.311391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S6</th>\n",
       "      <td>0.010562</td>\n",
       "      <td>0.046169</td>\n",
       "      <td>0.166261</td>\n",
       "      <td>0.396966</td>\n",
       "      <td>0.380042</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster        c1        c2        c3        c4        c5\n",
       "sample                                                   \n",
       "S1       0.005436  0.063044  0.150121  0.388767  0.392632\n",
       "S2       0.007362  0.024321  0.124481  0.413438  0.430398\n",
       "S3       0.010331  0.055854  0.097744  0.241583  0.594487\n",
       "S4       0.004880  0.096009  0.217312  0.298688  0.383112\n",
       "S5       0.010221  0.041330  0.202284  0.434774  0.311391\n",
       "S6       0.010562  0.046169  0.166261  0.396966  0.380042"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "props"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th>cluster</th>\n",
       "      <th>c1</th>\n",
       "      <th>c2</th>\n",
       "      <th>c3</th>\n",
       "      <th>c4</th>\n",
       "      <th>c5</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S1</th>\n",
       "      <td>-5.198679</td>\n",
       "      <td>-2.698113</td>\n",
       "      <td>-1.733536</td>\n",
       "      <td>-0.452733</td>\n",
       "      <td>-0.436502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S2</th>\n",
       "      <td>-4.893820</td>\n",
       "      <td>-3.688879</td>\n",
       "      <td>-1.950396</td>\n",
       "      <td>-0.350114</td>\n",
       "      <td>-0.280595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S3</th>\n",
       "      <td>-4.553587</td>\n",
       "      <td>-2.826251</td>\n",
       "      <td>-2.222005</td>\n",
       "      <td>-1.144124</td>\n",
       "      <td>0.381781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S4</th>\n",
       "      <td>-5.288267</td>\n",
       "      <td>-2.241485</td>\n",
       "      <td>-1.281479</td>\n",
       "      <td>-0.853905</td>\n",
       "      <td>-0.476938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S5</th>\n",
       "      <td>-4.566171</td>\n",
       "      <td>-3.142492</td>\n",
       "      <td>-1.372083</td>\n",
       "      <td>-0.262750</td>\n",
       "      <td>-0.793816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S6</th>\n",
       "      <td>-4.533900</td>\n",
       "      <td>-3.027010</td>\n",
       "      <td>-1.612283</td>\n",
       "      <td>-0.418396</td>\n",
       "      <td>-0.489623</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "cluster        c1        c2        c3        c4        c5\n",
       "sample                                                   \n",
       "S1      -5.198679 -2.698113 -1.733536 -0.452733 -0.436502\n",
       "S2      -4.893820 -3.688879 -1.950396 -0.350114 -0.280595\n",
       "S3      -4.553587 -2.826251 -2.222005 -1.144124  0.381781\n",
       "S4      -5.288267 -2.241485 -1.281479 -0.853905 -0.476938\n",
       "S5      -4.566171 -3.142492 -1.372083 -0.262750 -0.793816\n",
       "S6      -4.533900 -3.027010 -1.612283 -0.418396 -0.489623"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prop_trans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "cluster\n",
       "c5    0.409531\n",
       "c4    0.374093\n",
       "c3    0.156935\n",
       "c2    0.051161\n",
       "c1    0.008280\n",
       "Name: count, dtype: float64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# proportions of each cluster in all samples\n",
    "baseline_props = adata.obs['cluster'].value_counts()/adata.obs.shape[0]\n",
    "baseline_props"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Create design matrix using `create_design` function. The matrix has samples as rows and conditions and columns, whith 1s and 0s to specify which condition belongs to which sample (independent variables). The design matrix will be used as the X matrix in the linear model fit.\n",
    "* Parameters:\n",
    "    - data: anndata object, or a pandas dataframe\n",
    "    - samples: name of samples column\n",
    "    - conds: name of conditions/groups column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>group</th>\n",
       "      <th>cond_1</th>\n",
       "      <th>cond_2</th>\n",
       "      <th>cond_3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sample</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>S1</th>\n",
       "      <td>1</td>\n",
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       "      <th>S3</th>\n",
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       "      <th>S4</th>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>S5</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S6</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "group   cond_1  cond_2  cond_3\n",
       "sample                        \n",
       "S1           1       0       0\n",
       "S2           1       0       0\n",
       "S3           0       1       0\n",
       "S4           0       1       0\n",
       "S5           0       0       1\n",
       "S6           0       0       1"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "design = create_design(data=adata, samples='sample', conds='group')\n",
    "design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# for anova -> more than 2 conditions/groups\n",
