Position-specificFeaturesAsCpf1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fundamental-communist",
   "metadata": {},
   "source": [
    "# Position-specific feature importance analysis for AsCpf1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "perceived-facial",
   "metadata": {},
   "source": [
    "# Calculation of frequency of nucleotides at each location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "stuffed-spouse",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "481 58474\n",
      "positive off-targets\n",
      "Position-wise frequency of A in positive off-targets \n",
      " [0.010395010395010396, 0.0, 0.0, 0.21621621621621623, 0.033264033264033266, 0.0498960498960499, 0.033264033264033266, 0.3908523908523909, 0.0498960498960499, 0.3887733887733888, 0.21413721413721415, 0.15176715176715178, 0.3575883575883576, 0.16216216216216217, 0.6340956340956341, 0.1496881496881497, 0.3367983367983368, 0.11434511434511435, 0.4885654885654886, 0.44282744282744285, 0.1683991683991684, 0.4095634095634096, 0.26403326403326405, 0.3264033264033264, 0.5426195426195426, 0.20166320166320167, 0.15384615384615385] \n",
      "\n",
      "Position-wise frequency of T in positive off-targets \n",
      " [0.7713097713097713, 0.9875259875259875, 0.9916839916839917, 0.11642411642411643, 0.11850311850311851, 0.4490644490644491, 0.14137214137214138, 0.4386694386694387, 0.35550935550935553, 0.04781704781704782, 0.3804573804573805, 0.4095634095634096, 0.13721413721413722, 0.20374220374220375, 0.14345114345114346, 0.2702702702702703, 0.12058212058212059, 0.20166320166320167, 0.13097713097713098, 0.33264033264033266, 0.12474012474012475, 0.3804573804573805, 0.30145530145530147, 0.14760914760914762, 0.15592515592515593, 0.16632016632016633, 0.14345114345114346] \n",
      "\n",
      "Position-wise frequency of G in positive off-targets \n",
      " [0.2079002079002079, 0.002079002079002079, 0.002079002079002079, 0.3991683991683992, 0.5135135135135135, 0.3783783783783784, 0.6216216216216216, 0.058212058212058215, 0.4261954261954262, 0.37214137214137216, 0.316008316008316, 0.1101871101871102, 0.11850311850311851, 0.3264033264033264, 0.0997920997920998, 0.09355509355509356, 0.16632016632016633, 0.14553014553014554, 0.11434511434511435, 0.18087318087318088, 0.2806652806652807, 0.07484407484407485, 0.12474012474012475, 0.1496881496881497, 0.10395010395010396, 0.45114345114345117, 0.45114345114345117] \n",
      "\n",
      "Position-wise frequency of C in positive off-targets \n",
      " [0.010395010395010396, 0.010395010395010396, 0.006237006237006237, 0.2681912681912682, 0.33471933471933474, 0.12266112266112267, 0.20374220374220375, 0.11226611226611227, 0.16424116424116425, 0.19126819126819128, 0.0893970893970894, 0.3284823284823285, 0.3866943866943867, 0.3076923076923077, 0.12266112266112267, 0.48232848232848236, 0.3762993762993763, 0.5384615384615384, 0.2661122661122661, 0.04365904365904366, 0.42411642411642414, 0.13513513513513514, 0.3097713097713098, 0.3762993762993763, 0.19750519750519752, 0.18087318087318088, 0.2515592515592516] \n",
      "\n",
      "negative off-targets\n",
      "Position-wise frequency of A in negative off-targets \n",
      " [0.010395010395010396, 0.0, 0.0, 0.21621621621621623, 0.033264033264033266, 0.0498960498960499, 0.033264033264033266, 0.3908523908523909, 0.0498960498960499, 0.3887733887733888, 0.21413721413721415, 0.15176715176715178, 0.3575883575883576, 0.16216216216216217, 0.6340956340956341, 0.1496881496881497, 0.3367983367983368, 0.11434511434511435, 0.4885654885654886, 0.44282744282744285, 0.1683991683991684, 0.4095634095634096, 0.26403326403326405, 0.3264033264033264, 0.5426195426195426, 0.20166320166320167, 0.15384615384615385] \n",
      "\n",
      "Position-wise frequency of T in negative off-targets \n",
      " [0.7713097713097713, 0.9875259875259875, 0.9916839916839917, 0.11642411642411643, 0.11850311850311851, 0.4490644490644491, 0.14137214137214138, 0.4386694386694387, 0.35550935550935553, 0.04781704781704782, 0.3804573804573805, 0.4095634095634096, 0.13721413721413722, 0.20374220374220375, 0.14345114345114346, 0.2702702702702703, 0.12058212058212059, 0.20166320166320167, 0.13097713097713098, 0.33264033264033266, 0.12474012474012475, 0.3804573804573805, 0.30145530145530147, 0.14760914760914762, 0.15592515592515593, 0.16632016632016633, 0.14345114345114346] \n",
      "\n",
      "Position-wise frequency of G in negative off-targets \n",
      " [0.2079002079002079, 0.002079002079002079, 0.002079002079002079, 0.3991683991683992, 0.5135135135135135, 0.3783783783783784, 0.6216216216216216, 0.058212058212058215, 0.4261954261954262, 0.37214137214137216, 0.316008316008316, 0.1101871101871102, 0.11850311850311851, 0.3264033264033264, 0.0997920997920998, 0.09355509355509356, 0.16632016632016633, 0.14553014553014554, 0.11434511434511435, 0.18087318087318088, 0.2806652806652807, 0.07484407484407485, 0.12474012474012475, 0.1496881496881497, 0.10395010395010396, 0.45114345114345117, 0.45114345114345117] \n",
      "\n",
      "Position-wise frequency of C in negative off-targets \n",
      " [0.010395010395010396, 0.010395010395010396, 0.006237006237006237, 0.2681912681912682, 0.33471933471933474, 0.12266112266112267, 0.20374220374220375, 0.11226611226611227, 0.16424116424116425, 0.19126819126819128, 0.0893970893970894, 0.3284823284823285, 0.3866943866943867, 0.3076923076923077, 0.12266112266112267, 0.48232848232848236, 0.3762993762993763, 0.5384615384615384, 0.2661122661122661, 0.04365904365904366, 0.42411642411642414, 0.13513513513513514, 0.3097713097713098, 0.3762993762993763, 0.19750519750519752, 0.18087318087318088, 0.2515592515592516] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "  \n",
    "OT_data=pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_clean_dataset.csv\", encoding=\"cp1252\")\n",
    "\n",
    "df = pd.DataFrame(OT_data)\n",
    "POT = df[(df['Y'] == 1)]\n",
    "NOT = df[(df['Y'] == 0)]\n",
    "print(len(POT), len(NOT))\n",
    "l1=['A_OTSeqPosition1', 'A_OTSeqPosition2', 'A_OTSeqPosition3', 'A_OTSeqPosition4', 'A_OTSeqPosition5', 'A_OTSeqPosition6', 'A_OTSeqPosition7', 'A_OTSeqPosition8', 'A_OTSeqPosition9', 'A_OTSeqPosition10', 'A_OTSeqPosition11', 'A_OTSeqPosition12', 'A_OTSeqPosition13', 'A_OTSeqPosition14', 'A_OTSeqPosition15', 'A_OTSeqPosition16', 'A_OTSeqPosition17', 'A_OTSeqPosition18', 'A_OTSeqPosition19', 'A_OTSeqPosition20', 'A_OTSeqPosition21', 'A_OTSeqPosition22', 'A_OTSeqPosition23', 'A_OTSeqPosition24', 'A_OTSeqPosition25', 'A_OTSeqPosition26', 'A_OTSeqPosition27']\n",
    "l2=['T_OTSeqPosition1', 'T_OTSeqPosition2', 'T_OTSeqPosition3', 'T_OTSeqPosition4', 'T_OTSeqPosition5', 'T_OTSeqPosition6', 'T_OTSeqPosition7', 'T_OTSeqPosition8', 'T_OTSeqPosition9', 'T_OTSeqPosition10', 'T_OTSeqPosition11', 'T_OTSeqPosition12', 'T_OTSeqPosition13', 'T_OTSeqPosition14', 'T_OTSeqPosition15', 'T_OTSeqPosition16', 'T_OTSeqPosition17', 'T_OTSeqPosition18', 'T_OTSeqPosition19', 'T_OTSeqPosition20', 'T_OTSeqPosition21', 'T_OTSeqPosition22', 'T_OTSeqPosition23', 'T_OTSeqPosition24', 'T_OTSeqPosition25', 'T_OTSeqPosition26', 'T_OTSeqPosition27']\n",
    "l3=['G_OTSeqPosition1', 'G_OTSeqPosition2', 'G_OTSeqPosition3', 'G_OTSeqPosition4', 'G_OTSeqPosition5', 'G_OTSeqPosition6', 'G_OTSeqPosition7', 'G_OTSeqPosition8', 'G_OTSeqPosition9', 'G_OTSeqPosition10', 'G_OTSeqPosition11', 'G_OTSeqPosition12', 'G_OTSeqPosition13', 'G_OTSeqPosition14', 'G_OTSeqPosition15', 'G_OTSeqPosition16', 'G_OTSeqPosition17', 'G_OTSeqPosition18', 'G_OTSeqPosition19', 'G_OTSeqPosition20', 'G_OTSeqPosition21', 'G_OTSeqPosition22', 'G_OTSeqPosition23', 'G_OTSeqPosition24', 'G_OTSeqPosition25', 'G_OTSeqPosition26', 'G_OTSeqPosition27']\n",
    "l4=['C_OTSeqPosition1', 'C_OTSeqPosition2', 'C_OTSeqPosition3', 'C_OTSeqPosition4', 'C_OTSeqPosition5', 'C_OTSeqPosition6', 'C_OTSeqPosition7', 'C_OTSeqPosition8', 'C_OTSeqPosition9', 'C_OTSeqPosition10', 'C_OTSeqPosition11', 'C_OTSeqPosition12', 'C_OTSeqPosition13', 'C_OTSeqPosition14', 'C_OTSeqPosition15', 'C_OTSeqPosition16', 'C_OTSeqPosition17', 'C_OTSeqPosition18', 'C_OTSeqPosition19', 'C_OTSeqPosition20', 'C_OTSeqPosition21', 'C_OTSeqPosition22', 'C_OTSeqPosition23', 'C_OTSeqPosition24', 'C_OTSeqPosition25', 'C_OTSeqPosition26', 'C_OTSeqPosition27']\n",
    "print(\"positive off-targets\")\n",
    "POT_l1=[]\n",
    "POT_l2=[]\n",
    "POT_l3=[]\n",
    "POT_l4=[]\n",
    "\n",
    "for i, j, k, l in zip(l1, l2, l3, l4):\n",
    "    t0=POT[i].sum()\n",
    "    t1=POT[j].sum()\n",
    "    t2=POT[k].sum()\n",
    "    t3=POT[l].sum()\n",
    "    POT_A=t0/481\n",
    "    POT_T=t1/481\n",
    "    POT_G=t2/481\n",
    "    POT_C=t3/481\n",
    "    POT_l1.append(POT_A)\n",
    "    POT_l2.append(POT_T)\n",
    "    POT_l3.append(POT_G)\n",
    "    POT_l4.append(POT_C)\n",
    "print(\"Position-wise frequency of A in positive off-targets \\n\", POT_l1, \"\\n\")\n",
    "print(\"Position-wise frequency of T in positive off-targets \\n\", POT_l2, \"\\n\")\n",
    "print(\"Position-wise frequency of G in positive off-targets \\n\", POT_l3, \"\\n\")\n",
    "print(\"Position-wise frequency of C in positive off-targets \\n\", POT_l4, \"\\n\")\n",
    "print(\"negative off-targets\")\n",
    "NOT_l1=[]\n",
    "NOT_l2=[]\n",
    "NOT_l3=[]\n",
    "NOT_l4=[]\n",
    "for i, j, k, l in zip(l1, l2, l3, l4):\n",
    "    t0=NOT[i].sum()\n",
    "    t1=NOT[j].sum()\n",
    "    t2=NOT[k].sum()\n",
    "    t3=NOT[l].sum()\n",
    "    NOT_A=t0/58474\n",
    "    NOT_T=t1/58474\n",
    "    NOT_G=t2/58474\n",
    "    NOT_C=t3/58474\n",
    "    NOT_l1.append(NOT_A)\n",
    "    NOT_l2.append(NOT_T)\n",
    "    NOT_l3.append(NOT_G)\n",
    "    NOT_l4.append(NOT_C)\n",
    "print(\"Position-wise frequency of A in negative off-targets \\n\", POT_l1, \"\\n\")\n",
    "print(\"Position-wise frequency of T in negative off-targets \\n\", POT_l2, \"\\n\")\n",
    "print(\"Position-wise frequency of G in negative off-targets \\n\", POT_l3, \"\\n\")\n",
    "print(\"Position-wise frequency of C in negative off-targets \\n\", POT_l4, \"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "historic-mainland",
   "metadata": {},
   "source": [
    "# Enrichment analysis to study the position-specific favour and disfavour of Nucleotides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "invalid-course",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C_OTSeqPosition1\n",
      "ShapiroResult(statistic=0.07436710596084595, pvalue=5.6192068419425165e-43)\n",
      "ShapiroResult(statistic=0.5048091411590576, pvalue=1.7233495642348422e-34)\n",
      "Ttest_indResult(statistic=-10.563262488544801, pvalue=7.855092999137652e-24)\n",
      "\n",
      "\n",
      "G_OTSeqPosition1\n",
      "ShapiroResult(statistic=0.4982829689979553, pvalue=1.1713899929102465e-34)\n",
      "ShapiroResult(statistic=0.32916349172592163, pvalue=1.7341358564801726e-38)\n",
      "Ttest_indResult(statistic=5.0155682784630855, pvalue=6.40878744724475e-07)\n",
      "\n",
      "\n",
      "T_OTSeqPosition1\n",
      "ShapiroResult(statistic=0.5192270874977112, pvalue=4.1039639377625044e-34)\n",
      "ShapiroResult(statistic=0.6336281895637512, pvalue=9.487129965656043e-31)\n",
      "Ttest_indResult(statistic=7.62110437682325, pvalue=6.171696875097229e-14)\n",
      "\n",
      "\n",
      "A_OTSeqPosition1\n",
      "ShapiroResult(statistic=0.07436710596084595, pvalue=5.6192068419425165e-43)\n",
      "ShapiroResult(statistic=0.42270565032958984, pvalue=1.7581482435207314e-36)\n",
      "Ttest_indResult(statistic=-8.148510395015098, pvalue=2.4320611206937473e-15)\n",
      "\n",
      "\n",
      "C_OTSeqPosition2\n",
      "ShapiroResult(statistic=0.07436710596084595, pvalue=5.6192068419425165e-43)\n",
      "ShapiroResult(statistic=0.5307317972183228, pvalue=8.32672259993514e-34)\n",
      "Ttest_indResult(statistic=-11.499863886571012, pvalue=1.697618944157782e-27)\n",
      "\n",
      "\n",
      "G_OTSeqPosition2\n",
      "ShapiroResult(statistic=0.021795332431793213, pvalue=8.96831017167883e-44)\n",
      "ShapiroResult(statistic=0.3496665358543396, pvalue=4.553054969512416e-38)\n",
      "Ttest_indResult(statistic=-7.232849861087101, pvalue=1.7807289105904766e-12)\n",
      "\n",
      "\n",
      "T_OTSeqPosition2\n",
      "ShapiroResult(statistic=0.08509361743927002, pvalue=8.253647954873173e-43)\n",
      "ShapiroResult(statistic=0.6355574727058411, pvalue=1.0982004250805785e-30)\n",
      "Ttest_indResult(statistic=19.768508643484243, pvalue=1.6981882568404225e-65)\n",
      "\n",
      "\n",
      "A_OTSeqPosition2\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.393699049949646, pvalue=3.947864486869295e-37)\n",
      "Ttest_indResult(statistic=-8.427707457149516, pvalue=4.1364429460251914e-16)\n",
      "\n",
      "\n",
      "C_OTSeqPosition3\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "ShapiroResult(statistic=0.36510246992111206, pvalue=9.573998451588417e-38)\n",
      "Ttest_indResult(statistic=-7.139678721686124, pvalue=3.0385132160897857e-12)\n",
      "\n",
      "\n",
      "G_OTSeqPosition3\n",
      "ShapiroResult(statistic=0.021795332431793213, pvalue=8.96831017167883e-44)\n",
      "ShapiroResult(statistic=0.3970785140991211, pvalue=4.6840600648126615e-37)\n",
      "Ttest_indResult(statistic=-8.295288706467778, pvalue=1.0224798039660925e-15)\n",
      "\n",
      "\n",
      "T_OTSeqPosition3\n",
      "ShapiroResult(statistic=0.06296718120574951, pvalue=3.7414668997472616e-43)\n",
      "ShapiroResult(statistic=0.6161561012268066, pvalue=2.5909524727293456e-31)\n",
      "Ttest_indResult(statistic=16.58264413683971, pvalue=8.989183398075319e-50)\n",
      "\n",
      "\n",
      "A_OTSeqPosition3\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.4101952314376831, pvalue=9.164292860198492e-37)\n",
      "Ttest_indResult(statistic=-8.813691588675534, pvalue=2.233529633406677e-17)\n",
      "\n",
      "\n",
      "C_OTSeqPosition4\n",
      "ShapiroResult(statistic=0.5531560778617859, pvalue=3.4451586798571304e-33)\n",
