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b/notebooks/Evaluation-KIRC.ipynb |
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"cells": [ |
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{ |
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"cell_type": "code", |
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"execution_count": 66, |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"from core.utils_data import getCleanKIRC\n", |
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"from core.utils_analysis import *\n", |
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"from tqdm import tqdm" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"metadata": {}, |
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"source": [ |
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"### CCRCC Survival Outcome Prediction" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 64, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"text/html": [ |
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"<div>\n", |
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" vertical-align: middle;\n", |
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" }\n", |
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"\n", |
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" .dataframe tbody tr th {\n", |
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" vertical-align: top;\n", |
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" }\n", |
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"\n", |
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" .dataframe thead th {\n", |
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" text-align: right;\n", |
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" }\n", |
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"</style>\n", |
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"<table border=\"1\" class=\"dataframe\">\n", |
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" <thead>\n", |
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" <tr style=\"text-align: right;\">\n", |
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" <th></th>\n", |
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" <th>C-Index</th>\n", |
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" <th>P-Value (<50% vs. >50%)</th>\n", |
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" <th>P-Value (<25% vs. 25-50%)</th>\n", |
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" <th>P-Value (25-50% vs. 50-75%)</th>\n", |
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" <th>P-Value (50-75% vs. >75%)</th>\n", |
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" </tr>\n", |
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" </thead>\n", |
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" <tbody>\n", |
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" <tr>\n", |
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" <th>Cox (Age+Gender)</th>\n", |
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" <td>0.6300 ± 0.024</td>\n", |
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" <td>1.268123e-16</td>\n", |
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" <td>1.081791e-01</td>\n", |
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" <td>0.000012</td>\n", |
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" <td>3.595540e-01</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Cox (Grade)</th>\n", |
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" <td>0.6754 ± 0.036</td>\n", |
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" <td>4.418806e-17</td>\n", |
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" <td>1.249698e-07</td>\n", |
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" <td>0.000452</td>\n", |
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" <td>5.130931e-01</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Genomic SNN</th>\n", |
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" <td>0.6831 ± 0.026</td>\n", |
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" <td>2.773766e-20</td>\n", |
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" <td>3.264822e-01</td>\n", |
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" <td>0.002451</td>\n", |
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" <td>2.906279e-15</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Genomic (SNN + SNN)</th>\n", |
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" <td>0.6783 ± 0.027</td>\n", |
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" <td>4.605888e-19</td>\n", |
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" <td>4.157303e-01</td>\n", |
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" <td>0.022354</td>\n", |
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" <td>4.342929e-15</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Histology GCN</th>\n", |
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" <td>0.6482 ± 0.031</td>\n", |
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" <td>4.227995e-02</td>\n", |
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" <td>1.184032e-02</td>\n", |
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" <td>0.651703</td>\n", |
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" <td>1.439640e-01</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Histology (GCN + GCN)</th>\n", |
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" <td>0.6431 ± 0.027</td>\n", |
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" <td>1.454192e-02</td>\n", |
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" <td>8.767505e-02</td>\n", |
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" <td>0.467088</td>\n", |
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" <td>5.067014e-01</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Histology CNN</th>\n", |
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" <td>0.6706 ± 0.023</td>\n", |
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" <td>3.870304e-16</td>\n", |
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" <td>4.813033e-01</td>\n", |
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" <td>0.000174</td>\n", |
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" <td>4.194263e-04</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Histology (CNN + CNN)</th>\n", |
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" <td>0.6709 ± 0.023</td>\n", |
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" <td>3.449451e-14</td>\n", |
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" <td>2.007879e-01</td>\n", |
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" <td>0.001571</td>\n", |
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" <td>8.699393e-04</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Pathomic F. (CNN+SNN)</th>\n", |
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" <td>0.7140 ± 0.029</td>\n", |
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" <td>2.398952e-26</td>\n", |
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" <td>7.111202e-01</td>\n", |
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" <td>0.000061</td>\n", |
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" <td>2.776982e-12</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Pathomic F. (GCN+SNN)</th>\n", |
