quickstart.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quick start\n",
    "*Last updated: Jan 27, 2020*\n",
    "\n",
    "You have *whole genome* (WGS), *whole exome* (WES) or *targeted sequencing* (TGS) data from one or more tumour samples from the same patient and you wish to find clusters of mutations with similar *variant allele fractions* (VAF). You can use `clonosGP` to do that. The data can be either cross-sectional or longitudinal. In the case of longitudinal data, we can optionally exploit the temporal spacing of samples to improve clustering.\n",
    "\n",
    "## 1. First, import the necessary packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging as log\n",
    "import warnings as warn\n",
    "import pandas as pnd\n",
    "import pymc3 as pmc\n",
    "\n",
    "import clonosGP as cln\n",
    "\n",
    "log.getLogger('theano').setLevel(log.ERROR)             ## supress annoying warnings ...\n",
    "warn.filterwarnings(\"ignore\", category=FutureWarning)   ## from Theano"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Next, load your data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAMPLEID</th>\n",
       "      <th>TIME</th>\n",
       "      <th>MUTID</th>\n",
       "      <th>GENE</th>\n",
       "      <th>r</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>M1</td>\n",
       "      <td>ADAD1</td>\n",
       "      <td>22</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>M2</td>\n",
       "      <td>AMTN</td>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>M3</td>\n",
       "      <td>APBB2</td>\n",
       "      <td>19</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>M4</td>\n",
       "      <td>ASXL1</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>0</td>\n",
       "      <td>M5</td>\n",
       "      <td>ATM</td>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>e</td>\n",
       "      <td>35</td>\n",
       "      <td>M24</td>\n",
       "      <td>SEMA3E</td>\n",
       "      <td>36</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>e</td>\n",
       "      <td>35</td>\n",
       "      <td>M25</td>\n",
       "      <td>SF3B1</td>\n",
       "      <td>19</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>e</td>\n",
       "      <td>35</td>\n",
       "      <td>M26</td>\n",
       "      <td>SHROOM1</td>\n",
       "      <td>10</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>e</td>\n",
       "      <td>35</td>\n",
       "      <td>M27</td>\n",
       "      <td>SLC9A11</td>\n",
       "      <td>27</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>e</td>\n",
       "      <td>35</td>\n",
       "      <td>M28</td>\n",
       "      <td>SLITRK4</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SAMPLEID  TIME MUTID     GENE   r   R\n",
       "0          a     0    M1    ADAD1  22  38\n",
       "1          a     0    M2     AMTN   9  33\n",
       "2          a     0    M3    APBB2  19  43\n",
       "3          a     0    M4    ASXL1   1  40\n",
       "4          a     0    M5      ATM  31  37\n",
       "..       ...   ...   ...      ...  ..  ..\n",
       "135        e    35   M24   SEMA3E  36  58\n",
       "136        e    35   M25    SF3B1  19  32\n",
       "137        e    35   M26  SHROOM1  10  23\n",
       "138        e    35   M27  SLC9A11  27  41\n",
       "139        e    35   M28  SLITRK4  16  16\n",
       "\n",
       "[140 rows x 6 columns]"
      ]
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     "execution_count": 2,
     "metadata": {},
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    }
   ],
   "source": [
    "DATA = pnd.read_csv('data/cll_Schuh_2012_CLL003.csv')\n",
    "DATA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As you can see, the data are stored in long format. The minimum set of columns that must be present are `SAMPLEID`, `MUTID`, `r` and `R`, where `r` is the number of reads harbouring the mutation and `R` is the sum of `r` plus the number of reads harbouring the reference allele. In the case of longitudinal data (as above), column `TIME` may be present, indicating the relative time (in days or months) of sample collection. Each line should include information on a single mutation in a single sample. No missing values are permitted. Importantly, each mutation should be covered (i.e. `R>0`) in all samples, but not necessarily present in all samples, in which case `r=0`, as shown below:    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
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       "      <th>SAMPLEID</th>\n",
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       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "      <th>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "      <th>d</th>\n",
       "      <th>e</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TIME</th>\n",
       "      <th>0</th>\n",
       "      <th>19</th>\n",
       "      <th>25</th>\n",
       "      <th>31</th>\n",
       "      <th>35</th>\n",
       "      <th>0</th>\n",
       "      <th>19</th>\n",
       "      <th>25</th>\n",
       "      <th>31</th>\n",