    "# The coef parameter specify the columns in the design matrix containing conditions of interest\n",
    "# that you want to compare\n",
    "coef = np.arange(len(design.columns))\n",
    "coef"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Since we have more than 2 conditions, use the function `anova` to compare. If you have two conditions or want to only compare two, then specify a contras variable `cont = [1, -1]` with 1 and -1 match the columns of the design matrix with two conditions you want to compare. if the design matrix has more than two conditions but you only want to compare two, put 1 and -1 where they match the columns of interest and fill the rest with 0s, in this case, to compare 10x and celseq: `cont = [1, -1, 0]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_props_cond_1</th>\n",
       "      <th>mean_props_cond_2</th>\n",
       "      <th>mean_props_cond_3</th>\n",
       "      <th>f_statistics</th>\n",
       "      <th>p_values</th>\n",
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       "      <th>clusters</th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c1</th>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.010391</td>\n",
       "      <td>1.015703</td>\n",
       "      <td>0.362148</td>\n",
       "      <td>0.452685</td>\n",
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       "    <tr>\n",
       "      <th>c2</th>\n",
       "      <td>0.043683</td>\n",
       "      <td>0.075932</td>\n",
       "      <td>0.043750</td>\n",
       "      <td>1.907890</td>\n",
       "      <td>0.148393</td>\n",
       "      <td>0.370983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c3</th>\n",
       "      <td>0.137301</td>\n",
       "      <td>0.157528</td>\n",
       "      <td>0.184273</td>\n",
       "      <td>0.503052</td>\n",
       "      <td>0.604682</td>\n",
       "      <td>0.604682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c4</th>\n",
       "      <td>0.401103</td>\n",
       "      <td>0.270136</td>\n",
       "      <td>0.415870</td>\n",
       "      <td>2.019649</td>\n",
       "      <td>0.132702</td>\n",
       "      <td>0.370983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5</th>\n",
       "      <td>0.411515</td>\n",
       "      <td>0.488800</td>\n",
       "      <td>0.345716</td>\n",
       "      <td>1.347237</td>\n",
       "      <td>0.259957</td>\n",
       "      <td>0.433262</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean_props_cond_1  mean_props_cond_2  mean_props_cond_3   \n",
       "clusters                                                            \n",
       "c1                 0.006399           0.007605           0.010391  \\\n",
       "c2                 0.043683           0.075932           0.043750   \n",
       "c3                 0.137301           0.157528           0.184273   \n",
       "c4                 0.401103           0.270136           0.415870   \n",
       "c5                 0.411515           0.488800           0.345716   \n",
       "\n",
       "          f_statistics  p_values  adjusted_p_values  \n",
       "clusters                                             \n",
       "c1            1.015703  0.362148           0.452685  \n",
       "c2            1.907890  0.148393           0.370983  \n",
       "c3            0.503052  0.604682           0.604682  \n",
       "c4            2.019649  0.132702           0.370983  \n",
       "c5            1.347237  0.259957           0.433262  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "anova(props, prop_trans, design, coef, robust=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Additionally, you can specify cofactors of interest and add them to the design matrix, either specify cofactor columns in the adata.obs as parameter `cofactor` to the `create_design` function, or add it manually to the design matrix. In our case, we will add pair information that specifies which sample is a replicate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = design.iloc[:,coef]\n",
    "N = len(X)  # number of samples\n",
    "p = len(X.columns)  # number of conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>S6</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "group   cond_1  cond_2  cond_3  pair\n",
       "sample                              \n",
       "S1           1       0       0     1\n",
       "S2           1       0       0     2\n",
       "S3           0       1       0     1\n",
       "S4           0       1       0     2\n",
       "S5           0       0       1     1\n",
       "S6           0       0       1     2"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add pair information to the design matrix as cofactor\n",
    "cont = np.tile([1, 2], N//2)\n",
    "new_design = design.copy()\n",
    "new_design['pair'] = cont\n",
    "new_design"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_props_cond_1</th>\n",
       "      <th>mean_props_cond_2</th>\n",
       "      <th>mean_props_cond_3</th>\n",
       "      <th>f_statistics</th>\n",
       "      <th>p_values</th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>c1</th>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.010391</td>\n",
       "      <td>0.719279</td>\n",
       "      <td>0.487104</td>\n",
       "      <td>0.608879</td>\n",
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       "      <th>c2</th>\n",