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" <td>0.6853 ± 0.024</td>\n", |
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" <td>8.841046e-19</td>\n", |
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" <td>4.800574e-01</td>\n", |
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" <td>0.012675</td>\n", |
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" <td>7.066381e-16</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Pathomic F. (CNN+GCN+SNN)</th>\n", |
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" <td>0.7147 ± 0.027</td>\n", |
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" <td>1.872293e-22</td>\n", |
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" <td>1.384285e-01</td>\n", |
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" <td>0.019029</td>\n", |
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" <td>4.229939e-15</td>\n", |
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" </tr>\n", |
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" </tbody>\n", |
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"</table>\n", |
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"</div>" |
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], |
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"text/plain": [ |
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" C-Index P-Value (<50% vs. >50%) \\\n", |
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"Cox (Age+Gender) 0.6300 ± 0.024 1.268123e-16 \n", |
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"Cox (Grade) 0.6754 ± 0.036 4.418806e-17 \n", |
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"Genomic SNN 0.6831 ± 0.026 2.773766e-20 \n", |
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"Genomic (SNN + SNN) 0.6783 ± 0.027 4.605888e-19 \n", |
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"Histology GCN 0.6482 ± 0.031 4.227995e-02 \n", |
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"Histology (GCN + GCN) 0.6431 ± 0.027 1.454192e-02 \n", |
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"Histology CNN 0.6706 ± 0.023 3.870304e-16 \n", |
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"Histology (CNN + CNN) 0.6709 ± 0.023 3.449451e-14 \n", |
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"Pathomic F. (CNN+SNN) 0.7140 ± 0.029 2.398952e-26 \n", |
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"Pathomic F. (GCN+SNN) 0.6853 ± 0.024 8.841046e-19 \n", |
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"Pathomic F. (CNN+GCN+SNN) 0.7147 ± 0.027 1.872293e-22 \n", |
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"\n", |
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" P-Value (<25% vs. 25-50%) \\\n", |
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"Cox (Age+Gender) 1.081791e-01 \n", |
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"Cox (Grade) 1.249698e-07 \n", |
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"Genomic SNN 3.264822e-01 \n", |
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"Genomic (SNN + SNN) 4.157303e-01 \n", |
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"Histology GCN 1.184032e-02 \n", |
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"Histology (GCN + GCN) 8.767505e-02 \n", |
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"Histology CNN 4.813033e-01 \n", |
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"Histology (CNN + CNN) 2.007879e-01 \n", |
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"Pathomic F. (CNN+SNN) 7.111202e-01 \n", |
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"Pathomic F. (GCN+SNN) 4.800574e-01 \n", |
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"Pathomic F. (CNN+GCN+SNN) 1.384285e-01 \n", |
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"\n", |
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" P-Value (25-50% vs. 50-75%) \\\n", |
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"Cox (Age+Gender) 0.000012 \n", |
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"Cox (Grade) 0.000452 \n", |
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"Genomic SNN 0.002451 \n", |
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"Genomic (SNN + SNN) 0.022354 \n", |
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"Histology GCN 0.651703 \n", |
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"Histology (GCN + GCN) 0.467088 \n", |
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"Histology CNN 0.000174 \n", |
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"Histology (CNN + CNN) 0.001571 \n", |
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"Pathomic F. (CNN+SNN) 0.000061 \n", |
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"Pathomic F. (GCN+SNN) 0.012675 \n", |
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"Pathomic F. (CNN+GCN+SNN) 0.019029 \n", |
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"\n", |
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" P-Value (50-75% vs. >75%) \n", |
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"Cox (Age+Gender) 3.595540e-01 \n", |
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"Cox (Grade) 5.130931e-01 \n", |
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"Genomic SNN 2.906279e-15 \n", |
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"Genomic (SNN + SNN) 4.342929e-15 \n", |
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"Histology GCN 1.439640e-01 \n", |
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"Histology (GCN + GCN) 5.067014e-01 \n", |
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"Histology CNN 4.194263e-04 \n", |
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"Histology (CNN + CNN) 8.699393e-04 \n", |
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"Pathomic F. (CNN+SNN) 2.776982e-12 \n", |
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"Pathomic F. (GCN+SNN) 7.066381e-16 \n", |
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"Pathomic F. (CNN+GCN+SNN) 4.229939e-15 " |
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] |
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}, |
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"execution_count": 64, |
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"metadata": {}, |
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"output_type": "execute_result" |
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} |
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], |
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"source": [ |
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"cv_surv_all" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": 61, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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" <thead>\n", |
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" <tr style=\"text-align: right;\">\n", |
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" <th></th>\n", |
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" <th>C-Index</th>\n", |
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" <th>P-Value (<50% vs. >50%)</th>\n", |
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" <th>P-Value (<25% vs. 25-50%)</th>\n", |
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" <th>P-Value (25-50% vs. 50-75%)</th>\n", |