       "      <th>35</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MUTID</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>M1</th>\n",
       "      <td>38</td>\n",
       "      <td>48</td>\n",
       "      <td>29</td>\n",
       "      <td>37</td>\n",
       "      <td>42</td>\n",
       "      <td>22</td>\n",
       "      <td>18</td>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M10</th>\n",
       "      <td>39</td>\n",
       "      <td>34</td>\n",
       "      <td>41</td>\n",
       "      <td>50</td>\n",
       "      <td>35</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M11</th>\n",
       "      <td>38</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>44</td>\n",
       "      <td>35</td>\n",
       "      <td>16</td>\n",
       "      <td>15</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M12</th>\n",
       "      <td>46</td>\n",
       "      <td>33</td>\n",
       "      <td>44</td>\n",
       "      <td>49</td>\n",
       "      <td>32</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "      <td>17</td>\n",
       "      <td>34</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M13</th>\n",
       "      <td>31</td>\n",
       "      <td>37</td>\n",
       "      <td>26</td>\n",
       "      <td>48</td>\n",
       "      <td>33</td>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>8</td>\n",
       "      <td>29</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M14</th>\n",
       "      <td>42</td>\n",
       "      <td>45</td>\n",
       "      <td>46</td>\n",
       "      <td>22</td>\n",
       "      <td>25</td>\n",
       "      <td>15</td>\n",
       "      <td>18</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M15</th>\n",
       "      <td>31</td>\n",
       "      <td>28</td>\n",
       "      <td>39</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>16</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M16</th>\n",
       "      <td>46</td>\n",
       "      <td>37</td>\n",
       "      <td>35</td>\n",
       "      <td>42</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M17</th>\n",
       "      <td>54</td>\n",
       "      <td>27</td>\n",
       "      <td>39</td>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "      <td>20</td>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M18</th>\n",
       "      <td>46</td>\n",
       "      <td>30</td>\n",
       "      <td>34</td>\n",
       "      <td>47</td>\n",
       "      <td>33</td>\n",
       "      <td>25</td>\n",
       "      <td>13</td>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M19</th>\n",
       "      <td>39</td>\n",
       "      <td>28</td>\n",
       "      <td>40</td>\n",
       "      <td>34</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M2</th>\n",
       "      <td>33</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>46</td>\n",
       "      <td>25</td>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M20</th>\n",
       "      <td>42</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>11</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M21</th>\n",
       "      <td>42</td>\n",
       "      <td>34</td>\n",
       "      <td>33</td>\n",
       "      <td>53</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>27</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M22</th>\n",
       "      <td>45</td>\n",
       "      <td>37</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>23</td>\n",
       "      <td>20</td>\n",
       "      <td>18</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M23</th>\n",
       "      <td>43</td>\n",
       "      <td>33</td>\n",
       "      <td>28</td>\n",
       "      <td>38</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>17</td>\n",
       "      <td>15</td>\n",
       "      <td>16</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M24</th>\n",
       "      <td>58</td>\n",
       "      <td>47</td>\n",
       "      <td>45</td>\n",
       "      <td>49</td>\n",
       "      <td>58</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>24</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M25</th>\n",
       "      <td>44</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>44</td>\n",
       "      <td>32</td>\n",
       "      <td>22</td>\n",
       "      <td>21</td>\n",
       "      <td>10</td>\n",
       "      <td>18</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M26</th>\n",
       "      <td>50</td>\n",
       "      <td>32</td>\n",
       "      <td>31</td>\n",
       "      <td>43</td>\n",
       "      <td>23</td>\n",
       "      <td>28</td>\n",
       "      <td>19</td>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M27</th>\n",
       "      <td>36</td>\n",
       "      <td>43</td>\n",
       "      <td>42</td>\n",
       "      <td>64</td>\n",
       "      <td>41</td>\n",
       "      <td>20</td>\n",
       "      <td>25</td>\n",
       "      <td>16</td>\n",
       "      <td>32</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M28</th>\n",
       "      <td>27</td>\n",
       "      <td>20</td>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M3</th>\n",