       "      <td>0.043683</td>\n",
       "      <td>0.075932</td>\n",
       "      <td>0.043750</td>\n",
       "      <td>1.351087</td>\n",
       "      <td>0.258959</td>\n",
       "      <td>0.608879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c3</th>\n",
       "      <td>0.137301</td>\n",
       "      <td>0.157528</td>\n",
       "      <td>0.184273</td>\n",
       "      <td>0.356240</td>\n",
       "      <td>0.700304</td>\n",
       "      <td>0.700304</td>\n",
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       "    <tr>\n",
       "      <th>c4</th>\n",
       "      <td>0.401103</td>\n",
       "      <td>0.270136</td>\n",
       "      <td>0.415870</td>\n",
       "      <td>1.430231</td>\n",
       "      <td>0.239254</td>\n",
       "      <td>0.608879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5</th>\n",
       "      <td>0.411515</td>\n",
       "      <td>0.488800</td>\n",
       "      <td>0.345716</td>\n",
       "      <td>0.954057</td>\n",
       "      <td>0.385175</td>\n",
       "      <td>0.608879</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean_props_cond_1  mean_props_cond_2  mean_props_cond_3   \n",
       "clusters                                                            \n",
       "c1                 0.006399           0.007605           0.010391  \\\n",
       "c2                 0.043683           0.075932           0.043750   \n",
       "c3                 0.137301           0.157528           0.184273   \n",
       "c4                 0.401103           0.270136           0.415870   \n",
       "c5                 0.411515           0.488800           0.345716   \n",
       "\n",
       "          f_statistics  p_values  adjusted_p_values  \n",
       "clusters                                             \n",
       "c1            0.719279  0.487104           0.608879  \n",
       "c2            1.351087  0.258959           0.608879  \n",
       "c3            0.356240  0.700304           0.700304  \n",
       "c4            1.430231  0.239254           0.608879  \n",
       "c5            0.954057  0.385175           0.608879  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# run anova, taking into account pair information\n",
    "anova(props, prop_trans, new_design, coef, robust=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_props_cond_1</th>\n",
       "      <th>mean_props_cond_2</th>\n",
       "      <th>prop_ratio</th>\n",
       "      <th>t_statistics</th>\n",
       "      <th>p_values</th>\n",
       "      <th>adjusted_p_values</th>\n",
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       "      <th>c1</th>\n",
       "      <td>0.006399</td>\n",
       "      <td>0.007605</td>\n",
       "      <td>0.841368</td>\n",
       "      <td>-0.346112</td>\n",
       "      <td>0.734065</td>\n",
       "      <td>0.806602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c2</th>\n",
       "      <td>0.043683</td>\n",
       "      <td>0.075932</td>\n",
       "      <td>0.575292</td>\n",
       "      <td>-1.821747</td>\n",
       "      <td>0.088493</td>\n",
       "      <td>0.299088</td>\n",
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       "    <tr>\n",
       "      <th>c3</th>\n",
       "      <td>0.137301</td>\n",
       "      <td>0.157528</td>\n",
       "      <td>0.871598</td>\n",
       "      <td>-0.249180</td>\n",
       "      <td>0.806602</td>\n",
       "      <td>0.806602</td>\n",
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       "    <tr>\n",
       "      <th>c4</th>\n",
       "      <td>0.401103</td>\n",
       "      <td>0.270136</td>\n",
       "      <td>1.484819</td>\n",
       "      <td>1.650414</td>\n",
       "      <td>0.119635</td>\n",
       "      <td>0.299088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c5</th>\n",
       "      <td>0.411515</td>\n",
       "      <td>0.488800</td>\n",
       "      <td>0.841889</td>\n",
       "      <td>-0.858832</td>\n",
       "      <td>0.403949</td>\n",
       "      <td>0.673249</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          mean_props_cond_1  mean_props_cond_2  prop_ratio  t_statistics   \n",
       "clusters                                                                   \n",
       "c1                 0.006399           0.007605    0.841368     -0.346112  \\\n",
       "c2                 0.043683           0.075932    0.575292     -1.821747   \n",
       "c3                 0.137301           0.157528    0.871598     -0.249180   \n",
       "c4                 0.401103           0.270136    1.484819      1.650414   \n",
       "c5                 0.411515           0.488800    0.841889     -0.858832   \n",
       "\n",
       "          p_values  adjusted_p_values  \n",
       "clusters                               \n",
       "c1        0.734065           0.806602  \n",
       "c2        0.088493           0.299088  \n",
       "c3        0.806602           0.806602  \n",
       "c4        0.119635           0.299088  \n",
       "c5        0.403949           0.673249  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# to compare only cond_1 and cond_2, specify a contrast and run t_test\n",
    "contr = [1,-1,0]\n",
    "t_test(props, prop_trans, design, contr, robust=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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