{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import seaborn as sns\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "import pickle\n", "from scipy.stats import stats\n", "\n", "sns.set_style('white')\n", "%matplotlib inline\n", "\n", "mpl.rcParams['xtick.labelsize'] = 16\n", "mpl.rcParams['ytick.labelsize'] = 16\n", "mpl.rcParams['font.weight'] = 'bold'\n", "mpl.rcParams['pdf.fonttype'] = 42\n", "mpl.rcParams['ps.fonttype'] = 42\n", "\n", "pal = sns.cubehelix_palette(20, rot=-.25, light=0.7)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "df_rel_sort_list = pickle.load(open('pickle_files/rel_sort_lod_list.pkl', 'rb'))\n", "df_abs_sort_list = pickle.load(open('pickle_files/abs_sort_lod_list.pkl', 'rb'))\n", "\n", "df_pseudo_rel_sort_list = pickle.load(open('pickle_files/pseudo_rel_sort_lod_list.pkl', 'rb'))\n", "df_pseudo_abs_sort_list = pickle.load(open('pickle_files/pseudo_abs_sort_lod_list.pkl', 'rb'))\n", "\n", "df_col_names_list = pickle.load(open('pickle_files/col_names_lod_list.pkl', 'rb'))\n", "df_metadata = pickle.load(open('pickle_files/metadata_all.pkl', 'rb'))" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Read in the data file\n", "df=pd.read_csv('data_files/20210810_2 - Quantification Cq Results_0.csv')\n", "\n", "# Replace all Nan values for Cq with 40\n", "df['Cq'].fillna(40,inplace=True)\n", "\n", "# Define column names\n", "samples=[395, 185, 167, 322, 193, 419, 145, 199, \n", " 272, 343, 232, 388, 274, 238, 298, 180, \n", " 243, 408, 241, 207, 214, 302, 'H20', 'H20',\n", " 348, 349, 350, 351, 352, 353, 354, 356]\n", "\n", "# Define Rows used\n", "rows=['A','B','C','D','E','F','G','H']" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CqTypeCopies/uL
Sample
39519.049029+102033.479576
18519.263251+87954.014349
16719.521698+73528.399141
32221.057104+25365.999011
19340.000000+0.050335
\n", "
" ], "text/plain": [ " Cq Type Copies/uL\n", "Sample \n", "395 19.049029 + 102033.479576\n", "185 19.263251 + 87954.014349\n", "167 19.521698 + 73528.399141\n", "322 21.057104 + 25365.999011\n", "193 40.000000 + 0.050335" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_klebsiella = pd.DataFrame(columns=['Cq'])\n", "\n", "for index, row in enumerate(rows):\n", " df_klebsiella.loc[index] = df.loc[df['Well'].str.contains(row), 'Cq'].values.tolist()[0]\n", " df_klebsiella.loc[index+8] = df.loc[df['Well'].str.contains(row), 'Cq'].values.tolist()[1]\n", " df_klebsiella.loc[index+16] = df.loc[df['Well'].str.contains(row), 'Cq'].values.tolist()[2]\n", " df_klebsiella.loc[index+24] = df.loc[df['Well'].str.contains(row), 