diff --git a/.github/workflows/python-app.yml b/.github/workflows/python-app.yml
index e4452b5..b32cb4a 100644
--- a/.github/workflows/python-app.yml
+++ b/.github/workflows/python-app.yml
@@ -18,7 +18,8 @@ jobs:
 
 
     steps:
-    - uses: actions/checkout@v4
+    - name: Checkout code with submodules
+      uses: actions/checkout@v4
 
     - name: Set up Python
       uses: actions/setup-python@v5
@@ -36,4 +37,5 @@ jobs:
 
     - name: Test with pytest
       run: |
-        poetry run pytest
+        poetry run pytest -s
+      working-directory: ${{ github.workspace }}
diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 0000000..39a9e58
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "data/nf1_schwann_cell_painting_data"]
+	path = data/nf1_schwann_cell_painting_data
+	url = git@github.com:WayScience/nf1_schwann_cell_painting_data.git
diff --git a/README.md b/README.md
index 3dec6e3..c878769 100644
--- a/README.md
+++ b/README.md
@@ -1 +1,28 @@
-# pairwise_compare
\ No newline at end of file
+# Pairwise Compare
+This tool allows the user to compare groups of data specified in a tidy-wide pandas dataframe with ease.
+In this repo the capabilities of the PairwiseCompareManger are shown through an example using the nuclear speckle data (`example_pairwise_comparisons.ipynb`).
+Although, most of the development efforts can be found in the `src` folder.
+Users should almost exclusively interact with the PairwiseCompareManager, however, there may be rare exceptions.
+If you choose to interact with another component of the tool, then there will be less input validation safeguards available.
+
+## Data Formats
+When passing arguments to the `PairwiseCompareManager` you can specify the columns that remain the same in each group-to-group comparison, and the columns that will be different in these comparisons.
+These columns are parameterized by `_same_columns` and `_different_columns`, respectively.
+The column values in these columns uniquely define each group.
+During pairwise comparisons of groups, all of the column values of the columns specified in `_same_columns` will be the same between both groups compared for all paired combinations of groups.
+Likewise, all of the column values of the columns specified in `_different_columns` will be different between both groups compared for all paired combinations of groups.
+
+One of the following column arguments conditions must be satisified when using the `PairwiseCompareManager`:
+1. `_same_columns` must include at least one list element if `_different_columns` has less than two list elements.
+2. `_different_columns` must contain one or more list elements.
+3. `_same_columns` and `_different_columns` should not contain any of the same columns.
+
+## Limitations
+1. Input validation is enforced in the `PairwiseCompareManager` and `PairwiseCompare` classes.
+2. Additional column values are not tracked aside from columns used to compare groups (_same_columns, _different_columns).
+3. Output and input python data structures are limited.
+4. All of the data, in the supplied pandas dataframe, is used to compute comparisons.
+
+## Extensibility
+This tool compares features between any two groups of a tidy-wide pandas dataframe.
+To incorporate additional comparators for making comparisons, you must introduce the functionality as a class and inherit from the `Comparator` class (See `Comparator.py` for details).
diff --git a/data/Plate_4_bulk_feature_selected.parquet b/data/Plate_4_bulk_feature_selected.parquet
new file mode 100644
index 0000000..4052d9a
Binary files /dev/null and b/data/Plate_4_bulk_feature_selected.parquet differ
diff --git a/data/nf1_schwann_cell_painting_data b/data/nf1_schwann_cell_painting_data
new file mode 160000
index 0000000..74069b9
--- /dev/null
+++ b/data/nf1_schwann_cell_painting_data
@@ -0,0 +1 @@
+Subproject commit 74069b98883320bf0a5e755d76453edd6ae1e7e7
diff --git a/docs/example_pairwise_comparisons.ipynb b/docs/example_pairwise_comparisons.ipynb
new file mode 100644
index 0000000..1e7e6d9
--- /dev/null
+++ b/docs/example_pairwise_comparisons.ipynb
@@ -0,0 +1,1079 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jukit_cell_id": "iPcbHzi4Te"
+   },
+   "source": [
+    "# Examples of using the PairwiseCompareManager\n",
+    "This notebook shows examples on the NF1 dataset of how to compare instances of different groups.\n",
+    "Each instance is a unique well with Schwann Cells."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:49:54.609480Z",
+     "iopub.status.busy": "2024-11-20T20:49:54.609363Z",
+     "iopub.status.idle": "2024-11-20T20:49:54.802182Z",
+     "shell.execute_reply": "2024-11-20T20:49:54.801698Z"
+    },
+    "jukit_cell_id": "JlHVCFc6jg"
+   },
+   "outputs": [],
+   "source": [
+    "import pathlib\n",
+    "import sys\n",
+    "\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jukit_cell_id": "D89Os0yFLC"
+   },
+   "source": [
+    "# Custom Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:49:54.803965Z",
+     "iopub.status.busy": "2024-11-20T20:49:54.803771Z",
+     "iopub.status.idle": "2024-11-20T20:49:54.806859Z",
+     "shell.execute_reply": "2024-11-20T20:49:54.806473Z"
+    },
+    "jukit_cell_id": "KnQdvFxPkd"
+   },
+   "outputs": [],
+   "source": [
+    "from comparators.PearsonsCorrelation import PearsonsCorrelation\n",
+    "from comparison_tools.PairwiseCompareManager import PairwiseCompareManager"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jukit_cell_id": "fIxGUGaKkR"
+   },
+   "source": [
+    "# Inputs"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:49:54.808060Z",
+     "iopub.status.busy": "2024-11-20T20:49:54.807870Z",
+     "iopub.status.idle": "2024-11-20T20:49:54.858284Z",
+     "shell.execute_reply": "2024-11-20T20:49:54.857869Z"
+    },
+    "jukit_cell_id": "dlU1p2c9cE"
+   },
+   "outputs": [],
+   "source": [
+    "# Paths to original nuclear speckle data\n",
+    "data_dir = pathlib.Path(\n",
