{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Project Tasks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the first few assignments, we have learned how to infer part based components (known as mutational signatures) generated by particular mutational processes using Non-negative Matrix Factorization (NMF). By doing this, we are trying to reconstruct the mutation catalog in a given sample with mutational signatures and their contributions.\n",
"\n",
"In this group project, you will use similar mutational profiles and signature activities to predict cancer types but with much larger sample size. \n",
"You should:\n",
"* Separate the data into training and test groups within each cancer type.\n",
"* Find out which features are informative for the prediction of the cancer type (label). You should combine the profiles and activities and use each data type independently.\n",
"* Implement different models for classification of the samples given the input data and evaluate the model performance using test data to avoid overfitting. Explain briefly how does each model that you have used work.\n",
"* Report model performance, using standard machine learning metrics such as confusion matrices etc. \n",
"* Compare model performance across methods and across cancer types, are some types easier top predict than others.\n",
"* Submit a single Jupyter notebook as the final report and present that during the last assignment session "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The data include both mutational catalogs from multiple cancers and the predicted activities in the paper [\"Alexandrov LB, et al. (2020) The repertoire of mutational signatures in human cancer\"](https://www.nature.com/articles/s41586-020-1943-3). The data either are generated from whole human genome (WGS) or only exomes regions (WES). Since the exome region only constitutes about 1% of human genome, the total mutation numbers in these samples are, of course, much smaller. So if you plan to use WGS together with WES data, remember to normalize the profile for each sample to sum up to 1.\n",
"\n",
"Note that, the data is generated from different platforms by different research groups, some of them (e.g. labeled with PCAWG, TCGA) are processed with the same bioinformatics pipeline. Thus, these samples will have less variability related to data processing pipelines."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Cancer types might be labeled under the same tissue, e.g. 'Bone-Benign','Bone-Epith', which can also be combined together or take the one has more samples."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a link to background reading [\"Pan-Cancer Analysis of Whole Genomes\"](https://www.nature.com/collections/afdejfafdb). Have a look especially the paper [\"A deep learning system accurately classifies primary and metastatic cancers using passenger mutation patterns\"](https://www.nature.com/articles/s41467-019-13825-8)."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import re"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mutational catalogs and activities - WGS data"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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" }\n",
"\n",
" .dataframe thead th {\n",
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" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Mutation type</th>\n",
" <th>Trinucleotide</th>\n",
" <th>Biliary-AdenoCA::SP117655</th>\n",
" <th>Biliary-AdenoCA::SP117556</th>\n",
" <th>Biliary-AdenoCA::SP117627</th>\n",
" <th>Biliary-AdenoCA::SP117775</th>\n",
" <th>Biliary-AdenoCA::SP117332</th>\n",
" <th>Biliary-AdenoCA::SP117712</th>\n",
" <th>Biliary-AdenoCA::SP117017</th>\n",
" <th>Biliary-AdenoCA::SP117031</th>\n",
" <th>...</th>\n",
" <th>Uterus-AdenoCA::SP94540</th>\n",
" <th>Uterus-AdenoCA::SP95222</th>\n",
" <th>Uterus-AdenoCA::SP89389</th>\n",
" <th>Uterus-AdenoCA::SP90503</th>\n",
" <th>Uterus-AdenoCA::SP92460</th>\n",
" <th>Uterus-AdenoCA::SP92931</th>\n",
" <th>Uterus-AdenoCA::SP91265</th>\n",
" <th>Uterus-AdenoCA::SP89909</th>\n",
" <th>Uterus-AdenoCA::SP90629</th>\n",
" <th>Uterus-AdenoCA::SP95550</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>C>A</td>\n",
" <td>ACA</td>\n",
