Position-specificFeaturesLbCpf1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "acceptable-consumer",
   "metadata": {},
   "source": [
    "# Position-specific feature importance analysis for LbCpf1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "unlikely-factory",
   "metadata": {},
   "source": [
    "# Calculation of frequency of nucleotides at each location"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "adaptive-taiwan",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "524 80304\n",
      "positive off-targets\n",
      "Position-wise frequency of A in positive off-targets \n",
      " [0.02079002079002079, 0.0, 0.002079002079002079, 0.21621621621621623, 0.04365904365904366, 0.07692307692307693, 0.07068607068607069, 0.42203742203742206, 0.07276507276507277, 0.49480249480249483, 0.1600831600831601, 0.1683991683991684, 0.4407484407484408, 0.17255717255717257, 0.7505197505197505, 0.18087318087318088, 0.3762993762993763, 0.14137214137214138, 0.5550935550935551, 0.5509355509355509, 0.1787941787941788, 0.5343035343035343, 0.27442827442827444, 0.32224532224532226, 0.6943866943866944, 0.25363825363825365, 0.2203742203742204] \n",
      "\n",
      "Position-wise frequency of T in positive off-targets \n",
      " [0.7193347193347194, 1.0727650727650728, 1.0602910602910602, 0.12681912681912683, 0.1101871101871102, 0.3596673596673597, 0.15384615384615385, 0.5280665280665281, 0.340956340956341, 0.04781704781704782, 0.4885654885654886, 0.340956340956341, 0.15176715176715178, 0.19334719334719336, 0.08316008316008316, 0.2494802494802495, 0.08316008316008316, 0.1496881496881497, 0.13305613305613306, 0.288981288981289, 0.08523908523908524, 0.35550935550935553, 0.28482328482328484, 0.13097713097713098, 0.1995841995841996, 0.21205821205821207, 0.17255717255717257] \n",
      "\n",
      "Position-wise frequency of G in positive off-targets \n",
      " [0.3180873180873181, 0.0, 0.004158004158004158, 0.49064449064449067, 0.6486486486486487, 0.5426195426195426, 0.6632016632016632, 0.018711018711018712, 0.4864864864864865, 0.3700623700623701, 0.3201663201663202, 0.14553014553014554, 0.158004158004158, 0.44490644490644493, 0.14553014553014554, 0.0893970893970894, 0.16632016632016633, 0.1787941787941788, 0.15384615384615385, 0.20582120582120583, 0.2993762993762994, 0.08316008316008316, 0.12681912681912683, 0.15592515592515593, 0.16632016632016633, 0.6237006237006237, 0.6216216216216216] \n",
      "\n",
      "Position-wise frequency of C in positive off-targets \n",
      " [0.031185031185031187, 0.016632016632016633, 0.02286902286902287, 0.25571725571725573, 0.2869022869022869, 0.1101871101871102, 0.20166320166320167, 0.12058212058212059, 0.1891891891891892, 0.17671517671517672, 0.12058212058212059, 0.43451143451143454, 0.3388773388773389, 0.2785862785862786, 0.1101871101871102, 0.5675675675675675, 0.46361746361746364, 0.6195426195426196, 0.24740124740124741, 0.04365904365904366, 0.525987525987526, 0.11642411642411643, 0.40124740124740127, 0.4781704781704782, 0.2598752598752599, 0.23076923076923078, 0.30561330561330563] \n",
      "\n",
      "negative off-targets\n",
      "Position-wise frequency of A in negative off-targets \n",
      " [0.02079002079002079, 0.0, 0.002079002079002079, 0.21621621621621623, 0.04365904365904366, 0.07692307692307693, 0.07068607068607069, 0.42203742203742206, 0.07276507276507277, 0.49480249480249483, 0.1600831600831601, 0.1683991683991684, 0.4407484407484408, 0.17255717255717257, 0.7505197505197505, 0.18087318087318088, 0.3762993762993763, 0.14137214137214138, 0.5550935550935551, 0.5509355509355509, 0.1787941787941788, 0.5343035343035343, 0.27442827442827444, 0.32224532224532226, 0.6943866943866944, 0.25363825363825365, 0.2203742203742204] \n",
      "\n",
      "Position-wise frequency of T in negative off-targets \n",
      " [0.7193347193347194, 1.0727650727650728, 1.0602910602910602, 0.12681912681912683, 0.1101871101871102, 0.3596673596673597, 0.15384615384615385, 0.5280665280665281, 0.340956340956341, 0.04781704781704782, 0.4885654885654886, 0.340956340956341, 0.15176715176715178, 0.19334719334719336, 0.08316008316008316, 0.2494802494802495, 0.08316008316008316, 0.1496881496881497, 0.13305613305613306, 0.288981288981289, 0.08523908523908524, 0.35550935550935553, 0.28482328482328484, 0.13097713097713098, 0.1995841995841996, 0.21205821205821207, 0.17255717255717257] \n",
      "\n",
      "Position-wise frequency of G in negative off-targets \n",
      " [0.3180873180873181, 0.0, 0.004158004158004158, 0.49064449064449067, 0.6486486486486487, 0.5426195426195426, 0.6632016632016632, 0.018711018711018712, 0.4864864864864865, 0.3700623700623701, 0.3201663201663202, 0.14553014553014554, 0.158004158004158, 0.44490644490644493, 0.14553014553014554, 0.0893970893970894, 0.16632016632016633, 0.1787941787941788, 0.15384615384615385, 0.20582120582120583, 0.2993762993762994, 0.08316008316008316, 0.12681912681912683, 0.15592515592515593, 0.16632016632016633, 0.6237006237006237, 0.6216216216216216] \n",
      "\n",
      "Position-wise frequency of C in negative off-targets \n",
      " [0.031185031185031187, 0.016632016632016633, 0.02286902286902287, 0.25571725571725573, 0.2869022869022869, 0.1101871101871102, 0.20166320166320167, 0.12058212058212059, 0.1891891891891892, 0.17671517671517672, 0.12058212058212059, 0.43451143451143454, 0.3388773388773389, 0.2785862785862786, 0.1101871101871102, 0.5675675675675675, 0.46361746361746364, 0.6195426195426196, 0.24740124740124741, 0.04365904365904366, 0.525987525987526, 0.11642411642411643, 0.40124740124740127, 0.4781704781704782, 0.2598752598752599, 0.23076923076923078, 0.30561330561330563] \n",
      "\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_clean2.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": "useful-haiti",
   "metadata": {},
   "source": [
    "# Enrichment analysis to study the position-specific favour and disfavour of Nucleotides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "imported-director",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C_OTSeqPosition1\n",
      "ShapiroResult(statistic=0.15406674146652222, pvalue=4.161856439044707e-43)\n",
      "ShapiroResult(statistic=0.4603841304779053, pvalue=6.629083408824411e-37)\n",
      "Ttest_indResult(statistic=-8.083144148679956, pvalue=2.6742241553834104e-15)\n",
      "\n",
      "\n",
      "G_OTSeqPosition1\n",
      "ShapiroResult(statistic=0.5702968835830688, pvalue=6.512641861383606e-34)\n",
      "ShapiroResult(statistic=0.32479530572891235, pvalue=5.9773086645007235e-40)\n",
      "Ttest_indResult(statistic=8.522307566210817, pvalue=6.688441746558621e-17)\n",
      "\n",
      "\n",
      "T_OTSeqPosition1\n",
      "ShapiroResult(statistic=0.5980373024940491, pvalue=4.544268305737032e-33)\n",
      "ShapiroResult(statistic=0.6290885210037231, pvalue=4.29223474967585e-32)\n",
      "Ttest_indResult(statistic=2.968769285060489, pvalue=0.003058156805435945)\n",
      "\n",
      "\n",
      "A_OTSeqPosition1\n",
      "ShapiroResult(statistic=0.11582159996032715, pvalue=9.528829557408756e-44)\n",
      "ShapiroResult(statistic=0.4429128170013428, pvalue=2.4905001830907195e-37)\n",
      "Ttest_indResult(statistic=-8.3189228748493, pvalue=5.012022210487554e-16)\n",
      "\n",
      "\n",
      "C_OTSeqPosition2\n",
      "ShapiroResult(statistic=0.09859353303909302, pvalue=5.044674471569341e-44)\n",
      "ShapiroResult(statistic=0.5020115971565247, pvalue=7.609559523202898e-36)\n",
      "Ttest_indResult(statistic=-10.5316144064698, pvalue=5.7526021728066894e-24)\n",
      "\n",
      "\n",
      "G_OTSeqPosition2\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.3001370429992676, pvalue=1.8948778224139474e-40)\n",
      "Ttest_indResult(statistic=-6.7502012852436, pvalue=3.9256039386697614e-11)\n",
      "\n",
      "\n",
      "T_OTSeqPosition2\n",
      "ShapiroResult(statistic=0.09859293699264526, pvalue=5.044674471569341e-44)\n",
      "ShapiroResult(statistic=0.6309519410133362, pvalue=4.953700114562936e-32)\n",
      "Ttest_indResult(statistic=18.937270965669605, pvalue=8.518873285198759e-63)\n",
      "\n",
      "\n",
      "A_OTSeqPosition2\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.4213266968727112, pvalue=7.686166357853094e-38)\n",
      "Ttest_indResult(statistic=-9.490126448051152, pvalue=8.078401977238173e-20)\n",
      "\n",
      "\n",
      "C_OTSeqPosition3\n",
      "ShapiroResult(statistic=0.12396728992462158, pvalue=1.3032075718220799e-43)\n",
      "ShapiroResult(statistic=0.33640605211257935, pvalue=1.0390109632536718e-39)\n",
      "Ttest_indResult(statistic=-5.2969994262061695, pvalue=1.5446424432874147e-07)\n",
      "\n",
      "\n",
      "G_OTSeqPosition3\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.4098268151283264, pvalue=4.1687766113809284e-38)\n",
      "Ttest_indResult(statistic=-8.80780409485783, pvalue=1.614059194330801e-17)\n",
      "\n",
      "\n",
      "T_OTSeqPosition3\n",
      "ShapiroResult(statistic=0.1468701958656311, pvalue=3.1389085600875902e-43)\n",
      "ShapiroResult(statistic=0.6349253058433533, pvalue=6.737248305706823e-32)\n",
      "Ttest_indResult(statistic=19.206457748311152, pvalue=4.09056858546935e-65)\n",
      "\n",
      "\n",
      "A_OTSeqPosition3\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.520932137966156, pvalue=2.437697194243621e-35)\n",
      "Ttest_indResult(statistic=-12.357554868379466, pvalue=4.8823755353763615e-31)\n",
      "\n",
      "\n",
      "C_OTSeqPosition4\n",
      "ShapiroResult(statistic=0.5249063968658447, pvalue=3.33592698081236e-35)\n",
      "ShapiroResult(statistic=0.42689454555511475, pvalue=1.0373047982954485e-37)\n",
      "Ttest_indResult(statistic=3.476389721350473, pvalue=0.0005296758263017466)\n",
      "\n",
      "\n",
      "G_OTSeqPosition4\n",
      "ShapiroResult(statistic=0.6329589486122131, pvalue=6.145619060202088e-32)\n",
      "ShapiroResult(statistic=0.5712023973464966, pvalue=6.505468131601225e-34)\n",
      "Ttest_indResult(statistic=5.32782661123466, pvalue=1.2186336768057972e-07)\n",
      "\n",
      "\n",
      "T_OTSeqPosition4\n",
      "ShapiroResult(statistic=0.3725109100341797, pvalue=6.542375263747384e-39)\n",
      "ShapiroResult(statistic=0.5986630916595459, pvalue=4.468272595956371e-33)\n",
      "Ttest_indResult(statistic=-8.978030732303695, pvalue=1.5226684969133217e-18)\n",
      "\n",
      "\n",
      "A_OTSeqPosition4\n",
      "ShapiroResult(statistic=0.48801302909851074, pvalue=3.5127766046652823e-36)\n",
      "ShapiroResult(statistic=0.5020115971565247, pvalue=7.609559523202898e-36)\n",
      "Ttest_indResult(statistic=-0.5193129249762096, pvalue=0.6036523495180655)\n",
      "\n",
      "\n",
      "C_OTSeqPosition5\n",
      "ShapiroResult(statistic=0.5493968725204468, pvalue=1.6089394107944042e-34)\n",
      "ShapiroResult(statistic=0.48328107595443726, pvalue=2.4882958621336225e-36)\n",
      "Ttest_indResult(statistic=2.6691691363725982, pvalue=0.0077229394461348)\n",
      "\n",
      "\n",
      "G_OTSeqPosition5\n",
      "ShapiroResult(statistic=0.623160719871521, pvalue=2.9028077738435987e-32)\n",