      "ShapiroResult(statistic=0.4069744944572449, pvalue=7.763250889544585e-37)\n",
      "Ttest_indResult(statistic=5.115657311029885, pvalue=3.8177718998097987e-07)\n",
      "\n",
      "\n",
      "G_OTSeqPosition4\n",
      "ShapiroResult(statistic=0.621577262878418, pvalue=3.8559781156831425e-31)\n",
      "ShapiroResult(statistic=0.5594097375869751, pvalue=5.171581151628915e-33)\n",
      "Ttest_indResult(statistic=4.0518977344646885, pvalue=5.494379857063343e-05)\n",
      "\n",
      "\n",
      "T_OTSeqPosition4\n",
      "ShapiroResult(statistic=0.3725241422653198, pvalue=1.3758439337722953e-37)\n",
      "ShapiroResult(statistic=0.613891065120697, pvalue=2.1973036298225885e-31)\n",
      "Ttest_indResult(statistic=-9.799527020910077, pvalue=1.5633515950798176e-21)\n",
      "\n",
      "\n",
      "A_OTSeqPosition4\n",
      "ShapiroResult(statistic=0.5069360733032227, pvalue=1.956136246194782e-34)\n",
      "ShapiroResult(statistic=0.4982829689979553, pvalue=1.1713899929102465e-34)\n",
      "Ttest_indResult(statistic=0.3151870685601008, pvalue=0.7526881353199348)\n",
      "\n",
      "\n",
      "C_OTSeqPosition5\n",
      "ShapiroResult(statistic=0.5955262184143066, pvalue=5.937655889379188e-32)\n",
      "ShapiroResult(statistic=0.47243446111679077, pvalue=2.6388028685192724e-35)\n",
      "Ttest_indResult(statistic=5.366314711254275, pvalue=1.0160228024901859e-07)\n",
      "\n",
      "\n",
      "G_OTSeqPosition5\n",
      "ShapiroResult(statistic=0.6362714767456055, pvalue=1.159479980023973e-30)\n",
      "ShapiroResult(statistic=0.5563204288482666, pvalue=4.228892354961028e-33)\n",
      "Ttest_indResult(statistic=7.894018589058981, pvalue=8.0619098551106e-15)\n",
      "\n",
      "\n",
      "T_OTSeqPosition5\n",
      "ShapiroResult(statistic=0.37616437673568726, pvalue=1.6457565508805615e-37)\n",
      "ShapiroResult(statistic=0.5840963125228882, pvalue=2.693079304379351e-32)\n",
      "Ttest_indResult(statistic=-7.570742610838049, pvalue=9.593650659688459e-14)\n",
      "\n",
      "\n",
      "A_OTSeqPosition5\n",
      "ShapiroResult(statistic=0.1707882285118103, pvalue=2.053462769621587e-41)\n",
      "ShapiroResult(statistic=0.5152202844619751, pvalue=3.2179125711336465e-34)\n",
      "Ttest_indResult(statistic=-9.226564614953661, pvalue=3.8437735740948724e-19)\n",
      "\n",
      "\n",
      "C_OTSeqPosition6\n",
      "ShapiroResult(statistic=0.3833100199699402, pvalue=2.3450844146430503e-37)\n",
      "ShapiroResult(statistic=0.4101952314376831, pvalue=9.164292860198492e-37)\n",
      "Ttest_indResult(statistic=-0.7639574431852211, pvalue=0.44508086826817606)\n",
      "\n",
      "\n",
      "G_OTSeqPosition6\n",
      "ShapiroResult(statistic=0.6146578788757324, pvalue=2.3231736841176777e-31)\n",
      "ShapiroResult(statistic=0.5563204288482666, pvalue=4.228892354961028e-33)\n",
      "Ttest_indResult(statistic=3.5286584089639557, pvalue=0.0004376188763802074)\n",
      "\n",
      "\n",
      "T_OTSeqPosition6\n",
      "ShapiroResult(statistic=0.6327558159828186, pvalue=8.881495527807307e-31)\n",
      "ShapiroResult(statistic=0.593363344669342, pvalue=5.10560160880642e-32)\n",
      "Ttest_indResult(statistic=3.7923178032451403, pvalue=0.00015860148050935672)\n",
      "\n",
      "\n",
      "A_OTSeqPosition6\n",
      "ShapiroResult(statistic=0.22354954481124878, pvalue=1.7068936334247732e-40)\n",
      "ShapiroResult(statistic=0.5325759649276733, pvalue=9.338798440409167e-34)\n",
      "Ttest_indResult(statistic=-8.804394641377698, pvalue=9.917261350386679e-18)\n",
      "\n",
      "\n",
      "C_OTSeqPosition7\n",
      "ShapiroResult(statistic=0.49381202459335327, pvalue=9.012443441273105e-35)\n",
      "ShapiroResult(statistic=0.4982829689979553, pvalue=1.1713899929102465e-34)\n",
      "Ttest_indResult(statistic=-0.15932799088244537, pvalue=0.8734439841392977)\n",
      "\n",
      "\n",
      "G_OTSeqPosition7\n",
      "ShapiroResult(statistic=0.6146535277366638, pvalue=2.322423483622983e-31)\n",
      "ShapiroResult(statistic=0.5379865765571594, pvalue=1.310281216697875e-33)\n",
      "Ttest_indResult(statistic=12.544169848189098, pvalue=1.665891082740609e-33)\n",
      "\n",
      "\n",
      "T_OTSeqPosition7\n",
      "ShapiroResult(statistic=0.4133777618408203, pvalue=1.0804845212790306e-36)\n",
      "ShapiroResult(statistic=0.6182907819747925, pvalue=3.0284279768144206e-31)\n",
      "Ttest_indResult(statistic=-9.04620422543357, pvalue=9.494362427254673e-19)\n",
      "\n",
      "\n",
      "A_OTSeqPosition7\n",
      "ShapiroResult(statistic=0.1707882285118103, pvalue=2.053462769621587e-41)\n",
      "ShapiroResult(statistic=0.42270565032958984, pvalue=1.7581482435207314e-36)\n",
      "Ttest_indResult(statistic=-6.302888400470255, pvalue=5.127339701578535e-10)\n",
      "\n",
      "\n",
      "C_OTSeqPosition8\n",
      "ShapiroResult(statistic=0.36510246992111206, pvalue=9.573998451588417e-38)\n",
      "ShapiroResult(statistic=0.557874321937561, pvalue=4.6787147374283616e-33)\n",
      "Ttest_indResult(statistic=-6.499717900310178, pvalue=1.3585127785853077e-10)\n",
      "\n",
      "\n",
      "G_OTSeqPosition8\n",
      "ShapiroResult(statistic=0.24672436714172363, pvalue=4.492703006471796e-40)\n",
      "ShapiroResult(statistic=0.3496665358543396, pvalue=4.553054969512416e-38)\n",
      "Ttest_indResult(statistic=-2.605040480649892, pvalue=0.009337982856372447)\n",
      "\n",
      "\n",
      "T_OTSeqPosition8\n",
      "ShapiroResult(statistic=0.6310427188873291, pvalue=7.805351608527817e-31)\n",
      "ShapiroResult(statistic=0.5482719540596008, pvalue=2.516500510292261e-33)\n",
      "Ttest_indResult(statistic=5.839545785868173, pvalue=7.190400581313432e-09)\n",
      "\n",
      "\n",
      "A_OTSeqPosition8\n",
      "ShapiroResult(statistic=0.618972897529602, pvalue=3.1837234758970337e-31)\n",
      "ShapiroResult(statistic=0.5996679067611694, pvalue=7.942263433908486e-32)\n",
      "Ttest_indResult(statistic=1.5388718396392174, pvalue=0.12416536002458922)\n",
      "\n",
      "\n",
      "C_OTSeqPosition9\n",
      "ShapiroResult(statistic=0.44605517387390137, pvalue=6.119667068748541e-36)\n",
      "ShapiroResult(statistic=0.5609281063079834, pvalue=5.711660929088261e-33)\n",
      "Ttest_indResult(statistic=-4.307492058587992, pvalue=1.827251446848538e-05)\n",
      "\n",
      "\n",
      "G_OTSeqPosition9\n",
      "ShapiroResult(statistic=0.6285660266876221, pvalue=6.4812601956316426e-31)\n",
      "ShapiroResult(statistic=0.4821966886520386, pvalue=4.600421980820612e-35)\n",
      "Ttest_indResult(statistic=8.060892318967236, pvalue=2.3576190835226935e-15)\n",
      "\n",
      "\n",
      "T_OTSeqPosition9\n",
      "ShapiroResult(statistic=0.6054351329803467, pvalue=1.195674532540594e-31)\n",
      "ShapiroResult(statistic=0.5900065898895264, pvalue=4.044312411638272e-32)\n",
      "Ttest_indResult(statistic=1.0204730989474644, pvalue=0.3077613596564927)\n",
      "\n",
      "\n",
      "A_OTSeqPosition9\n",
      "ShapiroResult(statistic=0.22354954481124878, pvalue=1.7068936334247732e-40)\n",
      "ShapiroResult(statistic=0.47979533672332764, pvalue=4.0094209589862204e-35)\n",
      "Ttest_indResult(statistic=-6.889867284290804, pvalue=1.1849376363847236e-11)\n",
      "\n",
      "\n",
      "C_OTSeqPosition10\n",
      "ShapiroResult(statistic=0.47979533672332764, pvalue=4.0094209589862204e-35)\n",
      "ShapiroResult(statistic=0.4960593581199646, pvalue=1.0279570676189985e-34)\n",
      "Ttest_indResult(statistic=-0.5652779564095956, pvalue=0.5720168985534939)\n",
      "\n",
      "\n",
      "G_OTSeqPosition10\n",
      "ShapiroResult(statistic=0.6123144030570984, pvalue=1.9600498434142036e-31)\n",
      "ShapiroResult(statistic=0.49153822660446167, pvalue=7.893183408579461e-35)\n",
      "Ttest_indResult(statistic=5.945451112734385, pvalue=3.898972166319118e-09)\n",
      "\n",
      "\n",
      "T_OTSeqPosition10\n",
      "ShapiroResult(statistic=0.2174607515335083, pvalue=1.328907385657797e-40)\n",
      "ShapiroResult(statistic=0.5696666240692139, pvalue=1.0171061848773786e-32)\n",
      "Ttest_indResult(statistic=-10.618644115034066, pvalue=1.762015416520888e-24)\n",
      "\n",
      "\n",
      "A_OTSeqPosition10\n",
      "ShapiroResult(statistic=0.6182907819747925, pvalue=3.0284279768144206e-31)\n",
      "ShapiroResult(statistic=0.5738013982772827, pvalue=1.3408170518162848e-32)\n",
      "Ttest_indResult(statistic=2.9991480902852516, pvalue=0.002777489652848555)\n",
      "\n",
      "\n",
      "C_OTSeqPosition11\n",
      "ShapiroResult(statistic=0.3205522894859314, pvalue=1.1644567432083402e-38)\n",
      "ShapiroResult(statistic=0.4165235757827759, pvalue=1.272372638106712e-36)\n",
      "Ttest_indResult(statistic=-2.6202209741805382, pvalue=0.008931783743671668)\n",
      "\n",
      "\n",
      "G_OTSeqPosition11\n",
      "ShapiroResult(statistic=0.5853107571601868, pvalue=2.926622701644083e-32)\n",
      "ShapiroResult(statistic=0.4821966886520386, pvalue=4.600421980820612e-35)\n",
      "Ttest_indResult(statistic=4.405459690255935, pvalue=1.1775936506172224e-05)\n",
      "\n",
      "\n",
      "T_OTSeqPosition11\n",
      "ShapiroResult(statistic=0.615409255027771, pvalue=2.4537120966776226e-31)\n",
      "ShapiroResult(statistic=0.6261478662490845, pvalue=5.410619470704963e-31)\n",
      "Ttest_indResult(statistic=-1.119255007419186, pvalue=0.2633113903536616)\n",
      "\n",
      "\n",
      "A_OTSeqPosition11\n",
      "ShapiroResult(statistic=0.5048091411590576, pvalue=1.7233495642348422e-34)\n",
      "ShapiroResult(statistic=0.5325759649276733, pvalue=9.338798440409167e-34)\n",
      "Ttest_indResult(statistic=-1.0742645424001565, pvalue=0.2829746092461406)\n",
      "\n",
      "\n",
      "C_OTSeqPosition12\n",
      "ShapiroResult(statistic=0.5922608375549316, pvalue=4.72865289659236e-32)\n",
      "ShapiroResult(statistic=0.4257427453994751, pvalue=2.062970882382499e-36)\n",
      "Ttest_indResult(statistic=6.6415537210150974, pvalue=5.378552940702746e-11)\n",
      "\n",
      "\n",
      "G_OTSeqPosition12\n",
      "ShapiroResult(statistic=0.3613191246986389, pvalue=7.968728664354636e-38)\n",
      "ShapiroResult(statistic=0.4982829689979553, pvalue=1.1713899929102465e-34)\n",
      "Ttest_indResult(statistic=-4.176609178200454, pvalue=3.2461339330663936e-05)\n",
      "\n",
      "\n",
      "T_OTSeqPosition12\n",
      "ShapiroResult(statistic=0.6245303153991699, pvalue=4.79757048468898e-31)\n",
      "ShapiroResult(statistic=0.5955262184143066, pvalue=5.937655889379188e-32)\n",
      "Ttest_indResult(statistic=2.405933039415579, pvalue=0.016319724051864503)\n",
      "\n",
      "\n",
      "A_OTSeqPosition12\n",
      "ShapiroResult(statistic=0.42874568700790405, pvalue=2.4179296036956133e-36)\n",
      "ShapiroResult(statistic=0.5751444101333618, pvalue=1.4674042752148108e-32)\n",
      "Ttest_indResult(statistic=-5.558592148632521, pvalue=3.5752633815472633e-08)\n",
      "\n",
      "\n",
      "C_OTSeqPosition13\n",
      "ShapiroResult(statistic=0.6175906658172607, pvalue=2.877134330699747e-31)\n",
      "ShapiroResult(statistic=0.5710628032684326, pvalue=1.116306520485256e-32)\n",
      "Ttest_indResult(statistic=3.074789949909836, pvalue=0.0021663581707460984)\n",
      "\n",
      "\n",
      "G_OTSeqPosition13\n",
      "ShapiroResult(statistic=0.37616437673568726, pvalue=1.6457565508805615e-37)\n",
      "ShapiroResult(statistic=0.5211982131004333, pvalue=4.628270234072746e-34)\n",
      "Ttest_indResult(statistic=-4.6319520967134915, pvalue=4.1563014906099734e-06)\n",
      "\n",
      "\n",
      "T_OTSeqPosition13\n",
      "ShapiroResult(statistic=0.4069744944572449, pvalue=7.763250889544585e-37)\n",
      "ShapiroResult(statistic=0.5790814757347107, pvalue=1.9141159322878842e-32)\n",
      "Ttest_indResult(statistic=-6.416659365627601, pvalue=2.2628915979747106e-10)\n",
      "\n",
      "\n",
      "A_OTSeqPosition13\n",
      "ShapiroResult(statistic=0.6063467264175415, pvalue=1.2761129056707527e-31)\n",
      "ShapiroResult(statistic=0.4432511329650879, pvalue=5.256661288956569e-36)\n",
      "Ttest_indResult(statistic=7.081242989051183, pvalue=2.8767981266294272e-12)\n",
      "\n",
      "\n",
      "C_OTSeqPosition14\n",
      "ShapiroResult(statistic=0.5803600549697876, pvalue=2.087559682941331e-32)\n",
      "ShapiroResult(statistic=0.5269802808761597, pvalue=6.601101025525349e-34)\n",
      "Ttest_indResult(statistic=2.46767473174276, pvalue=0.013774014550670118)\n",
      "\n",
      "\n",
      "G_OTSeqPosition14\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n",
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ShapiroResult(statistic=0.5911405086517334, pvalue=4.374813521694703e-32)\n",
      "ShapiroResult(statistic=0.5288666486740112, pvalue=7.417305068841324e-34)\n",
      "Ttest_indResult(statistic=3.018050646317449, pvalue=0.002611834022875011)\n",
      "\n",
      "\n",
      "T_OTSeqPosition14\n",
      "ShapiroResult(statistic=0.49381202459335327, pvalue=9.012443441273105e-35)\n",
      "ShapiroResult(statistic=0.5449211001396179, pvalue=2.0318546586947938e-33)\n",
      "Ttest_indResult(statistic=-1.991645345467583, pvalue=0.04669487406597801)\n",
      "\n",
      "\n",
      "A_OTSeqPosition14\n",
      "ShapiroResult(statistic=0.4432511329650879, pvalue=5.256661288956569e-36)\n",
      "ShapiroResult(statistic=0.539747953414917, pvalue=1.4639823376911701e-33)\n",
      "Ttest_indResult(statistic=-3.4401376082565505, pvalue=0.0006071125720037283)\n",
      "\n",
      "\n",
      "C_OTSeqPosition15\n",
      "ShapiroResult(statistic=0.3833100199699402, pvalue=2.3450844146430503e-37)\n",
      "ShapiroResult(statistic=0.5414925217628479, pvalue=1.6345588739519692e-33)\n",
      "Ttest_indResult(statistic=-5.266176893662656, pvalue=1.7464510843222493e-07)\n",
      "\n",
      "\n",
      "G_OTSeqPosition15\n",
      "ShapiroResult(statistic=0.3416340947151184, pvalue=3.1102632168798623e-38)\n",
