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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Execution of Primer Scoring and data analysis code for BRD publication\n",
+    "\n",
+    "This workbook is intended to describe the usage of the `PrimerScore` functionality used during data analysis for the publication of *\"Detection of five viruses commonly implicated with Bovine Respiratory Disease using loop-mediated isothermal amplification\"*\n",
+    "\n",
+    "## Setup and initialization\n",
+    "Import `PrimerScore` and `matplotlib` dependencies for this module\n",
+    "\n",
+    "## License\n",
+    "\n",
+    "Copyright (C) 2025 Purdue Research Foundation. This work is protected under GNU Affero General Public License Version 3 with Commons Clause. Please see the `LICENSE` file for more information.\n",
+    "\n",
+    "## Import"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from matplotlib import pyplot as plt\n",
+    "\n",
+    "import PrimerScore"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Set file and input/output directory paths\n",
+    " \n",
+    "*Modify file paths and feel free to use patterns for common data types*"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set list of virus names\n",
+    "virusName = ['BAV-3', 'BHV-1', 'bPIV-3', 'BRSV', 'BVDV-1']\n",
+    "\n",
+    "# Set path for primer scoring data\n",
+    "screeningPathInputPattern = \"\"\n",
+    "coarseLODInputPath = \"\"\n",
+    "fineLODGenomicInputPathPattern = \"\"\n",
+    "fineLODVirusInputPath = \"\"\n",
+    "sensSpecInputPath = \"\"\n",
+    "\n",
+    "\n",
+    "# Set path for primer scoring output\n",
+    "screeningPathOutputPattern = \"\"\n",
+    "coarseLODOutputPath = \"\"\n",
+    "fineLODGenomicOutputPathPattern = \"\"\n",
+    "fineLODVirusOutputPath = \"\"\n",
+    "sensSpecOutputPath = \"\"\n",
+    "\n",
+    "# Define number of fine LOD rounds\n",
+    "numFineLODRounds = 2\n",
+    "\n",
+    "# Initialize PrimerScore class\n",
+    "PrimerScore.initialize([5,10,15,10,60], [3000, 200, 0.9, 0.2], 4, set_threshold_perc=0.2)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stage 1: Primer Scoring\n",
+    "\n",
+    "Execute the `scorePrimers` function of the `PrimerScore` module to score and rank primers for each virus."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Loop through each virus in our list of virus names\n",
+    "for virus in virusName:\n",
+    "    # Score primers for each virus and output results to csv file using output filename pattern\n",
+    "    PrimerScore.scorePrimers(screeningPathInputPattern.format(virus), screeningPathOutputPattern.format(virus))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Stage 2: Coarse LOD Analysis\n",
+    "\n",
+    "Execute the `scoreLOD` function of the `PrimerScore` module to determine and rank the LOD for each primer set that has progressed to Stage 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Run coarse LOD primer screening. Result is a dataframe that is additionally written to an output csv as defined by the output path\n",
+    "coarseLOD_df = PrimerScore.score_LOD(coarseLODInputPath, coarseLODOutputPath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Stage 3: Fine LOD Analysis\n",
+    "\n",
+    "Execute the `scoreLOD` function of the `PrimerScore` module to determine and rank the fine LOD for each primer set that has progressed to Stage 3. The fine LOD will be broken into successive rounds depending on if the fine LOD is below the minimum dilution level based on the coarse LOD of primer sets progressing to stage 3."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fineLODGenomicResults_dfList = []\n",
+    "for fineLODRound in range(1, numFineLODRounds+1):\n",
+    "    fineLODGenomicResults_dfList.append(PrimerScore.score_LOD(fineLODGenomicInputPathPattern.format(fineLODRound), fineLODGenomicOutputPathPattern.format(fineLODRound)))\n",
+    "\n",
+    "fineLODVirusResutls_df = PrimerScore.score_LOD(fineLODVirusInputPath, fineLODVirusOutputPath)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Sensitivity and Specificity Analysis\n",
+    "\n",
+    "Carry out sensitivity and specificity analysis for all primer sets tested."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Primer Set</th>\n",
+       "      <th>Concentration</th>\n",
+       "      <th>Tt</th>\n",
+       "      <th>TP</th>\n",
+       "      <th>TN</th>\n",
+       "      <th>FP</th>\n",
+       "      <th>FN</th>\n",
+       "      <th>FPR</th>\n",
+       "      <th>FNR</th>\n",
+       "      <th>Sensitivity</th>\n",
+       "      <th>Specificity</th>\n",
+       "      <th>Accuracy</th>\n",
+       "      <th>Error</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BAV-3.pV.3</td>\n",
+       "      <td>2x</td>\n",
+       "      <td>38</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BHV-1.gD.4</td>\n",
+       "      <td>2x</td>\n",
+       "      <td>24</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>bPIV-3.L.1</td>\n",
+       "      <td>2x</td>\n",
+       "      <td>34</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.066667</td>\n",
+       "      <td>0.933333</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>0.066667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BRSV.M2.1</td>\n",
+       "      <td>2x</td>\n",
+       "      <td>40</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>27.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>0.100000</td>\n",
+       "      <td>0.266667</td>\n",
+       "      <td>0.733333</td>\n",
+       "      <td>0.900000</td>\n",
+       "      <td>0.816667</td>\n",
+       "      <td>0.284800</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>BVDV-1.5UTR.3</td>\n",
+       "      <td>2x</td>\n",
+       "      <td>29</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.916667</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>BAV-3.pV.3</td>\n",
+       "      <td>1x</td>\n",
+       "      <td>45</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.033333</td>\n",
+       "      <td>0.966667</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.983333</td>\n",
+       "      <td>0.033333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>BHV-1.gD.4</td>\n",
+       "      <td>1x</td>\n",
+       "      <td>28</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>bPIV-3.L.1</td>\n",
+       "      <td>1x</td>\n",