      "ShapiroResult(statistic=0.5459467172622681, pvalue=1.2044900312017309e-34)\n",
      "Ttest_indResult(statistic=11.697488618134477, pvalue=9.000547227251385e-30)\n",
      "\n",
      "\n",
      "T_OTSeqPosition5\n",
      "ShapiroResult(statistic=0.3442775011062622, pvalue=1.6273377157096603e-39)\n",
      "ShapiroResult(statistic=0.5749449729919434, pvalue=8.409194548904862e-34)\n",
      "Ttest_indResult(statistic=-8.261245990039063, pvalue=5.078064425568564e-16)\n",
      "\n",
      "\n",
      "A_OTSeqPosition5\n",
      "ShapiroResult(statistic=0.19358372688293457, pvalue=2.0248762809493607e-42)\n",
      "ShapiroResult(statistic=0.51545250415802, pvalue=1.7334300022649944e-35)\n",
      "Ttest_indResult(statistic=-9.164848124164214, pvalue=4.738689828669838e-19)\n",
      "\n",
      "\n",
      "C_OTSeqPosition6\n",
      "ShapiroResult(statistic=0.3442775011062622, pvalue=1.6273377157096603e-39)\n",
      "ShapiroResult(statistic=0.4555196762084961, pvalue=5.034641622980284e-37)\n",
      "Ttest_indResult(statistic=-3.33212078434622, pvalue=0.0008934470616274163)\n",
      "\n",
      "\n",
      "G_OTSeqPosition6\n",
      "ShapiroResult(statistic=0.6365381479263306, pvalue=8.11502936513189e-32)\n",
      "ShapiroResult(statistic=0.5918077230453491, pvalue=2.7353990479214718e-33)\n",
      "Ttest_indResult(statistic=5.687490978165113, pvalue=1.6735336770300823e-08)\n",
      "\n",
      "\n",
      "T_OTSeqPosition6\n",
      "ShapiroResult(statistic=0.593158483505249, pvalue=3.2045555573949475e-33)\n",
      "ShapiroResult(statistic=0.5724630355834961, pvalue=7.091484667087271e-34)\n",
      "Ttest_indResult(statistic=1.2193162979810908, pvalue=0.22299906996146887)\n",
      "\n",
      "\n",
      "A_OTSeqPosition6\n",
      "ShapiroResult(statistic=0.27826154232025146, pvalue=7.544590931924815e-41)\n",
      "ShapiroResult(statistic=0.4854470491409302, pvalue=2.8268513647765327e-36)\n",
      "Ttest_indResult(statistic=-6.0812820536091285, pvalue=1.7634343434368745e-09)\n",
      "\n",
      "\n",
      "C_OTSeqPosition7\n",
      "ShapiroResult(statistic=0.4725441336631775, pvalue=1.4212775276266729e-36)\n",
      "ShapiroResult(statistic=0.4897134304046631, pvalue=3.639001624275186e-36)\n",
      "Ttest_indResult(statistic=-0.6108775157637866, pvalue=0.5414133605094229)\n",
      "\n",
      "\n",
      "G_OTSeqPosition7\n",
      "ShapiroResult(statistic=0.619103193283081, pvalue=2.1374710261404516e-32)\n",
      "ShapiroResult(statistic=0.4530501365661621, pvalue=4.381709630332819e-37)\n",
      "Ttest_indResult(statistic=16.324086420032025, pvalue=3.5310798891608576e-53)\n",
      "\n",
      "\n",
      "T_OTSeqPosition7\n",
      "ShapiroResult(statistic=0.4131584167480469, pvalue=5.320076142918184e-38)\n",
      "ShapiroResult(statistic=0.6278359293937683, pvalue=3.899307248877475e-32)\n",
      "Ttest_indResult(statistic=-10.662888138504824, pvalue=3.856601103205964e-25)\n",
      "\n",
      "\n",
      "A_OTSeqPosition7\n",
      "ShapiroResult(statistic=0.26410526037216187, pvalue=4.0248094492337396e-41)\n",
      "ShapiroResult(statistic=0.4810943007469177, pvalue=2.188536013729971e-36)\n",
      "Ttest_indResult(statistic=-6.277269464464659, pvalue=5.405751476041636e-10)\n",
      "\n",
      "\n",
      "C_OTSeqPosition8\n",
      "ShapiroResult(statistic=0.3622400164604187, pvalue=3.920948009654913e-39)\n",
      "ShapiroResult(statistic=0.5785624384880066, pvalue=1.0795832871803008e-33)\n",
      "Ttest_indResult(statistic=-7.97260379356804, pvalue=4.5648230061340445e-15)\n",
      "\n",
      "\n",
      "G_OTSeqPosition8\n",
      "ShapiroResult(statistic=0.10737556219100952, pvalue=6.866362475191604e-44)\n",
      "ShapiroResult(statistic=0.3687390089035034, pvalue=5.056928826639862e-39)\n",
      "Ttest_indResult(statistic=-6.467503024331347, pvalue=1.8796272263100777e-10)\n",
      "\n",
      "\n",
      "T_OTSeqPosition8\n",
      "ShapiroResult(statistic=0.6362048983573914, pvalue=7.906962163058942e-32)\n",
      "ShapiroResult(statistic=0.5505030155181885, pvalue=1.6235870576372336e-34)\n",
      "Ttest_indResult(statistic=7.54911501142634, pvalue=9.622998907168725e-14)\n",
      "\n",
      "\n",
      "A_OTSeqPosition8\n",
      "ShapiroResult(statistic=0.6178386211395264, pvalue=1.9440473980060782e-32)\n",
      "ShapiroResult(statistic=0.5785624384880066, pvalue=1.0795832871803008e-33)\n",
      "Ttest_indResult(statistic=2.821166948879289, pvalue=0.004875672869367704)\n",
      "\n",
      "\n",
      "C_OTSeqPosition9\n",
      "ShapiroResult(statistic=0.45838630199432373, pvalue=6.325758567444415e-37)\n",
      "ShapiroResult(statistic=0.5489991903305054, pvalue=1.4708202021052278e-34)\n",
      "Ttest_indResult(statistic=-3.514246765314314, pvalue=0.00046026691542303374)\n",
      "\n",
      "\n",
      "G_OTSeqPosition9\n",
      "ShapiroResult(statistic=0.6323803663253784, pvalue=5.876812889527802e-32)\n",
      "ShapiroResult(statistic=0.4721224904060364, pvalue=1.298466115917415e-36)\n",
      "Ttest_indResult(statistic=9.496566198344508, pvalue=1.5779614389009538e-20)\n",
      "\n",
      "\n",
      "T_OTSeqPosition9\n",
      "ShapiroResult(statistic=0.5835448503494263, pvalue=1.6257817882421654e-33)\n",
      "ShapiroResult(statistic=0.5918077230453491, pvalue=2.7353990479214718e-33)\n",
      "Ttest_indResult(statistic=-0.5077636153334563, pvalue=0.6117260861355645)\n",
      "\n",
      "\n",
      "A_OTSeqPosition9\n",
      "ShapiroResult(statistic=0.26889777183532715, pvalue=4.973208249888776e-41)\n",
      "ShapiroResult(statistic=0.5039899349212646, pvalue=8.580063414769088e-36)\n",
      "Ttest_indResult(statistic=-6.990332571877128, pvalue=5.474881814778773e-12)\n",
      "\n",
      "\n",
      "C_OTSeqPosition10\n",
      "ShapiroResult(statistic=0.44333189725875854, pvalue=2.725076873395431e-37)\n",
      "ShapiroResult(statistic=0.5244925022125244, pvalue=3.047310114304057e-35)\n",
      "Ttest_indResult(statistic=-2.9369306705334037, pvalue=0.003388687663940852)\n",
      "\n",
      "\n",
      "G_OTSeqPosition10\n",
      "ShapiroResult(statistic=0.5980409979820251, pvalue=4.545464738580978e-33)\n",
      "ShapiroResult(statistic=0.48328107595443726, pvalue=2.4882958621336225e-36)\n",
      "Ttest_indResult(statistic=5.390642459627768, pvalue=8.732403128138411e-08)\n",
      "\n",
      "\n",
      "T_OTSeqPosition10\n",
      "ShapiroResult(statistic=0.20560038089752197, pvalue=3.316873465056842e-42)\n",
      "ShapiroResult(statistic=0.5605806708335876, pvalue=3.1702944888432857e-34)\n",
      "Ttest_indResult(statistic=-10.879967394147835, pvalue=1.1555334712567635e-25)\n",
      "\n",
      "\n",
      "A_OTSeqPosition10\n",
      "ShapiroResult(statistic=0.6334913969039917, pvalue=6.404278951387252e-32)\n",
      "ShapiroResult(statistic=0.5673277378082275, pvalue=4.996755111454397e-34)\n",
      "Ttest_indResult(statistic=5.663804466513632, pvalue=1.9164178690258613e-08)\n",
      "\n",
      "\n",
      "C_OTSeqPosition11\n",
      "ShapiroResult(statistic=0.3622400164604187, pvalue=3.920948009654913e-39)\n",
      "ShapiroResult(statistic=0.47439873218536377, pvalue=1.4813217094725884e-36)\n",
      "Ttest_indResult(statistic=-3.4754095038918416, pvalue=0.0005319248216331581)\n",
      "\n",
      "\n",
      "G_OTSeqPosition11\n",
      "ShapiroResult(statistic=0.5715718865394592, pvalue=7.105063922726673e-34)\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=0.4766525626182556, pvalue=1.688544291447315e-36)\n",
      "Ttest_indResult(statistic=4.013032023861003, pvalue=6.431394776640518e-05)\n",
      "\n",
      "\n",
      "T_OTSeqPosition11\n",
      "ShapiroResult(statistic=0.6326701641082764, pvalue=6.009926457562093e-32)\n",
      "ShapiroResult(statistic=0.6057440042495728, pvalue=7.470390026382473e-33)\n",
      "Ttest_indResult(statistic=3.0581937796718033, pvalue=0.002283410072451574)\n",
      "\n",
      "\n",
      "A_OTSeqPosition11\n",
      "ShapiroResult(statistic=0.4217395782470703, pvalue=8.406314938406276e-38)\n",
      "ShapiroResult(statistic=0.5489991903305054, pvalue=1.4708202021052278e-34)\n",
      "Ttest_indResult(statistic=-4.695166808041283, pvalue=3.0344095456095854e-06)\n",
      "\n",
      "\n",
      "C_OTSeqPosition12\n",
      "ShapiroResult(statistic=0.6214951276779175, pvalue=2.5592780721498286e-32)\n",
      "ShapiroResult(statistic=0.4184969663619995, pvalue=6.605882390410278e-38)\n",
      "Ttest_indResult(statistic=9.640231242986692, pvalue=4.795170191800764e-21)\n",
      "\n",
      "\n",
      "G_OTSeqPosition12\n",
      "ShapiroResult(statistic=0.40127289295196533, pvalue=2.8483140107885384e-38)\n",
      "ShapiroResult(statistic=0.4897134304046631, pvalue=3.639001624275186e-36)\n",
      "Ttest_indResult(statistic=-2.8939289329308364, pvalue=0.0038854364628673694)\n",
      "\n",
      "\n",
      "T_OTSeqPosition12\n",
      "ShapiroResult(statistic=0.5835448503494263, pvalue=1.6257817882421654e-33)\n",
      "ShapiroResult(statistic=0.5854247212409973, pvalue=1.7424218682414377e-33)\n",
      "Ttest_indResult(statistic=-0.11195993550359315, pvalue=0.9108766337346093)\n",
      "\n",
      "\n",
      "A_OTSeqPosition12\n",
      "ShapiroResult(statistic=0.43276262283325195, pvalue=1.525410684582926e-37)\n",
      "ShapiroResult(statistic=0.5918077230453491, pvalue=2.7353990479214718e-33)\n",
      "Ttest_indResult(statistic=-6.683745015602438, pvalue=3.8952205265509646e-11)\n",
      "\n",
      "\n",
      "C_OTSeqPosition13\n",
      "ShapiroResult(statistic=0.5824108123779297, pvalue=1.501961274469292e-33)\n",
      "ShapiroResult(statistic=0.5519875288009644, pvalue=1.7904288508991536e-34)\n",
      "Ttest_indResult(statistic=1.5868412990158058, pvalue=0.1128512179745631)\n",
      "\n",
      "\n",
      "G_OTSeqPosition13\n",
      "ShapiroResult(statistic=0.418908953666687, pvalue=7.224336499329864e-38)\n",
      "ShapiroResult(statistic=0.5244925022125244, pvalue=3.047310114304057e-35)\n",
      "Ttest_indResult(statistic=-3.7075407362336215, pvalue=0.00022061835768844563)\n",
      "\n",
      "\n",
      "T_OTSeqPosition13\n",
      "ShapiroResult(statistic=0.41023433208465576, pvalue=4.5577145671659887e-38)\n",
      "ShapiroResult(statistic=0.5773703455924988, pvalue=9.940785627854569e-34)\n",
      "Ttest_indResult(statistic=-6.504447505298804, pvalue=1.2446608111256288e-10)\n",
      "\n",
      "\n",
      "A_OTSeqPosition13\n",
      "ShapiroResult(statistic=0.6231683492660522, pvalue=2.904514003893817e-32)\n",
      "ShapiroResult(statistic=0.4766525626182556, pvalue=1.688544291447315e-36)\n",
      "Ttest_indResult(statistic=7.873832217485309, pvalue=8.946538205478648e-15)\n",
      "\n",
      "\n",
      "C_OTSeqPosition14\n",
      "ShapiroResult(statistic=0.5432324409484863, pvalue=1.0759111530309274e-34)\n",
      "ShapiroResult(statistic=0.5412482023239136, pvalue=8.874937578903894e-35)\n",
      "Ttest_indResult(statistic=0.08883728671868249, pvalue=0.9292282140868273)\n",
      "\n",
      "\n",
      "G_OTSeqPosition14\n",