      "ShapiroResult(statistic=0.5764721632003784, pvalue=1.6046307510458353e-32)\n",
      "Ttest_indResult(statistic=-8.06097275568872, pvalue=2.6458946194754817e-15)\n",
      "\n",
      "\n",
      "T_OTSeqPosition15\n",
      "ShapiroResult(statistic=0.4165235757827759, pvalue=1.272372638106712e-36)\n",
      "ShapiroResult(statistic=0.47979533672332764, pvalue=4.0094209589862204e-35)\n",
      "Ttest_indResult(statistic=-1.988505651939088, pvalue=0.04704309989074757)\n",
      "\n",
      "\n",
      "A_OTSeqPosition15\n",
      "ShapiroResult(statistic=0.6098364591598511, pvalue=1.6390780546368658e-31)\n",
      "ShapiroResult(statistic=0.5343992710113525, pvalue=1.0464000918470454e-33)\n",
      "Ttest_indResult(statistic=13.187639528536751, pvalue=1.3868906227669272e-36)\n",
      "\n",
      "\n",
      "C_OTSeqPosition16\n",
      "ShapiroResult(statistic=0.6360868215560913, pvalue=1.14330169829338e-30)\n",
      "ShapiroResult(statistic=0.46482735872268677, pvalue=1.7210768365950798e-35)\n",
      "Ttest_indResult(statistic=10.56084918269732, pvalue=1.1717738969140649e-24)\n",
      "\n",
      "\n",
      "G_OTSeqPosition16\n",
      "ShapiroResult(statistic=0.32916349172592163, pvalue=1.7341358564801726e-38)\n",
      "ShapiroResult(statistic=0.42270565032958984, pvalue=1.7581482435207314e-36)\n",
      "Ttest_indResult(statistic=-2.580451857214151, pvalue=0.010020246395995032)\n",
      "\n",
      "\n",
      "T_OTSeqPosition16\n",
      "ShapiroResult(statistic=0.5547469854354858, pvalue=3.818588255858416e-33)\n",
      "ShapiroResult(statistic=0.5976288318634033, pvalue=6.880498161528254e-32)\n",
      "Ttest_indResult(statistic=-2.3158607539672103, pvalue=0.020776654929013042)\n",
      "\n",
      "\n",
      "A_OTSeqPosition16\n",
      "ShapiroResult(statistic=0.4257427453994751, pvalue=2.062970882382499e-36)\n",
      "ShapiroResult(statistic=0.5911405086517334, pvalue=4.374813521694703e-32)\n",
      "Ttest_indResult(statistic=-6.571106543591531, pvalue=8.461550504757269e-11)\n",
      "\n",
      "\n",
      "C_OTSeqPosition17\n",
      "ShapiroResult(statistic=0.613891065120697, pvalue=2.1973036298225885e-31)\n",
      "ShapiroResult(statistic=0.4404166340827942, pvalue=4.510752223949359e-36)\n",
      "Ttest_indResult(statistic=7.796589737821439, pvalue=1.7660783262707868e-14)\n",
      "\n",
      "\n",
      "G_OTSeqPosition17\n",
      "ShapiroResult(statistic=0.448827862739563, pvalue=7.116596624334193e-36)\n",
      "ShapiroResult(statistic=0.45961201190948486, pvalue=1.2875891879116198e-35)\n",
      "Ttest_indResult(statistic=-0.34260901597124455, pvalue=0.731967645973897)\n",
      "\n",
      "\n",
      "T_OTSeqPosition17\n",
      "ShapiroResult(statistic=0.3797592520713806, pvalue=1.9658843060943374e-37)\n",
      "ShapiroResult(statistic=0.5269802808761597, pvalue=6.601101025525349e-34)\n",
      "Ttest_indResult(statistic=-4.762293894613491, pvalue=2.2303615262288393e-06)\n",
      "\n",
      "\n",
      "A_OTSeqPosition17\n",
      "ShapiroResult(statistic=0.5965856909751892, pvalue=6.394857364165412e-32)\n",
      "ShapiroResult(statistic=0.6256217956542969, pvalue=5.202865420327074e-31)\n",
      "Ttest_indResult(statistic=-2.4689996018084694, pvalue=0.013722589171701072)\n",
      "\n",
      "\n",
      "C_OTSeqPosition18\n",
      "ShapiroResult(statistic=0.6343802213668823, pvalue=1.0043177349809692e-30)\n",
      "ShapiroResult(statistic=0.47979533672332764, pvalue=4.0094209589862204e-35)\n",
      "Ttest_indResult(statistic=11.979228640870993, pvalue=8.231050834397696e-31)\n",
      "\n",
      "\n",
      "G_OTSeqPosition18\n",
      "ShapiroResult(statistic=0.41963279247283936, pvalue=1.4965555501795072e-36)\n",
      "ShapiroResult(statistic=0.51112300157547, pvalue=2.5135538306447875e-34)\n",
      "Ttest_indResult(statistic=-3.013106222294548, pvalue=0.0026554984363120065)\n",
      "\n",
      "\n",
      "T_OTSeqPosition18\n",
      "ShapiroResult(statistic=0.49153822660446167, pvalue=7.893183408579461e-35)\n",
      "ShapiroResult(statistic=0.5828676223754883, pvalue=2.4761738541561576e-32)\n",
      "Ttest_indResult(statistic=-3.939050945385421, pvalue=8.78487021516024e-05)\n",
      "\n",
      "\n",
      "A_OTSeqPosition18\n",
      "ShapiroResult(statistic=0.36883705854415894, pvalue=1.148538749354781e-37)\n",
      "ShapiroResult(statistic=0.5466068983078003, pvalue=2.262359549999444e-33)\n",
      "Ttest_indResult(statistic=-5.884205768069083, pvalue=5.690738005180409e-09)\n",
      "\n",
      "\n",
      "C_OTSeqPosition19\n",
      "ShapiroResult(statistic=0.551547646522522, pvalue=3.105667462474007e-33)\n",
      "ShapiroResult(statistic=0.4749152660369873, pvalue=3.036723490498519e-35)\n",
      "Ttest_indResult(statistic=2.936611018846326, pvalue=0.003398652838891074)\n",
      "\n",
      "\n",
      "G_OTSeqPosition19\n",
      "ShapiroResult(statistic=0.36883705854415894, pvalue=1.148538749354781e-37)\n",
      "ShapiroResult(statistic=0.5563204288482666, pvalue=4.228892354961028e-33)\n",
      "Ttest_indResult(statistic=-6.326015788043638, pvalue=4.022427028944239e-10)\n",
      "\n",
      "\n",
      "T_OTSeqPosition19\n",
      "ShapiroResult(statistic=0.3970785140991211, pvalue=4.6840600648126615e-37)\n",
      "ShapiroResult(statistic=0.51112300157547, pvalue=2.5135538306447875e-34)\n",
      "Ttest_indResult(statistic=-3.6647209677136803, pvalue=0.0002617394145171427)\n",
      "\n",
      "\n",
      "A_OTSeqPosition19\n",
      "ShapiroResult(statistic=0.6363468170166016, pvalue=1.1661455031908758e-30)\n",
      "ShapiroResult(statistic=0.5790814757347107, pvalue=1.9141159322878842e-32)\n",
      "Ttest_indResult(statistic=5.896552754021886, pvalue=5.147983751823318e-09)\n",
      "\n",
      "\n",
      "C_OTSeqPosition20\n",
      "ShapiroResult(statistic=0.2048877477645874, pvalue=7.965120601068693e-41)\n",
      "ShapiroResult(statistic=0.37616437673568726, pvalue=1.6457565508805615e-37)\n",
      "Ttest_indResult(statistic=-4.28829801000725, pvalue=2.0173084814334085e-05)\n",
      "\n",
      "\n",
      "G_OTSeqPosition20\n",
      "ShapiroResult(statistic=0.46739113330841064, pvalue=1.9866246264684958e-35)\n",
      "ShapiroResult(statistic=0.5790814757347107, pvalue=1.9141159322878842e-32)\n",
      "Ttest_indResult(statistic=-4.552514536059855, pvalue=6.0038240242975575e-06)\n",
      "\n",
      "\n",
      "T_OTSeqPosition20\n",
      "ShapiroResult(statistic=0.5944548845291138, pvalue=5.509391559027156e-32)\n",
      "ShapiroResult(statistic=0.5790814757347107, pvalue=1.9141159322878842e-32)\n",
      "Ttest_indResult(statistic=0.8986109516790086, pvalue=0.3690853617100588)\n",
      "\n",
      "\n",
      "A_OTSeqPosition20\n",
      "ShapiroResult(statistic=0.6317695379257202, pvalue=8.244573191921604e-31)\n",
      "ShapiroResult(statistic=0.5482719540596008, pvalue=2.516500510292261e-33)\n",
      "Ttest_indResult(statistic=5.973599765630328, pvalue=3.2819521908011727e-09)\n",
      "\n",
      "\n",
      "C_OTSeqPosition21\n",
      "ShapiroResult(statistic=0.6281087398529053, pvalue=6.263315549627605e-31)\n",
      "ShapiroResult(statistic=0.5004816055297852, pvalue=1.3334827430770963e-34)\n",
      "Ttest_indResult(statistic=7.325751740494549, pvalue=5.164521414062217e-13)\n",
      "\n",
      "\n",
      "G_OTSeqPosition21\n",
      "ShapiroResult(statistic=0.5624300241470337, pvalue=6.303030831151845e-33)\n",
      "ShapiroResult(statistic=0.5004816055297852, pvalue=1.3334827430770963e-34)\n",
      "Ttest_indResult(statistic=2.553643536370599, pvalue=0.010815261640889416)\n",
      "\n",
      "\n",
      "T_OTSeqPosition21\n",
      "ShapiroResult(statistic=0.38681548833847046, pvalue=2.793425710460321e-37)\n",
      "ShapiroResult(statistic=0.43754804134368896, pvalue=3.8660785203572805e-36)\n",
      "Ttest_indResult(statistic=-1.4807834028247921, pvalue=0.1389955966199854)\n",
      "\n",
      "\n",
      "A_OTSeqPosition21\n",
      "ShapiroResult(statistic=0.4515693783760071, pvalue=8.267038538790506e-36)\n",
      "ShapiroResult(statistic=0.6245303153991699, pvalue=4.79757048468898e-31)\n",
      "Ttest_indResult(statistic=-8.5502782213726, pvalue=5.251914155173368e-17)\n",
      "\n",
      "\n",
      "C_OTSeqPosition22\n",
      "ShapiroResult(statistic=0.40371596813201904, pvalue=6.568589914638894e-37)\n",
      "ShapiroResult(statistic=0.5609281063079834, pvalue=5.711660929088261e-33)\n",
      "Ttest_indResult(statistic=-5.574601178772728, pvalue=3.2821502641712685e-08)\n",
      "\n",
      "\n",
      "G_OTSeqPosition22\n",
      "ShapiroResult(statistic=0.28831833600997925, pvalue=2.717629196265299e-39)\n",
      "ShapiroResult(statistic=0.38681548833847046, pvalue=2.793425710460321e-37)\n",
      "Ttest_indResult(statistic=-2.587986556882522, pvalue=0.009807051272742702)\n",
      "\n",
      "\n",
      "T_OTSeqPosition22\n",
      "ShapiroResult(statistic=0.615409255027771, pvalue=2.4537120966776226e-31)\n",
      "ShapiroResult(statistic=0.5343992710113525, pvalue=1.0464000918470454e-33)\n",
      "Ttest_indResult(statistic=4.563792082905631, pvalue=5.684983027145421e-06)\n",
      "\n",
      "\n",
      "A_OTSeqPosition22\n",
      "ShapiroResult(statistic=0.6245303153991699, pvalue=4.79757048468898e-31)\n",
      "ShapiroResult(statistic=0.5976288318634033, pvalue=6.880498161528254e-32)\n",
      "Ttest_indResult(statistic=2.2689577900779563, pvalue=0.023492456970202084)\n",
      "\n",
      "\n",
      "C_OTSeqPosition23\n",
      "ShapiroResult(statistic=0.5816184282302856, pvalue=2.2740490699002038e-32)\n",
      "ShapiroResult(statistic=0.5288666486740112, pvalue=7.417305068841324e-34)\n",
      "Ttest_indResult(statistic=2.4617921758187893, pvalue=0.014000473006345562)\n",
      "\n",
      "\n",
      "G_OTSeqPosition23\n",
      "ShapiroResult(statistic=0.38681548833847046, pvalue=2.793425710460321e-37)\n",
      "ShapiroResult(statistic=0.47243446111679077, pvalue=2.6388028685192724e-35)\n",
      "Ttest_indResult(statistic=-2.5906456921164525, pvalue=0.009728595562963148)\n",
      "\n",
      "\n",
      "T_OTSeqPosition23\n",
      "ShapiroResult(statistic=0.5764721632003784, pvalue=1.6046307510458353e-32)\n",
      "ShapiroResult(statistic=0.5449211001396179, pvalue=2.0318546586947938e-33)\n",
      "Ttest_indResult(statistic=1.5087818809316482, pvalue=0.13168431123406193)\n",
      "\n",
      "\n",
      "A_OTSeqPosition23\n",
      "ShapiroResult(statistic=0.549917995929718, pvalue=2.7966595686725907e-33)\n",
      "ShapiroResult(statistic=0.5803600549697876, pvalue=2.087559682941331e-32)\n",
      "Ttest_indResult(statistic=-1.4987102113823279, pvalue=0.13427826535639226)\n",
      "\n",
      "\n",
      "C_OTSeqPosition24\n",
      "ShapiroResult(statistic=0.613891065120697, pvalue=2.1973036298225885e-31)\n",
      "ShapiroResult(statistic=0.5449211001396179, pvalue=2.0318546586947938e-33)\n",
      "Ttest_indResult(statistic=3.9776585717805903, pvalue=7.488486634026625e-05)\n",
      "\n",
      "\n",
      "G_OTSeqPosition24\n",
      "ShapiroResult(statistic=0.4257427453994751, pvalue=2.062970882382499e-36)\n",
      "ShapiroResult(statistic=0.36510246992111206, pvalue=9.573998451588417e-38)\n",
      "Ttest_indResult(statistic=1.7210308556308909, pvalue=0.08557219370356091)\n",
      "\n",
      "\n",
      "T_OTSeqPosition24\n",
      "ShapiroResult(statistic=0.42270565032958984, pvalue=1.7581482435207314e-36)\n",
      "ShapiroResult(statistic=0.5231471061706543, pvalue=5.214692682451811e-34)\n",
      "Ttest_indResult(statistic=-3.3845502570319224, pvalue=0.0007426905996886382)\n",
      "\n",
      "\n",
      "A_OTSeqPosition24\n",
      "ShapiroResult(statistic=0.5911405086517334, pvalue=4.374813521694703e-32)\n",
      "ShapiroResult(statistic=0.615409255027771, pvalue=2.4537120966776226e-31)\n",
      "Ttest_indResult(statistic=-1.754565319164065, pvalue=0.07965308344047463)\n",
      "\n",
      "\n",
      "C_OTSeqPosition25\n",
      "ShapiroResult(statistic=0.4869193434715271, pvalue=6.037999053451904e-35)\n",
      "ShapiroResult(statistic=0.448827862739563, pvalue=7.116596624334193e-36)\n",
      "Ttest_indResult(statistic=1.2533591228810248, pvalue=0.21038156337479524)\n",
      "\n",
      "\n",
      "G_OTSeqPosition25\n",
      "ShapiroResult(statistic=0.3496665358543396, pvalue=4.553054969512416e-38)\n",
      "ShapiroResult(statistic=0.3613191246986389, pvalue=7.968728664354636e-38)\n",
      "Ttest_indResult(statistic=-0.3125098904253925, pvalue=0.7547209615935899)\n",
      "\n",
      "\n",
      "T_OTSeqPosition25\n",
      "ShapiroResult(statistic=0.4346469044685364, pvalue=3.30986499970698e-36)\n",
      "ShapiroResult(statistic=0.45961201190948486, pvalue=1.2875891879116198e-35)\n",
      "Ttest_indResult(statistic=-0.7806558094957495, pvalue=0.4351978016878857)\n",
      "\n",
      "\n",
      "A_OTSeqPosition25\n",
      "ShapiroResult(statistic=0.6338891983032227, pvalue=9.676528436646972e-31)\n",
      "ShapiroResult(statistic=0.6330589056015015, pvalue=9.087293555585428e-31)\n",
      "Ttest_indResult(statistic=-0.19406401826313444, pvalue=0.846166810977825)\n",
      "\n",
      "\n",
      "C_OTSeqPosition26\n",
      "ShapiroResult(statistic=0.46739113330841064, pvalue=1.9866246264684958e-35)\n",
      "ShapiroResult(statistic=0.3205522894859314, pvalue=1.1644567432083402e-38)\n",
      "Ttest_indResult(statistic=4.1828950423578455, pvalue=3.1648343627310227e-05)\n",
      "\n",
      "\n",
      "G_OTSeqPosition26\n",
      "ShapiroResult(statistic=0.6330546140670776, pvalue=9.084288992024726e-31)\n",
      "ShapiroResult(statistic=0.5379865765571594, pvalue=1.310281216697875e-33)\n",
      "Ttest_indResult(statistic=6.700019938445894, pvalue=3.581498796099481e-11)\n",
      "\n",
      "\n",
      "T_OTSeqPosition26\n",
      "ShapiroResult(statistic=0.448827862739563, pvalue=7.116596624334193e-36)\n",
      "ShapiroResult(statistic=0.448827862739563, pvalue=7.116596624334193e-36)\n",