'Cq'].values.tolist()[3]\n", " \n", "df_klebsiella.sort_index(inplace=True)\n", "df_klebsiella['Sample'] = samples\n", "df_klebsiella['Type'] = ['+']*22+['H20']*2+['-']*8\n", "df_klebsiella.set_index('Sample', inplace=True)\n", "df_klebsiella['Copies/uL'] = 2**(22.4-df_klebsiella['Cq'])*1000*10\n", "\n", "df_klebsiella.head()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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gO30hIaoDbG1tyczIIDk+jlK2hT8E7P7tCMo9NVF69KhRWFlaMm3SCGzsHahSqx6Z6amcP3yQxo0bc+rEiXyX9wn94+vrS+PGjfnuu+/4+OOPSUlJoWLFigwbNoy+ffu+8BM6mzdvzrVr11i3bh2bN28mKSkJR0dHRo0aRa9evTAzM3vNW1J8yWWfOmKA30AMy1em+9AxhbZZOmkkfd7pyNixY/Mtz83NZd++fdy8eRMTExPat28vE+yFeAly2acemDrlfdq0a0etRm/jWufZE0FHtm8l4uLv+G3e8Mw6Q0NDunTpookyhShxJER1RL169Qhcs4ZB/v1p3rUnLXr4YFPGnqhbfxO2dQM3/jjPgX37sLa21napQpQoEqI6xMvLiz9//51vV61i1YfjSUpKxNGxPEP9BzNowzpsbGy0XaIQJY6EqI6pVKkSC+bPZ8H8+douRQiBzBMVQgi1SIgKIYQaJESFEEINEqJCCKEGCVEhhFCDhKgQQqhBQlQIIdQgISqEEGqQEBVCCDVIiAohhBokRIUQQg0SokIIoQYJUSGEUIOEqBBCqEFCVAgh1CAhKoQQapAQFUIINUiICiGEGiRE9ZgePQ1biGJLQlTP/P777wwa7I91aRsMDQ1xdHJi2vTp3L17V9ulCaGXJET1yPdr1tC+YyfyypRn6a4jBF/6h+mrN/HXvVjqN2jA6dOntV2iEHpHnvapJ06cOMH0mR8xJygEJ5eqquUVXd9g8IxPqe3ZAq8ePbj611/Y2dlpsVIh9IuMRPXE518uxnv0xHwB+jSP1u2p3aQ5gYGBGq5MCP0mIaoHUlNTObB/Py26ehfZrpV3H9b/uFFDVQlRMkiI6oHExEQsrKwwt7Iqsl1ZJ2cePXyooaqEKBkkRPWAjY0NaSkppKUkF9kuNuouZe3tNVSVECWDhKgesLCwoGOnThzb+VOR7Y6EbGLQgP4aqkqIkkFCVE98OHUKoauWczfiRoHrzxz8hb/OnmTw4MGaLUwIPSdTnPREkyZN+PLzRUwe2IsOfQfRsocPNmXLcu/W3xwMDuLcob38sns3tra22i5VCL0iI1E9MmjQII4eDsMqI4mZPp0Z2LAGyyYMw6O6Cxf++AMPDw9tlyiE3tH4SDQlJYVly5axd+9eUlNTqVq1KmPGjKFt27YAZGVl4e7uTk5OTr7XOTs7ExYWpulydU7t2rVZHfAdqwO+03YpQpQIGh+JTp06laCgIOLi4jA0NOTSpUuMGTOGQ4cOARAREUFOTg4mJiY4ODiovuzlrLIQohjSaIjGxcVx9uxZzM3N2bNnD+fOnaNTp04AhIaGAnDt2jUAOnTowLFjx1RfwcHBmixVCCFeiEZ35+3s7AgPDyclJQUrKytSU1OJi4sDoFy5cgDcuPH47HKlSpU0WZoQQvwnWjmxZGVlxZ49e2jcuDFnz57Fw8ODCRMmAHD9+nUAfvnlFxo0aECTJk347LPPyMzM1EapQghRJK2dnY+MjCQ7OxuA9PR0YmNjgX9D9NatWygUCuLj41m/fj0ffvihtkoVQohCaS1E+/btS3h4OAMGDODy5cuMHTuW3NxcunXrhpeXFzt37iQ8PJzPP/8ceDwyjYiIeKaf4OBgvL298fb2Jj4+XtObIYQo4RRKLT9DIiEhgcaNGwOwc+dO3NzcnmnTqFEjEhMTWbp0KV26dCm0L29vb0JCQl5brUKIkqmobNHoiaW///6boKAgFAoFs2fPfmZ9ZmYmYWFhxMXF0atXL9XyJ3NGrZ5zlyIhhNA0jYaoqakpmzZtQqFQ0L59e5o2baq6SbC1tTVlypShd+/eAFhaWtK5c2eCg4NJTU3FwsKCOnXqaLJcIYR4Lo2GaIUKFfD19SU4OBh/f38sLS1JTU0FYMqUKTg7O6uGzRMnTmTWrFkkJz++vdvIkSPlum8hRLGj8cs+Z82ahZOTE6Ghody7d4833niDkSNH0rVrVwDmzp2Ls7MzO3fu5P79+7i4uDBw4ED695dbuAkhih+tn1h6leTEkhDidSgqW+QuTkIIoQYJUSGEUIOEqBBCqEFCVAgh1CAhKoQQapAQFUIINUiICiGEGiREhRBCDRKiQgihBglRIYRQg4SoEEKoQUJUCCHUICEqhBBqkBAVQgg1SIgKIYQaNH5TZvHfZGdns337drb+FEJSchKVKlRk2NAhNGrUSNulCVGiyUhUB/zxxx9UdXVl3pdLKVv7Leq/04t0Kzt69vahXYeOJCYmartEIUosGYkWc5GRkbTv2JGB0z/h7c5e/65o2wmvoWNY++kMunbz4sjhMAwNDbVXqBAllIxEi7mFn39OS+8++QP0fwwNDRkyaz4x8Qns3btXC9UJISREi7GMjAw2b9pMx76DCm1jaGhI+76D+WbVdxqsTAjxhIRoMRYdHY2ZhQVlHMoX2c61rjs3btzQUFVCiKdJiBZjpqamZGak87wHsmakpWJiaqqhqoQQT5MQLcYcHR0pX96JS2dOFNnu9N6f6dKpo4aqEkI8TUK0GFMoFEwYN5atX31JdlZmgW2i/4nk6I6tjB41SsPVCSFAQrTYGz58OG9UqsjCUQOJvPaXanlubi7nwvYzd1AvFsyfT5UqVbRYpRAll8wTLeYMDQ3ZuiWYRZ9/zhejB2JlY4e1rR1Rt/6mgrMzawK+o2vXrtouU4gSS0JUBxgaGjJj+nQ+mDqVc+fOkZKSgrOzMzVr1tR2aUKUeBKiOsTIyAhPT09tlyGEeIocExVCCDVIiAohhBokRIUQQg0SokIIoQYJUSGEUIOEqBBCqEFCVAgh1CAhKoQQapAQFUIINUiICiGEGiREhRBCDRKiQgihBglRIYRQg4SoEEKoQUJUCCHUICEqhBBqkJsy65i8vDyOHj3KzZs3MTMzo02bNjg7O2u7LCFKLI2PRFNSUpg3bx7NmjXD3d2dd999l0OHDuVrExAQQMuWLalTpw6+vr5cuHBB02UWS1u3bsX1DTeGjx3P1v2H+X7zNt6sVQvvXr2JiYnRdnlClEgvPBLdvn37S3Xco0ePApdPnTqVsLAwDA0NsbCw4NKlS4wZM4ZvvvmGtm3bsn79ehYvXoxCocDS0pI//vgDf39/fvnlF8qVK/dSNeiTNWvWMPPj2YxduIKaDZugUCgASEtJJvS7lTRt1ozTJ09StmxZLVcqRMnywiE6bdo01Qf3eRQKRYEhGhcXx9mzZzE3N2f79u1UrlyZiRMnsnfvXkJDQ2nTpg3ff/89APPmzaNHjx74+/tz9uxZNm7cyMSJE1+0XL3y6NEjJr8/hXmbduJc1TXfOgurUvR/fwaZGenM+OgjAlat0lKVQpRML7w737JlS8qVK4dSqcTW1hZXV1csLS1RKpWYmJhQvnx51Zejo2OBfdjZ2REeHs6vv/6Ki4sLaWlpxMXFAVCuXDkiIiJ48OABBgYGdO3aFSMjI9555x0ATp8+/Qo2VzetXbuWhm3aPxOgT+sxbCxbgreQlJSkwcqEEC8col27duXhw4fMnDmTkydP8vPPP3Pq1ClGjhxJXl4en332GWFhYaqvolhZWbFnzx4aN27M2bNn8fDwYMKECdy+fRuA0qVLY2ZmBqAK5MjIyP+4ibrvyLHjuLfqUGQbOwdHKlStxp9//qmhqoQQ8BIhunTpUszNzfHz81MtMzY2ZtKkSRgbG/Ppp5++1C+OjIwkOzsbgPT0dGJjY0lJSQHA3Nxc1c7U1BRAta4kys3LxdDI8LntDI2MyMvL00BFQognXjhE4+LiSE1N5cqVK/mWR0REkJ6eTlRU1Ev94r59+xIeHs6AAQO4fPkyY8eOfanXPxEcHIy3tzfe3t7Ex8f/pz6KO/d69fjr3Kki26QmJ3Hr2hVq1KihoaqEEPASJ5Y8PDz49ddf6devHx06dMDR0ZGHDx+yf/9+AOrXr/9Sv9jW1haA8ePHs2HDBm7fvq0KwYyMDFW7J99bWVkV2I+vry++vr4AeHt7v1QNumLkiBHUb9CAd0e+RylbuwLbHAgOon37Djg4OGi4OiFKthcO0dmzZzNgwAAePHjAzp07AVAqlQCUL1+eOXPmPLePv//+m6CgIBQKBbNnz35mfa1atQBISEggMzMTU1NToqOjAahcufKLlqp3XFxcGDFiBAtG+TF15Vpsy/0blEqlkuO7QtnzQwAnjh/XYpVClEwvHKIVK1Zk165dbNu2jd9//53k5GRsbW3x8PCge/fuWFpaPrcPU1NTNm3ahEKhoH379jRt2pTAwEAArK2tqVu3LnZ2dsTFxbFz50569uzJnj17AGjcuPF/3ET9sGjBAszNzJjcrTUNWrahSq16pKemcGbvzxgrIOzgQdzc3LRdphAljkL5ZDhZhD179mBnZ0eTJk3yLQ8ICKBJkybUrVv3hX/hxx9/THBwMACWlpakpqYC8Mknn+Dr60tgYCALFy4EwMLCgrS0NCwsLPjll18KnTr1hLe3NyEhIS9ciy6Ki4tjw4YNXL95E3MzMzp36kTr1q1feA6vEOLlFZUtRY5Ec3NzGTduHEeOHKFnz575QjQmJoZly5ahVCoZMWIEkyZNeqFiZs2ahZOTE6Ghody7d4833niDkSNH0rVrVwD8/f3Jy8sjKCiIhw8fUrduXWbMmPHcAC0p7OzsmDBhgrbLEEL8T5EhGhQUxOHDh4HHgfq06OhoKleuzK1btwgICMDNzY0uXbo89xcaGxszatQoRo0aVWiboUOHMnTo0BepXwghtKrIKU6hoaEoFAo++OADFi1alG9d3bp12bNnDwMGDECpVLJhw4bXWqgQQhRHRYZoZGQkZmZm+Pv7F7j+ScCamJjw999/