+       "      <td>53</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.916667</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>BRSV.M2.1</td>\n",
+       "      <td>1x</td>\n",
+       "      <td>42</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>0.166667</td>\n",
+       "      <td>0.333333</td>\n",
+       "      <td>0.666667</td>\n",
+       "      <td>0.833333</td>\n",
+       "      <td>0.750000</td>\n",
+       "      <td>0.372678</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>BVDV-1.5UTR.3</td>\n",
+       "      <td>1x</td>\n",
+       "      <td>36</td>\n",
+       "      <td>21.0</td>\n",
+       "      <td>30.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>9.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.300000</td>\n",
+       "      <td>0.700000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.850000</td>\n",
+       "      <td>0.300000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Primer Set Concentration  Tt    TP    TN   FP    FN       FPR       FNR  \\\n",
+       "0     BAV-3.pV.3            2x  38  30.0  30.0  0.0   0.0  0.000000  0.000000   \n",
+       "1     BHV-1.gD.4            2x  24  30.0  30.0  0.0   0.0  0.000000  0.000000   \n",
+       "2     bPIV-3.L.1            2x  34  28.0  30.0  0.0   2.0  0.000000  0.066667   \n",
+       "3      BRSV.M2.1            2x  40  22.0  27.0  3.0   8.0  0.100000  0.266667   \n",
+       "4  BVDV-1.5UTR.3            2x  29  25.0  30.0  0.0   5.0  0.000000  0.166667   \n",
+       "5     BAV-3.pV.3            1x  45  29.0  30.0  0.0   1.0  0.000000  0.033333   \n",
+       "6     BHV-1.gD.4            1x  28  30.0  30.0  0.0   0.0  0.000000  0.000000   \n",
+       "7     bPIV-3.L.1            1x  53  25.0  30.0  0.0   5.0  0.000000  0.166667   \n",
+       "8      BRSV.M2.1            1x  42  20.0  25.0  5.0  10.0  0.166667  0.333333   \n",
+       "9  BVDV-1.5UTR.3            1x  36  21.0  30.0  0.0   9.0  0.000000  0.300000   \n",
+       "\n",
+       "   Sensitivity  Specificity  Accuracy     Error  \n",
+       "0     1.000000     1.000000  1.000000  0.000000  \n",
+       "1     1.000000     1.000000  1.000000  0.000000  \n",
+       "2     0.933333     1.000000  0.966667  0.066667  \n",
+       "3     0.733333     0.900000  0.816667  0.284800  \n",
+       "4     0.833333     1.000000  0.916667  0.166667  \n",
+       "5     0.966667     1.000000  0.983333  0.033333  \n",
+       "6     1.000000     1.000000  1.000000  0.000000  \n",
+       "7     0.833333     1.000000  0.916667  0.166667  \n",
+       "8     0.666667     0.833333  0.750000  0.372678  \n",
+       "9     0.700000     1.000000  0.850000  0.300000  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "[optimalResults, allResults] = PrimerScore.executeSensSpecAnalysis(sensSpecInputPath, sensSpecOutputPath)\n",
+    "optimalResults"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate list of color blind colors\n",
+    "color_list = [\n",
+    "    ###########################################################################################\n",
+    "    # Colors from Points of View: Color Blindness (Nature Methods, 8, 441 (2011)) by B. Wong  #\n",
+    "    # https://www.nature.com/articles/nmeth.1618                                              #\n",
+    "    # RGB values extracted using ChatGPT v4o (https://chat.openai.com/) and verified by eye   #\n",
+    "    ###########################################################################################\n",
+    "    (230, 159, 0),      # Orange\n",
+    "    (86, 180, 233),     # Sky blue\n",
+    "    (0, 158, 115),      # Bluish green\n",
+    "    (240, 228, 66),     # Yellow\n",
+    "    (0, 114, 178),      # Blue\n",
+    "    (213, 94, 0),       # Vermillion\n",
+    "    (204, 121, 167),    # Reddish purple\n",
+    "    (0, 0, 0),          # Black\n",
+    "]\n",
+    "\n",
+    "# Create figure counter to start a new figure for each primer set\n",
+    "figCount = 1\n",
+    "\n",
+    "# Loop through each primer set and plot ROC curves for each concentration\n",
+    "for pSet in allResults['Primer Set'].unique():\n",
+    "    # Initialize new figure for this primer set\n",
+    "    plt.figure(figCount)\n",
+    "\n",
+    "    # Create color index counter for each concentration level being tested\n",
+    "    colorIdx = 0\n",
+    "\n",
+    "    # Loop through each concentration level and plot ROC curve\n",
+    "    for conc in allResults[allResults['Primer Set'] == pSet]['Concentration'].unique():\n",
+    "\n",
+    "        # Plot ROC curve using the a color blind list. If number of concentrations levels exceeds \n",
+    "        # number of colors in list, color will repeat\n",
+    "        plt.plot(allResults[(allResults['Primer Set'] == pSet) & (allResults['Concentration'] == conc)]['FPR'], \n",
+    "                 allResults[(allResults['Primer Set'] == pSet) & (allResults['Concentration'] == conc)]['Sensitivity'], \n",
+    "                linestyle='-', marker='o', label=conc, color=tuple(c / 255 for c in color_list[colorIdx % len(color_list)]))\n",
+    "        colorIdx += 1\n",
+    "\n",
+    "    # Format plot and add cutoff line of 95% sensitivity\n",
+    "    plt.legend()\n",
+    "    plt.xlabel('FPR')\n",
+    "    plt.ylabel('Sensitivity')\n",
+    "    plt.title(f\"ROC Curve for {pSet}\")\n",
+    "    plt.plot([0,1], [0.95, 0.95], linestyle='dashed')\n",
+    "    plt.xlim(0,1)\n",
+    "    plt.ylim(0,1)\n",
+    "\n",
+    "    # Increment figure counter for next primer set figure\n",
+    "    figCount += 1"
+   ]
+  },
+  {
+   "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": "3.9.20"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/PrimerScore.py b/PrimerScore.py
index d8a7e5d..04cb912 100644
--- a/PrimerScore.py
+++ b/PrimerScore.py
@@ -1,15 +1,30 @@
+#!/usr/bin/env python
+
+'''PrimerAnalysis.py is a python module for analyzing LAMP primer amplification data'''
+
+__author__ = 'Josiah Levi Davidson'
+__contact__ = 'Mohit S. Verma'
+__email__ = 'msverma@purdue.edu'
+__version__ = '1.3.0'
+__status__ = 'Development'
+__copyright__ = "Copyright (C) 2024 Purdue Research Foundation"
+__license__ = "GNU Affero General Public License Version 3 with Commons Clause"
+
+#### BEGIN IMPORTS
 import math
 import pandas as pd
 import numpy as np
 from os import path
 from matplotlib import pyplot as plt
 