      "Ttest_indResult(statistic=0.0, pvalue=1.0)\n",
      "\n",
      "\n",
      "A_OTSeqPosition26\n",
      "ShapiroResult(statistic=0.49153822660446167, pvalue=7.893183408579461e-35)\n",
      "ShapiroResult(statistic=0.6364971399307251, pvalue=1.1795651347789591e-30)\n",
      "Ttest_indResult(statistic=-10.018220291312366, pvalue=1.72836258749111e-22)\n",
      "\n",
      "\n",
      "C_OTSeqPosition27\n",
      "ShapiroResult(statistic=0.539747953414917, pvalue=1.4639823376911701e-33)\n",
      "ShapiroResult(statistic=0.393699049949646, pvalue=3.947864486869295e-37)\n",
      "Ttest_indResult(statistic=4.901867100686711, pvalue=1.1253399600101718e-06)\n",
      "\n",
      "\n",
      "G_OTSeqPosition27\n",
      "ShapiroResult(statistic=0.6330546140670776, pvalue=9.084288992024726e-31)\n",
      "ShapiroResult(statistic=0.4892408847808838, pvalue=6.906761125190948e-35)\n",
      "Ttest_indResult(statistic=8.635150625816102, pvalue=2.5679805947000674e-17)\n",
      "\n",
      "\n",
      "T_OTSeqPosition27\n",
      "ShapiroResult(statistic=0.4165235757827759, pvalue=1.272372638106712e-36)\n",
      "ShapiroResult(statistic=0.41963279247283936, pvalue=1.4965555501795072e-36)\n",
      "Ttest_indResult(statistic=-0.09160732402351228, pvalue=0.9270291441287375)\n",
      "\n",
      "\n",
      "A_OTSeqPosition27\n",
      "ShapiroResult(statistic=0.43171316385269165, pvalue=2.8305567118070635e-36)\n",
      "ShapiroResult(statistic=0.6355574727058411, pvalue=1.0982004250805785e-30)\n",
      "Ttest_indResult(statistic=-13.234923805121715, pvalue=1.412118657445263e-36)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "l1=['C_OTSeqPosition1','G_OTSeqPosition1', 'T_OTSeqPosition1', 'A_OTSeqPosition1', 'C_OTSeqPosition2', 'G_OTSeqPosition2', 'T_OTSeqPosition2', 'A_OTSeqPosition2', 'C_OTSeqPosition3', 'G_OTSeqPosition3', 'T_OTSeqPosition3', 'A_OTSeqPosition3', 'C_OTSeqPosition4', 'G_OTSeqPosition4', 'T_OTSeqPosition4', 'A_OTSeqPosition4', 'C_OTSeqPosition5', 'G_OTSeqPosition5', 'T_OTSeqPosition5', 'A_OTSeqPosition5', 'C_OTSeqPosition6', 'G_OTSeqPosition6', 'T_OTSeqPosition6', 'A_OTSeqPosition6', 'C_OTSeqPosition7', 'G_OTSeqPosition7', 'T_OTSeqPosition7', 'A_OTSeqPosition7', 'C_OTSeqPosition8', 'G_OTSeqPosition8', 'T_OTSeqPosition8', 'A_OTSeqPosition8', 'C_OTSeqPosition9', 'G_OTSeqPosition9', 'T_OTSeqPosition9', 'A_OTSeqPosition9', 'C_OTSeqPosition10', 'G_OTSeqPosition10', 'T_OTSeqPosition10', 'A_OTSeqPosition10', 'C_OTSeqPosition11', 'G_OTSeqPosition11', 'T_OTSeqPosition11', 'A_OTSeqPosition11', 'C_OTSeqPosition12', 'G_OTSeqPosition12', 'T_OTSeqPosition12', 'A_OTSeqPosition12', 'C_OTSeqPosition13', 'G_OTSeqPosition13', 'T_OTSeqPosition13', 'A_OTSeqPosition13', 'C_OTSeqPosition14', 'G_OTSeqPosition14', 'T_OTSeqPosition14', 'A_OTSeqPosition14', 'C_OTSeqPosition15', 'G_OTSeqPosition15', 'T_OTSeqPosition15', 'A_OTSeqPosition15', 'C_OTSeqPosition16', 'G_OTSeqPosition16', 'T_OTSeqPosition16', 'A_OTSeqPosition16', 'C_OTSeqPosition17', 'G_OTSeqPosition17', 'T_OTSeqPosition17', 'A_OTSeqPosition17', 'C_OTSeqPosition18', 'G_OTSeqPosition18', 'T_OTSeqPosition18', 'A_OTSeqPosition18', 'C_OTSeqPosition19', 'G_OTSeqPosition19', 'T_OTSeqPosition19', 'A_OTSeqPosition19', 'C_OTSeqPosition20', 'G_OTSeqPosition20', 'T_OTSeqPosition20', 'A_OTSeqPosition20', 'C_OTSeqPosition21', 'G_OTSeqPosition21', 'T_OTSeqPosition21', 'A_OTSeqPosition21', 'C_OTSeqPosition22', 'G_OTSeqPosition22', 'T_OTSeqPosition22', 'A_OTSeqPosition22', 'C_OTSeqPosition23', 'G_OTSeqPosition23', 'T_OTSeqPosition23', 'A_OTSeqPosition23', 'C_OTSeqPosition24', 'G_OTSeqPosition24', 'T_OTSeqPosition24', 'A_OTSeqPosition24', 'C_OTSeqPosition25', 'G_OTSeqPosition25', 'T_OTSeqPosition25', 'A_OTSeqPosition25', 'C_OTSeqPosition26', 'G_OTSeqPosition26', 'T_OTSeqPosition26', 'A_OTSeqPosition26', 'C_OTSeqPosition27', 'G_OTSeqPosition27', 'T_OTSeqPosition27', 'A_OTSeqPosition27']\n",
    "df= pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\")\n",
    "for i in l1:\n",
    "    #print(df.groupby(\"Y\")[i].describe())\n",
    "    POT = df[(df['Y'] == 1)]\n",
    "    NOT = df[(df['Y'] == 0)]\n",
    "    from scipy import stats\n",
    "    print(i)\n",
    "    print(stats.shapiro(POT[i]))\n",
    "    print(stats.shapiro(NOT[i]))\n",
    "    print(stats.ttest_ind(POT[i], NOT[i], equal_var = False))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "postal-positive",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "962\n",
      "positive off-targets\n",
      "[0.009541984732824428, 0.19083969465648856, 0.7080152671755725, 0.009541984732824428, 0.009541984732824428, 0.0019083969465648854, 0.9064885496183206, 0.0, 0.0057251908396946565, 0.0019083969465648854, 0.9103053435114504, 0.0, 0.24618320610687022, 0.366412213740458, 0.10687022900763359, 0.1984732824427481, 0.30725190839694655, 0.4713740458015267, 0.10877862595419847, 0.030534351145038167, 0.11259541984732824, 0.3473282442748092, 0.4122137404580153, 0.04580152671755725, 0.18702290076335878, 0.5706106870229007, 0.1297709923664122, 0.030534351145038167, 0.10305343511450382, 0.05343511450381679, 0.4026717557251908, 0.35877862595419846, 0.15076335877862596, 0.39122137404580154, 0.32633587786259544, 0.04580152671755725, 0.17557251908396945, 0.3416030534351145, 0.04389312977099236, 0.3568702290076336, 0.08206106870229007, 0.2900763358778626, 0.34923664122137404, 0.1965648854961832, 0.3015267175572519, 0.10114503816793893, 0.37595419847328243, 0.13931297709923665, 0.3549618320610687, 0.10877862595419847, 0.12595419847328243, 0.3282442748091603, 0.2824427480916031, 0.29961832061068705, 0.18702290076335878, 0.14885496183206107, 0.11259541984732824, 0.0916030534351145, 0.1316793893129771, 0.5820610687022901, 0.44274809160305345, 0.08587786259541985, 0.2480916030534351, 0.13740458015267176, 0.34541984732824427, 0.15267175572519084, 0.11068702290076336, 0.30916030534351147, 0.49427480916030536, 0.13358778625954199, 0.1851145038167939, 0.1049618320610687, 0.24427480916030533, 0.1049618320610687, 0.12022900763358779, 0.4484732824427481, 0.04007633587786259, 0.16603053435114504, 0.3053435114503817, 0.4064885496183206, 0.3893129770992366, 0.25763358778625955, 0.11450381679389313, 0.15458015267175573, 0.12404580152671756, 0.06870229007633588, 0.34923664122137404, 0.37595419847328243, 0.28435114503816794, 0.11450381679389313, 0.2767175572519084, 0.24236641221374045, 0.34541984732824427, 0.13740458015267176, 0.13549618320610687, 0.29961832061068705, 0.18129770992366412, 0.09541984732824428, 0.1431297709923664, 0.49809160305343514, 0.16603053435114504, 0.41412213740458015, 0.15267175572519084, 0.1851145038167939, 0.23091603053435114, 0.41412213740458015, 0.1316793893129771, 0.14122137404580154]\n",
      "negative off-targets\n",
      "[0.1961904761904762, 0.08571428571428572, 0.4990476190476191, 0.13523809523809524, 0.22095238095238096, 0.09523809523809523, 0.4819047619047619, 0.1180952380952381, 0.10285714285714286, 0.12, 0.5657142857142857, 0.12761904761904763, 0.12571428571428572, 0.25333333333333335, 0.34476190476190477, 0.19047619047619047, 0.16952380952380952, 0.24952380952380954, 0.2876190476190476, 0.2057142857142857, 0.12761904761904763, 0.24952380952380954, 0.3028571428571429, 0.22285714285714286, 0.19047619047619047, 0.22857142857142856, 0.35619047619047617, 0.13523809523809524, 0.25142857142857145, 0.09523809523809523, 0.24, 0.3142857142857143, 0.25523809523809526, 0.17714285714285713, 0.29714285714285715, 0.17523809523809525, 0.18857142857142858, 0.18476190476190477, 0.26666666666666666, 0.2723809523809524, 0.13142857142857142, 0.17714285714285713, 0.38095238095238093, 0.22285714285714286, 0.13714285714285715, 0.19047619047619047, 0.30666666666666664, 0.2742857142857143, 0.26857142857142857, 0.21142857142857144, 0.28, 0.14857142857142858, 0.21714285714285714, 0.21904761904761905, 0.2361904761904762, 0.23047619047619047, 0.23238095238095238, 0.2761904761904762, 0.17523809523809525, 0.22476190476190477, 0.16380952380952382, 0.13523809523809524, 0.31047619047619046, 0.29904761904761906, 0.14666666666666667, 0.16, 0.21714285714285714, 0.379047619047619, 0.17523809523809525, 0.2019047619047619, 0.2857142857142857, 0.23809523809523808, 0.17142857142857143, 0.24952380952380954, 0.2019047619047619, 0.28, 0.10857142857142857, 0.28, 0.28, 0.24, 0.19238095238095237, 0.19238095238095237, 0.14476190476190476, 0.37523809523809526, 0.25523809523809526, 0.11428571428571428, 0.22476190476190477, 0.31047619047619046, 0.21904761904761905, 0.16952380952380952, 0.2361904761904762, 0.2819047619047619, 0.2361904761904762, 0.10285714285714286, 0.21333333333333335, 0.3485714285714286, 0.1523809523809524, 0.10095238095238095, 0.16, 0.5028571428571429, 0.08190476190476191, 0.22857142857142856, 0.1523809523809524, 0.4533333333333333, 0.1180952380952381, 0.18285714285714286, 0.13333333333333333, 0.4819047619047619]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "  \n",
    "OT_data=pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", encoding=\"cp1252\")\n",
    "\n",
    "df = pd.DataFrame(OT_data)\n",
    "POT = df[(df['Y'] == 1)]\n",
    "NOT = df[(df['Y'] == 0)]\n",
    "print(len(df))\n",
    "l1=['C_OTSeqPosition1','G_OTSeqPosition1', 'T_OTSeqPosition1', 'A_OTSeqPosition1', 'C_OTSeqPosition2', 'G_OTSeqPosition2', 'T_OTSeqPosition2', 'A_OTSeqPosition2', 'C_OTSeqPosition3', 'G_OTSeqPosition3', 'T_OTSeqPosition3', 'A_OTSeqPosition3', 'C_OTSeqPosition4', 'G_OTSeqPosition4', 'T_OTSeqPosition4', 'A_OTSeqPosition4', 'C_OTSeqPosition5', 'G_OTSeqPosition5', 'T_OTSeqPosition5', 'A_OTSeqPosition5', 'C_OTSeqPosition6', 'G_OTSeqPosition6', 'T_OTSeqPosition6', 'A_OTSeqPosition6', 'C_OTSeqPosition7', 'G_OTSeqPosition7', 'T_OTSeqPosition7', 'A_OTSeqPosition7', 'C_OTSeqPosition8', 'G_OTSeqPosition8', 'T_OTSeqPosition8', 'A_OTSeqPosition8', 'C_OTSeqPosition9', 'G_OTSeqPosition9', 'T_OTSeqPosition9', 'A_OTSeqPosition9', 'C_OTSeqPosition10', 'G_OTSeqPosition10', 'T_OTSeqPosition10', 'A_OTSeqPosition10', 'C_OTSeqPosition11', 'G_OTSeqPosition11', 'T_OTSeqPosition11', 'A_OTSeqPosition11', 'C_OTSeqPosition12', 'G_OTSeqPosition12', 'T_OTSeqPosition12', 'A_OTSeqPosition12', 'C_OTSeqPosition13', 'G_OTSeqPosition13', 'T_OTSeqPosition13', 'A_OTSeqPosition13', 'C_OTSeqPosition14', 'G_OTSeqPosition14', 'T_OTSeqPosition14', 'A_OTSeqPosition14', 'C_OTSeqPosition15', 'G_OTSeqPosition15', 'T_OTSeqPosition15', 'A_OTSeqPosition15', 'C_OTSeqPosition16', 'G_OTSeqPosition16', 'T_OTSeqPosition16', 'A_OTSeqPosition16', 'C_OTSeqPosition17', 'G_OTSeqPosition17', 'T_OTSeqPosition17', 'A_OTSeqPosition17', 'C_OTSeqPosition18', 'G_OTSeqPosition18', 'T_OTSeqPosition18', 'A_OTSeqPosition18', 'C_OTSeqPosition19', 'G_OTSeqPosition19', 'T_OTSeqPosition19', 'A_OTSeqPosition19', 'C_OTSeqPosition20', 'G_OTSeqPosition20', 'T_OTSeqPosition20', 'A_OTSeqPosition20', 'C_OTSeqPosition21', 'G_OTSeqPosition21', 'T_OTSeqPosition21', 'A_OTSeqPosition21', 'C_OTSeqPosition22', 'G_OTSeqPosition22', 'T_OTSeqPosition22', 'A_OTSeqPosition22', 'C_OTSeqPosition23', 'G_OTSeqPosition23', 'T_OTSeqPosition23', 'A_OTSeqPosition23', 'C_OTSeqPosition24', 'G_OTSeqPosition24', 'T_OTSeqPosition24', 'A_OTSeqPosition24', 'C_OTSeqPosition25', 'G_OTSeqPosition25', 'T_OTSeqPosition25', 'A_OTSeqPosition25', 'C_OTSeqPosition26', 'G_OTSeqPosition26', 'T_OTSeqPosition26', 'A_OTSeqPosition26', 'C_OTSeqPosition27', 'G_OTSeqPosition27', 'T_OTSeqPosition27', 'A_OTSeqPosition27']\n",
    "print(\"positive off-targets\")\n",
    "POT_l1=[]\n",
    "for i in l1:\n",
    "    total = POT[i].sum()\n",
    "    POT_ratio=total/524\n",
    "    POT_l1.append(POT_ratio)\n",
    "print(POT_l1)\n",
    "print(\"negative off-targets\")\n",
    "NOT_l1=[]\n",
    "for i in l1:\n",
    "    total = NOT[i].sum()\n",
    "    NOT_ratio=total/525\n",
    "    NOT_l1.append(NOT_ratio)\n",
    "print(NOT_l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "wound-alexandria",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.04863632994886238, 2.2264631043257, 1.4187328826991432, 0.07055692936243414, 0.043185706764938145, 0.020038167938931296, 1.881053314425369, 0.0, 0.05566157760814249, 0.015903307888040712, 1.6091256072172104, 0.0, 1.9582755031228312, 1.446364001607071, 0.309982708447556, 1.0419847328244276, 1.8124410326786173, 1.889094458364897, 0.3782038319599616, 0.1484308736217133, 0.8822775435798107, 1.391964337742556, 1.3610831052858994, 0.20551967116852612, 0.9818702290076337, 2.4964217557251906, 0.36433032616238725, 0.22578217395978925, 0.40987161693268565, 0.5610687022900763, 1.677798982188295, 1.1415683553088134, 0.5906773384983479, 2.20850775671017, 1.0982457428068115, 0.26136740789910384, 0.9310663890816562, 1.8488825057055165, 0.16459923664122136, 1.310187903699354, 0.6243776966478592, 1.637527702536321, 0.9167461832061069, 0.8820219220982579, 2.1986323155216283, 0.5310114503816794, 1.2259376037172254, 0.5079118956743003, 1.3216663959720643, 0.5144935011347225, 0.4498364231188658, 2.209336465061656, 1.3007231820008036, 1.3678227680053106, 0.7918308298448657, 0.6458583054696865, 0.48452947065448626, 0.3316662279547249, 0.7514312977099236, 2.58967848363307, 2.7028226522279426, 0.6350123642619073, 0.7990680466444996, 0.4594739145232654, 2.3551353226925746, 0.9541984732824428, 0.5097428686219365, 0.8156239211323796, 2.820589943577829, 0.66163762062509, 0.6479007633587787, 0.44083969465648853, 1.4249363867684477, 0.42064856360351954, 0.5954738585625811, 1.601690294438386, 0.3691241462434713, 0.5929661941112322, 1.0905125408942202, 1.6937022900763359, 2.0236565641296953, 1.339184490968181, 0.790980313378867, 0.4119521835161003, 0.4860003418024381, 0.601145038167939, 1.553807090179842, 1.210895424530511, 1.2981247925655492, 0.6754438631100438, 1.1715864319133218, 0.8597457190014443, 1.4624630632849052, 1.33587786259542, 0.6351383587786259, 0.8595607558503316, 1.1897662213740456, 0.9451966008929858, 0.89456106870229, 0.9905230742539902, 2.0271169891709566, 1.8117843511450382, 1.0019083969465647, 0.40834081724292776, 1.955337355331199, 2.2647304389312977, 0.9875954198473282, 0.293048305826268]\n"
     ]
