From 06963da370277ce7082dfc78ed68754263271979 Mon Sep 17 00:00:00 2001
From: David Bikard <david.bikard@pasteur.fr>
Date: Thu, 1 Feb 2018 09:05:04 +0100
Subject: [PATCH] Added more explanations to code

---
 ...as9 binding position and orientation.ipynb |  18 +-
 README.md                                     |   2 +-
 off-target analysis.ipynb                     | 171 ++++++++++--------
 3 files changed, 102 insertions(+), 89 deletions(-)

diff --git a/Effect of dCas9 binding position and orientation.ipynb b/Effect of dCas9 binding position and orientation.ipynb
index 67002eb..6f886cc 100644
--- a/Effect of dCas9 binding position and orientation.ipynb	
+++ b/Effect of dCas9 binding position and orientation.ipynb	
@@ -7,6 +7,8 @@
     "# Effect of dCas9 binding position and orientation\n",
     "In this notebook we study the effect of dCas9 binding position and orientation with a special focus on polar effects. \n",
     "\n",
+    "In order to investigate the properties of dCas9 repression in E. coli we can analyze the effect of guides targeting essential genes. We expect guides that efficiently block the expression of these genes to be depleted from the library. Previous reports suggested that dCas9 efficiently blocks transcription elongation only when binding the coding strand (non-template strand)6,7. As expected guide RNAs targeting essential genes have on average a strong fitness effect when they bind to the coding strand, and no fitness effect when they bind to the template strand (Fig. 1a). \n",
+    "\n",
     "- **guide**: guide RNA sequence\n",
     "- **pos**: position in the genome\n",
     "- **ori**: orientation of the guide with regard to the chromosome\n",
@@ -175,22 +177,6 @@
     "data.head()"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In order to investigate the properties of dCas9 repression in E. coli we can analyze the effect of guides targeting essential genes. We expect guides that efficiently block the expression of these genes to be depleted from the library. Previous reports suggested that dCas9 efficiently blocks transcription elongation only when binding the coding strand (non-template strand)6,7. As expected guide RNAs targeting essential genes have on average a strong fitness effect when they bind to the coding strand, and no fitness effect when they bind to the template strand (Fig. 1a). "
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": 2,
diff --git a/README.md b/README.md
index a2602a3..1246566 100644
--- a/README.md
+++ b/README.md
@@ -16,7 +16,7 @@ during the course of the experiment, as measured through deep sequencing of the
 
 The log2FC data for all guides in the screen can be found in the file Supplementary_table_6-screen_data.csv
 
-The analysis is divded in three jupyter notebooks:
+The analysis is divided in three jupyter notebooks:
 * Effect of dCas9 binding position and orientation.ipynb
 > In this notebook we look at the effect of dCas9 binding position and orientation. In particular we perform an analysis of polar and reverse-polar effects.
 