      "ShapiroResult(statistic=0.6242295503616333, pvalue=3.147921855877062e-32)\n",
      "ShapiroResult(statistic=0.5262457132339478, pvalue=3.4030871401104757e-35)\n",
      "Ttest_indResult(statistic=6.0647709674924695, pvalue=1.8560582177052085e-09)\n",
      "\n",
      "\n",
      "T_OTSeqPosition14\n",
      "ShapiroResult(statistic=0.463201642036438, pvalue=8.314697035873825e-37)\n",
      "ShapiroResult(statistic=0.5563543438911438, pvalue=2.390966759420812e-34)\n",
      "Ttest_indResult(statistic=-3.7014240628804362, pvalue=0.00022581687139133379)\n",
      "\n",
      "\n",
      "A_OTSeqPosition14\n",
      "ShapiroResult(statistic=0.438102662563324, pvalue=2.042773440727963e-37)\n",
      "ShapiroResult(statistic=0.520932137966156, pvalue=2.437697194243621e-35)\n",
      "Ttest_indResult(statistic=-2.959048770502946, pvalue=0.0031566994235771744)\n",
      "\n",
      "\n",
      "C_OTSeqPosition15\n",
      "ShapiroResult(statistic=0.3442775011062622, pvalue=1.6273377157096603e-39)\n",
      "ShapiroResult(statistic=0.5380326509475708, pvalue=7.211544769840089e-35)\n",
      "Ttest_indResult(statistic=-6.437861382942715, pvalue=1.933251402547444e-10)\n",
      "\n",
      "\n",
      "G_OTSeqPosition15\n",
      "ShapiroResult(statistic=0.40127289295196533, pvalue=2.8483140107885384e-38)\n",
      "ShapiroResult(statistic=0.590776801109314, pvalue=2.5422603669645774e-33)\n",
      "Ttest_indResult(statistic=-7.591887735188315, pvalue=7.480983379236275e-14)\n",
      "\n",
      "\n",
      "T_OTSeqPosition15\n",
      "ShapiroResult(statistic=0.2917864918708801, pvalue=1.3883925054683855e-40)\n",
      "ShapiroResult(statistic=0.44548583030700684, pvalue=2.872216575265851e-37)\n",
      "Ttest_indResult(statistic=-4.394493341639863, pvalue=1.2352708109549574e-05)\n",
      "\n",
      "\n",
      "A_OTSeqPosition15\n",
      "ShapiroResult(statistic=0.5824106931686401, pvalue=1.5019265606517443e-33)\n",
      "ShapiroResult(statistic=0.5428290367126465, pvalue=9.8325832095713e-35)\n",
      "Ttest_indResult(statistic=15.603592800556445, pvalue=1.7190708255314773e-49)\n",
      "\n",
      "\n",
      "C_OTSeqPosition16\n",
      "ShapiroResult(statistic=0.6359067559242249, pvalue=7.725491696167698e-32)\n",
      "ShapiroResult(statistic=0.4480331540107727, pvalue=3.309488510045973e-37)\n",
      "Ttest_indResult(statistic=13.051373124230654, pvalue=5.64480520905642e-36)\n",
      "\n",
      "\n",
      "G_OTSeqPosition16\n",
      "ShapiroResult(statistic=0.3047347068786621, pvalue=2.5119255881947373e-40)\n",
      "ShapiroResult(statistic=0.47888416051864624, pvalue=1.9231368108194794e-36)\n",
      "Ttest_indResult(statistic=-5.17853680540326, pvalue=2.737204130655071e-07)\n",
      "\n",
      "\n",
      "T_OTSeqPosition16\n",
      "ShapiroResult(statistic=0.5195393562316895, pvalue=2.3837992285344213e-35)\n",
      "ShapiroResult(statistic=0.5797386169433594, pvalue=1.1713731410967015e-33)\n",
      "Ttest_indResult(statistic=-2.8483351775236843, pvalue=0.004481581671850791)\n",
      "\n",
      "\n",
      "A_OTSeqPosition16\n",
      "ShapiroResult(statistic=0.4484541416168213, pvalue=3.6216987047285595e-37)\n",
      "ShapiroResult(statistic=0.5938282012939453, pvalue=3.158875112238024e-33)\n",
      "Ttest_indResult(statistic=-6.307483707521823, pvalue=4.260044191492666e-10)\n",
      "\n",
      "\n",
      "C_OTSeqPosition17\n",
      "ShapiroResult(statistic=0.6284431219100952, pvalue=4.3409290154114877e-32)\n",
      "ShapiroResult(statistic=0.4603841304779053, pvalue=6.629083408824411e-37)\n",
      "Ttest_indResult(statistic=9.182397394815853, pvalue=2.437490372078798e-19)\n",
      "\n",
      "\n",
      "G_OTSeqPosition17\n",
      "ShapiroResult(statistic=0.43004995584487915, pvalue=1.3162092673895941e-37)\n",
      "ShapiroResult(statistic=0.40687263011932373, pvalue=3.5680377176473364e-38)\n",
      "Ttest_indResult(statistic=0.7138854054768571, pvalue=0.47545749216156974)\n",
      "\n",
      "\n",
      "T_OTSeqPosition17\n",
      "ShapiroResult(statistic=0.2917864918708801, pvalue=1.3883925054683855e-40)\n",
      "ShapiroResult(statistic=0.5505030155181885, pvalue=1.6235870576372336e-34)\n",
      "Ttest_indResult(statistic=-8.373991873084663, pvalue=2.2482032863374317e-16)\n",
      "\n",
      "\n",
      "A_OTSeqPosition17\n",
      "ShapiroResult(statistic=0.6008210182189941, pvalue=5.554989939984713e-33)\n",
      "ShapiroResult(statistic=0.6255295872688293, pvalue=3.269630483846088e-32)\n",
      "Ttest_indResult(statistic=-2.269985696504132, pvalue=0.023411280377093602)\n",
      "\n",
      "\n",
      "C_OTSeqPosition18\n",
      "ShapiroResult(statistic=0.6296417713165283, pvalue=4.7590520621253877e-32)\n",
      "ShapiroResult(statistic=0.4603841304779053, pvalue=6.629083408824411e-37)\n",
      "Ttest_indResult(statistic=14.417283669428901, pvalue=7.025484396749397e-43)\n",
      "\n",
      "\n",
      "G_OTSeqPosition18\n",
      "ShapiroResult(statistic=0.44590479135513306, pvalue=3.1428075329347276e-37)\n",
      "ShapiroResult(statistic=0.520932137966156, pvalue=2.437697194243621e-35)\n",
      "Ttest_indResult(statistic=-2.707148583312268, pvalue=0.006898602957574008)\n",
      "\n",
      "\n",
      "T_OTSeqPosition18\n",
      "ShapiroResult(statistic=0.4072800278663635, pvalue=3.900913084991075e-38)\n",
      "ShapiroResult(statistic=0.5820556282997131, pvalue=1.3763941413949899e-33)\n",
      "Ttest_indResult(statistic=-6.867188970133282, pvalue=1.1693188188323026e-11)\n",
      "\n",
      "\n",
      "A_OTSeqPosition18\n",
      "ShapiroResult(statistic=0.39513158798217773, pvalue=2.0706623069727097e-38)\n",
      "ShapiroResult(statistic=0.5534590482711792, pvalue=1.973252316379968e-34)\n",
      "Ttest_indResult(statistic=-5.710379318574496, pvalue=1.495148967736082e-08)\n",
      "\n",
      "\n",
      "C_OTSeqPosition19\n",
      "ShapiroResult(statistic=0.5177147388458252, pvalue=2.127924442772734e-35)\n",
      "ShapiroResult(statistic=0.450553834438324, pvalue=3.809619330819392e-37)\n",
      "Ttest_indResult(statistic=2.4244786818867454, pvalue=0.015501101713670577)\n",
      "\n",
      "\n",
      "G_OTSeqPosition19\n",
      "ShapiroResult(statistic=0.4131584167480469, pvalue=5.320076142918184e-38)\n",
      "ShapiroResult(statistic=0.5577765703201294, pvalue=2.62844899029931e-34)\n",
      "Ttest_indResult(statistic=-5.379857034107155, pvalue=9.30572729965219e-08)\n",
      "\n",
      "\n",
      "T_OTSeqPosition19\n",
      "ShapiroResult(statistic=0.3824275732040405, pvalue=1.0795737893810966e-38)\n",
      "ShapiroResult(statistic=0.5279818773269653, pvalue=3.797543267997162e-35)\n",
      "Ttest_indResult(statistic=-4.93905669791554, pvalue=9.221003033674148e-07)\n",
      "\n",
      "\n",
      "A_OTSeqPosition19\n",
      "ShapiroResult(statistic=0.6364175081253052, pvalue=8.039091254574419e-32)\n",
      "ShapiroResult(statistic=0.5809038877487183, pvalue=1.2702350629012368e-33)\n",
      "Ttest_indResult(statistic=6.755537577076005, pvalue=2.3642650148063876e-11)\n",
      "\n",
      "\n",
      "C_OTSeqPosition20\n",
      "ShapiroResult(statistic=0.19358372688293457, pvalue=2.0248762809493607e-42)\n",
      "ShapiroResult(statistic=0.39781540632247925, pvalue=2.223009233936561e-38)\n",
      "Ttest_indResult(statistic=-5.351393595137273, pvalue=1.1260386232178903e-07)\n",
      "\n",
      "\n",
      "G_OTSeqPosition20\n",
      "ShapiroResult(statistic=0.47707635164260864, pvalue=1.8485791229396343e-36)\n",
      "ShapiroResult(statistic=0.5809038877487183, pvalue=1.2702350629012368e-33)\n",
      "Ttest_indResult(statistic=-4.521451993296062, pvalue=6.861416034666287e-06)\n",
      "\n",
      "\n",
      "T_OTSeqPosition20\n",
      "ShapiroResult(statistic=0.5508972406387329, pvalue=1.7756987816985224e-34)\n",
      "ShapiroResult(statistic=0.5673277378082275, pvalue=4.996755111454397e-34)\n",
      "Ttest_indResult(statistic=-0.8088325941685707, pvalue=0.4187952962081779)\n",
      "\n",
      "\n",
      "A_OTSeqPosition20\n",
      "ShapiroResult(statistic=0.636499285697937, pvalue=8.090394530021707e-32)\n",
      "ShapiroResult(statistic=0.5489991903305054, pvalue=1.4708202021052278e-34)\n",
      "Ttest_indResult(statistic=8.341470622114173, pvalue=2.334004799928758e-16)\n",
      "\n",
      "\n",
      "C_OTSeqPosition21\n",
      "ShapiroResult(statistic=0.6361164450645447, pvalue=7.852671368719039e-32)\n",
      "ShapiroResult(statistic=0.4854470491409302, pvalue=2.8268513647765327e-36)\n",
      "Ttest_indResult(statistic=10.273676504610473, pvalue=1.3303989007890444e-23)\n",
      "\n",
      "\n",
      "G_OTSeqPosition21\n",
      "ShapiroResult(statistic=0.5581666231155396, pvalue=2.874187691459892e-34)\n",
      "ShapiroResult(statistic=0.5227212309837341, pvalue=2.7265746439722725e-35)\n",
      "Ttest_indResult(statistic=1.5795944780344382, pvalue=0.1145027997989398)\n",
      "\n",
      "\n",
      "T_OTSeqPosition21\n",
      "ShapiroResult(statistic=0.29616451263427734, pvalue=1.6948704926008662e-40)\n",
      "ShapiroResult(statistic=0.44548583030700684, pvalue=2.872216575265851e-37)\n",
      "Ttest_indResult(statistic=-4.281982477732712, pvalue=2.0392317023740356e-05)\n",
      "\n",
      "\n",
      "A_OTSeqPosition21\n",
      "ShapiroResult(statistic=0.44590479135513306, pvalue=3.1428075329347276e-37)\n",
      "ShapiroResult(statistic=0.6206803321838379, pvalue=2.2641333342406363e-32)\n",
      "Ttest_indResult(statistic=-8.655654178410801, pvalue=1.9966150662052255e-17)\n",
      "\n",
      "\n",
      "C_OTSeqPosition22\n",
      "ShapiroResult(statistic=0.3551868796348572, pvalue=2.7694267927006014e-39)\n",
      "ShapiroResult(statistic=0.5724630355834961, pvalue=7.091484667087271e-34)\n",
      "Ttest_indResult(statistic=-7.824330219571851, pvalue=1.3956152045620867e-14)\n",
      "\n",
      "\n",
      "G_OTSeqPosition22\n",
      "ShapiroResult(statistic=0.2917864918708801, pvalue=1.3883925054683855e-40)\n",
      "ShapiroResult(statistic=0.4429128170013428, pvalue=2.4905001830907195e-37)\n",
      "Ttest_indResult(statistic=-4.31216048712112, pvalue=1.784972347445137e-05)\n",
      "\n",
      "\n",
      "T_OTSeqPosition22\n",
      "ShapiroResult(statistic=0.5911122560501099, pvalue=2.770599042585853e-33)\n",
      "ShapiroResult(statistic=0.4918142557144165, pvalue=4.123512428905694e-36)\n",
      "Ttest_indResult(statistic=4.612157159440517, pvalue=4.489991966120954e-06)\n",
      "\n",
      "\n",
      "A_OTSeqPosition22\n",
      "ShapiroResult(statistic=0.6364124417304993, pvalue=8.03575461385961e-32)\n",
      "ShapiroResult(statistic=0.5928244590759277, pvalue=2.940670483617783e-33)\n",
      "Ttest_indResult(statistic=5.3660402345064195, pvalue=9.915422226571635e-08)\n",
      "\n",
      "\n",
      "C_OTSeqPosition23\n",
      "ShapiroResult(statistic=0.610823392868042, pvalue=1.1538274920111373e-32)\n",
      "ShapiroResult(statistic=0.5330858826637268, pvalue=5.252182227870907e-35)\n",
      "Ttest_indResult(statistic=4.4112338028100035, pvalue=1.1355833222876616e-05)\n",