" <td>269</td>\n",
" <td>114</td>\n",
" <td>105</td>\n",
" <td>217</td>\n",
" <td>52</td>\n",
" <td>192</td>\n",
" <td>54</td>\n",
" <td>196</td>\n",
" <td>...</td>\n",
" <td>117</td>\n",
" <td>233</td>\n",
" <td>94</td>\n",
" <td>114</td>\n",
" <td>257</td>\n",
" <td>139</td>\n",
" <td>404</td>\n",
" <td>97</td>\n",
" <td>250</td>\n",
" <td>170</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C>A</td>\n",
" <td>ACC</td>\n",
" <td>148</td>\n",
" <td>56</td>\n",
" <td>71</td>\n",
" <td>123</td>\n",
" <td>36</td>\n",
" <td>139</td>\n",
" <td>54</td>\n",
" <td>102</td>\n",
" <td>...</td>\n",
" <td>90</td>\n",
" <td>167</td>\n",
" <td>59</td>\n",
" <td>64</td>\n",
" <td>268</td>\n",
" <td>75</td>\n",
" <td>255</td>\n",
" <td>78</td>\n",
" <td>188</td>\n",
" <td>137</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 2782 columns</p>\n",
"</div>"
],
"text/plain": [
" Mutation type Trinucleotide Biliary-AdenoCA::SP117655 \\\n",
"0 C>A ACA 269 \n",
"1 C>A ACC 148 \n",
"\n",
" Biliary-AdenoCA::SP117556 Biliary-AdenoCA::SP117627 \\\n",
"0 114 105 \n",
"1 56 71 \n",
"\n",
" Biliary-AdenoCA::SP117775 Biliary-AdenoCA::SP117332 \\\n",
"0 217 52 \n",
"1 123 36 \n",
"\n",
" Biliary-AdenoCA::SP117712 Biliary-AdenoCA::SP117017 \\\n",
"0 192 54 \n",
"1 139 54 \n",
"\n",
" Biliary-AdenoCA::SP117031 ... Uterus-AdenoCA::SP94540 \\\n",
"0 196 ... 117 \n",
"1 102 ... 90 \n",
"\n",
" Uterus-AdenoCA::SP95222 Uterus-AdenoCA::SP89389 Uterus-AdenoCA::SP90503 \\\n",
"0 233 94 114 \n",
"1 167 59 64 \n",
"\n",
" Uterus-AdenoCA::SP92460 Uterus-AdenoCA::SP92931 Uterus-AdenoCA::SP91265 \\\n",
"0 257 139 404 \n",
"1 268 75 255 \n",
"\n",
" Uterus-AdenoCA::SP89909 Uterus-AdenoCA::SP90629 Uterus-AdenoCA::SP95550 \n",
"0 97 250 170 \n",
"1 78 188 137 \n",
"\n",
"[2 rows x 2782 columns]"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## PCAWG data is performed by the same pipeline\n",
"PCAWG_wgs_mut = pd.read_csv (\"./project_data/catalogs/WGS/WGS_PCAWG.96.csv\")\n",
"PCAWG_wgs_mut.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Accuracy is the cosine similarity of reconstruct catalog to the observed catalog "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"\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>Cancer Types</th>\n",
" <th>Sample Names</th>\n",
" <th>Accuracy</th>\n",
" <th>SBS1</th>\n",
" <th>SBS2</th>\n",
" <th>SBS3</th>\n",
" <th>SBS4</th>\n",
" <th>SBS5</th>\n",
" <th>SBS6</th>\n",
" <th>SBS7a</th>\n",
" <th>...</th>\n",
" <th>SBS51</th>\n",
" <th>SBS52</th>\n",
" <th>SBS53</th>\n",
" <th>SBS54</th>\n",
" <th>SBS55</th>\n",
" <th>SBS56</th>\n",
" <th>SBS57</th>\n",
" <th>SBS58</th>\n",
" <th>SBS59</th>\n",
" <th>SBS60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Biliary-AdenoCA</td>\n",
" <td>SP117655</td>\n",
" <td>0.968</td>\n",
" <td>1496</td>\n",
" <td>1296</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1825</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Biliary-AdenoCA</td>\n",
" <td>SP117556</td>\n",
" <td>0.963</td>\n",
" <td>985</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>922</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" Cancer Types Sample Names Accuracy SBS1 SBS2 SBS3 SBS4 SBS5 SBS6 \\\n",
"0 Biliary-AdenoCA SP117655 0.968 1496 1296 0 0 1825 0 \n",
"1 Biliary-AdenoCA SP117556 0.963 985 0 0 0 922 0 \n",
"\n",
" SBS7a ... SBS51 SBS52 SBS53 SBS54 SBS55 SBS56 SBS57 SBS58 SBS59 \\\n",
"0 0 ... 0 0 0 0 0 0 0 0 0 \n",
"1 0 ... 0 0 0 0 0 0 0 0 0 \n",
"\n",
" SBS60 \n",
"0 0 \n",
"1 0 \n",
"\n",
"[2 rows x 68 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Activities:\n",
"PCAWG_wgs_act = pd.read_csv (\"./project_data/activities/WGS/WGS_PCAWG.activities.csv\")\n",
"PCAWG_wgs_act.head(2)"
]
},
{
"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>Mutation type</th>\n",
" <th>Trinucleotide</th>\n",
" <th>ALL::PD4020a</th>\n",
" <th>ALL::SJBALL011_D</th>\n",
" <th>ALL::SJBALL012_D</th>\n",
" <th>ALL::SJBALL020013_D1</th>\n",
" <th>ALL::SJBALL020422_D1</th>\n",
" <th>ALL::SJBALL020579_D1</th>\n",
" <th>ALL::SJBALL020589_D1</th>\n",
" <th>ALL::SJBALL020625_D1</th>\n",
" <th>...</th>\n",
" <th>Stomach-AdenoCa::pfg316T</th>\n",
" <th>Stomach-AdenoCa::pfg317T</th>\n",
" <th>Stomach-AdenoCa::pfg344T</th>\n",
" <th>Stomach-AdenoCa::pfg373T</th>\n",