    }
   ],
   "source": [
    "enrichment_ratio=[]\n",
    "for i, j in zip(POT_l1, NOT_l1):\n",
    "    enrichment_ratio1=i/j\n",
    "    enrichment_ratio.append(enrichment_ratio1)\n",
    "print(enrichment_ratio)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "robust-exhibition",
   "metadata": {},
   "source": [
    "# mismatch distribution analysis AsCpf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "sublime-colon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "524\n",
      "0.33969465648854963\n",
      "0.015267175572519083\n",
      "0.026717557251908396\n",
      "0.7061068702290076\n",
      "0.04580152671755725\n",
      "0.08206106870229007\n",
      "0.07061068702290077\n",
      "0.04198473282442748\n",
      "0.09923664122137404\n",
      "0.11641221374045801\n",
      "0.06870229007633588\n",
      "0.08778625954198473\n",
      "0.08969465648854962\n",
      "0.04007633587786259\n",
      "0.04770992366412214\n",
      "0.16793893129770993\n",
      "0.20610687022900764\n",
      "0.1946564885496183\n",
      "0.08015267175572519\n",
      "0.03816793893129771\n",
      "0.07251908396946564\n",
      "0.07824427480916031\n",
      "0.4198473282442748\n",
      "0.18702290076335878\n",
      "0.14694656488549618\n",
      "0.25763358778625955\n",
      "0.17557251908396945\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "  \n",
    "OT_data=pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/LbCpf1/LbCpf1_dataset_features_POT.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "\n",
    "df = pd.DataFrame(OT_data)\n",
    "print(len(df))\n",
    "l1=['mismatch_POS1', 'mismatch_POS2', 'mismatch_POS3', 'mismatch_POS4', 'mismatch_POS5', 'mismatch_POS6', 'mismatch_POS7', 'mismatch_POS8', 'mismatch_POS9', 'mismatch_POS10', 'mismatch_POS11', 'mismatch_POS12', 'mismatch_POS13', 'mismatch_POS14', 'mismatch_POS15', 'mismatch_POS16', 'mismatch_POS17', 'mismatch_POS18', 'mismatch_POS19', 'mismatch_POS20', 'mismatch_POS21', 'mismatch_POS22', 'mismatch_POS23', 'mismatch_POS24', 'mismatch_POS25', 'mismatch_POS26', 'mismatch_POS27']\n",
    "for i in l1:\n",
    "    total = df[i].sum()\n",
    "    print(total/524)# mismatch distribution analysis AsCpf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "welsh-means",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.2286902286902287, 0.012474012474012475, 0.008316008316008316, 0.656964656964657, 0.04158004158004158, 0.060291060291060294, 0.058212058212058215, 0.04573804573804574, 0.09147609147609148, 0.11226611226611227, 0.07484407484407485, 0.07484407484407485, 0.09563409563409564, 0.08108108108108109, 0.08108108108108109, 0.2577962577962578, 0.2972972972972973, 0.2598752598752599, 0.12058212058212059, 0.0893970893970894, 0.12058212058212059, 0.12474012474012475, 0.41164241164241167, 0.24532224532224534, 0.23284823284823286, 0.34511434511434513, 0.2390852390852391]\n",
      "[0.019169800902713252, 0.005065901426615885, 0.004144990028560683, 0.02166804946535949, 0.00911170740977457, 0.010864319308716494, 0.010687162713076818, 0.00953568701751808, 0.013158347806965008, 0.014409374285423604, 0.012010623709242562, 0.012010623709242562, 0.013423255270420953, 0.012458843861206605, 0.012458843861206603, 0.019965457006128066, 0.020862227345782716, 0.020017706182070938, 0.014863410946153946, 0.013022836496933811, 0.014863410946153946, 0.01508172362634814, 0.022462604877311786, 0.01963941833788923, 0.01929107810338802, 0.021699195161276615, 0.01946810894004994]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.0, 0.21205821205821207, 0.23492723492723494, 0.4261954261954262, 0.20374220374220375, 0.28274428274428276, 0.2598752598752599, 0.25571725571725573, 0.31185031185031187, 0.2390852390852391, 0.26403326403326405, 0.2910602910602911, 0.27234927234927236, 0.23284823284823286, 0.24324324324324326, 0.2869022869022869, 0.26195426195426197, 0.24532224532224534, 0.34303534303534305, 0.2577962577962578, 0.24324324324324326, 0.2993762993762994, 0.24116424116424118, 0.4386694386694387, 0.21205821205821207, 0.20582120582120583, 0.0]\n",
      "[0.0, 0.01865752343542715, 0.01935073368638548, 0.022571778327035826, 0.01838428439798481, 0.02055480242545238, 0.020017706182070938, 0.01991261853101839, 0.021144330152607062, 0.01946810894004994, 0.020120454948323357, 0.02073363802838426, 0.020319076945492943, 0.01929107810338802, 0.019582941548904647, 0.020645286219080617, 0.020069370661442643, 0.019639418337889222, 0.021668049465359492, 0.019965457006128062, 0.019582941548904647, 0.02090405317297201, 0.019525840248027573, 0.022649437182433396, 0.01865752343542715, 0.018453705423725802, 0.0]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats  \n",
    "from scipy.stats import sem\n",
    "OT_data1=pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l2=[]\n",
    "l3=[]\n",
    "df = pd.DataFrame(OT_data1)\n",
    "POT = df[(df['Y'] == 1)]\n",
    "NOT = df[(df['Y'] == 0)]\n",
    "#print(len(df))\n",
    "l1=['mismatch_POS1', 'mismatch_POS2', 'mismatch_POS3', 'mismatch_POS4', 'mismatch_POS5', 'mismatch_POS6', 'mismatch_POS7', 'mismatch_POS8', 'mismatch_POS9', 'mismatch_POS10', 'mismatch_POS11', 'mismatch_POS12', 'mismatch_POS13', 'mismatch_POS14', 'mismatch_POS15', 'mismatch_POS16', 'mismatch_POS17', 'mismatch_POS18', 'mismatch_POS19', 'mismatch_POS20', 'mismatch_POS21', 'mismatch_POS22', 'mismatch_POS23', 'mismatch_POS24', 'mismatch_POS25', 'mismatch_POS26', 'mismatch_POS27']\n",
    "for i in l1:\n",
    "    total = POT[i].sum()\n",
    "    l2.append(total/481)\n",
    "    l3.append(sem(POT[i]))\n",
    "print(\"\\n Positive off-target \\n\")\n",
    "print(l2)\n",
    "print(l3)\n",
    "l2=[]\n",
    "l3=[]\n",
    "for i in l1:\n",
    "    total = NOT[i].sum()\n",
    "    l2.append(total/481)\n",
    "    l3.append(sem(NOT[i]))\n",
    "print(\"\\n Negative off-targets \\n\")\n",
    "print(l2)\n",
    "print(l3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "connected-worth",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mismatch_POS1\n",
      "ShapiroResult(statistic=0.5192276239395142, pvalue=4.104059446678509e-34)\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "Ttest_indResult(statistic=11.929713294928401, pvalue=6.507312732338707e-29)\n",
      "\n",
      "\n",
      "mismatch_POS2\n",
      "ShapiroResult(statistic=0.08509266376495361, pvalue=8.253647954873173e-43)\n",
      "ShapiroResult(statistic=0.5026566982269287, pvalue=1.5166114467882658e-34)\n",
      "Ttest_indResult(statistic=-10.323475886535435, pvalue=5.858995403198742e-23)\n",
      "\n",
      "\n",
      "mismatch_POS3\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "ShapiroResult(statistic=0.5250754952430725, pvalue=5.870221800867245e-34)\n",
      "Ttest_indResult(statistic=-11.450973956906834, pvalue=2.994158355601495e-27)\n",
      "\n",
      "\n",
      "mismatch_POS4\n",
      "ShapiroResult(statistic=0.5996636152267456, pvalue=7.939946534543015e-32)\n",
      "ShapiroResult(statistic=0.6285660266876221, pvalue=6.4812601956316426e-31)\n",
      "Ttest_indResult(statistic=7.375455985454559, pvalue=3.541711920778105e-13)\n",
      "\n",
      "\n",
      "mismatch_POS5\n",
      "ShapiroResult(statistic=0.19838988780975342, pvalue=6.129559742649615e-41)\n",
      "ShapiroResult(statistic=0.49381202459335327, pvalue=9.012443441273105e-35)\n",
      "Ttest_indResult(statistic=-7.903252095026851, pvalue=1.0520151919447733e-14)\n",
      "\n",
      "\n",
      "mismatch_POS6\n",
      "ShapiroResult(statistic=0.2522478699684143, pvalue=5.678593870860602e-40)\n",
      "ShapiroResult(statistic=0.5639110803604126, pvalue=6.947862950974797e-33)\n",
      "Ttest_indResult(statistic=-9.568138638422042, pvalue=1.64213249403714e-20)\n",
      "\n",
      "\n",
      "mismatch_POS7\n",
      "ShapiroResult(statistic=0.24672436714172363, pvalue=4.492703006471796e-40)\n",
      "ShapiroResult(statistic=0.5466068983078003, pvalue=2.262359549999444e-33)\n",
      "Ttest_indResult(statistic=-8.88699810456694, pvalue=4.812219964017644e-18)\n",
      "\n",
      "\n",
      "mismatch_POS8\n",
      "ShapiroResult(statistic=0.21124237775802612, pvalue=1.0308091633419787e-40)\n",
      "ShapiroResult(statistic=0.5432170033454895, pvalue=1.82330451211465e-33)\n",
      "Ttest_indResult(statistic=-9.510754159936843, pvalue=3.0907140427417067e-20)\n",
      "\n",
      "\n",
      "mismatch_POS9\n",
      "ShapiroResult(statistic=0.32488858699798584, pvalue=1.4223068710318212e-38)\n",
      "ShapiroResult(statistic=0.5828676223754883, pvalue=2.4761738541561576e-32)\n",
      "Ttest_indResult(statistic=-8.848837502350603, pvalue=5.56159112814874e-18)\n",
      "\n",
      "\n",
      "mismatch_POS10\n",
      "ShapiroResult(statistic=0.36510246992111206, pvalue=9.573998451588417e-38)\n",
      "ShapiroResult(statistic=0.5288666486740112, pvalue=7.417305068841324e-34)\n",
      "Ttest_indResult(statistic=-5.236003278940785, pvalue=2.051240389513299e-07)\n",
      "\n",
      "\n",
      "mismatch_POS11\n",
      "ShapiroResult(statistic=0.28831833600997925, pvalue=2.717629196265299e-39)\n",
      "ShapiroResult(statistic=0.549917995929718, pvalue=2.7966595686725907e-33)\n",
      "Ttest_indResult(statistic=-8.073752332671113, pvalue=2.5637485902170998e-15)\n",
      "\n",
      "\n",
      "mismatch_POS12\n",
      "ShapiroResult(statistic=0.28831833600997925, pvalue=2.717629196265299e-39)\n",
      "ShapiroResult(statistic=0.5696666240692139, pvalue=1.0171061848773786e-32)\n",
      "Ttest_indResult(statistic=-9.02359933972662, pvalue=1.4324472507709916e-18)\n",
      "\n",
      "\n",
      "mismatch_POS13\n",
      "ShapiroResult(statistic=0.3333784341812134, pvalue=2.11057366944407e-38)\n",
      "ShapiroResult(statistic=0.5563204288482666, pvalue=4.228892354961028e-33)\n",
      "Ttest_indResult(statistic=-7.256524755939033, pvalue=9.10897265134846e-13)\n",
      "\n",
      "\n",
      "mismatch_POS14\n",
      "ShapiroResult(statistic=0.3025544285774231, pvalue=5.1325484969686864e-39)\n",
      "ShapiroResult(statistic=0.5231471061706543, pvalue=5.214692682451811e-34)\n",
      "Ttest_indResult(statistic=-6.60877090569236, pvalue=6.967591843673063e-11)\n",
      "\n",
      "\n",
      "mismatch_POS15\n",
      "ShapiroResult(statistic=0.3025544285774231, pvalue=5.1325484969686864e-39)\n",
      "ShapiroResult(statistic=0.5325759649276733, pvalue=9.338798440409167e-34)\n",
      "Ttest_indResult(statistic=-6.9866701168749845, pvalue=5.8572979601555716e-12)\n",
      "\n",
      "\n",
      "mismatch_POS16\n",
      "ShapiroResult(statistic=0.5449211001396179, pvalue=2.0318546586947938e-33)\n",
      "ShapiroResult(statistic=0.5668240785598755, pvalue=8.421777113904461e-33)\n",
      "Ttest_indResult(statistic=-1.0134356472395412, pvalue=0.31110775475265334)\n",
      "\n",
      "\n",
      "mismatch_POS17\n",
      "ShapiroResult(statistic=0.5738013982772827, pvalue=1.3408170518162848e-32)\n",
      "ShapiroResult(statistic=0.5482719540596008, pvalue=2.516500510292261e-33)\n",
      "Ttest_indResult(statistic=1.2208960327727323, pvalue=0.22242560797510036)\n",
      "\n",
      "\n",
      "mismatch_POS18\n",
      "ShapiroResult(statistic=0.5466068983078003, pvalue=2.262359549999444e-33)\n",
      "ShapiroResult(statistic=0.5343992710113525, pvalue=1.0464000918470454e-33)\n",
      "Ttest_indResult(statistic=0.5189517523726817, pvalue=0.603914036390343)\n",
      "\n",
      "\n",
      "mismatch_POS19\n",
      "ShapiroResult(statistic=0.3797592520713806, pvalue=1.9658843060943374e-37)\n",
      "ShapiroResult(statistic=0.5996679067611694, pvalue=7.942263433908486e-32)\n",
      "Ttest_indResult(statistic=-8.466040390499883, pvalue=1.105829398566623e-16)\n",
      "\n",
      "\n",
      "mismatch_POS20\n",
      "ShapiroResult(statistic=0.3205522894859314, pvalue=1.1644567432083402e-38)\n",
      "ShapiroResult(statistic=0.5449211001396179, pvalue=2.0318546586947938e-33)\n",
      "Ttest_indResult(statistic=-7.0645430407784975, pvalue=3.4252324253226086e-12)\n",
      "\n",
      "\n",
      "mismatch_POS21\n",
      "ShapiroResult(statistic=0.3797592520713806, pvalue=1.9658843060943374e-37)\n",
      "ShapiroResult(statistic=0.5325759649276733, pvalue=9.338798440409167e-34)\n",
      "Ttest_indResult(statistic=-4.989306523300114, pvalue=7.279473247213597e-07)\n",
      "\n",
      "\n",
      "mismatch_POS22\n",
      "ShapiroResult(statistic=0.38681548833847046, pvalue=2.793425710460321e-37)\n",
      "ShapiroResult(statistic=0.5751444101333618, pvalue=1.4674042752148108e-32)\n",
      "Ttest_indResult(statistic=-6.774964676311061, pvalue=2.286146390273903e-11)\n",
      "\n",
      "\n",
      "mismatch_POS23\n",
      "ShapiroResult(statistic=0.6250859498977661, pvalue=4.999708963454121e-31)\n",
      "ShapiroResult(statistic=0.5307317972183228, pvalue=8.32672259993514e-34)\n",
      "Ttest_indResult(statistic=5.7278852081064615, pvalue=1.3674534366544403e-08)\n",
      "\n",
      "\n",
      "mismatch_POS24\n",
      "ShapiroResult(statistic=0.5343992710113525, pvalue=1.0464000918470454e-33)\n",
      "ShapiroResult(statistic=0.6310427188873291, pvalue=7.805351608527817e-31)\n",
      "Ttest_indResult(statistic=-6.449552665882835, pvalue=1.7911703390445472e-10)\n",
      "\n",
      "\n",
      "mismatch_POS25\n",
      "ShapiroResult(statistic=0.5231471061706543, pvalue=5.214692682451811e-34)\n",
      "ShapiroResult(statistic=0.5026566982269287, pvalue=1.5166114467882658e-34)\n",
      "Ttest_indResult(statistic=0.7746644553470982, pvalue=0.4387289232046594)\n",
      "\n",
      "\n",
      "mismatch_POS26\n",
      "ShapiroResult(statistic=0.6006634831428528, pvalue=8.520620172698086e-32)\n",
      "ShapiroResult(statistic=0.4960593581199646, pvalue=1.0279570676189985e-34)\n",
      "Ttest_indResult(statistic=4.890055027045432, pvalue=1.1860865726305476e-06)\n",
      "\n",
      "\n",
      "mismatch_POS27\n",