      "\n",
      "\n",
      "G_OTSeqPosition23\n",
      "ShapiroResult(statistic=0.3725109100341797, pvalue=6.542375263747384e-39)\n",
      "ShapiroResult(statistic=0.4603841304779053, pvalue=6.629083408824411e-37)\n",
      "Ttest_indResult(statistic=-2.7062652020050564, pvalue=0.0069181725890495194)\n",
      "\n",
      "\n",
      "T_OTSeqPosition23\n",
      "ShapiroResult(statistic=0.5478794574737549, pvalue=1.4566202533061686e-34)\n",
      "ShapiroResult(statistic=0.4938955307006836, pvalue=4.668997339463818e-36)\n",
      "Ttest_indResult(statistic=2.2122443503076763, pvalue=0.027166713332474533)\n",
      "\n",
      "\n",
      "A_OTSeqPosition23\n",
      "ShapiroResult(statistic=0.5400534868240356, pvalue=8.757823788456564e-35)\n",
      "ShapiroResult(statistic=0.6120439767837524, pvalue=1.1875387598144004e-32)\n",
      "Ttest_indResult(statistic=-4.21023625302339, pvalue=2.7726897457680957e-05)\n",
      "\n",
      "\n",
      "C_OTSeqPosition24\n",
      "ShapiroResult(statistic=0.6311017274856567, pvalue=5.32467202575024e-32)\n",
      "ShapiroResult(statistic=0.5314024686813354, pvalue=4.7178945036748084e-35)\n",
      "Ttest_indResult(statistic=6.876878237643551, pvalue=1.0623694867870264e-11)\n",
      "\n",
      "\n",
      "G_OTSeqPosition24\n",
      "ShapiroResult(statistic=0.41604894399642944, pvalue=6.202659317820198e-38)\n",
      "ShapiroResult(statistic=0.3618556261062622, pvalue=3.590866551189345e-39)\n",
      "Ttest_indResult(statistic=1.5894747283191162, pvalue=0.11225895204769042)\n",
      "\n",
      "\n",
      "T_OTSeqPosition24\n",
      "ShapiroResult(statistic=0.3791598677635193, pvalue=9.146691262291985e-39)\n",
      "ShapiroResult(statistic=0.4897134304046631, pvalue=3.639001624275186e-36)\n",
      "Ttest_indResult(statistic=-3.5407007152438945, pvalue=0.0004173440000964535)\n",
      "\n",
      "\n",
      "A_OTSeqPosition24\n",
      "ShapiroResult(statistic=0.5728336572647095, pvalue=7.745774006304572e-34)\n",
      "ShapiroResult(statistic=0.6322431564331055, pvalue=5.472696739624712e-32)\n",
      "Ttest_indResult(statistic=-5.083219212597266, pvalue=4.398581479205889e-07)\n",
      "\n",
      "\n",
      "C_OTSeqPosition25\n",
      "ShapiroResult(statistic=0.5283942222595215, pvalue=4.156877771336001e-35)\n",
      "ShapiroResult(statistic=0.5396491289138794, pvalue=8.00351342571997e-35)\n",
      "Ttest_indResult(statistic=-0.484536044465251, pvalue=0.6281068608421755)\n",
      "\n",
      "\n",
      "G_OTSeqPosition25\n",
      "ShapiroResult(statistic=0.43004995584487915, pvalue=1.3162092673895941e-37)\n",
      "ShapiroResult(statistic=0.5262457132339478, pvalue=3.4030871401104757e-35)\n",
      "Ttest_indResult(statistic=-3.4336667268112815, pvalue=0.0006194156873254265)\n",
      "\n",
      "\n",
      "T_OTSeqPosition25\n",
      "ShapiroResult(statistic=0.47024428844451904, pvalue=1.2446636089016828e-36)\n",
      "ShapiroResult(statistic=0.5809038877487183, pvalue=1.2702350629012368e-33)\n",
      "Ttest_indResult(statistic=-4.761245916192214, pvalue=2.203005117860729e-06)\n",
      "\n",
      "\n",
      "A_OTSeqPosition25\n",
      "ShapiroResult(statistic=0.6084824800491333, pvalue=9.711004216588651e-33)\n",
      "ShapiroResult(statistic=0.6105362176895142, pvalue=1.0622730859397864e-32)\n",
      "Ttest_indResult(statistic=0.16881331887257015, pvalue=0.8659760881320677)\n",
      "\n",
      "\n",
      "C_OTSeqPosition26\n",
      "ShapiroResult(statistic=0.5024311542510986, pvalue=8.330829770309251e-36)\n",
      "ShapiroResult(statistic=0.49799227714538574, pvalue=5.9697370116928556e-36)\n",
      "Ttest_indResult(statistic=0.1674270522994334, pvalue=0.8670663752030575)\n",
      "\n",
      "\n",
      "G_OTSeqPosition26\n",
      "ShapiroResult(statistic=0.628853440284729, pvalue=4.4796655606729374e-32)\n",
      "ShapiroResult(statistic=0.6142078638076782, pvalue=1.3944320838412586e-32)\n",
      "Ttest_indResult(statistic=6.454531085455811, pvalue=1.6575722872987916e-10)\n",
      "\n",
      "\n",
      "T_OTSeqPosition26\n",
      "ShapiroResult(statistic=0.4837043285369873, pvalue=2.72418771028346e-36)\n",
      "ShapiroResult(statistic=0.5459467172622681, pvalue=1.2044900312017309e-34)\n",
      "Ttest_indResult(statistic=-2.495023292111229, pvalue=0.012749526692895918)\n",
      "\n",
      "\n",
      "A_OTSeqPosition26\n",
      "ShapiroResult(statistic=0.5231343507766724, pvalue=2.984548301300058e-35)\n",
      "ShapiroResult(statistic=0.626010000705719, pvalue=3.3915656883512345e-32)\n",
      "Ttest_indResult(statistic=-12.404840283954282, pvalue=5.0822596545355067e-33)\n",
      "\n",
      "\n",
      "C_OTSeqPosition27\n",
      "ShapiroResult(statistic=0.5623444318771362, pvalue=3.8016428619428397e-34)\n",
      "ShapiroResult(statistic=0.520932137966156, pvalue=2.437697194243621e-35)\n",
      "Ttest_indResult(statistic=1.8599026271238053, pvalue=0.06318082548778894)\n",
      "\n",
      "\n",
      "G_OTSeqPosition27\n",
      "ShapiroResult(statistic=0.6292582154273987, pvalue=4.620929124915067e-32)\n",
      "ShapiroResult(statistic=0.5897303819656372, pvalue=2.360499369297689e-33)\n",
      "Ttest_indResult(statistic=8.289880297044556, pvalue=3.4598754410551755e-16)\n",
      "\n",
      "\n",
      "T_OTSeqPosition27\n",
      "ShapiroResult(statistic=0.438102662563324, pvalue=2.042773440727963e-37)\n",
      "ShapiroResult(statistic=0.5577765703201294, pvalue=2.62844899029931e-34)\n",
      "Ttest_indResult(statistic=-4.599767432122808, pvalue=4.767317682967083e-06)\n",
      "\n",
      "\n",
      "A_OTSeqPosition27\n",
      "ShapiroResult(statistic=0.49223583936691284, pvalue=4.5143747037939036e-36)\n",
      "ShapiroResult(statistic=0.6218305826187134, pvalue=2.469514972532337e-32)\n",
      "Ttest_indResult(statistic=-14.364595525635107, pvalue=1.0365581230819057e-42)\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/LbCpf1/LbCpf1_dataset-sampled-525PN.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": 5,
   "id": "wound-compilation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1049\n",
      "positive off-targets\n",
      "[0.02862595419847328, 0.2919847328244275, 0.6603053435114504, 0.019083969465648856, 0.015267175572519083, 0.0, 0.9847328244274809, 0.0, 0.02099236641221374, 0.003816793893129771, 0.9732824427480916, 0.0019083969465648854, 0.23473282442748092, 0.45038167938931295, 0.11641221374045801, 0.1984732824427481, 0.2633587786259542, 0.5954198473282443, 0.10114503816793893, 0.04007633587786259, 0.10114503816793893, 0.49809160305343514, 0.3301526717557252, 0.07061068702290077, 0.1851145038167939, 0.6087786259541985, 0.14122137404580154, 0.0648854961832061, 0.11068702290076336, 0.01717557251908397, 0.4847328244274809, 0.38740458015267176, 0.1736641221374046, 0.44656488549618323, 0.31297709923664124, 0.06679389312977099, 0.16221374045801526, 0.33969465648854963, 0.04389312977099236, 0.4541984732824427, 0.11068702290076336, 0.29389312977099236, 0.4484732824427481, 0.14694656488549618, 0.3988549618320611, 0.13358778625954199, 0.31297709923664124, 0.15458015267175573, 0.3110687022900763, 0.1450381679389313, 0.13931297709923665, 0.40458015267175573, 0.25572519083969464, 0.4083969465648855, 0.17748091603053434, 0.15839694656488548, 0.10114503816793893, 0.13358778625954199, 0.07633587786259542, 0.6889312977099237, 0.5209923664122137, 0.08206106870229007, 0.22900763358778625, 0.16603053435114504, 0.4255725190839695, 0.15267175572519084, 0.07633587786259542, 0.34541984732824427, 0.5687022900763359, 0.16412213740458015, 0.13740458015267176, 0.1297709923664122, 0.22709923664122136, 0.14122137404580154, 0.12213740458015267, 0.5095419847328244, 0.04007633587786259, 0.18893129770992367, 0.2652671755725191, 0.5057251908396947, 0.48282442748091603, 0.2748091603053435, 0.07824427480916031, 0.16412213740458015, 0.10687022900763359, 0.07633587786259542, 0.32633587786259544, 0.4904580152671756, 0.3683206106870229, 0.11641221374045801, 0.26145038167938933, 0.25190839694656486, 0.4389312977099237, 0.1431297709923664, 0.12022900763358779, 0.2958015267175573, 0.2385496183206107, 0.15267175572519084, 0.183206106870229, 0.6374045801526718, 0.21183206106870228, 0.5725190839694656, 0.1946564885496183, 0.23282442748091603, 0.28053435114503816, 0.5706106870229007, 0.15839694656488548, 0.20229007633587787]\n",
      "negative off-targets\n",
      "[0.17523809523809525, 0.09142857142857143, 0.5714285714285714, 0.1619047619047619, 0.21142857142857144, 0.08, 0.5619047619047619, 0.14666666666666667, 0.09714285714285714, 0.13904761904761906, 0.5333333333333333, 0.23047619047619047, 0.15047619047619049, 0.29333333333333333, 0.34095238095238095, 0.21142857142857144, 0.19428571428571428, 0.259047619047619, 0.29904761904761906, 0.22476190476190477, 0.17142857142857143, 0.32761904761904764, 0.29523809523809524, 0.1961904761904762, 0.2, 0.16952380952380952, 0.4228571428571429, 0.19238095238095237, 0.3047619047619048, 0.11428571428571428, 0.26476190476190475, 0.3047619047619048, 0.26285714285714284, 0.18476190476190477, 0.32761904761904764, 0.21333333333333335, 0.2342857142857143, 0.19428571428571428, 0.2780952380952381, 0.2876190476190476, 0.18666666666666668, 0.18857142857142858, 0.35619047619047617, 0.26285714285714284, 0.14476190476190476, 0.2, 0.3161904761904762, 0.32761904761904764, 0.26666666666666666, 0.2342857142857143, 0.3028571428571429, 0.18857142857142858, 0.25333333333333335, 0.2361904761904762, 0.2723809523809524, 0.23047619047619047, 0.24952380952380954, 0.32571428571428573, 0.16380952380952382, 0.25523809523809526, 0.1657142857142857, 0.19047619047619047, 0.30666666666666664, 0.3314285714285714, 0.17523809523809525, 0.13714285714285715, 0.26476190476190475, 0.41333333333333333, 0.17523809523809525, 0.23047619047619047, 0.31047619047619046, 0.26857142857142857, 0.1676190476190476, 0.2742857142857143, 0.23809523809523808, 0.30857142857142855, 0.13142857142857142, 0.30857142857142855, 0.2876190476190476, 0.26285714285714284, 0.1961904761904762, 0.23238095238095238, 0.16380952380952382, 0.3961904761904762, 0.29523809523809524, 0.1619047619047619, 0.2019047619047619, 0.3295238095238095, 0.2438095238095238, 0.17523809523809525, 0.2038095238095238, 0.37142857142857144, 0.2419047619047619, 0.11047619047619048, 0.2, 0.44571428571428573, 0.25142857142857145, 0.2361904761904762, 0.30857142857142855, 0.6323809523809524, 0.20761904761904762, 0.37714285714285717, 0.259047619047619, 0.5847619047619048, 0.23047619047619047, 0.3238095238095238, 0.2742857142857143, 0.6]\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "  \n",
    "OT_data=pd.read_csv(\"D://PhD_related/manuscript_obj1/seq_encoding_map/LbCpf1/LbCpf1_dataset-sampled-525PN.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": 6,