" <th>Stomach-AdenoCa::pfg375T</th>\n",
" <th>Stomach-AdenoCa::pfg378T</th>\n",
" <th>Stomach-AdenoCa::pfg398T</th>\n",
" <th>Stomach-AdenoCa::pfg413T</th>\n",
" <th>Stomach-AdenoCa::pfg416T</th>\n",
" <th>Stomach-AdenoCa::pfg424T</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>C>A</td>\n",
" <td>ACA</td>\n",
" <td>35</td>\n",
" <td>9</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>5</td>\n",
" <td>7</td>\n",
" <td>3</td>\n",
" <td>5</td>\n",
" <td>...</td>\n",
" <td>133</td>\n",
" <td>185</td>\n",
" <td>202</td>\n",
" <td>185</td>\n",
" <td>96</td>\n",
" <td>134</td>\n",
" <td>12</td>\n",
" <td>279</td>\n",
" <td>75</td>\n",
" <td>135</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C>A</td>\n",
" <td>ACC</td>\n",
" <td>16</td>\n",
" <td>2</td>\n",
" <td>4</td>\n",
" <td>10</td>\n",
" <td>5</td>\n",
" <td>9</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>48</td>\n",
" <td>70</td>\n",
" <td>126</td>\n",
" <td>88</td>\n",
" <td>35</td>\n",
" <td>54</td>\n",
" <td>16</td>\n",
" <td>112</td>\n",
" <td>31</td>\n",
" <td>91</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 1867 columns</p>\n",
"</div>"
],
"text/plain": [
" Mutation type Trinucleotide ALL::PD4020a ALL::SJBALL011_D \\\n",
"0 C>A ACA 35 9 \n",
"1 C>A ACC 16 2 \n",
"\n",
" ALL::SJBALL012_D ALL::SJBALL020013_D1 ALL::SJBALL020422_D1 \\\n",
"0 2 7 5 \n",
"1 4 10 5 \n",
"\n",
" ALL::SJBALL020579_D1 ALL::SJBALL020589_D1 ALL::SJBALL020625_D1 ... \\\n",
"0 7 3 5 ... \n",
"1 9 1 2 ... \n",
"\n",
" Stomach-AdenoCa::pfg316T Stomach-AdenoCa::pfg317T \\\n",
"0 133 185 \n",
"1 48 70 \n",
"\n",
" Stomach-AdenoCa::pfg344T Stomach-AdenoCa::pfg373T \\\n",
"0 202 185 \n",
"1 126 88 \n",
"\n",
" Stomach-AdenoCa::pfg375T Stomach-AdenoCa::pfg378T \\\n",
"0 96 134 \n",
"1 35 54 \n",
"\n",
" Stomach-AdenoCa::pfg398T Stomach-AdenoCa::pfg413T \\\n",
"0 12 279 \n",
"1 16 112 \n",
"\n",
" Stomach-AdenoCa::pfg416T Stomach-AdenoCa::pfg424T \n",
"0 75 135 \n",
"1 31 91 \n",
"\n",
"[2 rows x 1867 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nonPCAWG_wgs_mut = pd.read_csv (\"./project_data/catalogs/WGS/WGS_Other.96.csv\")\n",
"nonPCAWG_wgs_mut.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\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>Cancer Types</th>\n",
" <th>Sample Names</th>\n",
" <th>Accuracy</th>\n",
" <th>SBS1</th>\n",
" <th>SBS2</th>\n",
" <th>SBS3</th>\n",
" <th>SBS4</th>\n",
" <th>SBS5</th>\n",
" <th>SBS6</th>\n",
" <th>SBS7a</th>\n",
" <th>...</th>\n",
" <th>SBS51</th>\n",
" <th>SBS52</th>\n",
" <th>SBS53</th>\n",
" <th>SBS54</th>\n",
" <th>SBS55</th>\n",
" <th>SBS56</th>\n",
" <th>SBS57</th>\n",
" <th>SBS58</th>\n",
" <th>SBS59</th>\n",
" <th>SBS60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ALL</td>\n",
" <td>PD4020a</td>\n",
" <td>0.995</td>\n",
" <td>208</td>\n",
" <td>3006</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>365</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ALL</td>\n",
" <td>SJBALL011_D</td>\n",
" <td>0.905</td>\n",
" <td>66</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>144</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" Cancer Types Sample Names Accuracy SBS1 SBS2 SBS3 SBS4 SBS5 SBS6 \\\n",
"0 ALL PD4020a 0.995 208 3006 0 0 365 0 \n",
"1 ALL SJBALL011_D 0.905 66 0 0 0 144 0 \n",
"\n",
" SBS7a ... SBS51 SBS52 SBS53 SBS54 SBS55 SBS56 SBS57 SBS58 SBS59 \\\n",
"0 0 ... 0 0 0 0 0 0 0 0 0 \n",
"1 0 ... 0 0 0 0 0 0 0 0 0 \n",
"\n",
" SBS60 \n",
"0 0 \n",
"1 0 \n",
"\n",
"[2 rows x 68 columns]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"nonPCAWG_wgs_act = pd.read_csv (\"./project_data/activities/WGS/WGS_Other.activities.csv\")\n",
"nonPCAWG_wgs_act.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Mutational catalogs - WES data"
]
},
{
"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>Mutation type</th>\n",
" <th>Trinucleotide</th>\n",
" <th>AML::TCGA-AB-2802-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2803-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2804-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2805-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2806-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2807-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2808-03B-01W-0728-08</th>\n",
