example/HumanCellCycleSubset-ThresholdNotebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# python modules\n",
    "import os\n",
    "import glob\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# figure styles\n",
    "sns.set_style(\"darkgrid\", {\"axes.facecolor\": \"0.9\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# custom functions\n",
    "import combine_analysis_results as car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prefix as supplied to analysis program\n",
    "result_path = os.path.join(\"HumanCellCycleSubset-result\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "D, alpha = car.get_result_per_prefix(result_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "aplestin            nan\n",
      "cc_inflection       0.610\n",
      "density_min         0.690\n",
      "elo_clustering      nan\n",
      "gupta_clustering    nan\n",
      "mcr_2               nan\n",
      "mcr_3               nan\n",
      "mcr_max             nan\n",
      "rmt                 nan\n",
      "scale_free          0.820\n",
      "single_component    nan\n",
      "spectral_methods    0.790\n",
      "whole_graph         0.600\n",
      "Power-0.8           0.800\n",
      "TypeI-0.01          0.684\n"
     ]
    }
   ],
   "source": [
    "for name, t in D.items():\n",
    "    print(\"%-20s%0.3f\"%(name, t))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# alternatively, fetch dataframe of iterative results \n",
    "iterative_results = glob.glob(result_path + \"*.iterative.txt\")\n",
    "df = car.get_interative_t_values(iterative_results, {})\n",
    "df_plot = df.set_index(\"threshold\", drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>index</th>\n",
       "      <th>vertex-count</th>\n",
       "      <th>edge-count</th>\n",
       "      <th>connected-component-count</th>\n",
       "      <th>density</th>\n",
       "      <th>density-orig-V</th>\n",
       "      <th>largest-cc-size</th>\n",
       "      <th>2nd-largest-cc-size</th>\n",
       "      <th>clustering-coefficient</th>\n",
       "      <th>random-clustering-coefficient</th>\n",
       "      <th>...</th>\n",
       "      <th>maximal-clique-count</th>\n",
       "      <th>clique-number</th>\n",
       "      <th>poisson-chi2</th>\n",
       "      <th>poisson-pvalue</th>\n",
       "      <th>goe-chi2</th>\n",
       "      <th>goe-pvalue</th>\n",
       "      <th>scale-free-KS</th>\n",
       "      <th>scale-free-KS-p-value</th>\n",
       "      <th>scale-free-alpha</th>\n",
       "      <th>dNsv_dt</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>threshold</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",
       "      <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0.60</th>\n",
       "      <td>0</td>\n",
       "      <td>6639</td>\n",
       "      <td>15905</td>\n",
       "      <td>48</td>\n",
       "      <td>0.000722</td>\n",
       "      <td>0.000635</td>\n",
       "      <td>6533</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.475226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-16.480480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.61</th>\n",
       "      <td>1</td>\n",
       "      <td>6514</td>\n",
       "      <td>14532</td>\n",
       "      <td>53</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>0.000580</td>\n",
       "      <td>6394</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.457328</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-15.872587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.62</th>\n",
       "      <td>2</td>\n",
       "      <td>6365</td>\n",
       "      <td>13228</td>\n",
       "      <td>73</td>\n",
       "      <td>0.000653</td>\n",
       "      <td>0.000528</td>\n",
       "      <td>6197</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.433536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-14.659305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.63</th>\n",
       "      <td>3</td>\n",
       "      <td>6212</td>\n",
       "      <td>12037</td>\n",
       "      <td>105</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>5972</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.408152</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-14.166733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0.64</th>\n",
       "      <td>4</td>\n",
       "      <td>6046</td>\n",
       "      <td>10852</td>\n",
       "      <td>139</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>5722</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.375707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-13.711000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           index  vertex-count  edge-count  connected-component-count  \\\n",
       "threshold                                                               \n",
       "0.60           0          6639       15905                         48   \n",
       "0.61           1          6514       14532                         53   \n",
       "0.62           2          6365       13228                         73   \n",
       "0.63           3          6212       12037                        105   \n",
       "0.64           4          6046       10852                        139   \n",
       "\n",
       "            density  density-orig-V  largest-cc-size  2nd-largest-cc-size  \\\n",
       "threshold                                                                   \n",
       "0.60       0.000722        0.000635             6533                    5   \n",
       "0.61       0.000685        0.000580             6394                    5   \n",
       "0.62       0.000653        0.000528             6197                    5   \n",
       "0.63       0.000624        0.000481             5972                    5   \n",
       "0.64       0.000594        0.000433             5722                    6   \n",
       "\n",
       "           clustering-coefficient  random-clustering-coefficient  ...  \\\n",
       "threshold                                                         ...   \n",
       "0.60                          NaN                            NaN  ...   \n",
       "0.61                          NaN                            NaN  ...   \n",
       "0.62                          NaN                            NaN  ...   \n",
       "0.63                          NaN                            NaN  ...   \n",
       "0.64                          NaN                            NaN  ...   \n",
       "\n",
       "           maximal-clique-count  clique-number  poisson-chi2  poisson-pvalue  \\\n",
       "threshold                                                                      \n",
       "0.60                        NaN             -1           NaN             NaN   \n",
       "0.61                        NaN             -1           NaN             NaN   \n",
       "0.62                        NaN             -1           NaN             NaN   \n",