      "ShapiroResult(statistic=0.5288666486740112, pvalue=7.417305068841324e-34)\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "Ttest_indResult(statistic=12.280866098575768, pvalue=2.4526584079280263e-30)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n",
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "l1=['mismatch_POS1', 'mismatch_POS2', 'mismatch_POS3', 'mismatch_POS4', 'mismatch_POS5', 'mismatch_POS6', 'mismatch_POS7', 'mismatch_POS8', 'mismatch_POS9', 'mismatch_POS10', 'mismatch_POS11', 'mismatch_POS12', 'mismatch_POS13', 'mismatch_POS14', 'mismatch_POS15', 'mismatch_POS16', 'mismatch_POS17', 'mismatch_POS18', 'mismatch_POS19', 'mismatch_POS20', 'mismatch_POS21', 'mismatch_POS22', 'mismatch_POS23', 'mismatch_POS24', 'mismatch_POS25', 'mismatch_POS26', 'mismatch_POS27']\n",
    "df= pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\")\n",
    "for i in l1:\n",
    "    #print(df.groupby(\"Y\")[i].describe())\n",
    "    POT = df[(df['Y'] == 1)]\n",
    "    NOT = df[(df['Y'] == 0)]\n",
    "    from scipy import stats\n",
    "    print(i)\n",
    "    print(stats.shapiro(POT[i]))\n",
    "    print(stats.shapiro(NOT[i]))\n",
    "    print(stats.ttest_ind(POT[i], NOT[i], equal_var = False))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "southern-ceramic",
   "metadata": {},
   "source": [
    "# Position specific mismatch type analysis AsCpf1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "canadian-archive",
   "metadata": {},
   "source": [
    "# mismatch at position 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ceramic-transport",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "481\n",
      "481\n",
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.015267175572519083, 0.022900763358778626, 0.04580152671755725, 0.02480916030534351, 0.02480916030534351, 0.019083969465648856, 0.013358778625954198, 0.019083969465648856, 0.0057251908396946565, 0.011450381679389313, 0.0, 0.02480916030534351, 0.37595419847328243]\n",
      "[0.005837271168742748, 0.007118874706138849, 0.00993797788167744, 0.007401656182502244, 0.007401656182502244, 0.006512455346227478, 0.005466036101927195, 0.006512455346227478, 0.003593427446453971, 0.005065901426615886, 0.0, 0.007401656182502244, 0.022445360652324702]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.01904761904761905, 0.005714285714285714, 0.013333333333333334, 0.005714285714285714, 0.06666666666666667, 0.013333333333333334, 0.007619047619047619, 0.015238095238095238, 0.0038095238095238095, 0.011428571428571429, 0.009523809523809525, 0.013333333333333334, 0.2057142857142857]\n",
      "[0.006512455346227478, 0.003593427446453971, 0.005466036101927195, 0.0035934274464539703, 0.011855933548805004, 0.005466036101927195, 0.004144990028560683, 0.005837271168742748, 0.0029370886802191055, 0.005065901426615885, 0.004629379492684471, 0.005466036101927195, 0.019045860479753603]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats  \n",
    "from scipy.stats import sem\n",
    "OT_data1=pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l2=[]\n",
    "l3=[]\n",
    "df = pd.DataFrame(OT_data1)\n",
    "POT = df[(df['Y'] == 1)]\n",
    "NOT = df[(df['Y'] == 0)]\n",
    "print(len(POT))\n",
    "print(len(NOT))\n",
    "#print(len(df))\n",
    "l1=['MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS4', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS4', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS4', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS4', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS4', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS4', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS4', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS4', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS4', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS4', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS4', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS4', 'MM_type_other_POS4']\n",
    "for i in l1:\n",
    "    total = POT[i].sum()\n",
    "    l2.append(total/524)\n",
    "    l3.append(sem(POT[i]))\n",
    "print(\"\\n Positive off-target \\n\")\n",
    "print(l2)\n",
    "print(l3)\n",
    "l2=[]\n",
    "l3=[]\n",
    "for i in l1:\n",
    "    total = NOT[i].sum()\n",
    "    l2.append(total/525)\n",
    "    l3.append(sem(NOT[i]))\n",
    "print(\"\\n Negative off-targets \\n\")\n",
    "print(l2)\n",
    "print(l3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "experimental-serve",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS4\n",
      "ShapiroResult(statistic=0.10495269298553467, pvalue=1.6983737387616783e-42)\n",
      "ShapiroResult(statistic=0.12315070629119873, pvalue=3.33088644970009e-42)\n",
      "Ttest_indResult(statistic=-0.4754386521285774, pvalue=0.6345837925986958)\n",
      "\n",
      "\n",
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS4\n",
      "ShapiroResult(statistic=0.1400437355041504, pvalue=6.289027507889779e-42)\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "Ttest_indResult(statistic=2.3463849886300223, pvalue=0.019230665435105567)\n",
      "\n",
      "\n",
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS4\n",
      "ShapiroResult(statistic=0.22354954481124878, pvalue=1.7068936334247732e-40)\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "Ttest_indResult(statistic=3.1161203115226606, pvalue=0.0019027103229516961)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS4\n",
      "ShapiroResult(statistic=0.14807754755020142, pvalue=8.53811154313111e-42)\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "Ttest_indResult(statistic=2.5267917950908085, pvalue=0.011731974343826671)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS4\n",
      "ShapiroResult(statistic=0.14807754755020142, pvalue=8.53811154313111e-42)\n",
      "ShapiroResult(statistic=0.2834208011627197, pvalue=2.18893049606326e-39)\n",
      "Ttest_indResult(statistic=-3.2724534807852326, pvalue=0.0011116362232092092)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS4\n",
      "ShapiroResult(statistic=0.12315070629119873, pvalue=3.33088644970009e-42)\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "Ttest_indResult(statistic=0.7335643066073017, pvalue=0.46339880565615177)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS4\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "Ttest_indResult(statistic=0.9091952763387234, pvalue=0.3634918618413253)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS4\n",
      "ShapiroResult(statistic=0.12315070629119873, pvalue=3.33088644970009e-42)\n",
      "ShapiroResult(statistic=0.10495269298553467, pvalue=1.6983737387616783e-42)\n",
      "Ttest_indResult(statistic=0.4754386521285774, pvalue=0.6345837925986958)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS4\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "ShapiroResult(statistic=0.03733646869659424, pvalue=1.5274153261140506e-43)\n",
      "Ttest_indResult(statistic=0.44796082007366483, pvalue=0.6542865467565162)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS4\n",
      "ShapiroResult(statistic=0.08509266376495361, pvalue=8.253647954873173e-43)\n",
      "ShapiroResult(statistic=0.08509266376495361, pvalue=8.253647954873173e-43)\n",
      "Ttest_indResult(statistic=0.0, pvalue=1.0)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS4\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.07436710596084595, pvalue=5.6192068419425165e-43)\n",
      "Ttest_indResult(statistic=-2.24544356569536, pvalue=0.02519399786804975)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS4\n",
      "ShapiroResult(statistic=0.14807754755020142, pvalue=8.53811154313111e-42)\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "Ttest_indResult(statistic=1.3556942237593244, pvalue=0.1755428936943037)\n",
      "\n",
      "\n",
      "MM_type_other_POS4\n",
      "ShapiroResult(statistic=0.6245303153991699, pvalue=4.79757048468898e-31)\n",
      "ShapiroResult(statistic=0.5152202844619751, pvalue=3.2179125711336465e-34)\n",
      "Ttest_indResult(statistic=6.285659763559473, pvalue=5.000343243765838e-10)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df= pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l1=['MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS4', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS4', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS4', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS4', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS4', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS4', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS4', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS4', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS4', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS4', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS4', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS4', 'MM_type_other_POS4']\n",
    "for i in l1:\n",
    "    #print(df.groupby(\"Y\")[i].describe())\n",
    "    POT = df[(df['Y'] == 1)]\n",
    "    NOT = df[(df['Y'] == 0)]\n",
    "    from scipy import stats\n",
    "    print(i)\n",
    "    print(stats.shapiro(POT[i]))\n",
    "    print(stats.shapiro(NOT[i]))\n",
    "    print(stats.ttest_ind(POT[i], NOT[i], equal_var = False))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "encouraging-times",
   "metadata": {},
   "source": [
    "# mismatch at position 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "hired-midnight",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "481\n",
      "481\n",
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.04389312977099236, 0.0057251908396946565, 0.015267175572519083, 0.007633587786259542, 0.011450381679389313, 0.003816793893129771, 0.0, 0.003816793893129771, 0.0057251908396946565, 0.0, 0.1297709923664122, 0.0057251908396946565, 0.003816793893129771]\n",
      "[0.009739372172945534, 0.003593427446453971, 0.005837271168742748, 0.004144990028560683, 0.005065901426615886, 0.0029370886802191055, 0.0, 0.0029370886802191055, 0.003593427446453971, 0.0, 0.015902440785929468, 0.003593427446453971, 0.0029370886802191055]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.009523809523809525, 0.015238095238095238, 0.022857142857142857, 0.017142857142857144, 0.06095238095238095, 0.024761904761904763, 0.011428571428571429, 0.02095238095238095, 0.007619047619047619, 0.022857142857142857, 0.005714285714285714, 0.02095238095238095, 0.022857142857142857]\n",
      "[0.004629379492684471, 0.005837271168742748, 0.0071188747061388495, 0.006184812798604373, 0.011374503917030813, 0.007401656182502244, 0.005065901426615885, 0.0068230660663600885, 0.004144990028560683, 0.0071188747061388495, 0.0035934274464539716, 0.0068230660663600885, 0.0071188747061388495]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats  \n",
    "from scipy.stats import sem\n",
    "OT_data1=pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l2=[]\n",
    "l3=[]\n",
    "df = pd.DataFrame(OT_data1)\n",
    "POT = df[(df['Y'] == 1)]\n",
    "NOT = df[(df['Y'] == 0)]\n",
    "print(len(POT))\n",
    "print(len(NOT))\n",
    "#print(len(df))\n",
    "l1=['MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS16', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS16', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS16', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS16', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS16', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS16', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS16', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS16', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS16', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS16', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS16', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS16', 'MM_type_other_POS16']\n",
    "for i in l1:\n",
    "    total = POT[i].sum()\n",
    "    l2.append(total/524)\n",
    "    l3.append(sem(POT[i]))\n",
    "print(\"\\n Positive off-target \\n\")\n",
    "print(l2)\n",
    "print(l3)\n",
    "l2=[]\n",
    "l3=[]\n",
    "for i in l1:\n",
    "    total = NOT[i].sum()\n",
    "    l2.append(total/525)\n",
    "    l3.append(sem(NOT[i]))\n",
    "print(\"\\n Negative off-targets \\n\")\n",
    "print(l2)\n",
    "print(l3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "collective-cannon",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS16\n",
      "ShapiroResult(statistic=0.2174607515335083, pvalue=1.328907385657797e-40)\n",
      "ShapiroResult(statistic=0.07436710596084595, pvalue=5.6192068419425165e-43)\n",
      "Ttest_indResult(statistic=3.4702657403291757, pvalue=0.0005524803798140041)\n",
      "\n",
      "\n",
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS16\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "ShapiroResult(statistic=0.10495269298553467, pvalue=1.6983737387616783e-42)\n",
      "Ttest_indResult(statistic=-1.516486629670229, pvalue=0.12979230349472512)\n",
      "\n",
      "\n",
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS16\n",
      "ShapiroResult(statistic=0.10495269298553467, pvalue=1.6983737387616783e-42)\n",
      "ShapiroResult(statistic=0.1400437355041504, pvalue=6.289027507889779e-42)\n",
      "Ttest_indResult(statistic=-0.9033158918728195, pvalue=0.36659356403890264)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS16\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "ShapiroResult(statistic=0.11423343420028687, pvalue=2.3920164786024627e-42)\n",
      "Ttest_indResult(statistic=-1.3961796943056517, pvalue=0.1630297661218471)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS16\n",
      "ShapiroResult(statistic=0.08509266376495361, pvalue=8.253647954873173e-43)\n",
      "ShapiroResult(statistic=0.26823508739471436, pvalue=1.1277523924024339e-39)\n",
      "Ttest_indResult(statistic=-4.341128235233905, pvalue=1.6389459481957394e-05)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS16\n",
      "ShapiroResult(statistic=0.03733646869659424, pvalue=1.5274153261140506e-43)\n",