" <th>AML::TCGA-AB-2809-03D-01W-0755-09</th>\n",
" <th>...</th>\n",
" <th>Eye-Melanoma::TCGA-WC-A885-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-WC-A888-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-WC-A88A-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-WC-AA9A-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-WC-AA9E-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-YZ-A980-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-YZ-A982-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-YZ-A983-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-YZ-A984-01A-11D-A39W-08</th>\n",
" <th>Eye-Melanoma::TCGA-YZ-A985-01A-11D-A39W-08</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>C>A</td>\n",
" <td>ACA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C>A</td>\n",
" <td>ACC</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 9495 columns</p>\n",
"</div>"
],
"text/plain": [
" Mutation type Trinucleotide AML::TCGA-AB-2802-03B-01W-0728-08 \\\n",
"0 C>A ACA 0 \n",
"1 C>A ACC 0 \n",
"\n",
" AML::TCGA-AB-2803-03B-01W-0728-08 AML::TCGA-AB-2804-03B-01W-0728-08 \\\n",
"0 0 0 \n",
"1 2 0 \n",
"\n",
" AML::TCGA-AB-2805-03B-01W-0728-08 AML::TCGA-AB-2806-03B-01W-0728-08 \\\n",
"0 0 4 \n",
"1 0 0 \n",
"\n",
" AML::TCGA-AB-2807-03B-01W-0728-08 AML::TCGA-AB-2808-03B-01W-0728-08 \\\n",
"0 0 2 \n",
"1 1 3 \n",
"\n",
" AML::TCGA-AB-2809-03D-01W-0755-09 ... \\\n",
"0 0 ... \n",
"1 0 ... \n",
"\n",
" Eye-Melanoma::TCGA-WC-A885-01A-11D-A39W-08 \\\n",
"0 1 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-WC-A888-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-WC-A88A-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-WC-AA9A-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-WC-AA9E-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-YZ-A980-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-YZ-A982-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-YZ-A983-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 1 \n",
"\n",
" Eye-Melanoma::TCGA-YZ-A984-01A-11D-A39W-08 \\\n",
"0 0 \n",
"1 0 \n",
"\n",
" Eye-Melanoma::TCGA-YZ-A985-01A-11D-A39W-08 \n",
"0 0 \n",
"1 0 \n",
"\n",
"[2 rows x 9495 columns]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Performed by TCGA pipeline\n",
"TCGA_wes_mut = pd.read_csv (\"./project_data/catalogs/WES/WES_TCGA.96.csv\")\n",
"TCGA_wes_mut.head(2)"
]
},
{
"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",
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" 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>Cancer Types</th>\n",
" <th>Sample Names</th>\n",
" <th>Accuracy</th>\n",
" <th>SBS1</th>\n",
" <th>SBS2</th>\n",
" <th>SBS3</th>\n",
" <th>SBS4</th>\n",
" <th>SBS5</th>\n",
" <th>SBS6</th>\n",
" <th>SBS7a</th>\n",
" <th>...</th>\n",
" <th>SBS51</th>\n",
" <th>SBS52</th>\n",
" <th>SBS53</th>\n",
" <th>SBS54</th>\n",
" <th>SBS55</th>\n",
" <th>SBS56</th>\n",
" <th>SBS57</th>\n",
" <th>SBS58</th>\n",
" <th>SBS59</th>\n",
" <th>SBS60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>AML</td>\n",
" <td>TCGA-AB-2802-03B-01W-0728-08</td>\n",
" <td>0.811</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>AML</td>\n",
" <td>TCGA-AB-2803-03B-01W-0728-08</td>\n",
" <td>0.608</td>\n",
" <td>4</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>7</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" Cancer Types Sample Names Accuracy SBS1 SBS2 SBS3 \\\n",
"0 AML TCGA-AB-2802-03B-01W-0728-08 0.811 3 0 0 \n",
"1 AML TCGA-AB-2803-03B-01W-0728-08 0.608 4 0 0 \n",
"\n",
" SBS4 SBS5 SBS6 SBS7a ... SBS51 SBS52 SBS53 SBS54 SBS55 SBS56 \\\n",
"0 0 0 0 0 ... 0 0 0 0 0 0 \n",
"1 0 7 0 0 ... 0 0 0 0 0 0 \n",
"\n",
" SBS57 SBS58 SBS59 SBS60 \n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"\n",
"[2 rows x 68 columns]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"##Activities\n",
"TCGA_wes_act = pd.read_csv(\"./project_data/activities/WES/WES_TCGA.activities.csv\")\n",
"TCGA_wes_act.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Mutation type</th>\n",
" <th>Trinucleotide</th>\n",
" <th>ALL::TARGET-10-PAIXPH-03A-01D</th>\n",
" <th>ALL::TARGET-10-PAKHZT-03A-01R</th>\n",
" <th>ALL::TARGET-10-PAKMVD-09A-01D</th>\n",
" <th>ALL::TARGET-10-PAKSWW-03A-01D</th>\n",
" <th>ALL::TARGET-10-PALETF-03A-01D</th>\n",