+# BEGIN GLOBAL VARIABLES
 weights = {}
 thresholds = []
 replicates = 0
 primerSetName = ""
 rxnTypeDesignation = ""
 
+# BEGIN CONSTANTS
 MAX_INST_SIGNAL = 140000
 threshold_perc = 0.1
 
@@ -26,7 +41,7 @@
 #                       plateau phase ending, positive reaction calculation threshold,
 #                       and false positive threshold, respectively
 #   set_replicates := Number of replicates. Must be the same for all primer sets.
-def initialize(set_weights = [5, 5, 10, 20, 60], set_thresholds = [3000, 200, 0.9, 0.2], set_replicates = 4):
+def initialize(set_weights = [5, 5, 10, 20, 60], set_thresholds = [3000, 200, 0.9, 0.2], set_replicates = 4, set_instrument_signal = 140000, set_threshold_perc = 0.1):
     if not (len(set_weights) == 5):
         raise ValueError('Weights do not contain 5 values. Refer to readme for more information.')
     if not (len(set_thresholds) == 4):
@@ -34,11 +49,13 @@ def initialize(set_weights = [5, 5, 10, 20, 60], set_thresholds = [3000, 200, 0.
     if not (set_replicates >= 3):
         raise ValueError('Number of replicates is not greater than or equal to 3.')
     