      "ShapiroResult(statistic=0.14807754755020142, pvalue=8.53811154313111e-42)\n",
      "Ttest_indResult(statistic=-2.871873415672941, pvalue=0.004218219010379871)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS16\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.08509266376495361, pvalue=8.253647954873173e-43)\n",
      "Ttest_indResult(statistic=-2.46234804500437, pvalue=0.014153326791830779)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS16\n",
      "ShapiroResult(statistic=0.03733646869659424, pvalue=1.5274153261140506e-43)\n",
      "ShapiroResult(statistic=0.1317436695098877, pvalue=4.5962589629854e-42)\n",
      "Ttest_indResult(statistic=-2.5188583762455257, pvalue=0.012011715154836561)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS16\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "Ttest_indResult(statistic=-0.3789809637905569, pvalue=0.7047874883642872)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS16\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.1400437355041504, pvalue=6.289027507889779e-42)\n",
      "Ttest_indResult(statistic=-3.5044899619474146, pvalue=0.0005003496153487136)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS16\n",
      "ShapiroResult(statistic=0.4133777618408203, pvalue=1.0804845212790306e-36)\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "Ttest_indResult(statistic=8.28877805974403, pvalue=9.491060219219443e-16)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS16\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "ShapiroResult(statistic=0.1317436695098877, pvalue=4.5962589629854e-42)\n",
      "Ttest_indResult(statistic=-2.1567860873367524, pvalue=0.03134938847013414)\n",
      "\n",
      "\n",
      "MM_type_other_POS16\n",
      "ShapiroResult(statistic=0.03733646869659424, pvalue=1.5274153261140506e-43)\n",
      "ShapiroResult(statistic=0.1400437355041504, pvalue=6.289027507889779e-42)\n",
      "Ttest_indResult(statistic=-2.699664250137036, pvalue=0.007124694407124988)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n",
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df= pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l1=['MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS16', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS16', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS16', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS16', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS16', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS16', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS16', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS16', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS16', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS16', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS16', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS16', 'MM_type_other_POS16']\n",
    "for i in l1:\n",
    "    #print(df.groupby(\"Y\")[i].describe())\n",
    "    POT = df[(df['Y'] == 1)]\n",
    "    NOT = df[(df['Y'] == 0)]\n",
    "    from scipy import stats\n",
    "    print(i)\n",
    "    print(stats.shapiro(POT[i]))\n",
    "    print(stats.shapiro(NOT[i]))\n",
    "    print(stats.ttest_ind(POT[i], NOT[i], equal_var = False))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bulgarian-intermediate",
   "metadata": {},
   "source": [
    "# mismatch at position 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "headed-reverse",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "481\n",
      "481\n",
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.0019083969465648854, 0.007633587786259542, 0.17938931297709923, 0.015267175572519083, 0.030534351145038167, 0.0057251908396946565, 0.0057251908396946565, 0.003816793893129771, 0.007633587786259542, 0.003816793893129771, 0.003816793893129771, 0.007633587786259542, 0.0]\n",
      "[0.002079002079002079, 0.004144990028560683, 0.018098963918748563, 0.005837271168742748, 0.008185039370074648, 0.0035934274464539703, 0.0035934274464539716, 0.0029370886802191055, 0.004144990028560684, 0.0029370886802191055, 0.0029370886802191055, 0.004144990028560683, 0.0]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.02857142857142857, 0.022857142857142857, 0.022857142857142857, 0.022857142857142857, 0.0380952380952381, 0.017142857142857144, 0.013333333333333334, 0.017142857142857144, 0.007619047619047619, 0.013333333333333334, 0.005714285714285714, 0.009523809523809525, 0.02095238095238095]\n",
      "[0.00793364736095309, 0.0071188747061388495, 0.0071188747061388495, 0.0071188747061388495, 0.009111707409774572, 0.006184812798604373, 0.005466036101927195, 0.006184812798604373, 0.004144990028560683, 0.005466036101927195, 0.003593427446453971, 0.004629379492684471, 0.0068230660663600885]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats  \n",
    "from scipy.stats import sem\n",
    "OT_data1=pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l2=[]\n",
    "l3=[]\n",
    "df = pd.DataFrame(OT_data1)\n",
    "POT = df[(df['Y'] == 1)]\n",
    "NOT = df[(df['Y'] == 0)]\n",
    "print(len(POT))\n",
    "print(len(NOT))\n",
    "#print(len(df))\n",
    "l1=['MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS17', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS17', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS17', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS17', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS17', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS17', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS17', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS17', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS17', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS17', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS17', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS17', 'MM_type_other_POS17']\n",
    "for i in l1:\n",
    "    total = POT[i].sum()\n",
    "    l2.append(total/524)\n",
    "    l3.append(sem(POT[i]))\n",
    "print(\"\\n Positive off-target \\n\")\n",
    "print(l2)\n",
    "print(l3)\n",
    "l2=[]\n",
    "l3=[]\n",
    "for i in l1:\n",
    "    total = NOT[i].sum()\n",
    "    l2.append(total/525)\n",
    "    l3.append(sem(NOT[i]))\n",
    "print(\"\\n Negative off-targets \\n\")\n",
    "print(l2)\n",
    "print(l3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "injured-restaurant",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS17\n",
      "ShapiroResult(statistic=0.021795332431793213, pvalue=8.96831017167883e-44)\n",
      "ShapiroResult(statistic=0.16343194246292114, pvalue=1.5421289599894612e-41)\n",
      "Ttest_indResult(statistic=-3.548855802937077, pvalue=0.00042027287576574477)\n",
      "\n",
      "\n",
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS17\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "ShapiroResult(statistic=0.1400437355041504, pvalue=6.289027507889779e-42)\n",
      "Ttest_indResult(statistic=-2.019017860500275, pvalue=0.04383060141995808)\n",
      "\n",
      "\n",
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS17\n",
      "ShapiroResult(statistic=0.4845699667930603, pvalue=5.272728124248854e-35)\n",
      "ShapiroResult(statistic=0.1400437355041504, pvalue=6.289027507889779e-42)\n",
      "Ttest_indResult(statistic=8.765542646897936, pvalue=1.7488126876944712e-17)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS17\n",
      "ShapiroResult(statistic=0.10495269298553467, pvalue=1.6983737387616783e-42)\n",
      "ShapiroResult(statistic=0.1400437355041504, pvalue=6.289027507889779e-42)\n",
      "Ttest_indResult(statistic=-0.9033158918728195, pvalue=0.36659356403890264)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS17\n",
      "ShapiroResult(statistic=0.1707882285118103, pvalue=2.053462769621587e-41)\n",
      "ShapiroResult(statistic=0.19838988780975342, pvalue=6.129559742649615e-41)\n",
      "Ttest_indResult(statistic=-0.6789583151840052, pvalue=0.49732979078165374)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS17\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "ShapiroResult(statistic=0.11423343420028687, pvalue=2.3920164786024627e-42)\n",
      "Ttest_indResult(statistic=-1.743898701804142, pvalue=0.08157534448507009)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS17\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "Ttest_indResult(statistic=-1.2712834523274568, pvalue=0.20398414198978287)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS17\n",
      "ShapiroResult(statistic=0.03733646869659424, pvalue=1.5274153261140506e-43)\n",
      "ShapiroResult(statistic=0.11423343420028687, pvalue=2.3920164786024627e-42)\n",
      "Ttest_indResult(statistic=-2.1255267589643223, pvalue=0.033899343427399684)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS17\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "Ttest_indResult(statistic=0.0, pvalue=1.0)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS17\n",
      "ShapiroResult(statistic=0.03733646869659424, pvalue=1.5274153261140506e-43)\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "Ttest_indResult(statistic=-1.6752197124933457, pvalue=0.09431598610239865)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS17\n",
      "ShapiroResult(statistic=0.03733646869659424, pvalue=1.5274153261140506e-43)\n",
      "ShapiroResult(statistic=0.050718724727630615, pvalue=2.4382593279251817e-43)\n",
      "Ttest_indResult(statistic=-0.4479608200736647, pvalue=0.6542865467565163)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS17\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "ShapiroResult(statistic=0.07436710596084595, pvalue=5.6192068419425165e-43)\n",
      "Ttest_indResult(statistic=-0.334574802559103, pvalue=0.738019823134821)\n",
      "\n",
      "\n",
      "MM_type_other_POS17\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.1317436695098877, pvalue=4.5962589629854e-42)\n",
      "Ttest_indResult(statistic=-3.3517223263855693, pvalue=0.0008665151322558831)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df= pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l1=['MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS17', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS17', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS17', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS17', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS17', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS17', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS17', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS17', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS17', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS17', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS17', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS17', 'MM_type_other_POS17']\n",
    "for i in l1:\n",
    "    #print(df.groupby(\"Y\")[i].describe())\n",
    "    POT = df[(df['Y'] == 1)]\n",
    "    NOT = df[(df['Y'] == 0)]\n",
    "    from scipy import stats\n",
    "    print(i)\n",
    "    print(stats.shapiro(POT[i]))\n",
    "    print(stats.shapiro(NOT[i]))\n",
    "    print(stats.ttest_ind(POT[i], NOT[i], equal_var = False))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "textile-sheriff",
   "metadata": {},
   "source": [
    "# mismatch at position 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "marine-brunswick",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "481\n",
      "481\n",
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.013358778625954198, 0.003816793893129771, 0.003816793893129771, 0.01717557251908397, 0.0019083969465648854, 0.0019083969465648854, 0.02480916030534351, 0.009541984732824428, 0.0, 0.0, 0.16030534351145037, 0.0019083969465648854, 0.0]\n",
      "[0.005466036101927195, 0.0029370886802191055, 0.0029370886802191055, 0.006184812798604373, 0.0020790020790020796, 0.0020790020790020796, 0.0074016561825022455, 0.004629379492684471, 0.0, 0.0, 0.017328828409655623, 0.002079002079002079, 0.0]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.017142857142857144, 0.022857142857142857, 0.03428571428571429, 0.03238095238095238, 0.01904761904761905, 0.007619047619047619, 0.007619047619047619, 0.009523809523809525, 0.011428571428571429, 0.007619047619047619, 0.013333333333333334, 0.013333333333333334, 0.02857142857142857]\n",
      "[0.006184812798604373, 0.0071188747061388495, 0.008662855155925433, 0.00842786860112729, 0.006512455346227478, 0.004144990028560683, 0.004144990028560683, 0.004629379492684471, 0.005065901426615885, 0.004144990028560683, 0.005466036101927195, 0.005466036101927195, 0.007933647360953089]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats  \n",
    "from scipy.stats import sem\n",
    "OT_data1=pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l2=[]\n",
    "l3=[]\n",
    "df = pd.DataFrame(OT_data1)\n",
    "POT = df[(df['Y'] == 1)]\n",
    "NOT = df[(df['Y'] == 0)]\n",
    "print(len(POT))\n",
    "print(len(NOT))\n",
    "#print(len(df))\n",
    "l1=['MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS18', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS18', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS18', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS18', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS18', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS18', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS18', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS18', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS18', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS18', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS18', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS18', 'MM_type_other_POS18']\n",
    "for i in l1:\n",
    "    total = POT[i].sum()\n",
    "    l2.append(total/524)\n",
    "    l3.append(sem(POT[i]))\n",
    "print(\"\\n Positive off-target \\n\")\n",
    "print(l2)\n",
    "print(l3)\n",
    "l2=[]\n",
    "l3=[]\n",
    "for i in l1:\n",