" <th>ALL::TARGET-10-PALLSD-09A-01D</th>\n",
" <th>ALL::TARGET-10-PAMDKS-03A-01D</th>\n",
" <th>ALL::TARGET-10-PAPJIB-04A-01D</th>\n",
" <th>...</th>\n",
" <th>Head-SCC::V-109</th>\n",
" <th>Head-SCC::V-112</th>\n",
" <th>Head-SCC::V-116</th>\n",
" <th>Head-SCC::V-119</th>\n",
" <th>Head-SCC::V-123</th>\n",
" <th>Head-SCC::V-124</th>\n",
" <th>Head-SCC::V-125</th>\n",
" <th>Head-SCC::V-14</th>\n",
" <th>Head-SCC::V-29</th>\n",
" <th>Head-SCC::V-98</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>C>A</td>\n",
" <td>ACA</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>C>A</td>\n",
" <td>ACC</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 9693 columns</p>\n",
"</div>"
],
"text/plain": [
" Mutation type Trinucleotide ALL::TARGET-10-PAIXPH-03A-01D \\\n",
"0 C>A ACA 0 \n",
"1 C>A ACC 0 \n",
"\n",
" ALL::TARGET-10-PAKHZT-03A-01R ALL::TARGET-10-PAKMVD-09A-01D \\\n",
"0 0 0 \n",
"1 0 0 \n",
"\n",
" ALL::TARGET-10-PAKSWW-03A-01D ALL::TARGET-10-PALETF-03A-01D \\\n",
"0 1 0 \n",
"1 1 0 \n",
"\n",
" ALL::TARGET-10-PALLSD-09A-01D ALL::TARGET-10-PAMDKS-03A-01D \\\n",
"0 0 0 \n",
"1 0 0 \n",
"\n",
" ALL::TARGET-10-PAPJIB-04A-01D ... Head-SCC::V-109 Head-SCC::V-112 \\\n",
"0 2 ... 0 0 \n",
"1 0 ... 1 0 \n",
"\n",
" Head-SCC::V-116 Head-SCC::V-119 Head-SCC::V-123 Head-SCC::V-124 \\\n",
"0 0 0 0 0 \n",
"1 0 0 0 0 \n",
"\n",
" Head-SCC::V-125 Head-SCC::V-14 Head-SCC::V-29 Head-SCC::V-98 \n",
"0 0 0 0 1 \n",
"1 0 1 0 0 \n",
"\n",
"[2 rows x 9693 columns]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"other_wes_mut = pd.read_csv(\"./project_data/catalogs/WES/WES_Other.96.csv\")\n",
"other_wes_mut.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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" .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>Cancer Types</th>\n",
" <th>Sample Names</th>\n",
" <th>Accuracy</th>\n",
" <th>SBS1</th>\n",
" <th>SBS2</th>\n",
" <th>SBS3</th>\n",
" <th>SBS4</th>\n",
" <th>SBS5</th>\n",
" <th>SBS6</th>\n",
" <th>SBS7a</th>\n",
" <th>...</th>\n",
" <th>SBS51</th>\n",
" <th>SBS52</th>\n",
" <th>SBS53</th>\n",
" <th>SBS54</th>\n",
" <th>SBS55</th>\n",
" <th>SBS56</th>\n",
" <th>SBS57</th>\n",
" <th>SBS58</th>\n",
" <th>SBS59</th>\n",
" <th>SBS60</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>ALL</td>\n",
" <td>TARGET-10-PAIXPH-03A-01D</td>\n",
" <td>0.529</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
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" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>ALL</td>\n",
" <td>TARGET-10-PAKHZT-03A-01R</td>\n",
" <td>0.696</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>2 rows × 68 columns</p>\n",
"</div>"
],
"text/plain": [
" Cancer Types Sample Names Accuracy SBS1 SBS2 SBS3 SBS4 \\\n",
"0 ALL TARGET-10-PAIXPH-03A-01D 0.529 0 0 0 0 \n",
"1 ALL TARGET-10-PAKHZT-03A-01R 0.696 0 0 0 0 \n",
"\n",
" SBS5 SBS6 SBS7a ... SBS51 SBS52 SBS53 SBS54 SBS55 SBS56 SBS57 \\\n",
"0 0 0 0 ... 0 0 0 1 0 0 0 \n",
"1 0 0 0 ... 0 0 0 1 0 0 0 \n",
"\n",
" SBS58 SBS59 SBS60 \n",
"0 0 0 0 \n",
"1 0 0 0 \n",
"\n",
"[2 rows x 68 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"other_wes_act = pd.read_csv(\"./project_data/activities/WES/WES_Other.activities.csv\")\n",
"other_wes_act.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import sklearn\n",
"from sklearn.decomposition import PCA\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import torch \n",
"from sklearn.model_selection import cross_val_score\n",
"from sklearn.model_selection import train_test_split\n",
"from sklearn.ensemble import RandomForestClassifier\n",
"from sklearn.metrics import accuracy_score\n",
"#import re"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Dataset preprocess, combine some WGS and WES data"
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Missing entries in all mutations: 0\n",
"The shape of the all mutations data frame (12273, 97)\n",
"Sum of some rows:\n",
" AML::TCGA-AB-2802-03B-01W-0728-08 1.0\n",
"AML::TCGA-AB-2803-03B-01W-0728-08 1.0\n",
"AML::TCGA-AB-2804-03B-01W-0728-08 1.0\n",
"AML::TCGA-AB-2805-03B-01W-0728-08 1.0\n",
"AML::TCGA-AB-2806-03B-01W-0728-08 1.0\n",
"dtype: float64\n",
"Some tumor counts:\n",
" Breast-cancer 930\n",
"Prost-AdenoCA 770\n",