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" <th>P-Value (50-75% vs. >75%)</th>\n", |
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" </tr>\n", |
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" </thead>\n", |
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" <tbody>\n", |
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" <tr>\n", |
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" <th>Cox (Age+Gender)</th>\n", |
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" <td>0.6300 ± 0.024</td>\n", |
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" <td>1.268123e-16</td>\n", |
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" <td>1.081791e-01</td>\n", |
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" <td>0.000012</td>\n", |
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" <td>3.595540e-01</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Cox (Grade)</th>\n", |
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" <td>0.6754 ± 0.036</td>\n", |
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" <td>4.418806e-17</td>\n", |
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" <td>1.249698e-07</td>\n", |
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" <td>0.000452</td>\n", |
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" <td>5.130931e-01</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Genomic SNN</th>\n", |
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" <td>0.6843 ± 0.025</td>\n", |
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" <td>2.896933e-18</td>\n", |
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" <td>6.182972e-01</td>\n", |
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" <td>0.015148</td>\n", |
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" <td>3.034575e-15</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Genomic (SNN + SNN)</th>\n", |
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" <td>0.6783 ± 0.027</td>\n", |
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" <td>4.605888e-19</td>\n", |
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" <td>4.157303e-01</td>\n", |
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" <td>0.022354</td>\n", |
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" <td>4.342929e-15</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Histology GCN</th>\n", |
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" <td>0.6482 ± 0.031</td>\n", |
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" <td>4.227995e-02</td>\n", |
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280 |
" <td>1.184032e-02</td>\n", |
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" <td>0.651703</td>\n", |
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" <td>1.439640e-01</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Histology (GCN + GCN)</th>\n", |
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" <td>0.6431 ± 0.027</td>\n", |
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|
287 |
" <td>1.454192e-02</td>\n", |
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|
288 |
" <td>8.767505e-02</td>\n", |
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|
289 |
" <td>0.467088</td>\n", |
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" <td>5.067014e-01</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Histology CNN</th>\n", |
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" <td>0.6706 ± 0.023</td>\n", |
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295 |
" <td>3.870304e-16</td>\n", |
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296 |
" <td>4.813033e-01</td>\n", |
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" <td>0.000174</td>\n", |
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" <td>4.194263e-04</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Histology (CNN + CNN)</th>\n", |
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" <td>0.6709 ± 0.023</td>\n", |
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303 |
" <td>3.449451e-14</td>\n", |
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304 |
" <td>2.007879e-01</td>\n", |
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" <td>0.001571</td>\n", |
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" <td>8.699393e-04</td>\n", |
|
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307 |
" </tr>\n", |
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" <tr>\n", |
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" <th>Pathomic F. (CNN+SNN)</th>\n", |
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" <td>0.7188 ± 0.031</td>\n", |
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" <td>1.106133e-27</td>\n", |
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" <td>7.720058e-01</td>\n", |
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" <td>0.000007</td>\n", |
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" <td>5.768449e-12</td>\n", |
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" </tr>\n", |
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" <tr>\n", |
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" <th>Pathomic F. (GCN+SNN)</th>\n", |
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" <td>0.6853 ± 0.024</td>\n", |
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" <td>8.841046e-19</td>\n", |
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" <td>4.800574e-01</td>\n", |
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" <td>0.012675</td>\n", |
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" <td>7.066381e-16</td>\n", |
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|
323 |
" </tr>\n", |
|
|
324 |
" <tr>\n", |
|
|
325 |
" <th>Pathomic F. (CNN+GCN+SNN)</th>\n", |
|
|
326 |
" <td>0.7203 ± 0.028</td>\n", |
|
|
327 |
" <td>2.477594e-24</td>\n", |
|
|
328 |
" <td>8.655726e-02</td>\n", |
|
|
329 |
" <td>0.009373</td>\n", |
|
|
330 |
" <td>1.078824e-14</td>\n", |
|
|
331 |
" </tr>\n", |
|
|
332 |
" </tbody>\n", |
|
|
333 |
"</table>\n", |
|
|
334 |
"</div>" |
|
|
335 |
], |
|
|
336 |
"text/plain": [ |
|
|
337 |
" C-Index P-Value (<50% vs. >50%) \\\n", |
|
|
338 |
"Cox (Age+Gender) 0.6300 ± 0.024 1.268123e-16 \n", |
|
|
339 |
"Cox (Grade) 0.6754 ± 0.036 4.418806e-17 \n", |
|
|
340 |
"Genomic SNN 0.6843 ± 0.025 2.896933e-18 \n", |
|
|
341 |
"Genomic (SNN + SNN) 0.6783 ± 0.027 4.605888e-19 \n", |
|
|
342 |
"Histology GCN 0.6482 ± 0.031 4.227995e-02 \n", |
|
|
343 |
"Histology (GCN + GCN) 0.6431 ± 0.027 1.454192e-02 \n", |
|
|
344 |
"Histology CNN 0.6706 ± 0.023 3.870304e-16 \n", |
|
|
345 |
"Histology (CNN + CNN) 0.6709 ± 0.023 3.449451e-14 \n", |
|
|
346 |
"Pathomic F. (CNN+SNN) 0.7188 ± 0.031 1.106133e-27 \n", |
|
|
347 |
"Pathomic F. (GCN+SNN) 0.6853 ± 0.024 8.841046e-19 \n", |
|
|
348 |
"Pathomic F. (CNN+GCN+SNN) 0.7203 ± 0.028 2.477594e-24 \n", |
|
|
349 |
"\n", |
|
|
350 |
" P-Value (<25% vs. 25-50%) \\\n", |
|
|
351 |
"Cox (Age+Gender) 1.081791e-01 \n", |
|
|
352 |
"Cox (Grade) 1.249698e-07 \n", |
|
|
353 |
"Genomic SNN 6.182972e-01 \n", |
|
|
354 |
"Genomic (SNN + SNN) 4.157303e-01 \n", |
|
|
355 |
"Histology GCN 1.184032e-02 \n", |
|
|
356 |
"Histology (GCN + GCN) 8.767505e-02 \n", |
|
|
357 |
"Histology CNN 4.813033e-01 \n", |
|
|
358 |
"Histology (CNN + CNN) 2.007879e-01 \n", |
|
|
359 |
"Pathomic F. (CNN+SNN) 7.720058e-01 \n", |