    "    total = NOT[i].sum()\n",
    "    l2.append(total/525)\n",
    "    l3.append(sem(NOT[i]))\n",
    "print(\"\\n Negative off-targets \\n\")\n",
    "print(l2)\n",
    "print(l3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "manual-competition",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS18\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "ShapiroResult(statistic=0.11423343420028687, pvalue=2.3920164786024627e-42)\n",
      "Ttest_indResult(statistic=-0.5037527684340762, pvalue=0.6145524439566473)\n",
      "\n",
      "\n",
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS18\n",
      "ShapiroResult(statistic=0.03733646869659424, pvalue=1.5274153261140506e-43)\n",
      "ShapiroResult(statistic=0.1400437355041504, pvalue=6.289027507889779e-42)\n",
      "Ttest_indResult(statistic=-2.699664250137036, pvalue=0.007124694407124988)\n",
      "\n",
      "\n",
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS18\n",
      "ShapiroResult(statistic=0.03733646869659424, pvalue=1.5274153261140506e-43)\n",
      "ShapiroResult(statistic=0.1849304437637329, pvalue=3.582419524046395e-41)\n",
      "Ttest_indResult(statistic=-3.636520170387357, pvalue=0.00030055878174484055)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS18\n",
      "ShapiroResult(statistic=0.11423343420028687, pvalue=2.3920164786024627e-42)\n",
      "ShapiroResult(statistic=0.17795026302337646, pvalue=2.71935979986874e-41)\n",
      "Ttest_indResult(statistic=-1.591009679293147, pvalue=0.11196623011822733)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS18\n",
      "ShapiroResult(statistic=0.021795332431793213, pvalue=8.96831017167883e-44)\n",
      "ShapiroResult(statistic=0.12315070629119873, pvalue=3.33088644970009e-42)\n",
      "Ttest_indResult(statistic=-2.73702931657094, pvalue=0.00639084122701209)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS18\n",
      "ShapiroResult(statistic=0.021795332431793213, pvalue=8.96831017167883e-44)\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "Ttest_indResult(statistic=-1.345007518986661, pvalue=0.1790540033671336)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS18\n",
      "ShapiroResult(statistic=0.14807754755020142, pvalue=8.53811154313111e-42)\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "Ttest_indResult(statistic=2.2056438662814233, pvalue=0.02770987801556533)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS18\n",
      "ShapiroResult(statistic=0.07436710596084595, pvalue=5.6192068419425165e-43)\n",
      "ShapiroResult(statistic=0.07436710596084595, pvalue=5.6192068419425165e-43)\n",
      "Ttest_indResult(statistic=0.0, pvalue=1.0)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS18\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.08509266376495361, pvalue=8.253647954873173e-43)\n",
      "Ttest_indResult(statistic=-2.46234804500437, pvalue=0.014153326791830779)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS18\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "Ttest_indResult(statistic=-2.0062794503020767, pvalue=0.04538693145071601)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS18\n",
      "ShapiroResult(statistic=0.45961201190948486, pvalue=1.2875891879116198e-35)\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "Ttest_indResult(statistic=8.810074416578937, pvalue=1.478158742793826e-17)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS18\n",
      "ShapiroResult(statistic=0.021795332431793213, pvalue=8.96831017167883e-44)\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "Ttest_indResult(statistic=-2.1330173541664768, pvalue=0.033317910832462605)\n",
      "\n",
      "\n",
      "MM_type_other_POS18\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.16343194246292114, pvalue=1.5421289599894612e-41)\n",
      "Ttest_indResult(statistic=-3.930730692482512, pvalue=9.71451139666767e-05)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n",
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n",
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df= pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l1=['MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS18', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS18', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS18', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS18', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS18', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS18', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS18', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS18', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS18', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS18', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS18', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS18', 'MM_type_other_POS18']\n",
    "for i in l1:\n",
    "    #print(df.groupby(\"Y\")[i].describe())\n",
    "    POT = df[(df['Y'] == 1)]\n",
    "    NOT = df[(df['Y'] == 0)]\n",
    "    from scipy import stats\n",
    "    print(i)\n",
    "    print(stats.shapiro(POT[i]))\n",
    "    print(stats.shapiro(NOT[i]))\n",
    "    print(stats.ttest_ind(POT[i], NOT[i], equal_var = False))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cardiac-dream",
   "metadata": {},
   "source": [
    "# mismatch at position 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "caroline-status",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "481\n",
      "481\n",
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.026717557251908396, 0.0, 0.02099236641221374, 0.0, 0.02480916030534351, 0.019083969465648856, 0.01717557251908397, 0.009541984732824428, 0.0, 0.007633587786259542, 0.015267175572519083, 0.2366412213740458, 0.0]\n",
      "[0.007672850971622296, 0.0, 0.0068230660663600885, 0.0, 0.0074016561825022455, 0.006512455346227478, 0.006184812798604373, 0.004629379492684471, 0.0, 0.004144990028560683, 0.005837271168742748, 0.01996545700612807, 0.0]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.0038095238095238095, 0.022857142857142857, 0.02857142857142857, 0.030476190476190476, 0.013333333333333334, 0.02666666666666667, 0.011428571428571429, 0.013333333333333334, 0.013333333333333334, 0.011428571428571429, 0.009523809523809525, 0.01904761904761905, 0.017142857142857144]\n",
      "[0.0029370886802191055, 0.0071188747061388495, 0.007933647360953089, 0.008185039370074648, 0.005466036101927195, 0.007672850971622294, 0.005065901426615885, 0.005466036101927195, 0.005466036101927195, 0.005065901426615886, 0.004629379492684471, 0.006512455346227478, 0.006184812798604373]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from scipy import stats  \n",
    "from scipy.stats import sem\n",
    "OT_data1=pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l2=[]\n",
    "l3=[]\n",
    "df = pd.DataFrame(OT_data1)\n",
    "POT = df[(df['Y'] == 1)]\n",
    "NOT = df[(df['Y'] == 0)]\n",
    "print(len(POT))\n",
    "print(len(NOT))\n",
    "#print(len(df))\n",
    "l1=['MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS23', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS23', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS23', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS23', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS23', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS23', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS23', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS23', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS23', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS23', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS23', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS23', 'MM_type_other_POS23']\n",
    "for i in l1:\n",
    "    total = POT[i].sum()\n",
    "    l2.append(total/524)\n",
    "    l3.append(sem(POT[i]))\n",
    "print(\"\\n Positive off-target \\n\")\n",
    "print(l2)\n",
    "print(l3)\n",
    "l2=[]\n",
    "l3=[]\n",
    "for i in l1:\n",
    "    total = NOT[i].sum()\n",
    "    l2.append(total/525)\n",
    "    l3.append(sem(NOT[i]))\n",
    "print(\"\\n Negative off-targets \\n\")\n",
    "print(l2)\n",
    "print(l3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "adjusted-alias",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS23\n",
      "ShapiroResult(statistic=0.15586727857589722, pvalue=1.1511666884428372e-41)\n",
      "ShapiroResult(statistic=0.03733646869659424, pvalue=1.5274153261140506e-43)\n",
      "Ttest_indResult(statistic=3.036596420803866, pvalue=0.002493474382539817)\n",
      "\n",
      "\n",
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS23\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.1400437355041504, pvalue=6.289027507889779e-42)\n",
      "Ttest_indResult(statistic=-3.5044899619474146, pvalue=0.0005003496153487136)\n",
      "\n",
      "\n",
      "MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS23\n",
      "ShapiroResult(statistic=0.1317436695098877, pvalue=4.5962589629854e-42)\n",
      "ShapiroResult(statistic=0.16343194246292114, pvalue=1.5421289599894612e-41)\n",
      "Ttest_indResult(statistic=-0.7947194142390265, pvalue=0.4269775578135785)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS23\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.1707882285118103, pvalue=2.053462769621587e-41)\n",
      "Ttest_indResult(statistic=-4.064004064006096, pvalue=5.635163821035003e-05)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS23\n",
      "ShapiroResult(statistic=0.14807754755020142, pvalue=8.53811154313111e-42)\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "Ttest_indResult(statistic=1.3556942237593244, pvalue=0.1755428936943037)\n",
      "\n",
      "\n",
      "MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS23\n",
      "ShapiroResult(statistic=0.12315070629119873, pvalue=3.33088644970009e-42)\n",
      "ShapiroResult(statistic=0.15586727857589722, pvalue=1.1511666884428372e-41)\n",
      "Ttest_indResult(statistic=-0.8263099002245489, pvalue=0.4088390540645006)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS23\n",
      "ShapiroResult(statistic=0.11423343420028687, pvalue=2.3920164786024627e-42)\n",
      "ShapiroResult(statistic=0.08509266376495361, pvalue=8.253647954873173e-43)\n",
      "Ttest_indResult(statistic=0.7801423889801693, pvalue=0.43550682790314565)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS23\n",
      "ShapiroResult(statistic=0.07436710596084595, pvalue=5.6192068419425165e-43)\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "Ttest_indResult(statistic=-0.5804828481189924, pvalue=0.5617289705446323)\n",
      "\n",
      "\n",
      "MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS23\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.09526002407073975, pvalue=1.1925049931404193e-42)\n",
      "Ttest_indResult(statistic=-2.662443913951374, pvalue=0.00801796147350395)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS23\n",
      "ShapiroResult(statistic=0.06296706199645996, pvalue=3.7414668997472616e-43)\n",
      "ShapiroResult(statistic=0.08509266376495361, pvalue=8.253647954873173e-43)\n",
      "Ttest_indResult(statistic=-0.635240816804284, pvalue=0.5254286769495496)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS23\n",
      "ShapiroResult(statistic=0.10495269298553467, pvalue=1.6983737387616783e-42)\n",
      "ShapiroResult(statistic=0.07436710596084595, pvalue=5.6192068419425165e-43)\n",
      "Ttest_indResult(statistic=0.8371640269664739, pvalue=0.40271953431473284)\n",
      "\n",
      "\n",
      "MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS23\n",
      "ShapiroResult(statistic=0.5449211001396179, pvalue=2.0318546586947938e-33)\n",
      "ShapiroResult(statistic=0.12315070629119873, pvalue=3.33088644970009e-42)\n",
      "Ttest_indResult(statistic=11.285607620351206, pvalue=7.628392994496031e-27)\n",
      "\n",
      "\n",
      "MM_type_other_POS23\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.11423343420028687, pvalue=2.3920164786024627e-42)\n",
      "Ttest_indResult(statistic=-3.025316904537664, pvalue=0.002616978345609585)\n",
      "\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n",
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n",
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n",
      "c:\\users\\pragy\\appdata\\local\\programs\\python\\python38\\lib\\site-packages\\scipy\\stats\\morestats.py:1678: UserWarning: Input data for shapiro has range zero. The results may not be accurate.\n",
      "  warnings.warn(\"Input data for shapiro has range zero. The results \"\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df= pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/AsCpf1/AsCpf1_dataset-sampled-491PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l1=['MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS23', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS23', 'MM_type_AÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS23', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS23', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS23', 'MM_type_TÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS23', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS23', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS23', 'MM_type_GÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“C_POS23', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“A_POS23', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“T_POS23', 'MM_type_CÃƒÂ¢Ã¢â€šÂ¬Ã¢â‚¬Å“G_POS23', 'MM_type_other_POS23']\n",
    "for i in l1:\n",
    "    #print(df.groupby(\"Y\")[i].describe())\n",
    "    POT = df[(df['Y'] == 1)]\n",
    "    NOT = df[(df['Y'] == 0)]\n",
    "    from scipy import stats\n",
    "    print(i)\n",
    "    print(stats.shapiro(POT[i]))\n",
    "    print(stats.shapiro(NOT[i]))\n",
    "    print(stats.ttest_ind(POT[i], NOT[i], equal_var = False))\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "resistant-result",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}