"Liver-HCC 637\n",
"Lung-AdenoCA 566\n",
"ColoRect-AdenoCA 556\n",
"Name: tumor_types, dtype: int64\n",
"['AML', 'Adrenal-neoplasm', 'Biliary-AdenoCA', 'Bladder-TCC', 'Bone-Benign', 'Bone-Epith', 'Bone-Osteosarc', 'Breast-AdenoCA', 'Breast-DCIS', 'Breast-LobularCA', 'Breast-cancer', 'CNS-GBM', 'CNS-Medullo', 'CNS-Oligo', 'CNS-PiloAstro', 'Cervix-AdenoCA', 'Cervix-CA', 'Cervix-SCC', 'ColoRect-AdenoCA', 'DLBC', 'Eso-AdenoCA', 'Eye-Melanoma', 'Head-SCC', 'Kidney-ChRCC', 'Kidney-Papillary', 'Kidney-RCC', 'Liver-HCC', 'Lung-AdenoCA', 'Lung-SCC', 'Lymph-BNHL', 'Lymph-CLL', 'Mesothelium-Mesothelioma', 'Myeloid-AML', 'Myeloid-MDS', 'Myeloid-MPN', 'Ovary-AdenoCA', 'Panc-AdenoCA', 'Panc-Endocrine', 'Pheochromocytoma', 'Prost-AdenoCA', 'Sarcoma', 'Skin-Melanoma', 'SoftTissue-Leiomyo', 'SoftTissue-Liposarc', 'Stomach-AdenoCA', 'Testis-CA', 'Thy-AdenoCA', 'Thymoma', 'Transitional-cell-carcinoma', 'UCS', 'Uterus-AdenoCA']\n"
]
},
{
"data": {
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th>mut_tri</th>\n",
" <th>C>A_ACA</th>\n",
" <th>C>A_ACC</th>\n",
" <th>C>A_ACG</th>\n",
" <th>C>A_ACT</th>\n",
" <th>C>A_CCA</th>\n",
" <th>C>A_CCC</th>\n",
" <th>C>A_CCG</th>\n",
" <th>C>A_CCT</th>\n",
" <th>C>A_GCA</th>\n",
" <th>C>A_GCC</th>\n",
" <th>...</th>\n",
" <th>T>G_CTT</th>\n",
" <th>T>G_GTA</th>\n",
" <th>T>G_GTC</th>\n",
" <th>T>G_GTG</th>\n",
" <th>T>G_GTT</th>\n",
" <th>T>G_TTA</th>\n",
" <th>T>G_TTC</th>\n",
" <th>T>G_TTG</th>\n",
" <th>T>G_TTT</th>\n",
" <th>tumor_types</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>AML::TCGA-AB-2802-03B-01W-0728-08</th>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.066667</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>AML</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AML::TCGA-AB-2803-03B-01W-0728-08</th>\n",
" <td>0.00000</td>\n",
" <td>0.111111</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.055556</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>AML</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AML::TCGA-AB-2804-03B-01W-0728-08</th>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>AML</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AML::TCGA-AB-2805-03B-01W-0728-08</th>\n",
" <td>0.00000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.012658</td>\n",
" <td>0.000000</td>\n",
" <td>0.012658</td>\n",
" <td>0.012658</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.00000</td>\n",
" <td>...</td>\n",
" <td>0.012658</td>\n",
" <td>0.0</td>\n",
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" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>AML</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AML::TCGA-AB-2806-03B-01W-0728-08</th>\n",
" <td>0.00597</td>\n",
" <td>0.000000</td>\n",
" <td>0.004478</td>\n",
" <td>0.002985</td>\n",
" <td>0.029851</td>\n",
" <td>0.026866</td>\n",
" <td>0.014925</td>\n",
" <td>0.028358</td>\n",
" <td>0.008955</td>\n",
" <td>0.01194</td>\n",
" <td>...</td>\n",
" <td>0.001493</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.001493</td>\n",
" <td>0.000000</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>0.0</td>\n",
" <td>AML</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 97 columns</p>\n",
"</div>"
],
"text/plain": [
"mut_tri C>A_ACA C>A_ACC C>A_ACG C>A_ACT \\\n",
"AML::TCGA-AB-2802-03B-01W-0728-08 0.00000 0.000000 0.000000 0.000000 \n",
"AML::TCGA-AB-2803-03B-01W-0728-08 0.00000 0.111111 0.000000 0.000000 \n",
"AML::TCGA-AB-2804-03B-01W-0728-08 0.00000 0.000000 0.000000 0.000000 \n",
"AML::TCGA-AB-2805-03B-01W-0728-08 0.00000 0.000000 0.000000 0.012658 \n",
"AML::TCGA-AB-2806-03B-01W-0728-08 0.00597 0.000000 0.004478 0.002985 \n",
"\n",
"mut_tri C>A_CCA C>A_CCC C>A_CCG C>A_CCT \\\n",
"AML::TCGA-AB-2802-03B-01W-0728-08 0.000000 0.000000 0.000000 0.000000 \n",
"AML::TCGA-AB-2803-03B-01W-0728-08 0.000000 0.055556 0.000000 0.000000 \n",
"AML::TCGA-AB-2804-03B-01W-0728-08 0.000000 0.000000 0.000000 0.000000 \n",
"AML::TCGA-AB-2805-03B-01W-0728-08 0.000000 0.012658 0.012658 0.000000 \n",
"AML::TCGA-AB-2806-03B-01W-0728-08 0.029851 0.026866 0.014925 0.028358 \n",
"\n",
"mut_tri C>A_GCA C>A_GCC ... T>G_CTT T>G_GTA \\\n",
"AML::TCGA-AB-2802-03B-01W-0728-08 0.000000 0.00000 ... 0.000000 0.0 \n",
"AML::TCGA-AB-2803-03B-01W-0728-08 0.000000 0.00000 ... 0.000000 0.0 \n",