-    global weights, thresholds, replicates, primerSetName, rxnTypeDesignation
+    global weights, thresholds, replicates, primerSetName, rxnTypeDesignation, MAX_INST_SIGNAL, threshold_perc
     weights = {'Intensity_Avg': set_weights[0], 'Intensity_StdDev': set_weights[1], 'RxnTime_Std':set_weights[2],
                'RxnTime_Avg': set_weights[3], 'False_Positives':set_weights[4]}
     thresholds = set_thresholds
     replicates = set_replicates
+    MAX_INST_SIGNAL = set_instrument_signal
+    threshold_perc = set_threshold_perc
 
 # Main primer scoring method. Call to generate output
 # Arguments:
@@ -109,8 +126,8 @@ def determine_pos(rxnTimeSeries):
 
     idxIntersect = np.where(np.greater(revData.values, data.values)==False)[0][0]
 
-    dataVec = np.array([idxIntersect, data[idxIntersect] - data[0]])
-    revDataVec = np.array([idxIntersect, revData[idxIntersect] - revData[0]])
+    dataVec = np.array([idxIntersect, data.iloc[idxIntersect] - data.iloc[0]])
+    revDataVec = np.array([idxIntersect, revData.iloc[idxIntersect] - revData.iloc[0]])
 
     magnitude = (np.linalg.norm(dataVec)*np.linalg.norm(revDataVec))
     
@@ -199,7 +216,7 @@ def calc_overall_score_saliva(row, df_results):
         overall_score += calc_item_score(row, df_results, item)
     return overall_score
 
-def calc_FP_cardinal(name, df, df_results, n):
+def calc_FP_cardinal(name, df, df_results, n, retValue = 'Score'):
 
     # Calculate the total number of divisions given a number of replicates. This is just the sum of 1...num of replicates.
     totalDivisions = 0
@@ -212,15 +229,19 @@ def calc_FP_cardinal(name, df, df_results, n):
     # Select only primer sets that have at least n false positives for consideration in ranking
     subjectPrimerSets = df_results[df_results['False_Positives'] >= n]['PrimerSet']
 
-
-
     if(name not in subjectPrimerSets.values):
-        return totalPoints
+        if (retValue == 'Score'):
+            return totalPoints
+        elif (retValue == 'RxnTime'):
+            return np.nan
     else:
-        subjectRxnPenalty = df[(df['RxnType']=='-') & (df['ObservedType']==1) & (df['Primer'].isin(subjectPrimerSets))].groupby('Primer')['RxnPenalty'].apply(lambda x: x.sort_values(ascending=False).iloc[n-1])
-        maxSubjectRxnPenalty = subjectRxnPenalty.max()
+        subjectRxnPenalty = df[(df['RxnType']=='-') & (df['ObservedType']==1) & (df['Primer'].isin(subjectPrimerSets))].groupby('Primer')[['RxnPenalty', 'RxnTime']].apply(lambda x: x.sort_values(by='RxnPenalty', ascending=False).iloc[n-1])
+        maxSubjectRxnPenalty = subjectRxnPenalty['RxnPenalty'].max()
 
-        return (1 - (subjectRxnPenalty[name]/maxSubjectRxnPenalty)) * totalPoints
+        if (retValue == 'Score'):
+            return (1 - (subjectRxnPenalty['RxnPenalty'][name]/maxSubjectRxnPenalty)) * totalPoints
+        elif (retValue == 'RxnTime'):
+            return subjectRxnPenalty['RxnTime'][name]
 
 def calc_rxn_penalty(row):
     rxnTimePenalty = 60 - row['RxnTime']
@@ -244,6 +265,8 @@ def getPrimerSummary(fileName):
         results['FP_{}'.format(i+1)] = results.apply(lambda row: 0 if(row['TruePos'] < replicates) else calc_FP_cardinal(row['PrimerSet'], df, results, (i+1)), axis=1)
 
     results['Overall_Score'] = results.apply(lambda row:  0 if(row['TruePos'] < replicates) else calc_overall_score(row, results[results['TruePos']==replicates]), axis=1)
+    for i in range(0, replicates):
+        results['FP_{}_time'.format(i+1)] = results.apply(lambda row: 0 if(row['TruePos'] < replicates) else calc_FP_cardinal(row['PrimerSet'], df, results, (i+1), retValue='RxnTime'), axis=1)
     return results
 