diff --git a/off-target analysis.ipynb b/off-target analysis.ipynb
index 69c50c1..6531e97 100644
--- a/off-target analysis.ipynb	
+++ b/off-target analysis.ipynb	
@@ -19,7 +19,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -148,7 +148,7 @@
        "AAAAAATCTGCCCGTGTCGT  ATGACTGGAACAAAGCCTATAAAAAATCTGCCCGTGTCGTTGGTGA...  "
       ]
      },
-     "execution_count": 12,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -177,7 +177,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -244,7 +244,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -282,7 +282,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -313,14 +313,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
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EGYjYlgRFmUohz6l+p0ppHWIg/L+ir6F1KbLrv4cQXxgbqTZk138P73XcjHRmiqVDDoiG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SoRZcsldRZ7NhtfxwlW0w4WgoISjzTQJ+aW9P2f2wCUTJK2lsbADFXP4hFg/63AP33EdnCR9+2f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\nEhERERERkVYYqBIREREREZFW/j9ejaso14fH5AAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x13db2198>"
+       "<matplotlib.figure.Figure at 0xc3e89e8>"
       ]
      },
      "metadata": {},
@@ -349,7 +349,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -491,7 +491,7 @@
        "AAAAAATCTGCCCGTGTCGT    -2.405657  "
       ]
      },
-     "execution_count": 10,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -510,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -543,7 +543,7 @@
        "       [      1, 4608090]])"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -555,26 +555,34 @@
     "crUtils.off_targets(\"GCTTGACATCGTCGATGCTG\",9,verbose=1)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We looked for off-targets that displayed a perfect identity of 9nt or more between the seed sequence and regions where a strong fitness defect was consistently observed (i.e. essential or fitness genes). We found such off-targets for 24% (600/2499) of the guides that produced an unexpected fitness defect. As a control, we also looked at the proportion of guides with such off-targets among guides that target the same genes in the same orientation but produce no fitness defects. This occurs for 10.7% (3609/33612) of these guides, giving a measure of the false positive discovery rate. Guides that produce unexpected fitness defects are thus significantly more likely to have an off-target blocking the expression of a fitness or essential gene (Fisher exact test p-value<0.001). This enables to provide a conservative estimate that the fitness defect produced by 13% of these guides is due to their off-target activity. "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "85392it [12:31, 113.59it/s]\n"
+      "85392it [12:12, 116.64it/s]\n"
      ]
     }
    ],
    "source": [
     "from tqdm import tqdm\n",
     "for idx,g in tqdm(data.iterrows()):\n",
-    "    res=crUtils.off_targets(g.guide, 9)\n",
+    "    res=crUtils.off_targets(g.guide, 9) #get list of all positions with perfect match of 9nt in PAM-proximal region\n",
     "    res = res[res[:,1]!=g.pos] #remove actual target from list\n",
     "    if res.any():\n",
-    "        filtered_res = res[reg.predict(res)<threshold]\n",
+    "        filtered_res = res[reg.predict(res)<threshold] \n",
+    "        #are there off-targets in regions predicted to be important by the regression tree?\n",
     "        if filtered_res.shape[0]!=0:\n",
     "            #print filtered_res\n",
     "            data.loc[idx,\"off_target_pos9\"]=','.join([str(p) for p in filtered_res[:,1]])\n",
@@ -588,8 +596,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -627,9 +637,16 @@
     "print(\"\\nFisher exact test p-value: {:.4}\".format(stats.fisher_exact(contigency.values[:2,:2])[1]))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The same analysis was also performed while considering a minimum perfect match in the PAM-proximal region ranging from 6 to 15bp (Fig. 5a and Supplementary Fig. 14). A seed size of length 9 gave the largest difference between the positive detection rate (proportion of guides producing an unexpected fitness defect for which an off-target to an important region is detected) and false positives detection rate (proportion of guides that do not produce an effect and for which an off-target to an important region is detected), suggesting that 9nt of identity can be sufficient to block gene expression. "
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -637,24 +654,24 @@
      "output_type": "stream",
      "text": [
       "loading off-targets with seed of size 6 in cache...\n",
-      "6 2405\n",
+      "6 2419\n",
       "loading off-targets with seed of size 7 in cache...\n",
-      "7 1767\n",
+      "7 1751\n",
       "loading off-targets with seed of size 8 in cache...\n",
-      "8 831\n",
-      "9 277\n",
+      "8 800\n",
+      "9 261\n",
       "loading off-targets with seed of size 10 in cache...\n",
-      "10 72\n",
+      "10 73\n",
       "loading off-targets with seed of size 11 in cache...\n",
-      "11 16\n",
+      "11 20\n",
       "loading off-targets with seed of size 12 in cache...\n",
-      "12 7\n",
+      "12 11\n",
       "loading off-targets with seed of size 13 in cache...\n",
-      "13 3\n",
+      "13 8\n",
       "loading off-targets with seed of size 14 in cache...\n",
-      "14 3\n",
+      "14 6\n",
       "loading off-targets with seed of size 15 in cache...\n",
-      "15 3\n"
+      "15 6\n"
      ]
     }
    ],
@@ -693,14 +710,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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q8daWreWZlc/w5vo3qfXXMqHvBGYUzOCEPiccOI4qLHsF3v2Ns97qhOvgpN9A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\noap1OM8J+Q7OOf4NwIs4weS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NoyX9nndIzYZBF1rDqW2a0l+pzqLh0ILDIcwYYY2WbHD7ubHuhFvhyC4o+qjzi1OqE2k4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\nSUVORK5CYII=\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x188b7eb8>"
+       "<matplotlib.figure.Figure at 0x1538abe0>"
       ]
      },
      "metadata": {},
@@ -721,37 +738,36 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "What happens in the strain with reduced dCas9 concentration?"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
-     "ename": "KeyError",