"AML::TCGA-AB-2804-03B-01W-0728-08 0.000000 0.00000 ... 0.000000 0.0 \n",
"AML::TCGA-AB-2805-03B-01W-0728-08 0.000000 0.00000 ... 0.012658 0.0 \n",
"AML::TCGA-AB-2806-03B-01W-0728-08 0.008955 0.01194 ... 0.001493 0.0 \n",
"\n",
"mut_tri T>G_GTC T>G_GTG T>G_GTT T>G_TTA \\\n",
"AML::TCGA-AB-2802-03B-01W-0728-08 0.0 0.000000 0.066667 0.0 \n",
"AML::TCGA-AB-2803-03B-01W-0728-08 0.0 0.000000 0.000000 0.0 \n",
"AML::TCGA-AB-2804-03B-01W-0728-08 0.0 0.000000 0.000000 0.0 \n",
"AML::TCGA-AB-2805-03B-01W-0728-08 0.0 0.000000 0.000000 0.0 \n",
"AML::TCGA-AB-2806-03B-01W-0728-08 0.0 0.001493 0.000000 0.0 \n",
"\n",
"mut_tri T>G_TTC T>G_TTG T>G_TTT tumor_types \n",
"AML::TCGA-AB-2802-03B-01W-0728-08 0.0 0.0 0.0 AML \n",
"AML::TCGA-AB-2803-03B-01W-0728-08 0.0 0.0 0.0 AML \n",
"AML::TCGA-AB-2804-03B-01W-0728-08 0.0 0.0 0.0 AML \n",
"AML::TCGA-AB-2805-03B-01W-0728-08 0.0 0.0 0.0 AML \n",
"AML::TCGA-AB-2806-03B-01W-0728-08 0.0 0.0 0.0 AML \n",
"\n",
"[5 rows x 97 columns]"
]
},
"execution_count": 69,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"def prepare_mutation_df(raw_mutation_df):\n",
" # Make a copy of the original data frame and start processing from there\n",
" mutations = raw_mutation_df.copy()\n",
" \n",
" mutations['mut_tri'] = mutations.apply(lambda a: '{}_{}'.format(a['Mutation type'], a['Trinucleotide']), axis=1)\n",
" mutations = mutations.set_index('mut_tri').drop(['Mutation type', 'Trinucleotide'], axis=1)\n",
" mutations = mutations.T\n",
"\n",
" # Rename some index names\n",
" renamed_items = list(mutations.index)\n",
" index_items = list(mutations.index)\n",
"\n",
" for i in range(len(index_items)):\n",
" #result = re.sub(r'BC::', \"CA::\", items[i])\n",
" renamed_items[i] = index_items[i].replace('Ca', 'CA')\n",
" \n",
" mutations.rename(index=dict(zip(index_items, renamed_items)), inplace = True)\n",
" \n",
" # Normalize \n",
" row_sums = mutations.sum(axis=1)\n",
" mutations = mutations.divide(row_sums, axis = 0)\n",
"\n",
" # Figure out tumor types based on the first part of the index\n",
" tumor_types = [a.split(':')[0] for a in mutations.index]\n",
"\n",
" # Attach this back to the frame\n",
" mutations[\"tumor_types\"] = tumor_types\n",
"\n",
" # Prepare a list with all the types appearing only once\n",
" unique_tumor_types = sorted(list(set(tumor_types)))\n",
" return (mutations, unique_tumor_types)\n",
"\n",
"mutations_TCGA, tumor_t_TCGA = prepare_mutation_df(TCGA_wes_mut)\n",
"mutations_PCWGA, tumor_t_PCWGA = prepare_mutation_df(PCAWG_wgs_mut)\n",
"mutations_all = pd.concat([mutations_TCGA, mutations_PCWGA])\n",
"tumor_t_all = list(sorted(set(tumor_t_TCGA + tumor_t_PCWGA)))\n",
"\n",
"# Check if the data frame is ok\n",
"print(\"Missing entries in all mutations:\", mutations_all.isnull().sum().sum())\n",
"print(\"The shape of the all mutations data frame\", mutations_all.shape)\n",
"\n",
"# Check to see if the rows are normalized to one\n",
"print(\"Checking normalization: sum of some rows:\\n\", mutations_all.iloc[:,0:-1].sum(axis=1).head(5))\n",
"\n",
"# Check some counts of tumor types\n",
"print(\"Some tumor counts:\\n\", mutations_all[\"tumor_types\"].value_counts().head(5))\n",
"\n",
"# Tumor types\n",
"print(tumor_t_all)\n",
"mutations_all.head(5)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Check data content"
]
},
{
"cell_type": "code",
"execution_count": 70,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"<Figure size 720x864 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots(3,1)\n",
"fig.set_size_inches(10,12)\n",
"\n",
"sns.countplot(x=mutations_TCGA[\"tumor_types\"], \n",
" palette=sns.hls_palette(2),\n",
" ax=ax[0])\n",
"ax[0].tick_params(axis=\"x\", rotation=90)\n",
"\n",
"sns.countplot(x=mutations_PCWGA[\"tumor_types\"], \n",
" palette=sns.hls_palette(2),\n",
" ax=ax[1])\n",
"ax[1].tick_params(axis=\"x\", rotation=90)\n",
"\n",
"sns.countplot(x=mutations_all[\"tumor_types\"], \n",
" palette=sns.hls_palette(2),\n",
" ax=ax[2])\n",
"ax[2].tick_params(axis=\"x\", rotation=90)\n",
"\n",
"fig.tight_layout()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From the plots above, we see that at the moment there are tumor types with not that big sample size. Some combining of them may have to be done later."