 # Function to return primer names and DataFrame in a dictionary
@@ -267,4 +290,219 @@ def format_df(filename):
     df['ObservedType'] = df.apply(lambda row: determine_pos(row.drop(['Primer', 'RxnType'])), axis=1)
     df['RxnTime'] = df.apply(lambda row:  calc_rxn_time(row.drop(['Primer', 'RxnType','ObservedType'])), axis=1)
     df['RxnPenalty'] = df.apply(lambda row: calc_rxn_penalty(row), axis=1)
-    return df, primerNames
\ No newline at end of file
+
+    return df, primerNames
+
+# Function to score LOD data and return average and standard deviation at each concentration
+# Arguments:
+#   fileName: String, relative or absolute path to the file of interest containing time series data for primer sets.
+# TODO: Update variable names to be more descriptive
+def score_LOD(fileName, outputFile):
+    # Read in excel file and read sheet name
+    file = pd.ExcelFile(fileName)
+    sheets = file.sheet_names
+
+    # Concatenate sheets together in DataFrame by assigning different sheets a unique name corresponding to the sheet name.
+    df = pd.concat([pd.read_excel(fileName, header=None, sheet_name=sheet).T.assign(sheet_name=sheet) for sheet in sheets])
+
+    # Rearrange columns and assign column headers to DataFrame
+    cols = df.columns.tolist()
+    cols = cols[-1:] + cols[:-1]
+    df = df[cols]
+    col_header = ['Primer Set', 'Conc', 'Units', 'Template']
+    col_header.extend([str(x) for x in range(1, 61)])
+    df.columns = col_header
+
+    # Determine whether reactions are positive or negative and calculate reaction time
+    df['ObservedType'] = df.apply(lambda row: determine_pos(row.drop(['Primer Set', 'Conc', 'Units', 'Template'])), axis=1)
+    df['RxnTime'] = df.apply(lambda row: calc_rxn_time(row.drop(['Primer Set', 'Conc', 'Units', 'Template'])), axis=1)
+
+    # Create list of each unique concentration and primer set
+    totalConcs = df['Conc'].unique()
+    totalPrimerSets = df['Primer Set'].unique()
+
+    # Ensure concentration list is in descending order for assignment of LOD
+    totalConcs[totalConcs == "NTC"] = -1
+    totalConcs = totalConcs[totalConcs[:].astype(float).argsort()[::-1]]
+    totalConcs[-1] = "NTC"
+
+    # Insert columns corresponding to LOD and Pathogen
+    cols = np.insert(totalConcs, 0, "LOD", axis=0)
+    cols = np.insert(cols, 0, "Pathogen", axis=0)
+
+    # Create new dataframe for with all NaN for all concentrations and primer sets
+    results_df = pd.DataFrame(np.nan, index=totalPrimerSets, columns=cols, dtype=str)
+
+    # Loop trhough unique primer sets
+    for primerSet in totalPrimerSets:
+        # Split pathogen from primer set name
+        results_df.loc[primerSet, "Pathogen"] = primerSet.split(".")[0]
+
+        #Loop through unique concentrations and initialize flag that we have not yet found LOD
+        primerSet_df = df[df['Primer Set'] == primerSet]
+        lodFound = False
+
+        # Loop through unique concentrations
+        for conc in totalConcs:
+            # Get number of replicates for current concentration based on shape of df
+            # NOTE: if number of replicates is not consistent across the experiment, this could change the ability to compare across concentrations
+            numRep = primerSet_df[primerSet_df['Conc'] == conc].shape[0]
+
+            # Check whether sum of Observed type is greater than num reps for conc and if so, add average reaction rate to new df
+            numPos = primerSet_df[primerSet_df['Conc'] == conc]['ObservedType'].sum()
+
+            # Check if the concentration is a No Template Control (NTC) and assign number of positives. 
+            # NOTE: STRICT RULE - If false positives are present, the LOD is indeterminant.
+            if conc == 'NTC':
+                results_df.loc[primerSet, conc] = f"{numPos} of {numRep}"
+                if numPos > 0:
+                    results_df.loc[primerSet, 'LOD'] = np.nan
+            else:
+                # If number of positives is greater than number of replicates, add average reaction time to report dataframe along with standard deviation
+                if numPos == numRep:
+                    results_df.loc[primerSet, conc] = "{:0.2f}".format(primerSet_df[(primerSet_df['Conc'] == conc) & (primerSet_df['ObservedType'] == 1)]['RxnTime'].mean())
+                    results_df.loc[primerSet, f"{conc}_STD"] = primerSet_df[(primerSet_df['Conc'] == conc) & (primerSet_df['ObservedType'] == 1)]['RxnTime'].std()
+                else:
+                    # If number of positives is less than number of replicates, we have found our LOD. Assign the LOD as the last concentration up and 
+                    # flag to true
+                    if not lodFound:
+                        results_df.loc[primerSet, "LOD"] = totalConcs[[i for i, x in enumerate(totalConcs) if x == conc][0] - 1]
+                        lodFound = True
+                    
+                    # Add number of positives out of total replicates to report dataframe
+                    results_df.loc[primerSet, conc] = f"{numPos} of {numRep}"
+
+    results_df.to_csv(outputFile, encoding='utf-8')
+    # Return report dataframe
+    return results_df.sort_values(by=['Pathogen', 'LOD'])
+
+def formatSensSpecData(fileName):
+    # Open and read excel sheet names (which are named after the primer sets) before reading in data
+    dataFile = pd.ExcelFile(fileName)
+    sheetNames = dataFile.sheet_names
+
+    # Read in data, Transpose, and assign column names based on primer set information stored in the sheet name.
+    # Assign read and formatted data to a new DataFrame after concatenating all primer sets
+    df = pd.concat([pd.read_excel(fileName, header=None, sheet_name=sheet).T.assign(sheet_name=sheet) for sheet in sheetNames])
+
+    # Rearrange columns such that primer set name and concentraiton are first followed by data 
+    # and assign column headers to DataFrame
+    cols = df.columns.tolist()
+    cols = cols[-1:] + cols[:-1]
+    df = df[cols]
+
+    # Assign column header names
+    col_header = ['Primer Set', 'Conc']
+    col_header.extend([str(x) for x in range(1, 61)])
+    df.columns = col_header
+
+    # Determine if each reaction is positive or negative and calculate reaction time
+    df['ObservedType'] = df.apply(lambda row: determine_pos(row.drop(['Primer Set', 'Conc'])), axis=1)
+    df['RxnTime'] = df.apply(lambda row: calc_rxn_time(row.drop(['Primer Set', 'Conc'])), axis=1)
+
+    # Return formatted DataFrame
+    return df
+
+def calc_sens_spec(df: pd.DataFrame, concentration: str, primerSet: str) -> pd.DataFrame:
+    numTimePoints = 60