       "      <td>43</td>\n",
       "      <td>38</td>\n",
       "      <td>35</td>\n",
       "      <td>40</td>\n",
       "      <td>44</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M4</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>31</td>\n",
       "      <td>40</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>27</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M5</th>\n",
       "      <td>37</td>\n",
       "      <td>19</td>\n",
       "      <td>25</td>\n",
       "      <td>26</td>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M6</th>\n",
       "      <td>57</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>54</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>27</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M7</th>\n",
       "      <td>26</td>\n",
       "      <td>32</td>\n",
       "      <td>40</td>\n",
       "      <td>47</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>20</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M8</th>\n",
       "      <td>24</td>\n",
       "      <td>21</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>26</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M9</th>\n",
       "      <td>40</td>\n",
       "      <td>32</td>\n",
       "      <td>35</td>\n",
       "      <td>47</td>\n",
       "      <td>27</td>\n",
       "      <td>17</td>\n",
       "      <td>14</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           R                   r                \n",
       "SAMPLEID   a   b   c   d   e   a   b   c   d   e\n",
       "TIME      0   19  25  31  35  0   19  25  31  35\n",
       "MUTID                                           \n",
       "M1        38  48  29  37  42  22  18  11  20  23\n",
       "M10       39  34  41  50  35   0   0   6  19  12\n",
       "M11       38  34  34  44  35  16  15   2   0   0\n",
       "M12       46  33  44  49  32  16  13  17  34  15\n",
       "M13       31  37  26  48  33  19  20   8  29  18\n",
       "M14       42  45  46  22  25  15  18   2   1   0\n",
       "M15       31  28  39  34  27  18  13  14  16  12\n",
       "M16       46  37  35  42  28   2   0   6  19  13\n",
       "M17       54  27  39  45  37  20  12   2   0   0\n",
       "M18       46  30  34  47  33  25  13  14  25  20\n",
       "M19       39  28  40  34  32   0   0  10  12  14\n",
       "M2        33  30  40  46  25   9  16   3   0   0\n",
       "M20       42  30  40  35  32   0   0  11  12  14\n",
       "M21       42  34  33  53  32   0   0   6  27  19\n",
       "M22       45  37  40  44  23  20  18   3   0   0\n",
       "M23       43  33  28  38  29  21  17  15  16  13\n",
       "M24       58  47  45  49  58   5   2   6  24  36\n",
       "M25       44  33  35  44  32  22  21  10  18  19\n",
       "M26       50  32  31  43  23  28  19   6  19  10\n",
       "M27       36  43  42  64  41  20  25  16  32  27\n",
       "M28       27  20  17  20  16   0   0   8  18  16\n",
       "M3        43  38  35  40  44  19  17   2   0   0\n",
       "M4        40  40  31  40  35   1   2  13  27  17\n",
       "M5        37  19  25  26  19  31  16   1   0   0\n",
       "M6        57  51  52  54  24  24  27   3   0   0\n",
       "M7        26  32  40  47  33   0   0   9  20  14\n",
       "M8        24  21  23  23  26   9   7   4  10   9\n",
       "M9        40  32  35  47  27  17  14   3   0   0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DATA.pivot_table(index='MUTID', columns=['SAMPLEID', 'TIME'], values=['r', 'R'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following columns should also be included, if available:\n",
    "1. `PURITY`: the purity of each sample. If missing, it is assumed that `PURITY=1`. \n",
    "2. `CNn`: the local copy number at the locus of each mutation in the germline. If missing, it is assumed that `CNn=2`.\n",
    "3. `CNt`: the local copy number at the locus of each mutation in each sample. If missing, it is assumed that `CNt=2`.\n",
    "4. `CNm`: the number of chromosomes harbouring each mutation in each sample. If missing, it will be estimated from the data. \n",
    "\n",
    "Any additional columns are ignored.\n",
    "\n",
    "## 3. Run `clonosGP`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:clonosGP:No PURITY column in the data. Assuming all samples have purity 100%.\n",
      "INFO:clonosGP:No CNn column in the data. Assuming germline is diploid over all provided loci.\n",
      "INFO:clonosGP:No CNt column in the data. Assuming all tumour samples are diploid over all provided loci.\n",
      "INFO:clonosGP:No CNm column in the data. Multiplicity values will be approximated.\n",
      "Average Loss = 377.74: 100%|██████████| 10000/10000 [00:13<00:00, 718.83it/s]\n",
      "Finished [100%]: Average Loss = 377.74\n",
      "INFO:pymc3.variational.inference:Finished [100%]: Average Loss = 377.74\n",
      "INFO:clonosGP:Calculating posterior cluster weights and centres.\n",
      "INFO:clonosGP:Calculating posterior CCF values.\n",
      "INFO:clonosGP:Calculating posterior predictive distribution.\n",