|
|
360 |
"Pathomic F. (GCN+SNN) 4.800574e-01 \n", |
|
|
361 |
"Pathomic F. (CNN+GCN+SNN) 8.655726e-02 \n", |
|
|
362 |
"\n", |
|
|
363 |
" P-Value (25-50% vs. 50-75%) \\\n", |
|
|
364 |
"Cox (Age+Gender) 0.000012 \n", |
|
|
365 |
"Cox (Grade) 0.000452 \n", |
|
|
366 |
"Genomic SNN 0.015148 \n", |
|
|
367 |
"Genomic (SNN + SNN) 0.022354 \n", |
|
|
368 |
"Histology GCN 0.651703 \n", |
|
|
369 |
"Histology (GCN + GCN) 0.467088 \n", |
|
|
370 |
"Histology CNN 0.000174 \n", |
|
|
371 |
"Histology (CNN + CNN) 0.001571 \n", |
|
|
372 |
"Pathomic F. (CNN+SNN) 0.000007 \n", |
|
|
373 |
"Pathomic F. (GCN+SNN) 0.012675 \n", |
|
|
374 |
"Pathomic F. (CNN+GCN+SNN) 0.009373 \n", |
|
|
375 |
"\n", |
|
|
376 |
" P-Value (50-75% vs. >75%) \n", |
|
|
377 |
"Cox (Age+Gender) 3.595540e-01 \n", |
|
|
378 |
"Cox (Grade) 5.130931e-01 \n", |
|
|
379 |
"Genomic SNN 3.034575e-15 \n", |
|
|
380 |
"Genomic (SNN + SNN) 4.342929e-15 \n", |
|
|
381 |
"Histology GCN 1.439640e-01 \n", |
|
|
382 |
"Histology (GCN + GCN) 5.067014e-01 \n", |
|
|
383 |
"Histology CNN 4.194263e-04 \n", |
|
|
384 |
"Histology (CNN + CNN) 8.699393e-04 \n", |
|
|
385 |
"Pathomic F. (CNN+SNN) 5.768449e-12 \n", |
|
|
386 |
"Pathomic F. (GCN+SNN) 7.066381e-16 \n", |
|
|
387 |
"Pathomic F. (CNN+GCN+SNN) 1.078824e-14 " |
|
|
388 |
] |
|
|
389 |
}, |
|
|
390 |
"execution_count": 61, |
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|
391 |
"metadata": {}, |
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392 |
"output_type": "execute_result" |
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393 |
} |
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394 |
], |
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395 |
"source": [ |
|
|
396 |
"cv_surv_all" |
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|
397 |
] |
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|
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}, |
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399 |
{ |
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"cell_type": "code", |
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"execution_count": 62, |
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"metadata": {}, |
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"outputs": [ |
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{ |
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"data": { |
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"<table border=\"1\" class=\"dataframe\">\n", |
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" <thead>\n", |
|
|
423 |
" <tr style=\"text-align: right;\">\n", |
|
|
424 |
" <th></th>\n", |
|
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425 |
" <th>Split 1</th>\n", |
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426 |
" <th>Split 2</th>\n", |
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427 |
" <th>Split 3</th>\n", |
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428 |
" <th>Split 4</th>\n", |
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" <th>Split 5</th>\n", |
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" <th>Split 6</th>\n", |
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" <th>Split 7</th>\n", |
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" <th>Split 8</th>\n", |
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" <th>Split 9</th>\n", |
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" <th>Split 10</th>\n", |
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" <th>Split 11</th>\n", |
|
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436 |
" <th>Split 12</th>\n", |
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437 |
" <th>Split 13</th>\n", |
|
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438 |
" <th>Split 14</th>\n", |
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439 |
" <th>Split 15</th>\n", |
|
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440 |
" <th>C-Index</th>\n", |
|
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441 |
" </tr>\n", |
|
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442 |
" </thead>\n", |
|
|
443 |
" <tbody>\n", |
|
|
444 |
" <tr>\n", |
|
|
445 |
" <th>Cox (Age+Gender)</th>\n", |
|
|
446 |
" <td>0.649545</td>\n", |
|
|
447 |
" <td>0.620815</td>\n", |
|
|
448 |
" <td>0.671352</td>\n", |
|
|
449 |
" <td>0.630187</td>\n", |
|
|
450 |
" <td>0.637427</td>\n", |
|
|
451 |
" <td>0.675896</td>\n", |
|
|
452 |
" <td>0.605225</td>\n", |
|
|
453 |
" <td>0.615120</td>\n", |
|
|
454 |
" <td>0.672687</td>\n", |
|
|
455 |
" <td>0.553805</td>\n", |
|
|
456 |
" <td>0.551000</td>\n", |
|
|
457 |
" <td>0.581734</td>\n", |
|
|
458 |
" <td>0.640683</td>\n", |
|
|
459 |
" <td>0.654119</td>\n", |
|
|
460 |
" <td>0.690837</td>\n", |
|
|
461 |
" <td>0.6300 ± 0.024</td>\n", |
|
|
462 |
" </tr>\n", |
|
|
463 |
" <tr>\n", |
|
|
464 |
" <th>Cox (Grade)</th>\n", |
|
|
465 |
" <td>0.650909</td>\n", |
|
|
466 |
" <td>0.665575</td>\n", |
|
|
467 |
" <td>0.727577</td>\n", |
|
|
468 |
" <td>0.696501</td>\n", |
|
|
469 |
" <td>0.667032</td>\n", |
|
|
470 |
" <td>0.660586</td>\n", |
|
|
471 |
" <td>0.772369</td>\n", |
|
|
472 |
" <td>0.807131</td>\n", |
|
|
473 |
" <td>0.602643</td>\n", |
|
|
474 |
" <td>0.646810</td>\n", |
|
|
475 |
" <td>0.554000</td>\n", |
|
|
476 |
" <td>0.655295</td>\n", |
|
|
477 |
" <td>0.749443</td>\n", |
|
|
478 |
" <td>0.627841</td>\n", |
|
|
479 |
" <td>0.647809</td>\n", |
|
|
480 |
" <td>0.6754 ± 0.036</td>\n", |
|
|
481 |
" </tr>\n", |
|
|
482 |
" <tr>\n", |
|
|
483 |
" <th>Genomic SNN</th>\n", |
|
|
484 |
" <td>0.720909</td>\n", |
|
|
485 |
" <td>0.652111</td>\n", |
|
|
486 |
" <td>0.597055</td>\n", |
|
|
487 |
" <td>0.654190</td>\n", |
|
|
488 |
" <td>0.695175</td>\n", |
|
|
489 |
" <td>0.730945</td>\n", |
|
|
490 |
" <td>0.664996</td>\n", |
|
|
491 |
" <td>0.757302</td>\n", |
|
|
492 |
" <td>0.622467</td>\n", |
|
|
493 |
" <td>0.692929</td>\n", |
|
|
494 |
" <td>0.654667</td>\n", |
|
|
495 |
" <td>0.737029</td>\n", |
|
|
496 |
" <td>0.731997</td>\n", |
|
|
497 |
" <td>0.662997</td>\n", |
|
|
498 |
" <td>0.689243</td>\n", |
|
|
499 |
" <td>0.6843 ± 0.025</td>\n", |
|
|
500 |
" </tr>\n", |
|
|
501 |
" <tr>\n", |
|
|
502 |
" <th>Genomic (SNN + SNN)</th>\n", |
|
|
503 |
" <td>0.683182</td>\n", |
|
|
504 |
" <td>0.645197</td>\n", |
|
|
505 |
" <td>0.574967</td>\n", |
|
|
506 |
" <td>0.648495</td>\n", |
|
|
507 |
" <td>0.702485</td>\n", |
|
|
508 |
" <td>0.725733</td>\n", |
|
|
509 |
" <td>0.691482</td>\n", |
|
|
510 |
" <td>0.752148</td>\n", |
|
|
511 |
" <td>0.609692</td>\n", |
|
|
512 |
" <td>0.699462</td>\n", |
|
|
513 |
" <td>0.648333</td>\n", |
|
|
514 |
" <td>0.745203</td>\n", |
|
|
515 |
" <td>0.712324</td>\n", |
|
|
516 |
" <td>0.644176</td>\n", |
|
|
517 |
" <td>0.692032</td>\n", |
|
|
518 |
" <td>0.6783 ± 0.027</td>\n", |
|
|
519 |
" </tr>\n", |
|
|
520 |
" <tr>\n", |
|
|
521 |
" <th>Histology GCN</th>\n", |
|
|
522 |
" <td>0.646364</td>\n", |
|
|
523 |
" <td>0.637555</td>\n", |
|
|
524 |
" <td>0.625837</td>\n", |
|
|
525 |
" <td>0.652563</td>\n", |
|
|
526 |
" <td>0.712719</td>\n", |
|
|
527 |
" <td>0.632573</td>\n", |
|
|
528 |
" <td>0.613457</td>\n", |
|
|
529 |
" <td>0.744845</td>\n", |
|
|
530 |
" <td>0.691630</td>\n", |
|
|
531 |
" <td>0.630284</td>\n", |
|
|
532 |
" <td>0.526000</td>\n", |
|
|
533 |
" <td>0.735608</td>\n", |
|
|
534 |