       "0.63                        NaN             -1           NaN             NaN   \n",
       "0.64                        NaN             -1           NaN             NaN   \n",
       "\n",
       "           goe-chi2  goe-pvalue  scale-free-KS  scale-free-KS-p-value  \\\n",
       "threshold                                                               \n",
       "0.60            NaN         NaN       0.475226                    0.0   \n",
       "0.61            NaN         NaN       0.457328                    0.0   \n",
       "0.62            NaN         NaN       0.433536                    0.0   \n",
       "0.63            NaN         NaN       0.408152                    0.0   \n",
       "0.64            NaN         NaN       0.375707                    0.0   \n",
       "\n",
       "           scale-free-alpha    dNsv_dt  \n",
       "threshold                               \n",
       "0.60                    2.0 -16.480480  \n",
       "0.61                    2.0 -15.872587  \n",
       "0.62                    2.0 -14.659305  \n",
       "0.63                    2.0 -14.166733  \n",
       "0.64                    2.0 -13.711000  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_plot.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>threshold</th>\n",
       "      <th>vertex-count</th>\n",
       "      <th>edge-count</th>\n",
       "      <th>connected-component-count</th>\n",
       "      <th>density</th>\n",
       "      <th>density-orig-V</th>\n",
       "      <th>largest-cc-size</th>\n",
       "      <th>2nd-largest-cc-size</th>\n",
       "      <th>clustering-coefficient</th>\n",
       "      <th>...</th>\n",
       "      <th>maximal-clique-count</th>\n",
       "      <th>clique-number</th>\n",
       "      <th>poisson-chi2</th>\n",
       "      <th>poisson-pvalue</th>\n",
       "      <th>goe-chi2</th>\n",
       "      <th>goe-pvalue</th>\n",
       "      <th>scale-free-KS</th>\n",
       "      <th>scale-free-KS-p-value</th>\n",
       "      <th>scale-free-alpha</th>\n",
       "      <th>dNsv_dt</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>0.61</td>\n",
       "      <td>6514</td>\n",
       "      <td>14532</td>\n",
       "      <td>53</td>\n",
       "      <td>0.000685</td>\n",
       "      <td>0.000580</td>\n",
       "      <td>6394</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>-15.872587</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>0.62</td>\n",
       "      <td>6365</td>\n",
       "      <td>13228</td>\n",
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       "      <td>...</td>\n",
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       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.433536</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-14.659305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>0.63</td>\n",
       "      <td>6212</td>\n",
       "      <td>12037</td>\n",
       "      <td>105</td>\n",
       "      <td>0.000624</td>\n",
       "      <td>0.000481</td>\n",
       "      <td>5972</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.408152</td>\n",
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       "      <td>-14.166733</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>0.64</td>\n",
       "      <td>6046</td>\n",
       "      <td>10852</td>\n",
       "      <td>139</td>\n",
       "      <td>0.000594</td>\n",
       "      <td>0.000433</td>\n",
       "      <td>5722</td>\n",
       "      <td>6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.375707</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-13.711000</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   index  threshold  vertex-count  edge-count  connected-component-count  \\\n",
       "0      0       0.60          6639       15905                         48   \n",
       "1      1       0.61          6514       14532                         53   \n",
       "2      2       0.62          6365       13228                         73   \n",
       "3      3       0.63          6212       12037                        105   \n",
       "4      4       0.64          6046       10852                        139   \n",
       "\n",
       "    density  density-orig-V  largest-cc-size  2nd-largest-cc-size  \\\n",
       "0  0.000722        0.000635             6533                    5   \n",
       "1  0.000685        0.000580             6394                    5   \n",
       "2  0.000653        0.000528             6197                    5   \n",
       "3  0.000624        0.000481             5972                    5   \n",
       "4  0.000594        0.000433             5722                    6   \n",
       "\n",
       "   clustering-coefficient  ...  maximal-clique-count  clique-number  \\\n",
       "0                     NaN  ...                   NaN             -1   \n",
       "1                     NaN  ...                   NaN             -1   \n",
       "2                     NaN  ...                   NaN             -1   \n",
       "3                     NaN  ...                   NaN             -1   \n",
       "4                     NaN  ...                   NaN             -1   \n",
       "\n",
       "   poisson-chi2  poisson-pvalue  goe-chi2  goe-pvalue  scale-free-KS  \\\n",
       "0           NaN             NaN       NaN         NaN       0.475226   \n",
       "1           NaN             NaN       NaN         NaN       0.457328   \n",
       "2           NaN             NaN       NaN         NaN       0.433536   \n",
       "3           NaN             NaN       NaN         NaN       0.408152   \n",
       "4           NaN             NaN       NaN         NaN       0.375707   \n",
       "\n",
       "   scale-free-KS-p-value  scale-free-alpha    dNsv_dt  \n",
       "0                    0.0               2.0 -16.480480  \n",
       "1                    0.0               2.0 -15.872587  \n",
       "2                    0.0               2.0 -14.659305  \n",
       "3                    0.0               2.0 -14.166733  \n",
       "4                    0.0               2.0 -13.711000  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4cdea92588>"
      ]
     },
     "execution_count": 13,
     "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": [
    "df_plot[\"density\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "car.plot_t_vs_EV(df_plot, D)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4cde9589b0>"
      ]
     },
     "execution_count": 22,
     "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": [
    "df_plot[\"2nd-eigenvalue\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4cdb7c25f8>"
      ]
     },
     "execution_count": 23,
     "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": [
    "df_plot[\"almost-disconnected-component-count\"].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}