      "INFO:clonosGP:Calculating GP-related quantities.\n",
      "INFO:clonosGP:Calculating dispersion(s).\n",
      "INFO:clonosGP:Finished.\n"
     ]
    }
   ],
   "source": [
    "pmc.tt_rng(42)  # for reproducibility\n",
    "RES = cln.infer(DATA, \n",
    "                model_args={'prior': 'GP0', 'cov': 'Mat32'},    ## change \"GP0\" to \"Flat\", for cross-sectional data\n",
    "                pymc3_args={'niters': int(10e3)})               ## Run inference for 10K iterations \n",
    "\n",
    "## the above may take some time to start running the first time you try it.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Visualise the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x1080 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cln.viz.plot_summary(RES, figsize=(10, 15));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure that the algorithm has converged by inspecting the loss (top-left panel). If not, rerun with a larger number of iterations. Cluster weights (i.e. proportions of mutations belonging to each cluster) are illustrated at the top-right panel. A color-encoded hard clustering of mutations and the time course of each mutation cluster are provided in the two middle panels. Finally, estimated sample-specific dispersions, time scales (lengths) and amplitudes of the underlying Gaussian Process for modelling cluster dynamics are provided at the bottom three panels, respectively. For an overview of the fitted model, check below:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x504 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cln.viz.plot_samples(RES, figsize=(7, 7));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Inspect the results in more detail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SAMPLEID</th>\n",
       "      <th>PURITY</th>\n",
       "      <th>TIME</th>\n",
       "      <th>TIME2</th>\n",
       "      <th>MUTID</th>\n",
       "      <th>r</th>\n",
       "      <th>R</th>\n",
       "      <th>CNn</th>\n",
       "      <th>CNt</th>\n",
       "      <th>CNm</th>\n",
       "      <th>VAF</th>\n",
       "      <th>VAF0</th>\n",
       "      <th>CCF_</th>\n",
       "      <th>CCF</th>\n",
       "      <th>CCF_HI</th>\n",
       "      <th>CCF_LO</th>\n",
       "      <th>PPD</th>\n",
       "      <th>PPD_HI</th>\n",
       "      <th>PPD_LO</th>\n",
       "      <th>CLUSTERID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M1</td>\n",
       "      <td>22.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.578947</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980075</td>\n",
       "      <td>0.996485</td>\n",
       "      <td>0.903236</td>\n",
       "      <td>1.130544</td>\n",
       "      <td>1.615167</td>\n",
       "      <td>0.657822</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034178</td>\n",
       "      <td>0.076535</td>\n",
       "      <td>0.014375</td>\n",
       "      <td>7.600809</td>\n",
       "      <td>13.871916</td>\n",
       "      <td>3.426611</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M11</td>\n",
       "      <td>16.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.421053</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.842105</td>\n",
       "      <td>0.836695</td>\n",
       "      <td>0.904045</td>\n",
       "      <td>0.742396</td>\n",
       "      <td>2.269097</td>\n",
       "      <td>3.080643</td>\n",
       "      <td>1.447736</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M12</td>\n",
       "      <td>16.0</td>\n",
       "      <td>46.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.347826</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.695652</td>\n",
       "      <td>0.979337</td>\n",
       "      <td>0.996502</td>\n",
       "      <td>0.868062</td>\n",
       "      <td>1.519304</td>\n",
       "      <td>2.188371</td>\n",
       "      <td>0.957032</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>M13</td>\n",
       "      <td>19.0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.612903</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.980135</td>\n",
       "      <td>0.996485</td>\n",
       "      <td>0.904083</td>\n",
       "      <td>0.792584</td>\n",
       "      <td>1.166115</td>\n",
       "      <td>0.449369</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004338</td>\n",
       "      <td>0.033339</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>5.735527</td>\n",
       "      <td>8.875471</td>\n",
       "      <td>3.209190</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004338</td>\n",
       "      <td>0.033339</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>7.173190</td>\n",
       "      <td>11.144139</td>\n",
       "      <td>4.007911</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M7</td>\n",
       "      <td>14.0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.424242</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.848485</td>\n",
       "      <td>0.968441</td>\n",
       "      <td>0.995146</td>\n",
       "      <td>0.853384</td>\n",
       "      <td>2.045368</td>\n",