" <td>0.642910</td>\n", |
|
|
535 |
" <td>0.597301</td>\n", |
|
|
536 |
" <td>0.633466</td>\n", |
|
|
537 |
" <td>0.6482 ± 0.031</td>\n", |
|
|
538 |
" </tr>\n", |
|
|
539 |
" <tr>\n", |
|
|
540 |
" <th>Histology (GCN + GCN)</th>\n", |
|
|
541 |
" <td>0.682727</td>\n", |
|
|
542 |
" <td>0.588064</td>\n", |
|
|
543 |
" <td>0.621821</td>\n", |
|
|
544 |
" <td>0.644426</td>\n", |
|
|
545 |
" <td>0.684211</td>\n", |
|
|
546 |
" <td>0.631270</td>\n", |
|
|
547 |
" <td>0.617752</td>\n", |
|
|
548 |
" <td>0.721649</td>\n", |
|
|
549 |
" <td>0.651101</td>\n", |
|
|
550 |
" <td>0.641814</td>\n", |
|
|
551 |
" <td>0.562667</td>\n", |
|
|
552 |
" <td>0.737029</td>\n", |
|
|
553 |
" <td>0.655531</td>\n", |
|
|
554 |
" <td>0.584517</td>\n", |
|
|
555 |
" <td>0.622311</td>\n", |
|
|
556 |
" <td>0.6431 ± 0.027</td>\n", |
|
|
557 |
" </tr>\n", |
|
|
558 |
" <tr>\n", |
|
|
559 |
" <th>Histology CNN</th>\n", |
|
|
560 |
" <td>0.712727</td>\n", |
|
|
561 |
" <td>0.692868</td>\n", |
|
|
562 |
" <td>0.653949</td>\n", |
|
|
563 |
" <td>0.681855</td>\n", |
|
|
564 |
" <td>0.692982</td>\n", |
|
|
565 |
" <td>0.699674</td>\n", |
|
|
566 |
" <td>0.599857</td>\n", |
|
|
567 |
" <td>0.760309</td>\n", |
|
|
568 |
" <td>0.682819</td>\n", |
|
|
569 |
" <td>0.681783</td>\n", |
|
|
570 |
" <td>0.640667</td>\n", |
|
|
571 |
" <td>0.667377</td>\n", |
|
|
572 |
" <td>0.644395</td>\n", |
|
|
573 |
" <td>0.607244</td>\n", |
|
|
574 |
" <td>0.640637</td>\n", |
|
|
575 |
" <td>0.6706 ± 0.023</td>\n", |
|
|
576 |
" </tr>\n", |
|
|
577 |
" <tr>\n", |
|
|
578 |
" <th>Histology (CNN + CNN)</th>\n", |
|
|
579 |
" <td>0.714545</td>\n", |
|
|
580 |
" <td>0.688501</td>\n", |
|
|
581 |
" <td>0.654618</td>\n", |
|
|
582 |
" <td>0.682669</td>\n", |
|
|
583 |
" <td>0.681287</td>\n", |
|
|
584 |
" <td>0.700977</td>\n", |
|
|
585 |
" <td>0.610594</td>\n", |
|
|
586 |
" <td>0.768900</td>\n", |
|
|
587 |
" <td>0.680176</td>\n", |
|
|
588 |
" <td>0.684858</td>\n", |
|
|
589 |
" <td>0.640000</td>\n", |
|
|
590 |
" <td>0.670220</td>\n", |
|
|
591 |
" <td>0.639941</td>\n", |
|
|
592 |
" <td>0.599432</td>\n", |
|
|
593 |
" <td>0.647012</td>\n", |
|
|
594 |
" <td>0.6709 ± 0.023</td>\n", |
|
|
595 |
" </tr>\n", |
|
|
596 |
" <tr>\n", |
|
|
597 |
" <th>Pathomic F. (CNN+SNN)</th>\n", |
|
|
598 |
" <td>0.781818</td>\n", |
|
|
599 |
" <td>0.680131</td>\n", |
|
|
600 |
" <td>0.652945</td>\n", |
|
|
601 |
" <td>0.734744</td>\n", |
|
|
602 |
" <td>0.726608</td>\n", |
|
|
603 |
" <td>0.771987</td>\n", |
|
|
604 |
" <td>0.632069</td>\n", |
|
|
605 |
" <td>0.819158</td>\n", |
|
|
606 |
" <td>0.646696</td>\n", |
|
|
607 |
" <td>0.755957</td>\n", |
|
|
608 |
" <td>0.698333</td>\n", |
|
|
609 |
" <td>0.767235</td>\n", |
|
|
610 |
" <td>0.746474</td>\n", |
|
|
611 |
" <td>0.653764</td>\n", |
|
|
612 |
" <td>0.714741</td>\n", |
|
|
613 |
" <td>0.7188 ± 0.031</td>\n", |
|
|
614 |
" </tr>\n", |
|
|
615 |
" <tr>\n", |
|
|
616 |
" <th>Pathomic F. (GCN+SNN)</th>\n", |
|
|
617 |
" <td>0.702273</td>\n", |
|
|
618 |
" <td>0.643741</td>\n", |
|
|
619 |
" <td>0.606091</td>\n", |
|
|
620 |
" <td>0.653377</td>\n", |
|
|
621 |
" <td>0.707237</td>\n", |
|
|
622 |
" <td>0.724104</td>\n", |
|
|
623 |
" <td>0.684681</td>\n", |
|
|
624 |
" <td>0.759450</td>\n", |
|
|
625 |
" <td>0.624229</td>\n", |
|
|
626 |
" <td>0.701384</td>\n", |
|
|
627 |
" <td>0.659000</td>\n", |
|
|
628 |
" <td>0.740583</td>\n", |
|
|
629 |
" <td>0.722717</td>\n", |
|
|
630 |
" <td>0.662642</td>\n", |
|
|
631 |
" <td>0.688048</td>\n", |
|
|
632 |
" <td>0.6853 ± 0.024</td>\n", |
|
|
633 |
" </tr>\n", |
|
|
634 |
" <tr>\n", |
|
|
635 |
" <th>Pathomic F. (CNN+GCN+SNN)</th>\n", |
|
|
636 |
" <td>0.781818</td>\n", |
|
|
637 |
" <td>0.671761</td>\n", |
|
|
638 |
" <td>0.667336</td>\n", |
|
|
639 |
" <td>0.736371</td>\n", |
|
|
640 |
" <td>0.730994</td>\n", |
|
|
641 |
" <td>0.773290</td>\n", |
|
|
642 |
" <td>0.659270</td>\n", |
|
|
643 |
" <td>0.819158</td>\n", |
|
|
644 |
" <td>0.665198</td>\n", |
|
|
645 |
" <td>0.724443</td>\n", |
|
|
646 |
" <td>0.679667</td>\n", |
|
|
647 |
" <td>0.764748</td>\n", |
|
|
648 |
" <td>0.745731</td>\n", |
|
|
649 |
" <td>0.663352</td>\n", |
|
|
650 |
" <td>0.721912</td>\n", |
|
|
651 |
" <td>0.7203 ± 0.028</td>\n", |
|
|
652 |
" </tr>\n", |
|
|
653 |
" </tbody>\n", |
|
|
654 |
"</table>\n", |
|
|
655 |
"</div>" |
|
|
656 |
], |
|
|
657 |
"text/plain": [ |
|
|
658 |
" Split 1 Split 2 Split 3 Split 4 Split 5 \\\n", |
|
|
659 |
"Cox (Age+Gender) 0.649545 0.620815 0.671352 0.630187 0.637427 \n", |
|
|
660 |
"Cox (Grade) 0.650909 0.665575 0.727577 0.696501 0.667032 \n", |
|
|
661 |
"Genomic SNN 0.720909 0.652111 0.597055 0.654190 0.695175 \n", |
|
|
662 |
"Genomic (SNN + SNN) 0.683182 0.645197 0.574967 0.648495 0.702485 \n", |
|
|
663 |
"Histology GCN 0.646364 0.637555 0.625837 0.652563 0.712719 \n", |
|
|
664 |
"Histology (GCN + GCN) 0.682727 0.588064 0.621821 0.644426 0.684211 \n", |
|
|
665 |
"Histology CNN 0.712727 0.692868 0.653949 0.681855 0.692982 \n", |
|
|
666 |
"Histology (CNN + CNN) 0.714545 0.688501 0.654618 0.682669 0.681287 \n", |
|
|
667 |
"Pathomic F. (CNN+SNN) 0.781818 0.680131 0.652945 0.734744 0.726608 \n", |
|
|
668 |
"Pathomic F. (GCN+SNN) 0.702273 0.643741 0.606091 0.653377 0.707237 \n", |
|
|
669 |
"Pathomic F. (CNN+GCN+SNN) 0.781818 0.671761 0.667336 0.736371 0.730994 \n", |
|
|
670 |
"\n", |
|
|
671 |
" Split 6 Split 7 Split 8 Split 9 Split 10 \\\n", |
|
|
672 |
"Cox (Age+Gender) 0.675896 0.605225 0.615120 0.672687 0.553805 \n", |
|
|
673 |
"Cox (Grade) 0.660586 0.772369 0.807131 0.602643 0.646810 \n", |
|
|
674 |
"Genomic SNN 0.730945 0.664996 0.757302 0.622467 0.692929 \n", |
|
|
675 |
"Genomic (SNN + SNN) 0.725733 0.691482 0.752148 0.609692 0.699462 \n", |
|
|
676 |
"Histology GCN 0.632573 0.613457 0.744845 0.691630 0.630284 \n", |
|
|
677 |
"Histology (GCN + GCN) 0.631270 0.617752 0.721649 0.651101 0.641814 \n", |
|
|
678 |
"Histology CNN 0.699674 0.599857 0.760309 0.682819 0.681783 \n", |
|
|
679 |
"Histology (CNN + CNN) 0.700977 0.610594 0.768900 0.680176 0.684858 \n", |
|
|
680 |
"Pathomic F. (CNN+SNN) 0.771987 0.632069 0.819158 0.646696 0.755957 \n", |
|
|
681 |
"Pathomic F. (GCN+SNN) 0.724104 0.684681 0.759450 0.624229 0.701384 \n", |
|
|
682 |
"Pathomic F. (CNN+GCN+SNN) 0.773290 0.659270 0.819158 0.665198 0.724443 \n", |
|
|
683 |
"\n", |
|
|
684 |
" Split 11 Split 12 Split 13 Split 14 Split 15 \\\n", |
|
|
685 |
"Cox (Age+Gender) 0.551000 0.581734 0.640683 0.654119 0.690837 \n", |
|
|
686 |
"Cox (Grade) 0.554000 0.655295 0.749443 0.627841 0.647809 \n", |
|
|
687 |
"Genomic SNN 0.654667 0.737029 0.731997 0.662997 0.689243 \n", |
|
|
688 |
"Genomic (SNN + SNN) 0.648333 0.745203 0.712324 0.644176 0.692032 \n", |
|
|
689 |
"Histology GCN 0.526000 0.735608 0.642910 0.597301 0.633466 \n", |
|
|
690 |
"Histology (GCN + GCN) 0.562667 0.737029 0.655531 0.584517 0.622311 \n", |
|
|
691 |
"Histology CNN 0.640667 0.667377 0.644395 0.607244 0.640637 \n", |
|
|
692 |
"Histology (CNN + CNN) 0.640000 0.670220 0.639941 0.599432 0.647012 \n", |
|
|
693 |
"Pathomic F. (CNN+SNN) 0.698333 0.767235 0.746474 0.653764 0.714741 \n", |
|
|
694 |
"Pathomic F. (GCN+SNN) 0.659000 0.740583 0.722717 0.662642 0.688048 \n", |
|
|
695 |
"Pathomic F. (CNN+GCN+SNN) 0.679667 0.764748 0.745731 0.663352 0.721912 \n", |
|
|
696 |
"\n", |
|
|
697 |
" C-Index \n", |
|
|
698 |
"Cox (Age+Gender) 0.6300 ± 0.024 \n", |