+    # Set No Template Control (NTC) reactions as predicted negative and all other reactions as predicted positive
+    predPos = df[(df['Primer Set'] == primerSet) & (df['Conc'] == concentration)][['Conc', 'ObservedType', 'RxnTime']]
+    predNeg = df[(df['Primer Set'] == primerSet) & (df['Conc'] == 'NTC')][['Conc', 'ObservedType', 'RxnTime']]
+
+    # Initialize DataFrame to store sensitivity, specificity, false positive rate, false negative rate, accuracy, and error
+    SensSpecCounts = pd.DataFrame(np.nan, index=[i for i in range(1, numTimePoints + 1)], columns=['TP', 'TN', 'FP', 'FN', 'FPR', 'FNR', 'Sensitivity', 'Specificity', 'Accuracy', 'Error'])
+
+    # Initialize all reactions to the length of time points (or full length of reactions) as this will be the reaction time
+    # if a given reaction fails to amplify
+    predPos.loc[predPos['ObservedType']==0, 'RxnTime']= numTimePoints
+    predNeg.loc[predNeg['ObservedType']==0, 'RxnTime']= numTimePoints
+
+    # Loop through each time point and calculate the sensitivity, specificity, false positive rate, 
+    # false negative rate, accuracy, and error at that time
+    for cutoff_time in range(1, numTimePoints + 1):
+        # Count true/false positives/negatives
+        true_positives = predPos[(predPos['RxnTime'] <= cutoff_time)].shape[0]
+        true_negatives = predNeg[predNeg['RxnTime'] > cutoff_time].shape[0]
+        false_positives = predNeg[predNeg['RxnTime'] <= cutoff_time].shape[0]
+        false_negatives = predPos[(predPos['ObservedType'] == 0) | (predPos['RxnTime'] > cutoff_time)].shape[0]
+
+        
+        # Calculate sensitivity, specificity, false positive rate, false negative rate, accuracy
+        sens = true_positives / (true_positives + false_negatives)
+        spec = true_negatives / (true_negatives + false_positives)
+        false_positive_rate = false_positives / (false_positives + true_negatives)
+        false_negative_rate = false_negatives / (false_negatives + true_positives)
+        accuracy = (true_positives + true_negatives) / (true_positives + true_negatives + false_positives + false_negatives)
+        classificationError = math.sqrt((1-sens)**2 + (1-spec)**2)
+        
+        # Add to dataframe
+        SensSpecCounts.loc[cutoff_time] = [true_positives, true_negatives, false_positives, false_negatives, false_positive_rate, false_negative_rate, sens, spec, accuracy, classificationError]
+        
+    return SensSpecCounts
+
+def find_optimum_cutoff(df: pd.DataFrame):
+    # Find the minimum value of the error and return the corresponding time (located by the index). If multiple maximums exist, return the first one.
+    return df[df['Error'] == df['Error'].min()].index[0]
+
+def executeSensSpecAnalysis(fileName, outputFile) -> tuple[pd.DataFrame, pd.DataFrame]:
+    # Open and format Sensitivity and Specificity data
+    sensSpecData_df = formatSensSpecData(fileName)
+    
+    # Determine unique concentrations and primer sets
+    lodConcs = sensSpecData_df[sensSpecData_df['Conc'] != 'NTC']['Conc'].unique()
+    primerSets = sensSpecData_df['Primer Set'].unique()
+
+    # Initialize output DataFrame and set dtype of descriptor columns to string to avoid future warnings
+    optimalResults = pd.DataFrame(np.nan, index=[i for i in range(0, len(lodConcs) * len(primerSets))], columns=['Primer Set', 'Concentration', 'Tt', 'TP', 'TN', 'FP', 'FN', 'FPR', 'FNR', 'Sensitivity', 'Specificity', 'Accuracy', 'Error'])
+    optimalResults[['Primer Set', 'Concentration', 'Tt']] = optimalResults[['Primer Set', 'Concentration', 'Tt']].astype(str)
+
+    # Initialize output DataFrame for full sensitivity and specificity analysis results
+    sensSpecAnalysisResults = pd.DataFrame(columns=['Primer Set', 'Concentration', 'Tt', 'TP', 'TN', 'FP', 'FN', 'FPR', 'FNR', 'Sensitivity', 'Specificity', 'Accuracy', 'Error'])
+
+    # Initialize level counter to track row index for insertion into output DataFrame using iloc
+    levelCounter = 0
+
+    # Loop through each concentration and primer set
+    for concentration in lodConcs:
+        for primerSet in primerSets:
+
+            # Calculate sensitivity and specificity at each time point for the current concentration and primer set (level)
+            levelDf = calc_sens_spec(sensSpecData_df, concentration, primerSet)
+
+            # Calculate the optimum cutoff for the current level and the corresponding sensitivity and specificity data
+            run_optimumCutoff = find_optimum_cutoff(levelDf)
+            optimalSensSpec = levelDf.loc[run_optimumCutoff]
+
+            # Add Primer Set, template concentration, and the optimum cutoff to the optimal sensitivity and specificity data
+            rowDescriptors = pd.Series(index=['Primer Set', 'Concentration', 'Tt'], data=[primerSet, concentration, run_optimumCutoff])
+            optimalSensSpec = pd.concat([rowDescriptors, optimalSensSpec])
+
+            # Write optimal sensitivity and specificity data to output DataFrame for current level
+            optimalResults.iloc[levelCounter] = optimalSensSpec
+
+            # Add Primer Set and template concentration to sensitivity and specificity data
+            levelDf[['Primer Set', 'Concentration']] = [primerSet, concentration]
+
+            # Rearrange columns such that primer set name and concentraiton are first followed by data 
+            # and assign column headers to DataFrame
+            cols = levelDf.columns.tolist()
+            cols = cols[-2:] + cols[:-2]
+            levelDf = levelDf[cols]
+
+            # Write sensitivity and specificity data for current level to output DataFrame
+            # If sensSpecAnalysisResults is not empty, concatenate current level data to it
+            if sensSpecAnalysisResults.empty:
+                sensSpecAnalysisResults = levelDf
+            else:
+                sensSpecAnalysisResults = pd.concat([sensSpecAnalysisResults, levelDf])
+
+            # Increment level counter
+            levelCounter = levelCounter + 1
+
+    # Write optimal data to "optimalResults" sheet in output and write full data to "data" sheet in output excel file
+    with pd.ExcelWriter(outputFile) as writer:
+        optimalResults.to_excel(writer, sheet_name='optimalResults')
+        sensSpecAnalysisResults.to_excel(writer, sheet_name='data')
+    
+    # Return optimal sensitivity and specificity data and full sensitivity and specificity data
+    return (optimalResults, sensSpecAnalysisResults)
\ No newline at end of file
diff --git a/README.md b/README.md
index f4b09a4..b5bacb5 100644
--- a/README.md
+++ b/README.md
@@ -11,6 +11,8 @@ Copy the PrimerScore.py file to either a valid location in the search path or to
 # Usage
 
