{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Recover cell cycle in mESC\n",
    "\n",
    "Here we use the mESC dataset. For simplicity we have converted the dataset into TPM.\n",
    "The original count data is available at ArrayExpress: [E-MTAB-2805](https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-2805/). Tools to transform data are also provided and explained in the following sections."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pickle as pkl\n",
    "import sklearn as skl\n",
    "import sklearn.preprocessing\n",
    "\n",
    "import matplotlib as mpl\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Warning information from TensorFlow may occur. It doesn't matter."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/shaoheng/.conda/envs/tensorflow-gpu/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n"
     ]
    }
   ],
   "source": [
    "import cyclum\n",
    "from cyclum import writer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "input_file_mask = 'data/mESC/mesc-tpm'\n",
    "output_file_mask = './results/mESC_original/mesc-tpm'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data\n",
    "Here we have label, so we load both. However, the label is not used until evaluation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def preprocess(input_file_mask):\n",
    "    \"\"\"\n",
    "    Read in data and perform log transform (log2(x+1)), centering (mean = 1) and scaling (sd = 1).\n",
    "    \"\"\"\n",
    "    tpm = writer.read_df_from_binary(input_file_mask).T\n",
    "    sttpm = pd.DataFrame(data=skl.preprocessing.scale(np.log2(tpm.values + 1)), index=tpm.index, columns=tpm.columns)\n",
    "    \n",
    "    label = pd.read_csv(input_file_mask + '-label.txt', sep=\"\\t\", index_col=0).T\n",
    "    return sttpm, label\n",
    "\n",
    "sttpm, label = preprocess(input_file_mask)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is no convention whether cells should be columns or rows. Here we require cells to be rows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Gnai3</th>\n",
       "      <th>Pbsn</th>\n",
       "      <th>Cdc45</th>\n",
       "      <th>H19</th>\n",
       "      <th>Scml2</th>\n",
       "      <th>Apoh</th>\n",
       "      <th>Narf</th>\n",
       "      <th>Cav2</th>\n",
       "      <th>Klf6</th>\n",
       "      <th>Scmh1</th>\n",
       "      <th>...</th>\n",
       "      <th>RP23-345J21.2</th>\n",
       "      <th>AC121960.1</th>\n",
       "      <th>AC136147.1</th>\n",
       "      <th>AC122013.1</th>\n",
       "      <th>AC132389.1</th>\n",
       "      <th>Gm11392</th>\n",
       "      <th>AC160109.2</th>\n",
       "      <th>AC154675.1</th>\n",
       "      <th>AC156980.1</th>\n",
       "      <th>RP23-429I18.1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G1_cell1_count</th>\n",
       "      <td>-0.411123</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>-0.099416</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-0.144843</td>\n",
       "      <td>0.090295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1_cell2_count</th>\n",
       "      <td>-0.180800</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>0.777223</td>\n",
       "      <td>-0.165725</td>\n",
       "      <td>-0.820206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.362341</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>1.458881</td>\n",
       "      <td>0.207421</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>-0.824399</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-0.144843</td>\n",
       "      <td>-1.271033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1_cell3_count</th>\n",
       "      <td>-1.409101</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>-1.218187</td>\n",
       "      <td>-0.165725</td>\n",
       "      <td>-0.820206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.690715</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>-1.271394</td>\n",
       "      <td>-0.657735</td>\n",
       "      <td>...</td>\n",
       "      <td>2.593349</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.079577</td>\n",
       "      <td>-0.374972</td>\n",
       "      <td>-0.592938</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>-0.079861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.144843</td>\n",
       "      <td>-1.271033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1_cell4_count</th>\n",
       "      <td>-1.867558</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>0.923695</td>\n",
       "      <td>-0.165725</td>\n",
       "      <td>-0.820206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.903266</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>1.430708</td>\n",
       "      <td>-0.657735</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.146722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.079577</td>\n",
       "      <td>-0.374972</td>\n",
       "      <td>2.938898</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>-0.079861</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.144843</td>\n",
       "      <td>-1.271033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1_cell5_count</th>\n",
       "      <td>-1.646290</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>0.001887</td>\n",
       "      <td>-0.165725</td>\n",
       "      <td>-0.820206</td>\n",
       "      <td>0.0</td>\n",
       "      <td>-0.690715</td>\n",
       "      <td>-0.059028</td>\n",
       "      <td>-0.811233</td>\n",
       "      <td>-0.657735</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>-0.144843</td>\n",