|
|
699 |
"Cox (Grade) 0.6754 ± 0.036 \n", |
|
|
700 |
"Genomic SNN 0.6843 ± 0.025 \n", |
|
|
701 |
"Genomic (SNN + SNN) 0.6783 ± 0.027 \n", |
|
|
702 |
"Histology GCN 0.6482 ± 0.031 \n", |
|
|
703 |
"Histology (GCN + GCN) 0.6431 ± 0.027 \n", |
|
|
704 |
"Histology CNN 0.6706 ± 0.023 \n", |
|
|
705 |
"Histology (CNN + CNN) 0.6709 ± 0.023 \n", |
|
|
706 |
"Pathomic F. (CNN+SNN) 0.7188 ± 0.031 \n", |
|
|
707 |
"Pathomic F. (GCN+SNN) 0.6853 ± 0.024 \n", |
|
|
708 |
"Pathomic F. (CNN+GCN+SNN) 0.7203 ± 0.028 " |
|
|
709 |
] |
|
|
710 |
}, |
|
|
711 |
"execution_count": 62, |
|
|
712 |
"metadata": {}, |
|
|
713 |
"output_type": "execute_result" |
|
|
714 |
} |
|
|
715 |
], |
|
|
716 |
"source": [ |
|
|
717 |
"cv_surv" |
|
|
718 |
] |
|
|
719 |
}, |
|
|
720 |
{ |
|
|
721 |
"cell_type": "code", |
|
|
722 |
"execution_count": 12, |
|
|
723 |
"metadata": {}, |
|
|
724 |
"outputs": [ |
|
|
725 |
{ |
|
|
726 |
"name": "stdout", |
|
|
727 |
"output_type": "stream", |
|
|
728 |
"text": [ |
|
|
729 |
"C-Indices across Splits [0.6495454545454545, 0.6208151382823872, 0.6713520749665328, 0.630187144019528, 0.6374269005847953, 0.6758957654723127, 0.6052254831782391, 0.6151202749140894, 0.6726872246696035, 0.5538047655649501, 0.551, 0.5817341862117982, 0.6406829992576095, 0.6541193181818182, 0.6908366533864542]\n", |
|
|
730 |
"Average C-Index: 0.6300 ± 0.024\n", |
|
|
731 |
"Average P-Value: 0.08402941642069023\n" |
|
|
732 |
] |
|
|
733 |
} |
|
|
734 |
], |
|
|
735 |
"source": [ |
|
|
736 |
"trainCox(model='cox_agegender', normalize=False)" |
|
|
737 |
] |
|
|
738 |
}, |
|
|
739 |
{ |
|
|
740 |
"cell_type": "code", |
|
|
741 |
"execution_count": 13, |
|
|
742 |
"metadata": {}, |
|
|
743 |
"outputs": [ |
|
|
744 |
{ |
|
|
745 |
"name": "stdout", |
|
|
746 |
"output_type": "stream", |
|
|
747 |
"text": [ |
|
|
748 |
"C-Indices across Splits [0.7277272727272728, 0.7318049490538574, 0.8048862115127176, 0.7339300244100895, 0.6933479532163743, 0.7039087947882736, 0.7827487473156765, 0.8213058419243986, 0.6770925110132159, 0.6821675634127594, 0.5776666666666667, 0.6677327647476902, 0.7717149220489977, 0.6732954545454546, 0.7115537848605578]\n", |
|
|
749 |
"Average C-Index: 0.7174 ± 0.034\n", |
|
|
750 |
"Average P-Value: 0.033767264760414105\n" |
|
|
751 |
] |
|
|
752 |
} |
|
|
753 |
], |
|
|
754 |
"source": [ |
|
|
755 |
"trainCox(model='cox_all', normalize=False)" |
|
|
756 |
] |
|
|
757 |
}, |
|
|
758 |
{ |
|
|
759 |
"cell_type": "code", |
|
|
760 |
"execution_count": 14, |
|
|
761 |
"metadata": {}, |
|
|
762 |
"outputs": [ |
|
|
763 |
{ |
|
|
764 |
"name": "stdout", |
|
|
765 |
"output_type": "stream", |
|
|
766 |
"text": [ |
|
|
767 |
"C-Indices across Splits [0.6509090909090909, 0.6655749636098981, 0.7275769745649264, 0.6965012205044752, 0.66703216374269, 0.6605863192182411, 0.772369362920544, 0.8071305841924399, 0.6026431718061674, 0.6468101460415066, 0.554, 0.6552949538024165, 0.7494432071269488, 0.6278409090909091, 0.6478087649402391]\n", |
|
|
768 |
"Average C-Index: 0.6754 ± 0.036\n", |
|
|
769 |
"Average P-Value: 0.18670589420802652\n" |
|
|
770 |
] |
|
|
771 |
} |
|
|
772 |
], |
|
|
773 |
"source": [ |
|
|
774 |
"trainCox(model='cox_grade', normalize=False)" |
|
|
775 |
] |
|
|
776 |
}, |
|
|
777 |
{ |
|
|
778 |
"cell_type": "code", |
|
|
779 |
"execution_count": 1, |
|
|
780 |
"metadata": {}, |
|
|
781 |
"outputs": [], |
|
|
782 |
"source": [ |
|
|
783 |
"models = ['cox_agegender', 'cox_grade',\n", |
|
|
784 |
" 'omic', 'omicomic_fusion', \n", |
|
|
785 |
" 'graph', 'graphgraph_fusion', \n", |
|
|
786 |
" 'path', 'pathpath_fusion', \n", |
|
|
787 |
" 'pathomic_fusion', 'graphomic_fusion', 'pathgraphomic_fusion']\n", |
|
|
788 |
"model_names = ['Cox (Age+Gender)', 'Cox (Grade)',\n", |
|
|
789 |
" 'Genomic SNN', 'Genomic (SNN + SNN)',\n", |
|
|
790 |
" 'Histology GCN', 'Histology (GCN + GCN)',\n", |
|
|
791 |
" 'Histology CNN', 'Histology (CNN + CNN)',\n", |
|
|
792 |
" 'Pathomic F. (CNN+SNN)', 'Pathomic F. (GCN+SNN)', 'Pathomic F. (CNN+GCN+SNN)']" |
|
|
793 |
] |
|
|
794 |
}, |
|
|
795 |
{ |
|
|
796 |
"cell_type": "code", |
|
|
797 |
"execution_count": 63, |
|
|
798 |
"metadata": {}, |
|
|
799 |
"outputs": [ |
|
|
800 |
{ |
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|
801 |
"name": "stderr", |
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802 |
"output_type": "stream", |
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828 |
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829 |
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833 |
" 45%|████▌ | 5/11 [00:01<00:02, 2.45it/s]\u001b[A\n", |
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834 |
" 55%|█████▍ | 6/11 [00:02<00:02, 2.19it/s]\u001b[A\n", |
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835 |
" 64%|██████▎ | 7/11 [00:02<00:01, 2.03it/s]\u001b[A\n", |
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|
836 |
" 73%|███████▎ | 8/11 [00:03<00:01, 1.92it/s]\u001b[A\n", |
|
|
837 |
" 82%|████████▏ | 9/11 [00:04<00:01, 1.81it/s]\u001b[A\n", |
|
|
838 |
" 91%|█████████ | 10/11 [00:04<00:00, 1.75it/s]\u001b[A\n", |
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839 |
"100%|██████████| 11/11 [00:05<00:00, 2.02it/s]\u001b[A\n" |
|
|
840 |
] |
|
|
841 |
} |
|
|
842 |
], |
|
|
843 |
"source": [ |
|
|
844 |
"ckpt_name = './checkpoints/TCGA_KIRC/surv_15/'\n", |
|
|
845 |
"pvalue_surv_multi = [np.array(getPValAggSurv_KIRC_Multi(ckpt_name=ckpt_name, model=model, percentile=[25, 50, 75])) for model in tqdm(models)]\n", |
|
|
846 |
"pvalue_surv_multi = pd.DataFrame(np.array(pvalue_surv_multi))\n", |
|
|
847 |
"pvalue_surv_binary = [np.array(getPValAggSurv_KIRC_Binary(ckpt_name=ckpt_name, model=model, percentile=[50])) for model in tqdm(models)]\n", |
|
|
848 |
"pvalue_surv_binary = pd.DataFrame(np.array(pvalue_surv_binary))\n", |
|
|
849 |
"pvalue_surv = pd.concat([pvalue_surv_binary, pvalue_surv_multi],axis=1)\n", |
|
|
850 |
"pvalue_surv.index = model_names\n", |
|
|
851 |
"pvalue_surv.columns = ['P-Value (<50% vs. >50%)', 'P-Value (<25% vs. 25-50%)', 'P-Value (25-50% vs. 50-75%)', 'P-Value (50-75% vs. >75%)']\n", |
|
|
852 |
"\n", |
|
|
853 |
"cv_surv = [np.array(getPredAggSurv_KIRC(ckpt_name=ckpt_name, model=model)) for model in tqdm(models)]\n", |
|
|
854 |
"cv_surv = pd.DataFrame(np.array(cv_surv))\n", |
|
|
855 |
"cv_surv.columns = ['Split %s' % str(k) for k in range(1,16)]\n", |
|
|
856 |
"cv_surv.index = model_names\n", |
|
|
857 |
"cv_surv['C-Index'] = [CI_pm(cv_surv.loc[model]) for model in model_names]\n", |
|
|
858 |
"cv_surv_all = cv_surv[['C-Index']].join(pvalue_surv, how='inner')" |
|
|
859 |
] |
|
|
860 |
}, |
|
|
861 |
{ |
|
|
862 |
"cell_type": "markdown", |
|
|
863 |
"metadata": {}, |
|
|
864 |
"source": [ |
|
|
865 |
"### Hazard Histograms" |
|
|
866 |
] |
|
|
867 |
}, |
|
|
868 |
{ |
|
|
869 |
"cell_type": "code", |
|
|
870 |
"execution_count": 6, |
|
|
871 |
"metadata": {}, |
|
|
872 |
"outputs": [], |
|
|
873 |
"source": [ |
|
|
874 |
"def getHazardHistogramPlot_KIRC(ckpt_name='./checkpoints/TCGA_KIRC/surv_15/', model='pathgraphomic_fusion', \n", |
|
|
875 |
" split='test', zscore=True, agg_type='Hazard_mean', c=[(-1.5, -0.75), (-0.75, 0), (0, 0.75), (0.75, 1.5)]):\n", |
|
|
876 |
" data = getDataAggSurv_KIRC(ckpt_name=ckpt_name, model=model, split=split, use_rnaseq=True, agg_type=agg_type, zscore=zscore)\n", |