 ## Data formatting
+
+### Primer screening data format
 The input data file must be in .xls format **NOT .XLSX**. Additionally, the file **MUST** be formmatted in the following manner, with each column representing the data for a single reaction for a specific primer set:
   - First row should contain primer set IDs in the form of *{Target}*.*{SetID}*. For example, orf1ab.2.
   - Second row should contain reaction type annotation. 
@@ -18,11 +20,24 @@ The input data file must be in .xls format **NOT .XLSX**. Additionally, the file
     - For positive reactions, put **'+'** in the cell.
   - Rows three and onward should contain data.
  
- **NOTE:**
- 1. The data should be rectangular; this means that all data is of the same length. This has not been tested for data of different lengths.
- 2. There should be an equal amount of positive and negatives for a speific primer set. 
- 3. For proper scoring, each primer set should have the same number of replicates 
- 
+**NOTE:**
+1. The data should be rectangular; this means that all data is of the same length. This has not been tested for data of different lengths.
+2. There should be an equal amount of positive and negatives for a speific primer set. 
+3. For proper scoring, each primer set should have the same number of replicates 
+
+### LOD data format
+Data for LOD should should be in a `.xls` file. Each tab should correspond to the fluorescent data for one primer set. So, if uniqe primer sets are to be analzyed, there should be four tabs with four unique names.
+
+Each tab should contain a rectangular layout were each column corresponds to the time series of RFU values for a single replicate. The first row should annotate the concentration number, while the second row should annotate the concentration units. For instance, if the concentration for a replicate was 25 copies per reaction (cps/rxn), the first row would be 25 and the second row would be cps/rxn. The third row should contain the template for that replicate. For NTC reactions, the concentration units row should be empty. The following $n$ rows should be the fluorescent RFU values at each time point for that replicate.
+
+### Sens/Spec data format
+Similar to Sens/Spec data, data for sensitivity and specificity analysis should be contained in one `.xls` file. Each tab should correspond to the fluorescent data for one primer set. So, if uniqe primer sets are to be analzyed, there should be four tabs with four unique names.
+
+Each tab should contain a rectangular layout where each column corresponds to the time series of RFU values for a single replicate. The first row should contain the concentration designation. Do not indicate replicates, as the code will automatically determine the number of replicates. Then the following $n$ rows should be the flourescent RFU value at each time point for that replicate. 
+
+Data should be rectangular. As before, each concentration and NTC reactions should have the same number of replicates.
+
+
  ## Execution
  ### Importing
  If the PrimerScore.py file is located in a valid path or the current directory, simply execute the following line to import the module.
@@ -162,3 +177,32 @@ Once all primer sets have had primer set performance features calculated, an ove
 $S_k = \omega_{\bar{I}} \cdot \left( 1 - \frac{max \left( \bar{I} \right) - \bar{I}_k}{Range \left( \bar(I) \right)} \right) + \sum _x \omega _x \cdot \left( 1 - \frac{\text{min}(x) - x_k}{\text{Range}(x))} \right) + \sum _{i=0} ^n \left( i  \cdot \alpha \cdot \left( 1 - \frac{\phi \left( \Omega \right)_i}{\text{max}(\phi (\Omega))_i} \right) \right)$
 