-     "evalue": "'tree_pred18'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2441\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2442\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2443\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'tree_pred18'",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-23-1b09a3844f72>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mflt\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"ntargets\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"coding\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"essential\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m==\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m&\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"tree_pred18\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m>\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mcontigency\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcrosstab\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mflt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moff_target_pos9\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnotnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mflt\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit75\u001b[0m\u001b[1;33m<\u001b[0m\u001b[0mthreshold\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mmargins\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcontigency\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mcontigencyF\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcontigency\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcontigency\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"All\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m*\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcontigencyF\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1962\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1963\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1964\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1966\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1969\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1970\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1971\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1973\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   1643\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1644\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1645\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1646\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1647\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   3588\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3589\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3590\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3591\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3592\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misnull\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mD:\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   2442\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2443\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2444\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2445\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2446\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
-      "\u001b[1;31mKeyError\u001b[0m: 'tree_pred18'"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "fit75            False  True    All\n",
+      "off_target_pos9                    \n",
+      "False            31676   226  31902\n",
+      "True              3866   287   4153\n",
+      "All              35542   513  36055\n",
+      "fit75               False      True       All\n",
+      "off_target_pos9                              \n",
+      "False            0.891227  0.440546  0.884815\n",
+      "True             0.108773  0.559454  0.115185\n",
+      "All              1.000000  1.000000  1.000000\n",
+      "\n",
+      "Estimate of the proportion of guides producing an unexpectedly strong fitness defect that can be explained by a likely off-target: 45% ( 231 guides)\n",
+      "\n",
+      "Fisher exact test p-value: 7.96e-134\n"
      ]
     }
    ],
@@ -772,23 +788,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "guide                                     ACGCGGGTTGTGGACCTGGC\n",
-      "gene                                                      bioC\n",
-      "essential                                                False\n",
-      "pos                                                     810908\n",
-      "ori                                                          +\n",
-      "coding                                                   False\n",
-      "fit18                                                  -6.7067\n",
-      "fit75                                                 -6.47683\n",
-      "ntargets                                                     1\n",
-      "seq          TAAATACACCCACGTACTGGACGCGGGTTGTGGACCTGGCTGGATG...\n",
+      "guide                                           ACGCGGGTTGTGGACCTGGC\n",
+      "gene                                                            bioC\n",
+      "essential                                                      False\n",
+      "pos                                                           810908\n",
+      "ori                                                                +\n",
+      "coding                                                         False\n",
+      "fit18                                                        -6.7067\n",
+      "fit75                                                       -6.47683\n",
+      "ntargets                                                           1\n",
+      "seq                TAAATACACCCACGTACTGGACGCGGGTTGTGGACCTGGCTGGATG...\n",
+      "tree_pred18                                                 -1.15328\n",
+      "off_target_pos9                                              2964170\n",
       "Name: ACGCGGGTTGTGGACCTGGC, dtype: object\n"
      ]
     },
@@ -796,7 +814,7 @@
      "data": {
       "image/png": 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adggiJavEpicREakgShQiIlKQEoWIiBSkPgqRMpowQXegkf5PiUKkjH7xi1+E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\nTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x13d93a20>"
+       "<matplotlib.figure.Figure at 0x537d1a58>"
       ]
      },
      "metadata": {},
@@ -806,7 +824,7 @@
      "data": {
       "image/png": 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gTMysO8sYY0zULIkY45IFCxawYMECt8MwJibWnWWMS5YvX+52CMbEzFoixhhj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\n/X/XSch03a/J9QAAAABJRU5ErkJggg==\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0xd37c2e8>"
+       "<matplotlib.figure.Figure at 0xc5b0c18>"
       ]
      },
      "metadata": {},
@@ -816,7 +834,7 @@
      "data": {
       "image/png": 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EYD+CJsetmNk0M6s1s9q6XBkgkE4abJAWhx56KE0lfbjqztmMqh7HrbfdRlNT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\noEREJJKUoEREJJL+P9lyqdZHefIhAAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x16b13cf8>"
+       "<matplotlib.figure.Figure at 0x5388e278>"
       ]
      },
      "metadata": {},
@@ -834,6 +852,15 @@
     "    crUtils.plot_pos(pos-1000,pos+1000,data,\"fit75\",ax=ax)\n",
     "    plt.show()"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
-- 
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