From 2f2147d917070212303c384f05fbd9589dc718da Mon Sep 17 00:00:00 2001
From: npg2108 <npg2108@cumc.columbia.edu>
Date: Thu, 31 Jan 2019 12:05:18 -0500
Subject: [PATCH] fixed hypothesis_testing: marker instead of marker_type

---
 Introduction.ipynb | 168 ++++++++++++++++++++++-----------------------
 cohorts/cohort.py  |   2 +-
 2 files changed, 85 insertions(+), 85 deletions(-)

diff --git a/Introduction.ipynb b/Introduction.ipynb
index 8d8b82c..59efdbb 100644
--- a/Introduction.ipynb
+++ b/Introduction.ipynb
@@ -29,7 +29,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Sat Jan 12 16:42:16 EST 2019\r\n"
+      "Thu Jan 31 12:02:39 EST 2019\r\n"
      ]
     }
    ],
@@ -1972,8 +1972,8 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[('t-test', <function ttest_ind at 0x10ef72620>), ('Wilcoxon_RankSum_test', <function ranksums at 0x10ef69c80>)]\n",
-      "[('t-test', <function ttest_ind at 0x10ef72620>), ('Wilcoxon_RankSum_test', <function ranksums at 0x10ef69c80>), ('ks_2samp', <function kruskal at 0x10ef850d0>)]\n",
+      "[('t-test', <function ttest_ind at 0x108cce620>), ('Wilcoxon_RankSum_test', <function ranksums at 0x108cc4c80>)]\n",
+      "[('t-test', <function ttest_ind at 0x108cce620>), ('Wilcoxon_RankSum_test', <function ranksums at 0x108cc4c80>), ('ks_2samp', <function kruskal at 0x108cbd0d0>)]\n",
       "A list of statistical tests are required. For example: [( \"t-test\",scipy.stats.ttest_ind )]. More than one can be given.\n"
      ]
     },
@@ -2007,252 +2007,252 @@
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>E7EX29</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.016216</td>\n",
        "      <td>3.033787</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P03950</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.002775</td>\n",
        "      <td>4.256394</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P05067</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.000048</td>\n",
        "      <td>7.901340</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P08253</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.028716</td>\n",
        "      <td>2.662005</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P15144</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.000058</td>\n",
        "      <td>7.682007</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P27338</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.013073</td>\n",
        "      <td>3.176138</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P52209</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.003509</td>\n",
        "      <td>4.085061</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>7</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P62191</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.000967</td>\n",
        "      <td>5.068448</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>8</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q8N302</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.030395</td>\n",
        "      <td>2.625397</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>9</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q99460</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.022634</td>\n",
        "      <td>2.815977</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>10</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q9UJX5</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.013623</td>\n",
        "      <td>3.148812</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>11</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q9Y5C1</td>\n",
        "      <td>t-test</td>\n",
        "      <td>0.017585</td>\n",
        "      <td>2.980566</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>12</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>E7EX29</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>13</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P03950</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>14</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P05067</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>15</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P08253</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>16</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P15144</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>17</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P27338</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.016294</td>\n",
        "      <td>2.402272</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>18</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P52209</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>19</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P62191</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>20</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q8N302</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>21</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q99460</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>22</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q9UJX5</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>23</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q9Y5C1</td>\n",
        "      <td>Wilcoxon_RankSum_test</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>2.611165</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>E7EX29</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>25</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P03950</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>26</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P05067</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>27</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P08253</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>28</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P15144</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>29</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P27338</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.016294</td>\n",
        "      <td>5.770909</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>30</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P52209</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>31</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>P62191</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>32</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q8N302</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>33</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q99460</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>34</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q9UJX5</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>35</th>\n",
-       "      <td>protein</td>\n",
+       "      <td>Q9Y5C1</td>\n",
        "      <td>ks_2samp</td>\n",
        "      <td>0.009023</td>\n",
        "      <td>6.818182</td>\n",
@@ -2262,43 +2262,43 @@
        "</div>"
       ],