   "id": "capable-shoot",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.16335462993693992, 3.193583015267176, 1.1555343511450382, 0.11787157611136058, 0.07220961419434702, 0.0, 1.752490619743822, 0.0, 0.2160978895374944, 0.027449545121823692, 1.8249045801526718, 0.0082802346855088, 1.559933326891487, 1.5353920888272032, 0.34143247046782377, 0.9387249845265112, 1.3555231252806466, 2.2984957341715315, 0.3382238537462926, 0.17830573165998187, 0.5900127226463104, 1.5203377418782176, 1.1182590494951983, 0.3599088416215816, 0.9255725190839694, 3.591109872201733, 0.333969465648855, 0.3372760940216159, 0.3631917938931298, 0.15028625954198474, 1.8308254160030755, 1.2711712786259541, 0.6606787255227349, 2.416974895726765, 0.9553080063909107, 0.3130963740458015, 0.6923757214671383, 1.748428378985182, 0.1578348844504862, 1.5791668773065062, 0.5929661941112323, 1.5585241730279897, 1.2590827448258972, 0.5590358446730833, 2.7552480916030535, 0.6679389312977099, 0.9898372114411846, 0.4718289543759986, 1.1665076335877862, 0.6190653509588531, 0.45999567910125305, 2.145500809622947, 1.0094415427882681, 1.7290999753755232, 0.6515907756365771, 0.6872594788972304, 0.40535225219975524, 0.41013794027052364, 0.4660039055565418, 2.699171129087387, 3.1439194524874967, 0.43082061068702293, 0.7467640225688683, 0.5009541984732825, 2.4285388317291736, 1.1132315521628497, 0.28831896315009065, 0.8356931790199458, 3.245311981413873, 0.7121001829537569, 0.4425607642954152, 0.483189865194088, 1.3548534004163775, 0.5148695928753181, 0.5129770992366413, 1.6512934690415606, 0.3049286425489545, 0.6122773536895675, 0.9222865375865731, 1.9239545303684038, 2.460998295412436, 1.1825804029533225, 0.47765400319545537, 0.41425058719906044, 0.36197980792908147, 0.4714863044454423, 1.6162861875270058, 1.488384150377267, 1.5106900047709924, 0.6643088284102223, 1.2828172932867234, 0.6782149148561362, 1.814479773997716, 1.295571202948144, 0.601145038167939, 0.663657271481699, 0.9487768910478834, 0.6463925141590741, 0.5937234944868534, 1.007943989699255, 1.020292037257511, 1.5180430256766133, 0.7514312977099237, 0.3981525225650844, 1.2171944987697938, 1.7621800628648405, 0.5774888676844783, 0.33715012722646315]\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 LbCpf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "vocal-holmes",
   "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "retired-providence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.3700623700623701, 0.016632016632016633, 0.029106029106029108, 0.7692307692307693, 0.0498960498960499, 0.0893970893970894, 0.07692307692307693, 0.04573804573804574, 0.10810810810810811, 0.12681912681912683, 0.07484407484407485, 0.09563409563409564, 0.09771309771309772, 0.04365904365904366, 0.05197505197505198, 0.18295218295218296, 0.22453222453222454, 0.21205821205821207, 0.08731808731808732, 0.04158004158004158, 0.079002079002079, 0.08523908523908524, 0.4573804573804574, 0.20374220374220375, 0.1600831600831601, 0.2806652806652807, 0.19126819126819128]\n",
      "[0.02070932382738571, 0.005361517315159861, 0.007051263991269128, 0.019919530182343232, 0.009141311149779889, 0.012001198928772477, 0.01120168127071716, 0.008769625904068082, 0.013073459917960828, 0.01402404451420261, 0.011060608744653248, 0.012374017694957778, 0.01249470442978419, 0.008576527973779575, 0.009320477142539429, 0.016345659556385485, 0.017687905595261004, 0.017313073786290586, 0.011873152142926117, 0.00837815005900203, 0.011340385069193272, 0.011743116308228536, 0.02158072351783838, 0.017050444522637424, 0.015481641008780649, 0.01912315536690247, 0.016636181722584138]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.0, 0.21621621621621623, 0.2577962577962578, 0.4968814968814969, 0.2702702702702703, 0.3097713097713098, 0.30145530145530147, 0.24532224532224534, 0.35550935550935553, 0.2785862785862786, 0.2785862785862786, 0.3367983367983368, 0.2515592515592516, 0.22453222453222454, 0.23700623700623702, 0.3097713097713098, 0.28482328482328484, 0.3201663201663202, 0.3471933471933472, 0.2993762993762994, 0.3076923076923077, 0.30561330561330563, 0.24324324324324326, 0.44490644490644493, 0.22661122661122662, 0.18087318087318088, 0.0]\n",
      "[0.0, 0.017411363027616325, 0.018554877095301417, 0.021754896434112093, 0.018855813633393243, 0.019695277750199667, 0.019532186054926466, 0.018235314646244224, 0.02047268193758213, 0.019046528503859406, 0.019046528503859406, 0.020178360321610217, 0.018397483445758877, 0.01765854670388847, 0.0180114380926523, 0.019695277750199667, 0.019184531570500027, 0.019889431430093307, 0.020345801449790507, 0.019490311804720047, 0.019655160109539785, 0.019614607424760785, 0.01818017525752857, 0.021466544165085834, 0.017718826980112394, 0.016243205345437967, 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/LbCpf1/LbCpf1_dataset-sampled-525PN.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": 9,
   "id": "placed-forty",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mismatch_POS1\n",
      "ShapiroResult(statistic=0.5980409979820251, pvalue=4.545464738580978e-33)\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "Ttest_indResult(statistic=16.402981542030954, pvalue=4.351230827635782e-49)\n",
      "\n",
      "\n",
      "mismatch_POS2\n",
      "ShapiroResult(statistic=0.09859353303909302, pvalue=5.044674471569341e-44)\n",
      "ShapiroResult(statistic=0.4875909090042114, pvalue=3.2086766322371485e-36)\n",
      "Ttest_indResult(statistic=-10.035483354033676, pvalue=4.5529833387280975e-22)\n",
      "\n",
      "\n",
      "mismatch_POS3\n",
      "ShapiroResult(statistic=0.14687281847000122, pvalue=3.1389085600875902e-43)\n",
      "ShapiroResult(statistic=0.5262457132339478, pvalue=3.4030871401104757e-35)\n",
      "Ttest_indResult(statistic=-10.553042470709938, pvalue=3.39721425597659e-24)\n",
      "\n",
      "\n",
      "mismatch_POS4\n",
      "ShapiroResult(statistic=0.5715742111206055, pvalue=7.106134724611875e-34)\n",
      "ShapiroResult(statistic=0.6336241960525513, pvalue=6.090118094428014e-32)\n",
      "Ttest_indResult(statistic=8.50493910900247, pvalue=6.257747926648364e-17)\n",
      "\n",
      "\n",
      "mismatch_POS5\n",
      "ShapiroResult(statistic=0.21142542362213135, pvalue=4.2235135714749987e-42)\n",
      "ShapiroResult(statistic=0.5363994836807251, pvalue=6.492892982317941e-35)\n",
      "Ttest_indResult(statistic=-9.631069985687448, pvalue=8.665821859206207e-21)\n",
      "\n",
      "\n",
      "mismatch_POS6\n",
      "ShapiroResult(statistic=0.3047347068786621, pvalue=2.5119255881947373e-40)\n",
      "ShapiroResult(statistic=0.5646729469299316, pvalue=4.174961041487286e-34)\n",
      "Ttest_indResult(statistic=-8.747457808363327, pvalue=1.1241583873424401e-17)\n",
      "\n",
      "\n",
      "mismatch_POS7\n",
      "ShapiroResult(statistic=0.27826154232025146, pvalue=7.544590931924815e-41)\n",
      "ShapiroResult(statistic=0.5591868162155151, pvalue=2.8879660005372157e-34)\n",
      "Ttest_indResult(statistic=-9.13026344067856, pvalue=5.045535120311492e-19)\n",
      "\n",
      "\n",
      "mismatch_POS8\n",
      "ShapiroResult(statistic=0.1996554136276245, pvalue=2.596606054393886e-42)\n",
      "ShapiroResult(statistic=0.51545250415802, pvalue=1.7334300022649944e-35)\n",
      "Ttest_indResult(statistic=-9.032968817012472, pvalue=1.3796949292208198e-18)\n",
      "\n",
      "\n",
      "mismatch_POS9\n",
      "ShapiroResult(statistic=0.3405500650405884, pvalue=1.3592511026042866e-39)\n",
      "ShapiroResult(statistic=0.590776801109314, pvalue=2.5422603669645774e-33)\n",
      "Ttest_indResult(statistic=-9.323573141114457, pvalue=8.648976315000489e-20)\n",
      "\n",
      "\n",
      "mismatch_POS10\n",
      "ShapiroResult(statistic=0.3725109100341797, pvalue=6.542375263747384e-39)\n",
      "ShapiroResult(statistic=0.5428290367126465, pvalue=9.8325832095713e-35)\n",
      "Ttest_indResult(statistic=-5.869378290806184, pvalue=6.014383584634025e-09)\n",
      "\n",
      "\n",
      "mismatch_POS11\n",
      "ShapiroResult(statistic=0.27361589670181274, pvalue=6.1320820798854e-41)\n",
      "ShapiroResult(statistic=0.5428290367126465, pvalue=9.8325832095713e-35)\n",
      "Ttest_indResult(statistic=-8.469217668230023, pvalue=1.0939947136399377e-16)\n",
      "\n",
      "\n",
      "mismatch_POS12\n",
      "ShapiroResult(statistic=0.3171526789665222, pvalue=4.473743438249481e-40)\n",
      "ShapiroResult(statistic=0.5809038877487183, pvalue=1.2702350629012368e-33)\n",
      "Ttest_indResult(statistic=-9.327523518456834, pvalue=8.759124113192768e-20)\n",
      "\n",
      "\n",
      "mismatch_POS13\n",
      "ShapiroResult(statistic=0.321181058883667, pvalue=5.404906267793322e-40)\n",
      "ShapiroResult(statistic=0.520932137966156, pvalue=2.437697194243621e-35)\n",
      "Ttest_indResult(statistic=-6.330313332930041, pvalue=3.8145906685106407e-10)\n",
      "\n",
      "\n",
      "mismatch_POS14\n",
      "ShapiroResult(statistic=0.19358372688293457, pvalue=2.0248762809493607e-42)\n",
      "ShapiroResult(statistic=0.49595433473587036, pvalue=5.281638391517101e-36)\n",
      "Ttest_indResult(statistic=-8.43751656169058, pvalue=1.6349457805699556e-16)\n",
      "\n",
      "\n",
      "mismatch_POS15\n",
      "ShapiroResult(statistic=0.2171357274055481, pvalue=5.359966626042425e-42)\n",
      "ShapiroResult(statistic=0.5078887939453125, pvalue=1.0882608174725509e-35)\n",
      "Ttest_indResult(statistic=-8.354632422271393, pvalue=2.9601823599821803e-16)\n",
      "\n",
      "\n",
      "mismatch_POS16\n",
      "ShapiroResult(statistic=0.4509754776954651, pvalue=4.1692965491631315e-37)\n",
      "ShapiroResult(statistic=0.5646729469299316, pvalue=4.174961041487286e-34)\n",
      "Ttest_indResult(statistic=-4.527146500512257, pvalue=6.687957066430591e-06)\n",
      "\n",
      "\n",
      "mismatch_POS17\n",
      "ShapiroResult(statistic=0.49637508392333984, pvalue=5.782361031078749e-36)\n",
      "ShapiroResult(statistic=0.5474821329116821, pvalue=1.331623648251748e-34)\n",
      "Ttest_indResult(statistic=-2.1018261208607583, pvalue=0.03580847323366071)\n",
      "\n",
      "\n",
      "mismatch_POS18\n",
      "ShapiroResult(statistic=0.4837043285369873, pvalue=2.72418771028346e-36)\n",
      "ShapiroResult(statistic=0.5712023973464966, pvalue=6.505468131601225e-34)\n",
      "Ttest_indResult(statistic=-3.742132355753197, pvalue=0.00019256762206707883)\n",
      "\n",
      "\n",
      "mismatch_POS19\n",
      "ShapiroResult(statistic=0.3004802465438843, pvalue=2.065219663737272e-40)\n",
      "ShapiroResult(statistic=0.5865209102630615, pvalue=1.8819875778305986e-33)\n",
      "Ttest_indResult(statistic=-10.100796710561255, pvalue=1.0092202052116592e-22)\n",
      "\n",
      "\n",
      "mismatch_POS20\n",
      "ShapiroResult(statistic=0.1873791217803955, pvalue=1.5722568769724448e-42)\n",
      "ShapiroResult(statistic=0.5577765703201294, pvalue=2.62844899029931e-34)\n",
      "Ttest_indResult(statistic=-11.12988680747007, pvalue=1.2288527626529785e-26)\n",
      "\n",
      "\n",
      "mismatch_POS21\n",
      "ShapiroResult(statistic=0.2828368544578552, pvalue=9.2637038879585e-41)\n",
      "ShapiroResult(statistic=0.5633245706558228, pvalue=3.8120583847395983e-34)\n",
      "Ttest_indResult(statistic=-9.227265600274352, pvalue=2.2107992264025876e-19)\n",
      "\n",
      "\n",
      "mismatch_POS22\n",