v5YCxeujVCo5ePAg3Xt6U6FiJSpUrIR3r94cOXKEFzhULoTgOSFqbGxMXl5ekVfBGBoaYmBgQFZW1isvTrw+2dnZ9OnXj+FjxlK+fhNmrtvKjMAtlKnpzsAhwxg4aPAzh3CEEM8qMkTd3NzIysoq8oz3jh07yMjI4I033njlxYnXZ8rUD4i4F8P8rb/QoY8fDhUq4VixMp37+7Pwp31cvBnBjJkfabtMIYq9Ik8s+fr6Eh4ezieffMKtW7do3bo1Dg4OKJVKoqOjOXLkCD/++CMKhQIfHx9N1SzU9OjRI9atW8eyX45jamb+zHozCwvGLVzBlO5tmTljOtbW1lqoUgjdUGSIenl5cerUKUJDQwkMDFRNjH+aUqmka9euvPvuu6+tSPFqbdu2DfcWrSltV6bQNnYOjtRq1ITQ0FAGDRqkweqE0C3PvWJpwYIFuLu7ExgYyK1bt/Ktc3JyYsiQIQwYMOC1FShevaioKMpVdHluO4dKVbh///7rL0gIHfZCl336+Pjg4+NDbGws9+/fJy8vj3LlyuHk5PS66xOvga2tLck3Ip/bLulRLKUb1H79BQmhw17qQXX29vbUrVuX+vXrS4DqMG9vb07v301melqhbdJSkjl3+EChz8oSQjwmz50vgSpXrkyrVq35cfH8AueD5uXlseGLT3nnna6UL19eCxUKoTskREuowDXfE/XXnyyeMIzrf4SjVCpRKpVcCT/DF+P8iYu8yXfffqPtMoUo9l74VnhCv9jY2PDrsaN8++23fD1jIrExMSiVShzLl2fcmNGMGjUq32NahBAFkxAtwSwtLZkyZQrvv/8+iYmJwOOHBMpt9YR4cRKiAoVCgY2NjbbLEEInyTFRIYRQg4xEdURubq7qMdMVK1Z8ocexCCFePxmJFnOZmZksWrSIKlWr0axlKzq+0w0n5woMHzGSyMhIcnNzSUhIIDs7W9ulClEiyUi0GMvIyKBj5y6kY8i4xd/iWufxY6njYx6w9Zul1K1fn7zcXEBBdnYWHTt1ZsrkSbRo0UK7hQtRgshItBibOfMjcs0smfp1oCpAAe78fZ1T+3bRsf8Qlu0+xvrz11h74iJO7p749O3HkqVLtVi1ECWLjESLqdTUVNYGBrJg214MDQ1VyxMexrJ08mimLF9NrUaequXmVlZ06ONHg5Zt+Lh/D+rXq0ebNm20UboQJYqMRIup48ePU9mtBuWcK+RbfmjbJhq165QvQJ9Wtrwz3mMm8eUSGY0KoQkSosVUamoqVtbPzt08uXcnrXsWfQPs5u/04MjhMNeXRnYAABiKSURBVFJTU19XeUKI/5EQLaYqVarEPzeuPXODkNSkJGzsHYp8ram5BeYWliQlJb3OEoUQSIgWWx4eHliYmXLh1PF8y23syxF9+1Yhr3osOSGejPR0uQpJCA2QEC2mFAoFn86dQ8DHU4m+c1u1vKXXuxzY8mORrz0cEkz37t3lBiJCaICcnS/GevfuTUxsLDN8utCimzcN279DRVc3tny1mMOhWwo8Nhpx+QI713zDoQP7tVCxECWPhGgxN3bMGLp17cq3q1axY+UisrKyaOrZhOBlC7l44ghtffxwqlKNxEexHNu+lWM//8Sa1atxd3fXdulClAgSojqgUqVKLJg/nwVPLUtISGDdunWs+fIT7t+/R6lS1vj07sXy8HAqV66stVqFKGkkRHWUjY0NEydOZOLEidouRYgSTU4sCSGEGiREhRBCDRKiQgihBglRIYRQg4SoEEKoQc7O66krV66wPiiI6AcxlLGzpW+fPrz11lvaLksIvSMjUT2TlJREV6/uNG/VmusPkzCt7MadtFy69ehJsxYtefDggbZLFEKvyEhUj2RlZdGpcxesK1bh64NnMDYxUa3rNXYyW79aTKvWbThz+hTW1tZarFQI/SEjUT2ybds2UrJzGTZ7Yb4ABTA0NMR3wlTKVq7K6tWrtVShEPpHQlSPrPzmWzoPHI6BQcF/VoVCQZdBI/j621UarkwI/SUhqkeuXL7Mmx6NimzzRv23uHM7kqysLA1VJYR+kxDVI0bGRmQ/Jxxzc3JQKpWFjlaFEC9HPkl6pHnzFpw9uLfINufC9uPRqDFGRv+eU7x58yaT33+f2vXq4/ZmTbx69GTv3r3k5eW97pKF0HkSonpkwrix/LL+e1KTC362UnZWJj+v+Zr3xo9TLVu5ciUNGzcmMjEDv4/mM2rRVzi/9TbjJk+hfcdOpKSkaKp8IXSShKgeadWqFV5du/DZsL78c/1qvnXR/0TyxVh/alV3xdfXF4CtW7ey4IsvWbDlFwZM/Yjqdd2p7PYm7X36s2DrL1DKhn4D/LSxKULoDJknqkcUCgVfr1zJl4sXs2BEP+ydKlKuYiXiH0Tzz42rjB49mjmzZ2NgYIBSqWT23E8YPmcR5SpUfKYvQyMjRn7yJePaN+HixYvUqVNHC1skRPGn8RDNzMxkzZo17NixgwcPHuDs7IyPjw9+fn4YGBjwzz//0L59+2de16hRI4KCgjRdrs5RKBRMnTKFie+9R1hYGA8ePMDOzo62bdvme3Dd77//TnJKKnU8mxfal5GxMa3f7cPatYEsXbpEE+ULoXM0HqLTp09n9+7dAFhbW3Pz5k3mz59PcnIy48aN4/r16wBYWFhQqlQp1etsbW01XapOMzY2pmPHjoWuv3PnDhWqVX/uWXrnqtWJPBn2qssTQm9o9JjovXv3VAH6ww8/cO7cOT744AMA1qxZg1KpVIWon58fx44dU32tWLFCk6XqvVKlSpGcEPfcdknxcfn+MxNC5KfREE1OTqZjx460bNmSJk2aANCyZUsA0tLSePToETdu3AAeP5xNvD5vv/020Xduc+/W30W2O7krBJ9e72qoKiF0j0ZD1M3NjRUrVhAQEKBaFh4eDjzefbexsVGNRNetW0f9+vVp3rw5X3/9tcxZfMVMTU0ZPWoU6xfNJSc7u8A2R3ZsIzMliU6dOmm4OiF0h1anOEVERLBkyeMTFl5eXuTl5REZGQnAjRs3MDAwICYmhhUrVrB48WItVqqfPp41i7IWpswf3o9LZ06iVCoBiL13lw1fziN46Xx+3rEDQ0NDLVcqRPGltSlOkZGRDB48mISEBBwcHJg0aRIZGRn4+fmRmJjI+PHjcXJy4ptvvmH58uX88MMPjB49Gisrq3z9BAcHExwcDEB8fLw2NkVnGRsbs3PHdlatWsWK+R8RGxuLqZkZmenp9B/Qn3NnzshhFSGeQ6F8MvzQoMjISPz8/IiJicHGxoYffviBGjVqFNg2JSVFdUf2LVu2UK9evUL79fb2JiQk5LXUrO+USiX3798nKysLR0dHzMzMtF2SEMVGUdmi8ZFoXFwcQ4YMUQVoYGCgKkBjYmL4888/yc7OpkuXLgBkP3W87v+PQsWro1AocHJy0nYZQugcrcwTjYqKwtjYmNWrV1OzZk3Vurt37zJu3DiMjY1xdHSkQYMGrFu3DgBHR0cqV66s6XKFEKJIGg3RS5cuceTIEdXP48aNy7d+8+bNeHp6curUKfr164elpaXqBhjvv/9+vjsPCSFEcaDRVDp69Kjq++zs7GcempaXl8fKlStZsWIFBw8e5OHDh9SoUYORI0eqdu+FEKI40cqJpdelpJxYio+PZ926dWz/+WfS0tKp4uLCyOHDaNOmDQqFQtvlCaF3itWJJaGevXv30q9/f+q93ZJmvv5YWlsTefUvRowdj7NDOXbu2I6NjY22yxSixJAQ1SHnz5+nv58fU74KpEaDhqrlNRo0or2vH4GffYRX9x4cPXJYRqRCaIjclFmHfDLvM94dMzlfgD5haGjIkI8+I+pBDIcPH9ZCdUKUTBKiOiI2NpYjRw7TsnvvQtsYGBjQxmcAqwLkufJCaIrszuuIu3fvYu9QngNbNhBx+QIKhQLXOvVp1aM3ltalVe1catRk+/6ftVipECWLjER1REhICHdu3+LWXxep37w19d5uybU/zjO6XRMObtuoapealISFhXkRPQkhXiUZieqA7wICWLfhR5bvPopDhX9vCNKqR2+iIm7y6bB+mJqa0bybNyd3h9LTy0uL1QpRskiIFnMZGRnMmDmTWYFb8wXoE85VXZmyPIDPxw/FztGJCyePsT0oUAuVClEySYgWc6GhoVSpUYtK1d0KbeNapz42ZexZNGYQ27ZskXmiQmiQhGgxd+XKFarWbcBf58+wd2MgF04eJyc7G+eqrrT36U/zbt6YmplTo0FD3mnbSu5CL4SGyYmlYs7ExIQLJ4+x8sMJ1HBvyNKdYXx3+Bx9Jkzl9P49zOzrRWLcI/Jyc6hataq2yxWixJGRaDGXnJJCUnwcn/+0l1I2/z422r15a+o3a8WGJfP5YvwwYu/e5vPpU7RYqRAlk4xEi7G8vDw2B29h/MLl+QL0CYVCQf9J04m9fxcnR0dq166thSqFKNlkJFqMnTlzBkMTU96o/1ahbQwMDGjv0x9F7D0NViaEeEJGosVYTEwMDhUqPfdmIo4VXUjLSNdQVUKIp0mIFmN2dnY8iol+brtH0fexL1NWAxUJIf4/CdFizNPTk9SEOCL+ulhoG6VSyZ4Na7h06VK+JwcIITRDQrQYMzIy4v1Jk1j76QzSU1MLbLP9+6+xti1DnQ5e+PTtx/IVKzRcpRAlm5xYKuYmTpzIX1euMqtvN7oNG4tnx3cwNjHl5sU/2PXDaiIuX2DOui2UcXSiQYvWfNy/Bw3c3WnevLm2SxeiRJCRaDFnYGDA6oDvWPr5QnZ/v5J+7q741KrIsqnjqFqzDgu37KaM4+Pnxds7VaD7iPEsXrpMy1ULUXLISFQHKBQKunfvzuQpU1m0dQ8ubjUxLOTx0S27vcuQhXPIzs7G2NhYw5UKUfLISFSHJCUlYmfvUGiAAphbWWFiYkpqIcdQhRCvloSoDnF0LM+927eKbBMf8wBlXh6lSpXSUFVClGwSojrEf9BAwrZuKLLNwa0/0qdvXwwNDTVUlRAlm4SoDvH39+fq+dMc/zmkwPVXfzvLvo2BvD95koYrE6LkkhNLOsTW1pb9e/fSsXNnfjtygDa9B1C+sgvxsTEcDQ3m9P7dbPrxR958801tlypEiSEhqmPq1KnDlcuXWbduHWuXLyDmQTSlS9vg69Ob77+8gJOTk7ZLFKJEkRDVQaVLl+a9997jvffe03YpQpR4ckxUCCHUICEqhBBqkBAVQgg1SIgKIYQa5MSSnsrJyWHfvn1