       "text/plain": [
-       "    protein                   test    pvalue  statistic\n",
-       "0   protein                 t-test  0.016216   3.033787\n",
-       "1   protein                 t-test  0.002775   4.256394\n",
-       "2   protein                 t-test  0.000048   7.901340\n",
-       "3   protein                 t-test  0.028716   2.662005\n",
-       "4   protein                 t-test  0.000058   7.682007\n",
-       "5   protein                 t-test  0.013073   3.176138\n",
-       "6   protein                 t-test  0.003509   4.085061\n",
-       "7   protein                 t-test  0.000967   5.068448\n",
-       "8   protein                 t-test  0.030395   2.625397\n",
-       "9   protein                 t-test  0.022634   2.815977\n",
-       "10  protein                 t-test  0.013623   3.148812\n",
-       "11  protein                 t-test  0.017585   2.980566\n",
-       "12  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "13  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "14  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "15  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "16  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "17  protein  Wilcoxon_RankSum_test  0.016294   2.402272\n",
-       "18  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "19  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "20  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "21  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "22  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "23  protein  Wilcoxon_RankSum_test  0.009023   2.611165\n",
-       "24  protein               ks_2samp  0.009023   6.818182\n",
-       "25  protein               ks_2samp  0.009023   6.818182\n",
-       "26  protein               ks_2samp  0.009023   6.818182\n",
-       "27  protein               ks_2samp  0.009023   6.818182\n",
-       "28  protein               ks_2samp  0.009023   6.818182\n",
-       "29  protein               ks_2samp  0.016294   5.770909\n",
-       "30  protein               ks_2samp  0.009023   6.818182\n",
-       "31  protein               ks_2samp  0.009023   6.818182\n",
-       "32  protein               ks_2samp  0.009023   6.818182\n",
-       "33  protein               ks_2samp  0.009023   6.818182\n",
-       "34  protein               ks_2samp  0.009023   6.818182\n",
-       "35  protein               ks_2samp  0.009023   6.818182"
+       "   protein                   test    pvalue  statistic\n",
+       "0   E7EX29                 t-test  0.016216   3.033787\n",
+       "1   P03950                 t-test  0.002775   4.256394\n",
+       "2   P05067                 t-test  0.000048   7.901340\n",
+       "3   P08253                 t-test  0.028716   2.662005\n",
+       "4   P15144                 t-test  0.000058   7.682007\n",
+       "5   P27338                 t-test  0.013073   3.176138\n",
+       "6   P52209                 t-test  0.003509   4.085061\n",
+       "7   P62191                 t-test  0.000967   5.068448\n",
+       "8   Q8N302                 t-test  0.030395   2.625397\n",
+       "9   Q99460                 t-test  0.022634   2.815977\n",
+       "10  Q9UJX5                 t-test  0.013623   3.148812\n",
+       "11  Q9Y5C1                 t-test  0.017585   2.980566\n",
+       "12  E7EX29  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "13  P03950  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "14  P05067  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "15  P08253  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "16  P15144  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "17  P27338  Wilcoxon_RankSum_test  0.016294   2.402272\n",
+       "18  P52209  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "19  P62191  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "20  Q8N302  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "21  Q99460  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "22  Q9UJX5  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "23  Q9Y5C1  Wilcoxon_RankSum_test  0.009023   2.611165\n",
+       "24  E7EX29               ks_2samp  0.009023   6.818182\n",
+       "25  P03950               ks_2samp  0.009023   6.818182\n",
+       "26  P05067               ks_2samp  0.009023   6.818182\n",
+       "27  P08253               ks_2samp  0.009023   6.818182\n",
+       "28  P15144               ks_2samp  0.009023   6.818182\n",
+       "29  P27338               ks_2samp  0.016294   5.770909\n",
+       "30  P52209               ks_2samp  0.009023   6.818182\n",
+       "31  P62191               ks_2samp  0.009023   6.818182\n",
+       "32  Q8N302               ks_2samp  0.009023   6.818182\n",
+       "33  Q99460               ks_2samp  0.009023   6.818182\n",
+       "34  Q9UJX5               ks_2samp  0.009023   6.818182\n",
+       "35  Q9Y5C1               ks_2samp  0.009023   6.818182"
       ]
      },
      "execution_count": 10,
@@ -2339,7 +2339,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10fbc5cf8>"
+       "<matplotlib.figure.Figure at 0x1098f9c18>"
       ]
      },
      "metadata": {},
@@ -2367,7 +2367,7 @@
      "data": {
       "image/png": 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\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x113162d30>"
+       "<matplotlib.figure.Figure at 0x1098377b8>"
       ]
      },
      "metadata": {},
@@ -2476,9 +2476,9 @@
     {
      "data": {
       "text/plain": [
-       "{'patient_cohort_1': <cohorts.cohort.Cohort at 0x113320d68>,\n",
-       " 'patient_cohort_2': <cohorts.cohort.Cohort at 0x113339fd0>,\n",
-       " 'patient_cohort_3': <cohorts.cohort.Cohort at 0x1133397b8>}"
+       "{'patient_cohort_1': <cohorts.cohort.Cohort at 0x1a13564be0>,\n",
+       " 'patient_cohort_2': <cohorts.cohort.Cohort at 0x1a133c5c50>,\n",
+       " 'patient_cohort_3': <cohorts.cohort.Cohort at 0x1a1357b7f0>}"
       ]
      },
      "execution_count": 14,
@@ -2514,9 +2514,9 @@
     {
      "data": {
       "text/plain": [
-       "{'patient_cohort_1': <cohorts.cohort.Cohort at 0x113320d68>,\n",
-       " 'patient_cohort_2': <cohorts.cohort.Cohort at 0x113339fd0>,\n",
-       " 'patient_cohort_3': <cohorts.cohort.Cohort at 0x1133397b8>}"
+       "{'patient_cohort_1': <cohorts.cohort.Cohort at 0x1a13564be0>,\n",
+       " 'patient_cohort_2': <cohorts.cohort.Cohort at 0x1a133c5c50>,\n",
+       " 'patient_cohort_3': <cohorts.cohort.Cohort at 0x1a1357b7f0>}"
       ]
      },
      "execution_count": 16,
diff --git a/cohorts/cohort.py b/cohorts/cohort.py
index 045a1b6..824b243 100644
--- a/cohorts/cohort.py
+++ b/cohorts/cohort.py
@@ -883,7 +883,7 @@ def hypothesis_testing(self,df,df_groups,tests=None):
 				pval = hyp[1](a,b)[1]
 
 				#put data in pandas series
-				row = pd.Series([self.marker_type,test,pval,stat],index=df.columns)
+				row = pd.Series([marker,test,pval,stat],index=df.columns)
 
 				#add row to dataframe
 				df = df.append(row,ignore_index=True)