      "ShapiroResult(statistic=0.29616451263427734, pvalue=1.6948704926008662e-40)\n",
      "ShapiroResult(statistic=0.5619610548019409, pvalue=3.4779959767941067e-34)\n",
      "Ttest_indResult(statistic=-8.825253760110444, pvalue=6.056382020360444e-18)\n",
      "\n",
      "\n",
      "mismatch_POS23\n",
      "ShapiroResult(statistic=0.6271321773529053, pvalue=3.926711254804607e-32)\n",
      "ShapiroResult(statistic=0.5135911703109741, pvalue=1.5449841381206474e-35)\n",
      "Ttest_indResult(statistic=6.981051599065322, pvalue=5.279226264671945e-12)\n",
      "\n",
      "\n",
      "mismatch_POS24\n",
      "ShapiroResult(statistic=0.4748212695121765, pvalue=1.6215818654518646e-36)\n",
      "ShapiroResult(statistic=0.624015212059021, pvalue=2.9139499909214554e-32)\n",
      "Ttest_indResult(statistic=-8.046827851845908, pvalue=2.4033858315139698e-15)\n",
      "\n",
      "\n",
      "mismatch_POS25\n",
      "ShapiroResult(statistic=0.4217395782470703, pvalue=8.406314938406276e-38)\n",
      "ShapiroResult(statistic=0.49799227714538574, pvalue=5.9697370116928556e-36)\n",
      "Ttest_indResult(statistic=-2.578569291799921, pvalue=0.010059068535439693)\n",
      "\n",
      "\n",
      "mismatch_POS26\n",
      "ShapiroResult(statistic=0.5447965860366821, pvalue=1.1910701101481636e-34)\n",
      "ShapiroResult(statistic=0.4480331540107727, pvalue=3.309488510045973e-37)\n",
      "Ttest_indResult(statistic=3.663499698735858, pvalue=0.00026158808574389564)\n",
      "\n",
      "\n",
      "mismatch_POS27\n",
      "ShapiroResult(statistic=0.4608069062232971, pvalue=7.255901027498474e-37)\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "Ttest_indResult(statistic=10.553654799624137, pvalue=9.779578124412288e-24)\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/LbCpf1/LbCpf1_dataset-sampled-525PN.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": "simplified-paintball",
   "metadata": {},
   "source": [
    "# Position specific mismatch type analysis LbCpf1"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "obvious-central",
   "metadata": {},
   "source": [
    "# mismatch at position 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "distant-shareware",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "524\n",
      "525\n",
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.0019083969465648854, 0.0057251908396946565, 0.04389312977099236, 0.02480916030534351, 0.030534351145038167, 0.02099236641221374, 0.01717557251908397, 0.0, 0.0057251908396946565, 0.019083969465648856, 0.0, 0.02480916030534351, 0.5114503816793893]\n",
      "[0.0019083969465648856, 0.0032991142647998697, 0.008957785309326786, 0.006801426274655497, 0.00752332314205558, 0.006268633708777109, 0.005681234789524287, 0.0, 0.0032991142647998697, 0.00598273033698274, 0.0, 0.006801426274655497, 0.021857739626199305]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.02857142857142857, 0.007619047619047619, 0.007619047619047619, 0.013333333333333334, 0.06857142857142857, 0.01904761904761905, 0.013333333333333334, 0.011428571428571429, 0.007619047619047619, 0.009523809523809525, 0.009523809523809525, 0.005714285714285714, 0.25333333333333335]\n",
      "[0.007277894749602436, 0.003798603030792554, 0.003798603030792555, 0.005010590988355261, 0.011040296398684476, 0.005971434426915686, 0.005010590988355261, 0.004643381398134032, 0.003798603030792554, 0.004242889556209342, 0.004242889556209342, 0.0032928422990792304, 0.01899957590705663]\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/LbCpf1/LbCpf1_dataset-sampled-525PN.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_AÃ¢â‚¬â€œT_POS4', 'MM_type_AÃ¢â‚¬â€œC_POS4', 'MM_type_AÃ¢â‚¬â€œG_POS4', 'MM_type_TÃ¢â‚¬â€œC_POS4', 'MM_type_TÃ¢â‚¬â€œG_POS4', 'MM_type_TÃ¢â‚¬â€œA_POS4', 'MM_type_GÃ¢â‚¬â€œA_POS4', 'MM_type_GÃ¢â‚¬â€œT_POS4', 'MM_type_GÃ¢â‚¬â€œC_POS4', 'MM_type_CÃ¢â‚¬â€œA_POS4', 'MM_type_CÃ¢â‚¬â€œT_POS4', 'MM_type_CÃ¢â‚¬â€œG_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": 13,
   "id": "unlikely-mumbai",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM_type_AÃ¢â‚¬â€œT_POS4\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.1538635492324829, pvalue=3.839557792249999e-43)\n",
      "Ttest_indResult(statistic=-3.543757395791489, pvalue=0.0004253816268768453)\n",
      "\n",
      "\n",
      "MM_type_AÃ¢â‚¬â€œC_POS4\n",
      "ShapiroResult(statistic=0.04745042324066162, pvalue=8.407790785948902e-45)\n",
      "ShapiroResult(statistic=0.05888634920120239, pvalue=1.1210387714598537e-44)\n",
      "Ttest_indResult(statistic=-0.3764182676987785, pvalue=0.7066837079381533)\n",
      "\n",
      "\n",
      "MM_type_AÃ¢â‚¬â€œG_POS4\n",
      "ShapiroResult(statistic=0.20560038089752197, pvalue=3.316873465056842e-42)\n",
      "ShapiroResult(statistic=0.05888634920120239, pvalue=1.1210387714598537e-44)\n",
      "Ttest_indResult(statistic=3.728097448371174, pvalue=0.0002083999243724627)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œC_POS4\n",
      "ShapiroResult(statistic=0.13947057723999023, pvalue=2.3541814200656927e-43)\n",
      "ShapiroResult(statistic=0.08930468559265137, pvalue=3.2229864679470793e-44)\n",
      "Ttest_indResult(statistic=1.3584387895808356, pvalue=0.1746429721256183)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œG_POS4\n",
      "ShapiroResult(statistic=0.16106730699539185, pvalue=5.479076995510035e-43)\n",
      "ShapiroResult(statistic=0.27329450845718384, pvalue=5.63125800873571e-41)\n",
      "Ttest_indResult(statistic=-2.8470951223060843, pvalue=0.004509761193404014)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œA_POS4\n",
      "ShapiroResult(statistic=0.12396728992462158, pvalue=1.3032075718220799e-43)\n",
      "ShapiroResult(statistic=0.11566287279129028, pvalue=8.828180325246348e-44)\n",
      "Ttest_indResult(statistic=0.22462928282862302, pvalue=0.8223116195471276)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œA_POS4\n",
      "ShapiroResult(statistic=0.10737556219100952, pvalue=6.866362475191604e-44)\n",
      "ShapiroResult(statistic=0.08930468559265137, pvalue=3.2229864679470793e-44)\n",
      "Ttest_indResult(statistic=0.5072182256589852, pvalue=0.612110215111503)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œT_POS4\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.07971543073654175, pvalue=2.2420775429197073e-44)\n",
      "Ttest_indResult(statistic=-2.461260544560062, pvalue=0.014166175898024254)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œC_POS4\n",
      "ShapiroResult(statistic=0.04745042324066162, pvalue=8.407790785948902e-45)\n",
      "ShapiroResult(statistic=0.05888634920120239, pvalue=1.1210387714598537e-44)\n",
      "Ttest_indResult(statistic=-0.3764182676987785, pvalue=0.7066837079381533)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œA_POS4\n",
      "ShapiroResult(statistic=0.11582159996032715, pvalue=9.528829557408756e-44)\n",
      "ShapiroResult(statistic=0.06961178779602051, pvalue=1.5414283107572988e-44)\n",
      "Ttest_indResult(statistic=1.3034473682309973, pvalue=0.19274012913393337)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œT_POS4\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.06961178779602051, pvalue=1.5414283107572988e-44)\n",
      "Ttest_indResult(statistic=-2.2446517632945957, pvalue=0.025206931337515046)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œG_POS4\n",
      "ShapiroResult(statistic=0.13947057723999023, pvalue=2.3541814200656927e-43)\n",
      "ShapiroResult(statistic=0.047379910945892334, pvalue=7.006492321624085e-45)\n",
      "Ttest_indResult(statistic=2.5269133673501614, pvalue=0.0117100154511772)\n",
      "\n",
      "\n",
      "MM_type_other_POS4\n",
      "ShapiroResult(statistic=0.6363593339920044, pvalue=8.002644464479999e-32)\n",
      "ShapiroResult(statistic=0.5412482023239136, pvalue=8.874937578903894e-35)\n",
      "Ttest_indResult(statistic=8.912546412252281, pvalue=2.231506398656497e-18)\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/LbCpf1/LbCpf1_dataset-sampled-525PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l1=['MM_type_AÃ¢â‚¬â€œT_POS4', 'MM_type_AÃ¢â‚¬â€œC_POS4', 'MM_type_AÃ¢â‚¬â€œG_POS4', 'MM_type_TÃ¢â‚¬â€œC_POS4', 'MM_type_TÃ¢â‚¬â€œG_POS4', 'MM_type_TÃ¢â‚¬â€œA_POS4', 'MM_type_GÃ¢â‚¬â€œA_POS4', 'MM_type_GÃ¢â‚¬â€œT_POS4', 'MM_type_GÃ¢â‚¬â€œC_POS4', 'MM_type_CÃ¢â‚¬â€œA_POS4', 'MM_type_CÃ¢â‚¬â€œT_POS4', 'MM_type_CÃ¢â‚¬â€œG_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": "elder-stable",
   "metadata": {},
   "source": [
    "# mismatch at position 16"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "functioning-software",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "524\n",
      "525\n",
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.009541984732824428, 0.003816793893129771, 0.0019083969465648854, 0.007633587786259542, 0.003816793893129771, 0.0019083969465648854, 0.0019083969465648854, 0.003816793893129771, 0.0057251908396946565, 0.0, 0.12213740458015267, 0.003816793893129771, 0.0019083969465648854]\n",
      "[0.004250955411715787, 0.0026962994176698628, 0.0019083969465648856, 0.003805831322207491, 0.002696299417669863, 0.0019083969465648856, 0.0019083969465648856, 0.0026962994176698628, 0.0032991142647998697, 0.0, 0.014318145501344127, 0.0026962994176698623, 0.0019083969465648856]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.009523809523809525, 0.02095238095238095, 0.022857142857142857, 0.015238095238095238, 0.07428571428571429, 0.015238095238095238, 0.022857142857142857, 0.017142857142857144, 0.015238095238095238, 0.017142857142857144, 0.017142857142857144, 0.022857142857142857, 0.013333333333333334]\n",
      "[0.004242889556209343, 0.006256809829762718, 0.006528664610651999, 0.005351374171027442, 0.011455801801765223, 0.005351374171027442, 0.006528664610651999, 0.0056704974390079125, 0.005351374171027442, 0.0056704974390079125, 0.0056704974390079125, 0.006528664610651999, 0.005010590988355261]\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/LbCpf1/LbCpf1_dataset-sampled-525PN.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_AÃ¢â‚¬â€œT_POS16', 'MM_type_AÃ¢â‚¬â€œC_POS16', 'MM_type_AÃ¢â‚¬â€œG_POS16', 'MM_type_TÃ¢â‚¬â€œC_POS16', 'MM_type_TÃ¢â‚¬â€œG_POS16', 'MM_type_TÃ¢â‚¬â€œA_POS16', 'MM_type_GÃ¢â‚¬â€œA_POS16', 'MM_type_GÃ¢â‚¬â€œT_POS16', 'MM_type_GÃ¢â‚¬â€œC_POS16', 'MM_type_CÃ¢â‚¬â€œA_POS16', 'MM_type_CÃ¢â‚¬â€œT_POS16', 'MM_type_CÃ¢â‚¬â€œG_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": 16,
   "id": "afraid-shaft",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM_type_AÃ¢â‚¬â€œT_POS16\n",
      "ShapiroResult(statistic=0.06971222162246704, pvalue=1.6815581571897805e-44)\n",
      "ShapiroResult(statistic=0.06961178779602051, pvalue=1.5414283107572988e-44)\n",
      "Ttest_indResult(statistic=0.0030261460617221144, pvalue=0.9975860649340222)\n",
      "\n",
      "\n",
      "MM_type_AÃ¢â‚¬â€œC_POS16\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.12379896640777588, pvalue=1.2051166793193427e-43)\n",
      "Ttest_indResult(statistic=-2.5151111358066585, pvalue=0.012118598686519414)\n",
      "\n",
      "\n",