|
|
877 |
" norm = True\n", |
|
|
878 |
" fig, ax = plt.subplots(dpi=600)\n", |
|
|
879 |
"\n", |
|
|
880 |
" low = data[data['OS_month'] <= 12*3.5]\n", |
|
|
881 |
" low = low[low['censored'] == 1]\n", |
|
|
882 |
" high = data[data['OS_month'] > 12*3.5]\n", |
|
|
883 |
" high = high[high['censored'] == 1]\n", |
|
|
884 |
"\n", |
|
|
885 |
" sns.distplot(low['Hazard'], bins=15, kde=False, norm_hist=norm,\n", |
|
|
886 |
" #kde_kws={\"color\": \"k\", \"lw\": 2},\n", |
|
|
887 |
" hist_kws={'histtype':'stepfilled', \"linewidth\": 1, \"alpha\": 0.5, \"color\": \"r\"}, ax=ax)\n", |
|
|
888 |
" sns.distplot(high['Hazard'], bins=15, kde=False, norm_hist=norm,\n", |
|
|
889 |
" #kde_kws={\"color\": \"k\", \"lw\": 2},\n", |
|
|
890 |
" hist_kws={'histtype':'stepfilled', \"linewidth\": 1, \"alpha\": 0.5, \"color\": \"b\"}, ax=ax)\n", |
|
|
891 |
"\n", |
|
|
892 |
" ax.set_xlabel('')\n", |
|
|
893 |
" ax.spines[\"right\"].set_visible(False)\n", |
|
|
894 |
" ax.spines[\"top\"].set_visible(False)\n", |
|
|
895 |
" ax.tick_params(axis='y', which='both', labelsize=15)\n", |
|
|
896 |
" ax.tick_params(axis='x', which='both', labelsize=15)\n", |
|
|
897 |
" ax.set_xticks(np.arange(-1.5, 1.51, 0.5))\n", |
|
|
898 |
" plt.xlim([-1.75, 1.75])\n", |
|
|
899 |
" if norm:\n", |
|
|
900 |
" ax.set_yticks(np.arange(0, 2.1, 1))\n", |
|
|
901 |
" plt.ylim([0, 2])\n", |
|
|
902 |
"\n", |
|
|
903 |
" fig.savefig(ckpt_name+'/%s_HHP.png' % (model))\n", |
|
|
904 |
" \n", |
|
|
905 |
" clusters = []\n", |
|
|
906 |
" for i in range(len(c)):\n", |
|
|
907 |
" cluster = data[data['Hazard'] > c[i][0]]\n", |
|
|
908 |
" cluster = cluster[cluster['Hazard'] < c[i][1]]\n", |
|
|
909 |
" cluster_size = cluster.shape[0]\n", |
|
|
910 |
" cluster_I = (cluster['Neoplasm Histologic Grade'] == '1').sum() / cluster_size\n", |
|
|
911 |
" cluster_II = (cluster['Neoplasm Histologic Grade'] == '2').sum() / cluster_size\n", |
|
|
912 |
" cluster_III = (cluster['Neoplasm Histologic Grade'] == '3').sum() / cluster_size\n", |
|
|
913 |
" cluster_IV = (cluster['Neoplasm Histologic Grade'] == '4').sum() / cluster_size\n", |
|
|
914 |
" cluster_summary = [cluster_I, cluster_II, cluster_III, cluster_IV]\n", |
|
|
915 |
" clusters.append(cluster_summary)\n", |
|
|
916 |
"\n", |
|
|
917 |
" cluster_results = pd.DataFrame(clusters)\n", |
|
|
918 |
" cluster_results.index = ['%0.2f < Hazard < %0.2f' % c[i] for i in range(len(c))]\n", |
|
|
919 |
" cluster_results.index.name = 'Density Region'\n", |
|
|
920 |
" cluster_results.columns = ['Grade ' + str(g) + ' (%)' for g in range(1, 5)]\n", |
|
|
921 |
" cluster_results *= 100\n", |
|
|
922 |
" pd.options.display.float_format = '{:.2f}'.format\n", |
|
|
923 |
" return cluster_results" |
|
|
924 |
] |
|
|
925 |
}, |
|
|
926 |
{ |
|
|
927 |
"cell_type": "code", |
|
|
928 |
"execution_count": 129, |
|
|
929 |
"metadata": {}, |
|
|
930 |
"outputs": [ |
|
|
931 |
{ |
|
|
932 |
"data": { |
|
|
933 |
"text/html": [ |
|
|
934 |
"<div>\n", |
|
|
935 |
"<style scoped>\n", |
|
|
936 |
" .dataframe tbody tr th:only-of-type {\n", |
|
|
937 |
" vertical-align: middle;\n", |
|
|
938 |
" }\n", |
|
|
939 |
"\n", |
|
|
940 |
" .dataframe tbody tr th {\n", |
|
|
941 |
" vertical-align: top;\n", |
|
|
942 |
" }\n", |
|
|
943 |
"\n", |
|
|
944 |
" .dataframe thead th {\n", |
|
|
945 |
" text-align: right;\n", |
|
|
946 |
" }\n", |
|
|
947 |
"</style>\n", |
|
|
948 |
"<table border=\"1\" class=\"dataframe\">\n", |
|
|
949 |
" <thead>\n", |
|
|
950 |
" <tr style=\"text-align: right;\">\n", |
|
|
951 |
" <th></th>\n", |
|
|
952 |
" <th>Grade 1 (%)</th>\n", |
|
|
953 |
" <th>Grade 2 (%)</th>\n", |
|
|
954 |
" <th>Grade 3 (%)</th>\n", |
|
|
955 |
" <th>Grade 4 (%)</th>\n", |
|
|
956 |
" </tr>\n", |
|
|
957 |
" <tr>\n", |
|
|
958 |
" <th>Density Region</th>\n", |
|
|
959 |
" <th></th>\n", |
|
|
960 |
" <th></th>\n", |
|
|
961 |
" <th></th>\n", |
|
|
962 |
" <th></th>\n", |
|
|
963 |
" </tr>\n", |
|
|
964 |
" </thead>\n", |
|
|
965 |
" <tbody>\n", |
|
|
966 |
" <tr>\n", |
|
|
967 |
" <th>-1.50 < Hazard < -0.75</th>\n", |
|
|
968 |
" <td>4.14</td>\n", |
|
|
969 |
" <td>50.59</td>\n", |
|
|
970 |
" <td>38.17</td>\n", |
|
|
971 |
" <td>7.10</td>\n", |
|
|
972 |
" </tr>\n", |
|
|
973 |
" <tr>\n", |
|
|
974 |
" <th>-0.75 < Hazard < 0.00</th>\n", |
|
|
975 |
" <td>5.15</td>\n", |
|
|
976 |
" <td>43.30</td>\n", |
|
|
977 |
" <td>41.92</td>\n", |
|
|
978 |
" <td>9.62</td>\n", |
|
|
979 |
" </tr>\n", |
|
|
980 |
" <tr>\n", |
|
|
981 |
" <th>0.00 < Hazard < 0.75</th>\n", |
|
|
982 |
" <td>2.53</td>\n", |
|
|
983 |
" <td>41.88</td>\n", |
|
|
984 |
" <td>41.52</td>\n", |
|
|
985 |
" <td>14.08</td>\n", |
|
|
986 |
" </tr>\n", |
|
|
987 |
" <tr>\n", |
|
|
988 |
" <th>0.75 < Hazard < 1.50</th>\n", |
|
|
989 |
" <td>0.52</td>\n", |
|
|
990 |
" <td>39.18</td>\n", |
|
|
991 |
" <td>41.24</td>\n", |
|
|
992 |
" <td>19.07</td>\n", |
|
|
993 |
" </tr>\n", |
|
|
994 |
" <tr>\n", |
|
|
995 |
" <th>-1.50 < Hazard < -0.75</th>\n", |
|
|
996 |
" <td>5.80</td>\n", |
|
|
997 |
" <td>56.04</td>\n", |
|
|
998 |
" <td>33.33</td>\n", |
|
|
999 |
" <td>4.83</td>\n", |
|
|
1000 |
" </tr>\n", |
|
|
1001 |
" <tr>\n", |
|
|
1002 |
" <th>-0.75 < Hazard < 0.00</th>\n", |
|
|
1003 |
" <td>1.60</td>\n", |
|
|
1004 |
" <td>48.88</td>\n", |
|
|
1005 |
" <td>38.98</td>\n", |
|
|
1006 |
" <td>10.54</td>\n", |
|
|
1007 |
" </tr>\n", |
|
|
1008 |
" <tr>\n", |
|
|
1009 |
" <th>0.00 < Hazard < 0.75</th>\n", |
|
|
1010 |
" <td>3.01</td>\n", |
|
|
1011 |
" <td>38.55</td>\n", |
|
|
1012 |
" <td>45.78</td>\n", |
|
|
1013 |
" <td>12.65</td>\n", |
|
|
1014 |
" </tr>\n", |
|
|
1015 |
" <tr>\n", |
|
|
1016 |
" <th>0.75 < Hazard < 1.50</th>\n", |
|
|
1017 |
" <td>2.07</td>\n", |
|
|
1018 |
" <td>31.54</td>\n", |
|
|
1019 |
" <td>43.57</td>\n", |
|
|
1020 |
" <td>22.82</td>\n", |
|
|
1021 |
" </tr>\n", |
|
|
1022 |
" <tr>\n", |
|
|
1023 |
" <th>-1.50 < Hazard < -0.75</th>\n", |
|
|
1024 |
" <td>3.97</td>\n", |
|
|
1025 |
" <td>51.04</td>\n", |
|
|
1026 |
" <td>38.75</td>\n", |
|
|
1027 |
" <td>6.24</td>\n", |
|
|
1028 |
" </tr>\n", |
|
|
1029 |
" <tr>\n", |
|
|
1030 |
" <th>-0.75 < Hazard < 0.00</th>\n", |
|
|
1031 |
" <td>5.37</td>\n", |
|
|
1032 |
" <td>50.00</td>\n", |
|
|
1033 |
" <td>36.36</td>\n", |
|
|
1034 |
" <td>8.26</td>\n", |
|
|
1035 |
" </tr>\n", |
|
|
1036 |
" <tr>\n", |
|
|
1037 |
" <th>0.00 < Hazard < 0.75</th>\n", |
|
|
1038 |
" <td>2.70</td>\n", |
|
|
1039 |
" <td>54.05</td>\n", |
|
|
1040 |
" <td>35.14</td>\n", |
|
|
1041 |
" <td>8.11</td>\n", |
|
|
1042 |
" </tr>\n", |
|
|
1043 |
" <tr>\n", |
|
|
1044 |
" <th>0.75 < Hazard < 1.50</th>\n", |
|
|
1045 |
" <td>1.48</td>\n", |
|
|
1046 |
" <td>31.28</td>\n", |
|
|
1047 |
" <td>44.83</td>\n", |
|
|
1048 |
" <td>22.41</td>\n", |
|
|
1049 |
" </tr>\n", |
|
|
1050 |
" <tr>\n", |
|
|
1051 |
" <th>-1.50 < Hazard < -0.75</th>\n", |
|
|
1052 |
" <td>4.91</td>\n", |
|
|
1053 |
" <td>50.64</td>\n", |
|
|
1054 |
" <td>39.10</td>\n", |
|
|
1055 |
" <td>5.34</td>\n", |
|
|
1056 |
" </tr>\n", |
|
|
1057 |
" <tr>\n", |
|
|
1058 |
" <th>-0.75 < Hazard < 0.00</th>\n", |
|
|
1059 |
" <td>4.10</td>\n", |
|
|
1060 |
" <td>48.12</td>\n", |
|
|
1061 |
" <td>38.91</td>\n", |
|
|
1062 |
" <td>8.87</td>\n", |
|
|
1063 |
" </tr>\n", |
|
|
1064 |
" <tr>\n", |
|
|
1065 |
" <th>0.00 < Hazard < 0.75</th>\n", |
|
|
1066 |
" <td>1.36</td>\n", |
|
|
1067 |
" <td>48.98</td>\n", |