       "      <td>-0.111558</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38293 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Gnai3      Pbsn     Cdc45       H19     Scml2  Apoh  \\\n",
       "G1_cell1_count -0.411123 -0.059028 -0.099416  5.385822 -0.691219   0.0   \n",
       "G1_cell2_count -0.180800 -0.059028  0.777223 -0.165725 -0.820206   0.0   \n",
       "G1_cell3_count -1.409101 -0.059028 -1.218187 -0.165725 -0.820206   0.0   \n",
       "G1_cell4_count -1.867558 -0.059028  0.923695 -0.165725 -0.820206   0.0   \n",
       "G1_cell5_count -1.646290 -0.059028  0.001887 -0.165725 -0.820206   0.0   \n",
       "\n",
       "                    Narf      Cav2      Klf6     Scmh1      ...        \\\n",
       "G1_cell1_count -0.690715 -0.059028 -1.051909 -0.350978      ...         \n",
       "G1_cell2_count  0.362341 -0.059028  1.458881  0.207421      ...         \n",
       "G1_cell3_count -0.690715 -0.059028 -1.271394 -0.657735      ...         \n",
       "G1_cell4_count  0.903266 -0.059028  1.430708 -0.657735      ...         \n",
       "G1_cell5_count -0.690715 -0.059028 -0.811233 -0.657735      ...         \n",
       "\n",
       "                RP23-345J21.2  AC121960.1  AC136147.1  AC122013.1  AC132389.1  \\\n",
       "G1_cell1_count      -0.146722         0.0   -0.079577   -0.374972   -0.824399   \n",
       "G1_cell2_count      -0.146722         0.0   -0.079577   -0.374972   -0.824399   \n",
       "G1_cell3_count       2.593349         0.0   -0.079577   -0.374972   -0.592938   \n",
       "G1_cell4_count      -0.146722         0.0   -0.079577   -0.374972    2.938898   \n",
       "G1_cell5_count      -0.146722         0.0   -0.079577   -0.374972   -0.824399   \n",
       "\n",
       "                 Gm11392  AC160109.2  AC154675.1  AC156980.1  RP23-429I18.1  \n",
       "G1_cell1_count -0.059028   -0.079861         0.0   -0.144843       0.090295  \n",
       "G1_cell2_count -0.059028   -0.079861         0.0   -0.144843      -1.271033  \n",
       "G1_cell3_count -0.059028   -0.079861         0.0   -0.144843      -1.271033  \n",
       "G1_cell4_count -0.059028   -0.079861         0.0   -0.144843      -1.271033  \n",
       "G1_cell5_count -0.059028   -0.079861         0.0   -0.144843      -0.111558  \n",
       "\n",
       "[5 rows x 38293 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sttpm.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>stage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>G1_cell1_count</th>\n",
       "      <td>g0/g1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1_cell2_count</th>\n",
       "      <td>g0/g1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1_cell3_count</th>\n",
       "      <td>g0/g1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1_cell4_count</th>\n",
       "      <td>g0/g1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G1_cell5_count</th>\n",
       "      <td>g0/g1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                stage\n",
       "G1_cell1_count  g0/g1\n",
       "G1_cell2_count  g0/g1\n",
       "G1_cell3_count  g0/g1\n",
       "G1_cell4_count  g0/g1\n",
       "G1_cell5_count  g0/g1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "label.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set up the model and fit the model\n",
    "\n",
    "Fitting the model may take some time. Using a GTX 960M GPU it takes 6 minutes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = cyclum.PreloadCyclum(sttpm.values, q_circular=3, q_linear=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pretrain burnin\n",
      "step  2000: loss [0.1452561, 45035228.0, 141884.6] time 9.83\n",
      "pretrain train\n",
      "step  2000: loss [0.14524707, 45035230.0, 141884.84] time 7.32\n",
      "step  4000: loss [0.14524722, 45035230.0, 141884.84] time 6.24\n",
      "midtrain burnin\n",
      "step  2000: loss [0.14524722, 13479.106, 65288.434] time 31.53\n",
      "midtrain train\n",
      "step  2000: loss [0.14524722, 13475.677, 65288.434] time 31.44\n",
      "step  4000: loss [0.14524722, 13475.676, 65288.434] time 29.15\n",
      "finaltrain train\n",
      "step  2000: loss [0.23462683, 12895.94, 35502.785] time 41.88\n",
      "step  4000: loss [0.25569192, 12895.507, 18958.936] time 38.46\n",
      "step  6000: loss [0.276326, 12895.623, 9609.924] time 38.45\n",
      "finaltrain refine\n",
      "step  2000: loss [0.28275946, 12895.442, 7700.0806] time 42.01\n",
      "step  4000: loss [0.28499442, 12895.469, 6226.207] time 41.03\n",
      "step  6000: loss [0.28545943, 12895.44, 5038.1367] time 39.55\n",
      "step  8000: loss [0.2893271, 12895.435, 4072.1836] time 40.60\n",
      "step 10000: loss [0.29161924, 12895.42, 3288.5647] time 40.55\n",
      "Full time 438.50\n"
     ]
    }
   ],
   "source": [
    "pseudotime, rotation = model.fit()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Illustrations\n",
    "We illustrate the results on a circle, to show its circular nature. \n",
    "There is virtually no start and end of the circle.\n",
    "Red, green and blue represents G0/G1, S and G2/M phase respectively.\n",
    "The inner lines represents single cells. The cells spread across the\n",
    "The areas outside"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%aimport cyclum.illustration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "color_map = {'stage': {\"g0/g1\": \"red\", \"s\": \"green\", \"g2/m\": \"blue\"},\n",
    "                 'subcluster': {\"intact\": \"cyan\", \"perturbed\": \"violet\"}}\n",
    "cyclum.illustration.plot_round_distr_color(pseudotime, label['stage'], color_map['stage'])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}