      "MM_type_AÃ¢â‚¬â€œG_POS16\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.13166457414627075, pvalue=1.6255062186167878e-43)\n",
      "Ttest_indResult(statistic=-3.0798508023492346, pvalue=0.002163754324073748)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œC_POS16\n",
      "ShapiroResult(statistic=0.05897265672683716, pvalue=1.1210387714598537e-44)\n",
      "ShapiroResult(statistic=0.09845596551895142, pvalue=4.624284932271896e-44)\n",
      "Ttest_indResult(statistic=-1.1580408251249505, pvalue=0.24713974068479352)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œG_POS16\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.2870124578475952, pvalue=1.0420335640412205e-40)\n",
      "Ttest_indResult(statistic=-5.98775779985798, pvalue=3.7251696993034566e-09)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œA_POS16\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.09845596551895142, pvalue=4.624284932271896e-44)\n",
      "Ttest_indResult(statistic=-2.3461676539794296, pvalue=0.01926468583281722)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œA_POS16\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.13166457414627075, pvalue=1.6255062186167878e-43)\n",
      "Ttest_indResult(statistic=-3.0798508023492346, pvalue=0.002163754324073748)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œT_POS16\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.10722720623016357, pvalue=6.445972935894159e-44)\n",
      "Ttest_indResult(statistic=-2.1223567063704074, pvalue=0.03413518700340471)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œC_POS16\n",
      "ShapiroResult(statistic=0.04745042324066162, pvalue=8.407790785948902e-45)\n",
      "ShapiroResult(statistic=0.09845596551895142, pvalue=4.624284932271896e-44)\n",
      "Ttest_indResult(statistic=-1.513202984791976, pvalue=0.13059051449306164)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œA_POS16\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.10722720623016357, pvalue=6.445972935894159e-44)\n",
      "Ttest_indResult(statistic=-3.0231663671920095, pvalue=0.0026239106331308205)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œT_POS16\n",
      "ShapiroResult(statistic=0.3824275732040405, pvalue=1.0795737893810966e-38)\n",
      "ShapiroResult(statistic=0.10722720623016357, pvalue=6.445972935894159e-44)\n",
      "Ttest_indResult(statistic=6.81777267838023, pvalue=2.035566040283443e-11)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œG_POS16\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.13166457414627075, pvalue=1.6255062186167878e-43)\n",
      "Ttest_indResult(statistic=-2.695584513439305, pvalue=0.007195834172495926)\n",
      "\n",
      "\n",
      "MM_type_other_POS16\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.08930468559265137, pvalue=3.2229864679470793e-44)\n",
      "Ttest_indResult(statistic=-2.130835704701895, pvalue=0.03346461683666276)\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/LbCpf1/LbCpf1_dataset-sampled-525PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l1=['MM_type_AÃ¢â‚¬â€œT_POS16', 'MM_type_AÃ¢â‚¬â€œC_POS16', 'MM_type_AÃ¢â‚¬â€œG_POS16', 'MM_type_TÃ¢â‚¬â€œC_POS16', 'MM_type_TÃ¢â‚¬â€œG_POS16', 'MM_type_TÃ¢â‚¬â€œA_POS16', 'MM_type_GÃ¢â‚¬â€œA_POS16', 'MM_type_GÃ¢â‚¬â€œT_POS16', 'MM_type_GÃ¢â‚¬â€œC_POS16', 'MM_type_CÃ¢â‚¬â€œA_POS16', 'MM_type_CÃ¢â‚¬â€œT_POS16', 'MM_type_CÃ¢â‚¬â€œG_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": "amber-covering",
   "metadata": {},
   "source": [
    "# mismatch at position 17"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "round-leonard",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "524\n",
      "525\n",
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.0019083969465648854, 0.003816793893129771, 0.14694656488549618, 0.0019083969465648854, 0.009541984732824428, 0.0057251908396946565, 0.013358778625954198, 0.003816793893129771, 0.0057251908396946565, 0.003816793893129771, 0.003816793893129771, 0.0057251908396946565, 0.0]\n",
      "[0.0019083969465648856, 0.0026962994176698623, 0.01548164100878065, 0.0019083969465648856, 0.004250955411715788, 0.0032991142647998697, 0.00502009759329711, 0.0026962994176698623, 0.003299114264799869, 0.0026962994176698623, 0.0026962994176698623, 0.0032991142647998697, 0.0]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.024761904761904763, 0.024761904761904763, 0.01904761904761905, 0.024761904761904763, 0.024761904761904763, 0.04, 0.013333333333333334, 0.017142857142857144, 0.007619047619047619, 0.017142857142857144, 0.007619047619047619, 0.017142857142857144, 0.022857142857142857]\n",
      "[0.0067886232899349455, 0.006788623289934947, 0.005971434426915686, 0.0067886232899349455, 0.006788623289934947, 0.008560516500077062, 0.005010590988355261, 0.0056704974390079125, 0.003798603030792554, 0.0056704974390079125, 0.003798603030792554, 0.0056704974390079125, 0.006528664610651999]\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/LbCpf1/LbCpf1_dataset-sampled-525PN.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_AÃ¢â‚¬â€œT_POS17', 'MM_type_AÃ¢â‚¬â€œC_POS17', 'MM_type_AÃ¢â‚¬â€œG_POS17', 'MM_type_TÃ¢â‚¬â€œC_POS17', 'MM_type_TÃ¢â‚¬â€œG_POS17', 'MM_type_TÃ¢â‚¬â€œA_POS17', 'MM_type_GÃ¢â‚¬â€œA_POS17', 'MM_type_GÃ¢â‚¬â€œT_POS17', 'MM_type_GÃ¢â‚¬â€œC_POS17', 'MM_type_CÃ¢â‚¬â€œA_POS17', 'MM_type_CÃ¢â‚¬â€œT_POS17', 'MM_type_CÃ¢â‚¬â€œG_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": 18,
   "id": "naval-orlando",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM_type_AÃ¢â‚¬â€œT_POS17\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.13928401470184326, pvalue=2.1720126197034665e-43)\n",
      "Ttest_indResult(statistic=-3.2408211076048796, pvalue=0.0012571431699250233)\n",
      "\n",
      "\n",
      "MM_type_AÃ¢â‚¬â€œC_POS17\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.13928401470184326, pvalue=2.1720126197034665e-43)\n",
      "Ttest_indResult(statistic=-2.8674332971783785, pvalue=0.004265274533808534)\n",
      "\n",
      "\n",
      "MM_type_AÃ¢â‚¬â€œG_POS17\n",
      "ShapiroResult(statistic=0.4217395782470703, pvalue=8.406314938406276e-38)\n",
      "ShapiroResult(statistic=0.11566287279129028, pvalue=8.828180325246348e-44)\n",
      "Ttest_indResult(statistic=7.707843928945487, pvalue=4.586963680171943e-14)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œC_POS17\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.13928401470184326, pvalue=2.1720126197034665e-43)\n",
      "Ttest_indResult(statistic=-3.2408211076048796, pvalue=0.0012571431699250233)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œG_POS17\n",
      "ShapiroResult(statistic=0.06971222162246704, pvalue=1.6815581571897805e-44)\n",
      "ShapiroResult(statistic=0.13928401470184326, pvalue=2.1720126197034665e-43)\n",
      "Ttest_indResult(statistic=-1.9001751582391653, pvalue=0.05773677236895585)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œA_POS17\n",
      "ShapiroResult(statistic=0.04745042324066162, pvalue=8.407790785948902e-45)\n",
      "ShapiroResult(statistic=0.1933377981185913, pvalue=1.8637269575520067e-42)\n",
      "Ttest_indResult(statistic=-3.7359853062104422, pvalue=0.00020276261208925026)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œA_POS17\n",
      "ShapiroResult(statistic=0.08943074941635132, pvalue=3.5032461608120427e-44)\n",
      "ShapiroResult(statistic=0.08930468559265137, pvalue=3.2229864679470793e-44)\n",
      "Ttest_indResult(statistic=0.003587496657737334, pvalue=0.9971382813515856)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œT_POS17\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.10722720623016357, pvalue=6.445972935894159e-44)\n",
      "Ttest_indResult(statistic=-2.1223567063704074, pvalue=0.03413518700340471)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œC_POS17\n",
      "ShapiroResult(statistic=0.04745042324066162, pvalue=8.407790785948902e-45)\n",
      "ShapiroResult(statistic=0.05888634920120239, pvalue=1.1210387714598537e-44)\n",
      "Ttest_indResult(statistic=-0.3764182676987786, pvalue=0.7066837079381533)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œA_POS17\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.10722720623016357, pvalue=6.445972935894159e-44)\n",
      "Ttest_indResult(statistic=-2.1223567063704074, pvalue=0.03413518700340471)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œT_POS17\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.05888634920120239, pvalue=1.1210387714598537e-44)\n",
      "Ttest_indResult(statistic=-0.8162385006882964, pvalue=0.41456949370750174)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œG_POS17\n",
      "ShapiroResult(statistic=0.04745042324066162, pvalue=8.407790785948902e-45)\n",
      "ShapiroResult(statistic=0.10722720623016357, pvalue=6.445972935894159e-44)\n",
      "Ttest_indResult(statistic=-1.7403948643345135, pvalue=0.08215498613319558)\n",
      "\n",
      "\n",
      "MM_type_other_POS17\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.13166457414627075, pvalue=1.6255062186167878e-43)\n",
      "Ttest_indResult(statistic=-3.5010441216186448, pvalue=0.0005029722612375902)\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/LbCpf1/LbCpf1_dataset-sampled-525PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l1=['MM_type_AÃ¢â‚¬â€œT_POS17', 'MM_type_AÃ¢â‚¬â€œC_POS17', 'MM_type_AÃ¢â‚¬â€œG_POS17', 'MM_type_TÃ¢â‚¬â€œC_POS17', 'MM_type_TÃ¢â‚¬â€œG_POS17', 'MM_type_TÃ¢â‚¬â€œA_POS17', 'MM_type_GÃ¢â‚¬â€œA_POS17', 'MM_type_GÃ¢â‚¬â€œT_POS17', 'MM_type_GÃ¢â‚¬â€œC_POS17', 'MM_type_CÃ¢â‚¬â€œA_POS17', 'MM_type_CÃ¢â‚¬â€œT_POS17', 'MM_type_CÃ¢â‚¬â€œG_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": "norman-fisher",
   "metadata": {},
   "source": [
    "# mismatch at position 18"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "brazilian-syria",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "524\n",
      "525\n",
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.0019083969465648854, 0.0019083969465648854, 0.003816793893129771, 0.0019083969465648854, 0.0019083969465648854, 0.003816793893129771, 0.026717557251908396, 0.007633587786259542, 0.0, 0.0, 0.1431297709923664, 0.0019083969465648854, 0.0]\n",
      "[0.0019083969465648856, 0.0019083969465648856, 0.0026962994176698628, 0.0019083969465648856, 0.0019083969465648856, 0.0026962994176698628, 0.007051263991269127, 0.003805831322207491, 0.0, 0.0, 0.015313401549409562, 0.0019083969465648856, 0.0]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.030476190476190476, 0.030476190476190476, 0.022857142857142857, 0.030476190476190476, 0.01904761904761905, 0.03619047619047619, 0.030476190476190476, 0.007619047619047619, 0.017142857142857144, 0.009523809523809525, 0.01904761904761905, 0.009523809523809525, 0.030476190476190476]\n",