 where $k$ is a given primer set, $x$ indicates a given feature, $\omega_x$ is the weight allocated to feature $x$, $x_k$ is the feature value for primer set $k$, $\alpha$ is the false positive weighting factor, $n$ is the number of replicates, $\Omega_i$ is the reaction penalty for reaction $i$, and $\phi$ is the set of reaction penalities for each *false positive* reaction for a specific primer set ordered from smallest to largest such that an element $\Omega \in \phi$ if a given primer set has at least $i$ false positives, and $\text{max}(\phi (\Omega))_i $ is the maximum value of the $i$th reaction penalty of each primer set containing at least $i$ false positives.
+
+## LOD Determination and Scoring
+
+The Limit of Detection for a primer set was determined by observing the lowest concentration, $C$, at which all replicates, $n$, at that concentration level amplified and all replicates of all concentrations higher than that level. All reactions are determined to be positive using the same positive detection methodology as above.
+
+If any false positives (i.e. deteremined positive amplifications in designated NTC reactions) are detected, then the LOD is indeterminant for that primer set.
+
+## Sensitivity and Specificity Analysis
+
+Sensitivity and specificity analyses were typically conducted at 2x and 1x the concentration of LOD (but this is not all the case). In any event, the current process identifies the number of "distinct" concentrations along with a common negative control set of reactions and determines analytical sensitivity and specificity on each of those distinct concentrations.
+
+For all input reactions, the algorithm determines any reaction that is *not* an NTC reaction to be a predicted positive reaction. All NTC reactions are predicted negatives. Positive reaction detection is then applied to all reactions to determine observed positive and observed negative reactions along with reaction times. Observed positves were further designated at a given time point, $t_{set}$, only if $t_{set} < t_{rxn}$. Otherwise, the reaction was counted as an observed negative at that time point.
+
+For each time point, $t_set$ in the LAMP reaction, the following were calculated.
+- True Positive  (TP): Predicted Positive and Observed Positive
+- True Negative  (TN): Predicted Negative and Observed Negative
+- False Positive (FP): Predicted Negative and Observed Positive
+- False Negative (FN): Predicted Positive and Observed Negative
+
+From these counts, the following were then calculated for each tiem point:
+- $\text{Sens} = \frac{\text{TP}}{\text{TP} + \text{TN}}$
+- $\text{Spec} = \frac{\text{TN}}{\text{TN} + \text{FP}}$
+- $\text{FPR}  = \frac{\text{FP}}{\text{FP} + \text{TN}}$
+- $\text{FNR}  = \frac{\text{FN}}{\text{FN} + \text{TP}}$
+- $\text{Acc}  = \frac{\text{TP} + \text{TN}}{\text{TP} + \text{TN} + \text{FP} + \text{FN}}$
+- $\text{Classification Error} = \sqrt{\left( 1 - \text{Sens}\right)^2 + \left( 1 - \text{Spec}\right)^2}$
+
+The reaction time which minimized the classification error was then chosen as the optimum point for reporting final primer set Sensitivity and Specificity metrics.
+