cv34dMzMz2rVrR/Xq1bVdlhB6R0aiemj9+vVUcnHhg1lzOPzHZULDjtOoiSftO3bizp072i5PCL0iI1E98/U33zB/0edMXLYa1zr1VcuHZGawa10ATZs148ypUzKfVIhXREaieuThw4fMmDGTmd9vyhegACamZniPnECjTl5Mmz5DSxUKoX8kRPXImjVraNS2I46VXApt03XwSHbs2EFcXJzmChNCj0mI6pFfT52iXos2RbYpbVcGF7caXLhwQUNVCaHfJET1SF5uHgrF8/+kBgYG5OXlaaAiIfSfhKgeaejxFpdPHy+yTWpSIn//dZnatWtrqCoh9JuEqB4ZMXw4v+7ZQdyD6ELb7N34Ax07daJcuXIarEwI/SUhqkecnJz4YOpU5o/oT/Q/kfnW5eXlcXDLjxzYFMjC+Z9pp0Ah9JDME9UzM2fMwNzcnJm+XanRwIMqteuTkZbK2f27KWtnx7EjR6hWrZq2yxRCb8hIVM8oFArenzyZu3f+YeSAvrhYGFLP2Z6fgjfze/h5atSooe0ShdArGh+JZmZmsmbNGnbs2MGDBw9wdnbGx8cHPz8/DAwMyMnJYfHixezYsYOUlBQ8PDz4+OOPcXFx0XSpOs3S0pLBgwdruwwh9J7GR6LTp09n+fLlREZGYmxszM2bN5k/fz7ffPMNAF988QVr164lISEBQ0NDTpw4wZAhQ0hPT9d0qUII8VwaDdF79+6xe/duAH744QfOnTvHBx98ADy+2iYlJYWNGzeqfj558iQuLi5ERUWxa9cuTZYqhBAvRKMhmpycTMeOHWnZsiVNmjQBoGXLlgCkpaVx4MABsrKysLe3x9PTE3Nzczp06ADA6dOnNVmqEEK8EI0eE3Vzc2PFihX5loWHhwNgYWFBSkoKAA4ODqr1jo6OAERGRmqmSCGEeAlaPTsfERHBkiVLAPDy8lKFqLm5uaqNqakpgGqdEEIUJ1qbJxoZGcngwYNJSEjAwcGBSZMmsWnTppfuJzg4mODgYADi4+NfdZk6KykpiaCgIH7Zv5+srCxqutVg1KiRMsVJiFdMKyPRyMhI/Pz8ePDgATY2NgQEBGBjY4OlpSUAGRkZqrZPvreysiqwL19fX0JCQggJCcHW1vb1F68Ddu7cSWWXKmzevQ+3Vp3x6N6Xe5lKmrVshf+QoWRnZ2u7RCH0hsZHonFxcQwZMoSYmBhsbGwIDAxUjY4qVqwIwIMHD1Tto6MfXwdeuXJlTZeqk44ePYr/0GFMD9iQ78bMHq3b02PEeJa8N5zRY8by/eoALVYphP7QyjzRqKgojI2NWb16NTVr1lSta9CgAUZGRsTExHDy5EnS09M5ePAgAI0bN9Z0qcVaREQEH3z4Ie06dKRD5y7Mnz+fmJgYps/8iMEzPnnmzvYAZhYWTFoWwE8hIfz9999aqFoI/aPREL106RJHjhxR/Txu3DhatGih+kpPT6dv374A+Pv707RpU27duoWDgwPdunXTZKnFllKpZMrUD3irYSNuPEqmcS8/3vLy4eiff1G1mitXr16lcfsuhb7e3NKSVj19CFi9WoNVC6G/NLo7f/ToUdX32dnZ+Xbb4fFjfqdNm4aZmRkhISGkpKTg6enJrFmzsLCw0GSpxdbsOXPYfeAQy/Yco5TNv8eAG7bpSNXa9TmyfQtGxsZF9lG1dj2uHd9PSkoKf/75J7m5ubi5ueWbWiaEeDEaDdGxY8cyduzY57abMmUKU6ZM0UBFuiU+Pp7ly1ew5OfD+QL0iXIVKqJUKp/bT3pqCteuXadCxUo4uVTB2NiEW9ev0K5de+bP+1TO4AvxEuRWeDpkw4YNNGjZBttyBY8Y36j3FlERN3l4P4qy5Z0L7efwT5sxKWXD56GBqnapyUkc3LKBZi1acHD/furXf/aYqhDiWXIrPB1y4+ZNKr9Zp9D15paWtOzei6AvPyt0RHrx9K/8c/M6MwKC8gWtZSlrug8dw8Bpc+nl4yPPYBLiBUmI6hBzc3MyUou+cqvvex9y+exJlk8dS/Sd26rlmelp7N8cxILRg5iwaAXGJqYFvv7tLt1RGJuqZkUIIYomu/M65J0uXRg0bDi9x05GoVAU2EapzCMnK5OaFRz5qE9XHJwrYmpuzu0b16hatSpu9dxp2KZDob9DoVDQuFM39vzyi+rmL0KIwslIVIc0b94ccxMTDoduKbRN6HcradeuPau+/ZaoO3f4/puvWLrgMy788Qfjx47F3rHwY6VPmFtakZ6e8dx2QggZieoUhULBti3BtGrThviYaDr0Gag6S/8o+h6hAV9x49xJTvz6+LHJ5ubmvP3226rXu7q68vdfF1AqlYWOZAEiL1+gU9OGr3djhNATMhLVMTVr1uTUiRPkPrjD+A5Nmd2/OzN9ujC1R3tcbC05fepkoY9DfvvttzECLp05UWj/iXGPOHtoHwMHDnxNWyCEfpGRqA6qVq0amzf+yMOHD7l27RoGBgbUqVOn0Ju0PKFQKFi0YD6jx41n5ppNVKhaPd/6lMQEvhznz6hRo7C3t3+dmyCE3pAQ1WFly5albNmyL/Uab29vkpKSeK9vdzxat+OtNp0wMjbmyrlTHNm+hcEDB7FAnksvxAuTEC2BBg8eTLdu3Vi7di1h+0LJzc2lXp06fHn2rDyTXoiXJCFaQpUpU4apU6cydepUbZcihE6TE0tCCKEGCVEhhFCDhKgQQqhBQlQIIdQgISqEEGqQEBVCCDXo1RSnqKgovL29tV1GsRMfHy+PkxYvRN4rBYuKiip0nUL5Is+TEDrN29ubkJAQbZchdIC8V16e7M4LIYQaJESFEEINEqIlgK+vr7ZLEDpC3isvT46JCiGEGmQkquPkqZziZcl75tXSqylOxVmbNm2KnCbRs2dPFi5c+FJ9XrhwgTlz5sjZVD0l7xndICGqYaVLl8bMzKzA5S/j4sWL+Pr6FqtRxd27d2nbti3Ozs6EhYVpuxy9oc/vGX0gIaph06ZNeyUXBKSlpcmHoYSQ90zxJsdEi5kzZ87g5uZGp06duHDhAr6+vtSpU4dOnTpx8OBBVZunHyTn5ubGypUrAUhNTWXu3Lk0adKEunXr0qdPH06dOlVg/6tWraJx48a0bduWlJQUAI4ePUr//v1xd3enYcOGjB8/nlu3bmnwX0C8LHnPaJeEaDEVHx+Pv78/N27cICsri1u3bjF58mTi4uIwMTHJd2meg4MDVlZWKJVKxowZw8aNG0lOTsbMzIzff/+dYcOGce7cuXz93717l6VLl5KXl6d6/fbt2xk5ciTnz58nLy+P1NRU9u/fj4+PDxEREZr+JxAvSd4z2iEhqmHTp0/Hzc3tma8rV67ka5eQkICvry/nz5/nxx9/BCAzM5Pw8HDc3d1Zvny5qu2xY8fw9/fn+PHjnD59mkqVKnH8+HHOnj3LnDlzyMnJ4auvvsrXf3Z2NuPHj+fcuXN89dVXZGVlMX/+fJRKJT4+PoSHh3PixAnq1KlDUlISCxYsKHB77t69q9qGtm3bAo+vM36yrE2bNq/yn69E0rf3jL6RY6IaVthJAmNj42eW+fv7Y2BggIeHB7a2tsTHx5Oamlpo32fPngUgJiaGHj16AP9OZwkPDyc7Oztfey8vLwDs7Ow4deoUiYmJGBoaMm3aNIyMjLC1tWX8+PGMGDGCkydPkpmZiampab4+jIyMcHBwUP2u2NhYDA0NVU8hlUcvq0/f3jP6RkJUw17mJMHTu1/m5ubEx8cXeWIgMTERgIyMDDIyMvKty87OJiEhId+ypwPu0aNHqt9paWmpWl6hQgUAcnJySEhIUAXmE46Ojhw7dgz49+y8o6OjnJ1/hfTtPQOPr4y6f/++6md/f3/8/f2fu33FkYRoMWZk9O+fR6FQ5Fv3/3+Gx0/whMfzC7/99lsAsrKyUCgUqlHL08epnh7dPHntk5HLkw/F3bt3gcejHrlFWvGnK++Z2NhYHjx4oPr5yUkqXSTHRHWUoaGh6vuUlBRycnJ46623ADhx4gQXL14EYOXKlbi7uzNu3Lhn+nj6Q+Xu7o6lpSW5ubl8/vnnqlHEk+NizZo1w8TE5HVuknjNitN7JiwsjGvXrqm+xo8f/8q2U9NkJKphCxcuZNmyZc8sr169OmvWrHnhfp7sMgG0aNGC3r17M336dNzd3fn999/p1asX1tbWJCUlAdClS5ci+zMzM+PDDz/k448/ZvPmzezYsYPs7GxycnKwsbFh2rRpL1TTtWvXXngbxIvR5/eMPpCRqIYlJiby4MGDZ74ePnz4Uv04ODgwevRo1e6SlZUVAN999x19+vTB3t6ezMxM3NzcWLJkyXM/EPD4ONWqVavw8PBAoVBgbm5Ox44dCQ4OxsXF5aW3Vbwa8p4p3uQuTkIIoQYZiQohhBokRIUQQg0SokIIoQYJUSGEUIOEqBBCqEFCVAgh1CAhKoQQapAQFUIINUiICiGEGv4PyDsxpxBERS8AAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.set_style(\"ticks\", {\"xtick.major.size\": 8, \"ytick.major.size\": 8})\n", "\n", "fig = plt.figure(figsize=(5,5))\n", "ax1 = fig.add_subplot(111)\n", "\n", "sns.stripplot(x='Type', y='Cq', data=df_klebsiella, order=['+', '-'], s=10, palette=['lightblue','lightgrey'], edgecolor='k', linewidth=1)\n", "ax1.set_ylabel('Cq', fontsize=16, fontweight='bold')\n", "ax1.set_xlabel('')\n", "ax1.set_xticklabels(['Entero +','Entero -'], fontsize=16, fontweight='bold')\n", "fig.savefig('Kleb_Entero_stripplot.pdf', bbox_inches='tight', transparent=True)" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "df_klebsiella = df_klebsiella.merge(df_metadata[['Weight (mL)']], left_index=True, right_index=True)\n", "df_klebsiella['Weight (mL)'] = df_klebsiella['Weight (mL)'].astype('float')\n", "df_klebsiella['Copies/mL'] = df_klebsiella['Copies/uL']*100/df_klebsiella['Weight (mL)']" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "df_merge = df_klebsiella.merge(df_abs_sort_list[4][['ASV255']], left_index=True, right_index=True)\n", "df_merge['Log Copies/mL'] = np.log10(df_merge['Copies/mL']+1)\n", "df_merge['Log Entero'] = np.log10(df_merge['ASV255']+1)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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CqTypeCopies/uLWeight (mL)Copies/mLASV255Log Copies/mLLog Entero
14522.497330+9347.6104331.770005.281136e+055.256522e+065.7227286.720699
16719.521698+73528.3991411.277605.755197e+061.124314e+086.7600608.050888
18032.594624+8.5332091.845104.624795e+023.504757e+052.6660315.544659
18519.263251+87954.0143490.938149.375361e+061.537657e+086.9719888.186860
19340.000000+0.0503351.000005.033523e+001.839212e+070.7805717.264632
\n", "
" ], "text/plain": [ " Cq Type Copies/uL Weight (mL) Copies/mL ASV255 \\\n", "145 22.497330 + 9347.610433 1.77000 5.281136e+05 5.256522e+06 \n", "167 19.521698 + 73528.399141 1.27760 5.755197e+06 1.124314e+08 \n", "180 32.594624 + 8.533209 1.84510 4.624795e+02 3.504757e+05 \n", "185 19.263251 + 87954.014349 0.93814 9.375361e+06 1.537657e+08 \n", "193 40.000000 + 0.050335 1.00000 5.033523e+00 1.839212e+07 \n", "\n", " Log Copies/mL Log Entero \n", "145 5.722728 6.720699 \n", "167 6.760060 8.050888 \n", "180 2.666031 5.544659 \n", "185 6.971988 8.186860 \n", "193 0.780571 7.264632 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_merge.head()" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(5,5))\n", "ax1 = fig.add_subplot(111)\n", "\n", "sns.scatterplot(ax=ax1, x='Log Entero', y='Log Copies/mL', data=df_merge, s=120, color='lightblue', edgecolor='k')\n", "\n", "ax1.set_xlim(4,8.5)\n", "\n", "ax1.set_ylabel('Klebsiella Log ~Copies/mL', fontsize=16, fontweight='bold')\n", "ax1.set_xlabel('Enterobacteriaceae Log Copies/mL', fontsize=16, fontweight='bold')\n", "fig.savefig('Kleb_Entero_scatter.pdf', bbox_inches='tight', transparent=True)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(0.8827247048393103, 5.968164622358843e-06)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_df = df_merge[(df_merge['Cq']<37)&(df_merge['Type']=='+')]\n", "\n", "x = _df['Log Copies/mL'].tolist()\n", "y = _df['Log Entero'].tolist()\n", "\n", "stats.pearsonr(x,y)" ] }, { "cell_type": "code", "execution_count": null, "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": "3.6.10" } }, "nbformat": 4, "nbformat_minor": 4 }