|
|
1068 |
" <td>40.14</td>\n", |
|
|
1069 |
" <td>9.52</td>\n", |
|
|
1070 |
" </tr>\n", |
|
|
1071 |
" <tr>\n", |
|
|
1072 |
" <th>0.75 < Hazard < 1.50</th>\n", |
|
|
1073 |
" <td>1.01</td>\n", |
|
|
1074 |
" <td>34.34</td>\n", |
|
|
1075 |
" <td>41.92</td>\n", |
|
|
1076 |
" <td>22.73</td>\n", |
|
|
1077 |
" </tr>\n", |
|
|
1078 |
" </tbody>\n", |
|
|
1079 |
"</table>\n", |
|
|
1080 |
"</div>" |
|
|
1081 |
], |
|
|
1082 |
"text/plain": [ |
|
|
1083 |
" Grade 1 (%) Grade 2 (%) Grade 3 (%) Grade 4 (%)\n", |
|
|
1084 |
"Density Region \n", |
|
|
1085 |
"-1.50 < Hazard < -0.75 4.14 50.59 38.17 7.10\n", |
|
|
1086 |
"-0.75 < Hazard < 0.00 5.15 43.30 41.92 9.62\n", |
|
|
1087 |
"0.00 < Hazard < 0.75 2.53 41.88 41.52 14.08\n", |
|
|
1088 |
"0.75 < Hazard < 1.50 0.52 39.18 41.24 19.07\n", |
|
|
1089 |
"-1.50 < Hazard < -0.75 5.80 56.04 33.33 4.83\n", |
|
|
1090 |
"-0.75 < Hazard < 0.00 1.60 48.88 38.98 10.54\n", |
|
|
1091 |
"0.00 < Hazard < 0.75 3.01 38.55 45.78 12.65\n", |
|
|
1092 |
"0.75 < Hazard < 1.50 2.07 31.54 43.57 22.82\n", |
|
|
1093 |
"-1.50 < Hazard < -0.75 3.97 51.04 38.75 6.24\n", |
|
|
1094 |
"-0.75 < Hazard < 0.00 5.37 50.00 36.36 8.26\n", |
|
|
1095 |
"0.00 < Hazard < 0.75 2.70 54.05 35.14 8.11\n", |
|
|
1096 |
"0.75 < Hazard < 1.50 1.48 31.28 44.83 22.41\n", |
|
|
1097 |
"-1.50 < Hazard < -0.75 4.91 50.64 39.10 5.34\n", |
|
|
1098 |
"-0.75 < Hazard < 0.00 4.10 48.12 38.91 8.87\n", |
|
|
1099 |
"0.00 < Hazard < 0.75 1.36 48.98 40.14 9.52\n", |
|
|
1100 |
"0.75 < Hazard < 1.50 1.01 34.34 41.92 22.73" |
|
|
1101 |
] |
|
|
1102 |
}, |
|
|
1103 |
"execution_count": 129, |
|
|
1104 |
"metadata": {}, |
|
|
1105 |
"output_type": "execute_result" |
|
|
1106 |
}, |
|
|
1107 |
{ |
|
|
1108 |
"data": { |
|
|
1109 |
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|
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|
1110 |
"text/plain": [ |
|
|
1111 |
"<Figure size 3600x2400 with 1 Axes>" |
|
|
1112 |
] |
|
|
1113 |
}, |
|
|
1114 |
"metadata": { |
|
|
1115 |
"needs_background": "light" |
|
|
1116 |
}, |
|
|
1117 |
"output_type": "display_data" |
|
|
1118 |
}, |
|
|
1119 |
{ |
|
|
1120 |
"data": { |
|
|
1121 |
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1122 |
"text/plain": [ |
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1123 |
"<Figure size 3600x2400 with 1 Axes>" |
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1124 |
] |
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1125 |
}, |
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1126 |
"metadata": { |
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1127 |
"needs_background": "light" |
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1128 |
}, |
|
|
1129 |
"output_type": "display_data" |
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1130 |
}, |
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1131 |
{ |
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1132 |
"data": { |
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|
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|
1134 |
"text/plain": [ |
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|
1135 |
"<Figure size 3600x2400 with 1 Axes>" |
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1136 |
] |
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1137 |
}, |
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|
1138 |
"metadata": { |
|
|
1139 |
"needs_background": "light" |
|
|
1140 |
}, |
|
|
1141 |
"output_type": "display_data" |
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|
1142 |
}, |
|
|
1143 |
{ |
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|
1144 |
"data": { |
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|
1145 |
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\n", |
|
|
1146 |
"text/plain": [ |
|
|
1147 |
"<Figure size 3600x2400 with 1 Axes>" |
|
|
1148 |
] |
|
|
1149 |
}, |
|
|
1150 |
"metadata": { |
|
|
1151 |
"needs_background": "light" |
|
|
1152 |
}, |
|
|
1153 |
"output_type": "display_data" |
|
|
1154 |
} |
|
|
1155 |
], |
|
|
1156 |
"source": [ |
|
|
1157 |
"cluster_p = getHazardHistogramPlot_KIRC(model='path')\n", |
|
|
1158 |
"cluster_g = getHazardHistogramPlot_KIRC(model='graph')\n", |
|
|
1159 |
"cluster_o = getHazardHistogramPlot_KIRC(model='omic')\n", |
|
|
1160 |
"cluster_pgo = getHazardHistogramPlot_KIRC(model='pathgraphomic_fusion')\n", |
|
|
1161 |
"cluster_results = pd.concat([cluster_p, cluster_g, cluster_o, cluster_pgo])\n", |
|
|
1162 |
"cluster_results" |
|
|
1163 |
] |
|
|
1164 |
}, |
|
|
1165 |
{ |
|
|
1166 |
"cell_type": "code", |
|
|
1167 |
"execution_count": 135, |
|
|
1168 |
"metadata": {}, |
|
|
1169 |
"outputs": [ |
|
|
1170 |
{ |
|
|
1171 |
"name": "stderr", |
|
|
1172 |
"output_type": "stream", |
|
|
1173 |
"text": [ |
|
|
1174 |
"100%|██████████| 4/4 [00:28<00:00, 7.02s/it]\n" |
|
|
1175 |
] |
|
|
1176 |
} |
|
|
1177 |
], |
|
|
1178 |
"source": [ |
|
|
1179 |
"for model in tqdm(['path', 'graph', 'omic', 'pathgraphomic_fusion']):\n", |
|
|
1180 |
" makeKaplanMeierPlot_KIRC_Binary(model=model)\n", |
|
|
1181 |
" makeKaplanMeierPlot_KIRC_Multi(model=model)" |
|
|
1182 |
] |
|
|
1183 |
}, |
|
|
1184 |
{ |
|
|
1185 |
"cell_type": "code", |
|
|
1186 |
"execution_count": 26, |
|
|
1187 |
"metadata": {}, |
|
|
1188 |
"outputs": [ |
|
|
1189 |
{ |
|
|
1190 |
"data": { |
|
|
1191 |
"image/png": 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|
|
|
1192 |
"text/plain": [ |
|
|
1193 |
"<Figure size 6000x6000 with 1 Axes>" |
|
|
1194 |
] |
|
|
1195 |
}, |
|
|
1196 |
"metadata": { |
|
|
1197 |
"needs_background": "light" |
|
|
1198 |
}, |
|
|
1199 |
"output_type": "display_data" |
|
|
1200 |
} |
|
|
1201 |
], |
|
|
1202 |
"source": [ |
|
|
1203 |
"makeKaplanMeierPlot_KIRC_Multi(model='pathgraphomic_fusion')" |
|
|
1204 |
] |
|
|
1205 |
}, |
|
|
1206 |
{ |
|
|
1207 |
"cell_type": "code", |
|
|
1208 |
"execution_count": 16, |
|
|
1209 |
"metadata": {}, |
|
|
1210 |
"outputs": [ |
|
|
1211 |
{ |
|
|
1212 |
"name": "stdout", |
|
|
1213 |
"output_type": "stream", |
|
|
1214 |
"text": [ |
|
|
1215 |
"1\n", |
|
|
1216 |
"2\n", |
|
|
1217 |
"3\n" |
|
|
1218 |
] |
|
|
1219 |
}, |
|
|
1220 |
{ |
|
|
1221 |
"data": { |
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1222 |
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|
|
|
1223 |
"text/plain": [ |
|
|
1224 |
"<Figure size 6000x6000 with 1 Axes>" |
|
|
1225 |
] |
|
|
1226 |
}, |
|
|
1227 |
"metadata": { |
|
|
1228 |
"needs_background": "light" |
|
|
1229 |
}, |
|
|
1230 |
"output_type": "display_data" |
|
|
1231 |
} |
|
|
1232 |
], |
|
|
1233 |
"source": [ |
|
|
1234 |
"makeKaplanMeierPlot_KIRC_Multi(model='path')" |
|
|
1235 |
] |
|
|
1236 |
} |
|
|
1237 |
], |
|
|
1238 |
"metadata": { |
|
|
1239 |
"kernelspec": { |
|
|
1240 |
"display_name": "Python 3", |
|
|
1241 |
"language": "python", |
|
|
1242 |
"name": "python3" |
|
|
1243 |
}, |
|
|
1244 |
"language_info": { |
|
|
1245 |
"codemirror_mode": { |
|
|
1246 |
"name": "ipython", |
|
|
1247 |
"version": 3 |
|
|
1248 |
}, |
|
|
1249 |
"file_extension": ".py", |
|
|
1250 |
"mimetype": "text/x-python", |
|
|
1251 |
"name": "python", |
|
|
1252 |
"nbconvert_exporter": "python", |
|
|
1253 |
"pygments_lexer": "ipython3", |
|
|
1254 |
"version": "3.7.3" |
|
|
1255 |
} |
|
|
1256 |
}, |
|
|
1257 |
"nbformat": 4, |
|
|
1258 |
"nbformat_minor": 4 |
|
|
1259 |
} |