      "[0.00750920456677449, 0.00750920456677449, 0.0065286646106519994, 0.00750920456677449, 0.005971434426915686, 0.008158815494544103, 0.00750920456677449, 0.003798603030792554, 0.0056704974390079125, 0.004242889556209343, 0.005971434426915686, 0.004242889556209342, 0.00750920456677449]\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/LbCpf1/LbCpf1_dataset-sampled-525PN.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_AÃ¢â‚¬â€œT_POS18', 'MM_type_AÃ¢â‚¬â€œC_POS18', 'MM_type_AÃ¢â‚¬â€œG_POS18', 'MM_type_TÃ¢â‚¬â€œC_POS18', 'MM_type_TÃ¢â‚¬â€œG_POS18', 'MM_type_TÃ¢â‚¬â€œA_POS18', 'MM_type_GÃ¢â‚¬â€œA_POS18', 'MM_type_GÃ¢â‚¬â€œT_POS18', 'MM_type_GÃ¢â‚¬â€œC_POS18', 'MM_type_CÃ¢â‚¬â€œA_POS18', 'MM_type_CÃ¢â‚¬â€œT_POS18', 'MM_type_CÃ¢â‚¬â€œG_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": 21,
   "id": "timely-pencil",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM_type_AÃ¢â‚¬â€œT_POS18\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.1608561873435974, pvalue=5.05868745621259e-43)\n",
      "Ttest_indResult(statistic=-3.687160552099762, pvalue=0.0002476444065083602)\n",
      "\n",
      "\n",
      "MM_type_AÃ¢â‚¬â€œC_POS18\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.1608561873435974, pvalue=5.05868745621259e-43)\n",
      "Ttest_indResult(statistic=-3.687160552099762, pvalue=0.0002476444065083602)\n",
      "\n",
      "\n",
      "MM_type_AÃ¢â‚¬â€œG_POS18\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.13166457414627075, pvalue=1.6255062186167878e-43)\n",
      "Ttest_indResult(statistic=-2.6955845134393046, pvalue=0.007195834172495933)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œC_POS18\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.1608561873435974, pvalue=5.05868745621259e-43)\n",
      "Ttest_indResult(statistic=-3.687160552099762, pvalue=0.0002476444065083602)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œG_POS18\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.11566287279129028, pvalue=8.828180325246348e-44)\n",
      "Ttest_indResult(statistic=-2.7339765624870176, pvalue=0.006433316357317949)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œA_POS18\n",
      "ShapiroResult(statistic=0.03488314151763916, pvalue=5.605193857299268e-45)\n",
      "ShapiroResult(statistic=0.18080121278762817, pvalue=1.1210387714598537e-42)\n",
      "Ttest_indResult(statistic=-3.767533608344902, pvalue=0.00018015617058038242)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œA_POS18\n",
      "ShapiroResult(statistic=0.14687281847000122, pvalue=3.1389085600875902e-43)\n",
      "ShapiroResult(statistic=0.1608561873435974, pvalue=5.05868745621259e-43)\n",
      "Ttest_indResult(statistic=-0.36488407349485835, pvalue=0.7152719146486024)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œT_POS18\n",
      "ShapiroResult(statistic=0.05897265672683716, pvalue=1.1210387714598537e-44)\n",
      "ShapiroResult(statistic=0.05888634920120239, pvalue=1.1210387714598537e-44)\n",
      "Ttest_indResult(statistic=0.0027040659996857503, pvalue=0.9978429852313967)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œC_POS18\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.10722720623016357, pvalue=6.445972935894159e-44)\n",
      "Ttest_indResult(statistic=-3.0231663671920095, pvalue=0.0026239106331308205)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œA_POS18\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.06961178779602051, pvalue=1.5414283107572988e-44)\n",
      "Ttest_indResult(statistic=-2.2446517632945957, pvalue=0.025206931337515046)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œT_POS18\n",
      "ShapiroResult(statistic=0.41604894399642944, pvalue=6.202659317820198e-38)\n",
      "ShapiroResult(statistic=0.11566287279129028, pvalue=8.828180325246348e-44)\n",
      "Ttest_indResult(statistic=7.549186637628393, pvalue=1.4163882532781699e-13)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œG_POS18\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.06961178779602051, pvalue=1.5414283107572988e-44)\n",
      "Ttest_indResult(statistic=-1.6369060897558205, pvalue=0.10208248787025376)\n",
      "\n",
      "\n",
      "MM_type_other_POS18\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.1608561873435974, pvalue=5.05868745621259e-43)\n",
      "Ttest_indResult(statistic=-4.058511151905034, pvalue=5.6918917422593785e-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/LbCpf1/LbCpf1_dataset-sampled-525PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l1=['MM_type_AÃ¢â‚¬â€œT_POS18', 'MM_type_AÃ¢â‚¬â€œC_POS18', 'MM_type_AÃ¢â‚¬â€œG_POS18', 'MM_type_TÃ¢â‚¬â€œC_POS18', 'MM_type_TÃ¢â‚¬â€œG_POS18', 'MM_type_TÃ¢â‚¬â€œA_POS18', 'MM_type_GÃ¢â‚¬â€œA_POS18', 'MM_type_GÃ¢â‚¬â€œT_POS18', 'MM_type_GÃ¢â‚¬â€œC_POS18', 'MM_type_CÃ¢â‚¬â€œA_POS18', 'MM_type_CÃ¢â‚¬â€œT_POS18', 'MM_type_CÃ¢â‚¬â€œG_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": "sudden-joseph",
   "metadata": {},
   "source": [
    "# mismatch at position 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "funded-accessory",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "524\n",
      "525\n",
      "\n",
      " Positive off-target \n",
      "\n",
      "[0.009541984732824428, 0.0, 0.011450381679389313, 0.0, 0.019083969465648856, 0.015267175572519083, 0.013358778625954198, 0.0057251908396946565, 0.0, 0.007633587786259542, 0.011450381679389313, 0.3244274809160305, 0.0019083969465648854]\n",
      "[0.004250955411715788, 0.0, 0.004652199964544339, 0.0, 0.00598273033698274, 0.005361517315159861, 0.0050200975932971105, 0.003299114264799869, 0.0, 0.003805831322207491, 0.004652199964544339, 0.02047123106284786, 0.0019083969465648856]\n",
      "\n",
      " Negative off-targets \n",
      "\n",
      "[0.013333333333333334, 0.030476190476190476, 0.011428571428571429, 0.017142857142857144, 0.015238095238095238, 0.02666666666666667, 0.02095238095238095, 0.005714285714285714, 0.017142857142857144, 0.01904761904761905, 0.0019047619047619048, 0.022857142857142857, 0.02095238095238095]\n",
      "[0.005010590988355261, 0.00750920456677449, 0.004643381398134032, 0.0056704974390079125, 0.005351374171027442, 0.007038004189300933, 0.006256809829762718, 0.0032928422990792304, 0.0056704974390079125, 0.005971434426915686, 0.0019047619047619056, 0.006528664610651999, 0.006256809829762718]\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/LbCpf1/LbCpf1_dataset-sampled-525PN.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_AÃ¢â‚¬â€œT_POS23', 'MM_type_AÃ¢â‚¬â€œC_POS23', 'MM_type_AÃ¢â‚¬â€œG_POS23', 'MM_type_TÃ¢â‚¬â€œC_POS23', 'MM_type_TÃ¢â‚¬â€œG_POS23', 'MM_type_TÃ¢â‚¬â€œA_POS23', 'MM_type_GÃ¢â‚¬â€œA_POS23', 'MM_type_GÃ¢â‚¬â€œT_POS23', 'MM_type_GÃ¢â‚¬â€œC_POS23', 'MM_type_CÃ¢â‚¬â€œA_POS23', 'MM_type_CÃ¢â‚¬â€œT_POS23', 'MM_type_CÃ¢â‚¬â€œG_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": 24,
   "id": "simplified-stability",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MM_type_AÃ¢â‚¬â€œT_POS23\n",
      "ShapiroResult(statistic=0.06971222162246704, pvalue=1.6815581571897805e-44)\n",
      "ShapiroResult(statistic=0.08930468559265137, pvalue=3.2229864679470793e-44)\n",
      "Ttest_indResult(statistic=-0.576991315927017, pvalue=0.5640726112881687)\n",
      "\n",
      "\n",
      "MM_type_AÃ¢â‚¬â€œC_POS23\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.1608561873435974, pvalue=5.05868745621259e-43)\n",
      "Ttest_indResult(statistic=-4.058511151905034, pvalue=5.6918917422593785e-05)\n",
      "\n",
      "\n",
      "MM_type_AÃ¢â‚¬â€œG_POS23\n",
      "ShapiroResult(statistic=0.07982927560806274, pvalue=2.5223372357846707e-44)\n",
      "ShapiroResult(statistic=0.07971543073654175, pvalue=2.2420775429197073e-44)\n",
      "Ttest_indResult(statistic=0.003318172088653855, pvalue=0.997353118679428)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œC_POS23\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.10722720623016357, pvalue=6.445972935894159e-44)\n",
      "Ttest_indResult(statistic=-3.0231663671920095, pvalue=0.0026239106331308205)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œG_POS23\n",
      "ShapiroResult(statistic=0.11582159996032715, pvalue=9.528829557408756e-44)\n",
      "ShapiroResult(statistic=0.09845596551895142, pvalue=4.624284932271896e-44)\n",
      "Ttest_indResult(statistic=0.47912640860437583, pvalue=0.6319500099689651)\n",
      "\n",
      "\n",
      "MM_type_TÃ¢â‚¬â€œA_POS23\n",
      "ShapiroResult(statistic=0.09859353303909302, pvalue=5.044674471569341e-44)\n",
      "ShapiroResult(statistic=0.14667779207229614, pvalue=2.9006878211523713e-43)\n",
      "Ttest_indResult(statistic=-1.2884328831611422, pvalue=0.1978998842713464)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œA_POS23\n",
      "ShapiroResult(statistic=0.08943074941635132, pvalue=3.5032461608120427e-44)\n",
      "ShapiroResult(statistic=0.12379896640777588, pvalue=1.2051166793193427e-43)\n",
      "Ttest_indResult(statistic=-0.9466224126596177, pvalue=0.3440598024197635)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œT_POS23\n",
      "ShapiroResult(statistic=0.04745042324066162, pvalue=8.407790785948902e-45)\n",
      "ShapiroResult(statistic=0.047379910945892334, pvalue=7.006492321624085e-45)\n",
      "Ttest_indResult(statistic=0.002339543521356175, pvalue=0.9981337617218948)\n",
      "\n",
      "\n",
      "MM_type_GÃ¢â‚¬â€œC_POS23\n",
      "ShapiroResult(statistic=1.0, pvalue=1.0)\n",
      "ShapiroResult(statistic=0.10722720623016357, pvalue=6.445972935894159e-44)\n",
      "Ttest_indResult(statistic=-3.0231663671920095, pvalue=0.0026239106331308205)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œA_POS23\n",
      "ShapiroResult(statistic=0.05897265672683716, pvalue=1.1210387714598537e-44)\n",
      "ShapiroResult(statistic=0.11566287279129028, pvalue=8.828180325246348e-44)\n",
      "Ttest_indResult(statistic=-1.6118943785985171, pvalue=0.10733979321129199)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œT_POS23\n",
      "ShapiroResult(statistic=0.07982927560806274, pvalue=2.5223372357846707e-44)\n",
      "ShapiroResult(statistic=0.0202905535697937, pvalue=2.802596928649634e-45)\n",
      "Ttest_indResult(statistic=1.8988567784294825, pvalue=0.05799803780775564)\n",
      "\n",
      "\n",
      "MM_type_CÃ¢â‚¬â€œG_POS23\n",
      "ShapiroResult(statistic=0.5900723934173584, pvalue=2.5736897798277037e-33)\n",
      "ShapiroResult(statistic=0.13166457414627075, pvalue=1.6255062186167878e-43)\n",
      "Ttest_indResult(statistic=14.03495901459156, pvalue=4.030746023101553e-39)\n",
      "\n",
      "\n",
      "MM_type_other_POS23\n",
      "ShapiroResult(statistic=0.02032226324081421, pvalue=2.802596928649634e-45)\n",
      "ShapiroResult(statistic=0.12379896640777588, pvalue=1.2051166793193427e-43)\n",
      "Ttest_indResult(statistic=-2.911309988101577, pvalue=0.003728338150254819)\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/LbCpf1/LbCpf1_dataset-sampled-525PN.csv\", index_col=[0], encoding=\"cp1252\")\n",
    "l1=['MM_type_AÃ¢â‚¬â€œT_POS23', 'MM_type_AÃ¢â‚¬â€œC_POS23', 'MM_type_AÃ¢â‚¬â€œG_POS23', 'MM_type_TÃ¢â‚¬â€œC_POS23', 'MM_type_TÃ¢â‚¬â€œG_POS23', 'MM_type_TÃ¢â‚¬â€œA_POS23', 'MM_type_GÃ¢â‚¬â€œA_POS23', 'MM_type_GÃ¢â‚¬â€œT_POS23', 'MM_type_GÃ¢â‚¬â€œC_POS23', 'MM_type_CÃ¢â‚¬â€œA_POS23', 'MM_type_CÃ¢â‚¬â€œT_POS23', 'MM_type_CÃ¢â‚¬â€œG_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": "standard-complex",
   "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
}