       "      <td>2.678318</td>\n",
       "      <td>1.316128</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M8</td>\n",
       "      <td>9.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.346154</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.692308</td>\n",
       "      <td>0.984233</td>\n",
       "      <td>0.997697</td>\n",
       "      <td>0.739820</td>\n",
       "      <td>1.169704</td>\n",
       "      <td>1.639214</td>\n",
       "      <td>0.833463</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>e</td>\n",
       "      <td>1.0</td>\n",
       "      <td>35</td>\n",
       "      <td>1.0</td>\n",
       "      <td>M9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.004338</td>\n",
       "      <td>0.033339</td>\n",
       "      <td>0.000393</td>\n",
       "      <td>8.014729</td>\n",
       "      <td>12.493506</td>\n",
       "      <td>4.415217</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>140 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    SAMPLEID  PURITY  TIME  TIME2 MUTID     r     R  CNn  CNt  CNm       VAF  \\\n",
       "0          a     1.0     0    0.0    M1  22.0  38.0  2.0  2.0  1.0  0.578947   \n",
       "1          a     1.0     0    0.0   M10   0.0  39.0  2.0  2.0  1.0  0.000000   \n",
       "2          a     1.0     0    0.0   M11  16.0  38.0  2.0  2.0  1.0  0.421053   \n",
       "3          a     1.0     0    0.0   M12  16.0  46.0  2.0  2.0  1.0  0.347826   \n",
       "4          a     1.0     0    0.0   M13  19.0  31.0  2.0  2.0  1.0  0.612903   \n",
       "..       ...     ...   ...    ...   ...   ...   ...  ...  ...  ...       ...   \n",
       "135        e     1.0    35    1.0    M5   0.0  19.0  2.0  2.0  1.0  0.000000   \n",
       "136        e     1.0    35    1.0    M6   0.0  24.0  2.0  2.0  1.0  0.000000   \n",
       "137        e     1.0    35    1.0    M7  14.0  33.0  2.0  2.0  1.0  0.424242   \n",
       "138        e     1.0    35    1.0    M8   9.0  26.0  2.0  2.0  1.0  0.346154   \n",
       "139        e     1.0    35    1.0    M9   0.0  27.0  2.0  2.0  1.0  0.000000   \n",
       "\n",
       "     VAF0      CCF_       CCF    CCF_HI    CCF_LO       PPD     PPD_HI  \\\n",
       "0     0.5  1.000000  0.980075  0.996485  0.903236  1.130544   1.615167   \n",
       "1     0.5  0.000000  0.034178  0.076535  0.014375  7.600809  13.871916   \n",
       "2     0.5  0.842105  0.836695  0.904045  0.742396  2.269097   3.080643   \n",
       "3     0.5  0.695652  0.979337  0.996502  0.868062  1.519304   2.188371   \n",
       "4     0.5  1.000000  0.980135  0.996485  0.904083  0.792584   1.166115   \n",
       "..    ...       ...       ...       ...       ...       ...        ...   \n",
       "135   0.5  0.000000  0.004338  0.033339  0.000393  5.735527   8.875471   \n",
       "136   0.5  0.000000  0.004338  0.033339  0.000393  7.173190  11.144139   \n",
       "137   0.5  0.848485  0.968441  0.995146  0.853384  2.045368   2.678318   \n",
       "138   0.5  0.692308  0.984233  0.997697  0.739820  1.169704   1.639214   \n",
       "139   0.5  0.000000  0.004338  0.033339  0.000393  8.014729  12.493506   \n",
       "\n",
       "       PPD_LO CLUSTERID  \n",
       "0    0.657822         3  \n",
       "1    3.426611         1  \n",
       "2    1.447736         2  \n",
       "3    0.957032         3  \n",
       "4    0.449369         3  \n",
       "..        ...       ...  \n",
       "135  3.209190         2  \n",
       "136  4.007911         2  \n",
       "137  1.316128         1  \n",
       "138  0.833463         3  \n",
       "139  4.415217         2  \n",
       "\n",
       "[140 rows x 20 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RES['data']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`clonosGP` returns the original data matrix with additional columns attached. \n",
    "1. `CLUSTERID` returns the most likely cluster assigment. Mutations with the same `CLUSTERID` were assigned to the same cluster.\n",
    "2. Columns `CCF`, `CCF_HI` and `CCF_LO` are the estimated median *cancer cell fraction* (CCF) along with 95% *credible intervals*. Notice that if `PURITY` is not provided by the user, it is assumed to be 100%. In this case, CCF should be interpreted as the *cellular prevalence* of the mutation (i.e. the fraction of both normal and cancer cells harbouring the mutation), not as the cancer cell fraction. `CCF_` is a first, rough estimate of CCF.\n",
    "3. `PPD`, `PPD_HI`, `PPD_LO` are the median *posterior predictive distribution* and 95% credible intervals, resepctively.\n",
    "4. `VAF` is the *variant allele fraction* of the mutation, calculated as `r/R`; `VAF0` would be the expected VAF if the mutation was clonal (i.e. if the mutation had CCF=1).\n",
    "5. `TIME2` is `TIME` scaled to fit in the interval [0, 1].\n",
    "6. `PURITY`, `CNn`, `CNt` and `CNm` are the quantities we used internally, in case they were not provided by the user.\n",
    "\n",
    "**This is the end of this quick introduction to `clonosGP`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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