]
},
{
"cell_type": "code",
"execution_count": 72,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"target AML\n",
"(8591, 96) (3682, 96)\n"
]
}
],
"source": [
"target_type = list(tumor_t_all)[0]\n",
"print(\"target\", target_type)\n",
"\n",
"X = mutations_all.drop(\"tumor_types\", axis=1)\n",
"wgs_labels = [1 if tumor_type == target_type else 0 for tumor_type in mutations_all[\"tumor_types\"]]\n",
"X_train, X_test, y_train, y_test = train_test_split(X, wgs_labels, test_size = 0.3, random_state=1)\n",
"\n",
"print(X_train.shape, X_test.shape)\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Testing with a single RandomForest classifier"
]
},
{
"cell_type": "code",
"execution_count": 73,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"0.9921238457360131\n"
]
},
{
"data": {
"text/plain": [
"(0.0, 2.0)"
]
},
"execution_count": 73,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 288x216 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"model = RandomForestClassifier(n_estimators=100, random_state=0)\n",
"model.fit(X_train, y_train)\n",
"y_model = model.predict(X_test)\n",
"print(accuracy_score(y_test, y_model))\n",
"\n",
"cm = sklearn.metrics.confusion_matrix(y_model, y_test)\n",
"plt.figure(figsize=(4,3))\n",
"sns.heatmap(cm, annot=True, fmt='d', cmap='viridis')\n",
"plt.xlabel('Predicted')\n",
"plt.ylabel('True')\n",
"plt.ylim(0, 2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Multiple models experiment"
]
},
{
"cell_type": "code",
"execution_count": 74,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Accuracy score for AML: 0.9921238457360131\n",
"Accuracy score for Adrenal-neoplasm: 0.9926670287887018\n",
"Accuracy score for Biliary-AdenoCA: 0.9926670287887018\n",
"Accuracy score for Bladder-TCC: 0.9983704508419338\n",
"Accuracy score for Bone-Benign: 0.9986420423682781\n",
"Accuracy score for Bone-Epith: 0.9986420423682781\n",
"Accuracy score for Bone-Osteosarc: 0.9970124932102118\n",
"Accuracy score for Breast-AdenoCA: 0.9869636067354699\n",
"Accuracy score for Breast-DCIS: 1.0\n",
"Accuracy score for Breast-LobularCA: 0.9994568169473113\n",
"Accuracy score for Breast-cancer: 0.9220532319391636\n",
"Accuracy score for CNS-GBM: 0.9703965236284627\n",
"Accuracy score for CNS-Medullo: 0.9880499728408474\n",
"Accuracy score for CNS-Oligo: 0.9991852254209669\n",
"Accuracy score for CNS-PiloAstro: 0.993481803367735\n",
"Accuracy score for Cervix-AdenoCA: 0.9997284084736556\n",
"Accuracy score for Cervix-CA: 0.9796306355241716\n",
"Accuracy score for Cervix-SCC: 0.997827267789245\n",
"Accuracy score for ColoRect-AdenoCA: 0.9617055947854427\n",
"Accuracy score for DLBC: 0.9573601303639326\n",
"Accuracy score for Eso-AdenoCA: 0.9809885931558935\n",
"Accuracy score for Eye-Melanoma: 0.9929386203150462\n",
"Accuracy score for Head-SCC: 0.9568169473112439\n",
"Accuracy score for Kidney-ChRCC: 0.993481803367735\n",
"Accuracy score for Kidney-Papillary: 0.9785442694187941\n",
"Accuracy score for Kidney-RCC: 0.9720260727865291\n",
"Accuracy score for Liver-HCC: 0.973384030418251\n",
"Accuracy score for Lung-AdenoCA: 0.9630635524171646\n",
"Accuracy score for Lung-SCC: 0.9695817490494296\n",
"Accuracy score for Lymph-BNHL: 0.9940249864204237\n",
"Accuracy score for Lymph-CLL: 0.9915806626833242\n",
"Accuracy score for Mesothelium-Mesothelioma: 0.9926670287887018\n",
"Accuracy score for Myeloid-AML: 1.0\n",
"Accuracy score for Myeloid-MDS: 0.9997284084736556\n",
"Accuracy score for Myeloid-MPN: 0.996469310157523\n",
"Accuracy score for Ovary-AdenoCA: 0.9606192286800652\n",
"Accuracy score for Panc-AdenoCA: 0.9785442694187941\n",
"Accuracy score for Panc-Endocrine: 0.9940249864204237\n",
"Accuracy score for Pheochromocytoma: 0.9880499728408474\n",
"Accuracy score for Prost-AdenoCA: 0.9519282998370451\n",
"Accuracy score for Sarcoma: 0.9826181423139598\n",
"Accuracy score for Skin-Melanoma: 0.9923954372623575\n",
"Accuracy score for SoftTissue-Leiomyo: 0.9994568169473113\n",
"Accuracy score for SoftTissue-Liposarc: 0.9989136338946225\n",
"Accuracy score for Stomach-AdenoCA: 0.965507876154264\n",
"Accuracy score for Testis-CA: 0.9894079304725693\n",
"Accuracy score for Thy-AdenoCA: 0.9714828897338403\n",
"Accuracy score for Thymoma: 0.9885931558935361\n",
"Accuracy score for Transitional-cell-carcinoma: 0.9646931015752308\n",
"Accuracy score for UCS: 0.9945681694731124\n",
"Accuracy score for Uterus-AdenoCA: 0.9627919608908202\n"
]
}
],
"source": [
"for tgt_tumor_type in tumor_t_all:\n",
" wgs_labels = [1 if tumor_type == tgt_tumor_type else 0 for tumor_type in mutations_all[\"tumor_types\"]]\n",
" X_train, X_test, y_train, y_test = train_test_split(X, wgs_labels, test_size = 0.3, random_state=1)\n",
" model = RandomForestClassifier(n_estimators=100, random_state=0)\n",
" model.fit(X_train, y_train)\n",
" y_model = model.predict(X_test)\n",
" print(f\"Accuracy score for {tgt_tumor_type}: {accuracy_score(y_test, y_model)}\")\n"
]
}
],
"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": "3.9.5"
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": true,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 2
}