+    "    \"../data/nf1_schwann_cell_painting_data/3.processing_features/data/bulk_profiles\"\n",
+    ").resolve(strict=True)\n",
+    "\n",
+    "plate4df = pd.read_parquet(data_dir / \"Plate_4_bulk_feature_selected.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jukit_cell_id": "ecmR6GvGEY"
+   },
+   "source": [
+    "# Process Plate 4\n",
+    "Each instance (row) represents a unique well."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:49:54.859806Z",
+     "iopub.status.busy": "2024-11-20T20:49:54.859654Z",
+     "iopub.status.idle": "2024-11-20T20:49:54.879948Z",
+     "shell.execute_reply": "2024-11-20T20:49:54.879552Z"
+    },
+    "jukit_cell_id": "gQ5lHlQ04X"
+   },
+   "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>Metadata_WellRow</th>\n",
+       "      <th>Metadata_WellCol</th>\n",
+       "      <th>Metadata_gene_name</th>\n",
+       "      <th>Metadata_genotype</th>\n",
+       "      <th>Metadata_seed_density</th>\n",
+       "      <th>Metadata_siRNA</th>\n",
+       "      <th>Metadata_RNAiMax</th>\n",
+       "      <th>Metadata_Concentration</th>\n",
+       "      <th>Metadata_Plate</th>\n",
+       "      <th>Metadata_Well</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DAPI_3_00_256</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DAPI_3_01_256</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_DAPI_3_02_256</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_GFP_3_02_256</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_RFP_3_00_256</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_RFP_3_01_256</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_RFP_3_02_256</th>\n",
+       "      <th>Nuclei_Texture_InverseDifferenceMoment_RFP_3_03_256</th>\n",
+       "      <th>Nuclei_Texture_SumEntropy_DAPI_3_03_256</th>\n",
+       "      <th>Nuclei_Texture_SumVariance_DAPI_3_01_256</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>B</td>\n",
+       "      <td>2</td>\n",
+       "      <td>NF1</td>\n",
+       "      <td>WT</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>No siRNA</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>Plate_4</td>\n",
+       "      <td>B2</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.165689</td>\n",
+       "      <td>-0.446846</td>\n",
+       "      <td>-0.312296</td>\n",
+       "      <td>-0.401454</td>\n",
+       "      <td>-2.032668</td>\n",
+       "      <td>-2.252355</td>\n",
+       "      <td>-2.122525</td>\n",
+       "      <td>-2.177496</td>\n",
+       "      <td>-1.448158</td>\n",
+       "      <td>0.031227</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>B</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NF1</td>\n",
+       "      <td>WT</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>Scramble</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.050</td>\n",
+       "      <td>Plate_4</td>\n",
+       "      <td>B3</td>\n",
+       "      <td>...</td>\n",
+       "      <td>-0.960711</td>\n",
+       "      <td>-1.438404</td>\n",
+       "      <td>-0.707251</td>\n",
+       "      <td>0.738356</td>\n",
+       "      <td>0.188400</td>\n",
+       "      <td>-0.844323</td>\n",
+       "      <td>-0.846457</td>\n",
+       "      <td>-0.342180</td>\n",
+       "      <td>1.306905</td>\n",
+       "      <td>0.344522</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>B</td>\n",
+       "      <td>4</td>\n",
+       "      <td>NF1</td>\n",
+       "      <td>WT</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>Scramble</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>Plate_4</td>\n",
+       "      <td>B4</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1.910170</td>\n",
+       "      <td>1.887803</td>\n",
+       "      <td>2.496062</td>\n",
+       "      <td>-1.858108</td>\n",
+       "      <td>0.149212</td>\n",
+       "      <td>-1.231724</td>\n",
+       "      <td>-1.045144</td>\n",
+       "      <td>-0.340360</td>\n",
+       "      <td>-1.806753</td>\n",
+       "      <td>0.169433</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>B</td>\n",
+       "      <td>5</td>\n",
+       "      <td>NF1</td>\n",
+       "      <td>WT</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>No siRNA</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.000</td>\n",
+       "      <td>Plate_4</td>\n",
+       "      <td>B5</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2.573592</td>\n",
+       "      <td>2.273321</td>\n",
+       "      <td>2.652510</td>\n",
+       "      <td>0.884645</td>\n",
+       "      <td>0.187687</td>\n",
+       "      <td>-0.199618</td>\n",
+       "      <td>-0.262197</td>\n",
+       "      <td>-0.310826</td>\n",
+       "      <td>-1.312691</td>\n",
+       "      <td>-0.182742</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>B</td>\n",
+       "      <td>6</td>\n",
+       "      <td>NF1</td>\n",
+       "      <td>WT</td>\n",
+       "      <td>1000</td>\n",
+       "      <td>Scramble</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.005</td>\n",
+       "      <td>Plate_4</td>\n",
+       "      <td>B6</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.929752</td>\n",
+       "      <td>1.058821</td>\n",
+       "      <td>-0.080929</td>\n",
+       "      <td>-0.245173</td>\n",
+       "      <td>0.298632</td>\n",
+       "      <td>-0.254777</td>\n",
+       "      <td>-1.608270</td>\n",
+       "      <td>-0.996690</td>\n",
+       "      <td>0.151230</td>\n",
+       "      <td>2.010409</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 1182 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Metadata_WellRow  Metadata_WellCol Metadata_gene_name Metadata_genotype  \\\n",
+       "0                B                 2                NF1                WT   \n",
+       "1                B                 3                NF1                WT   \n",
+       "2                B                 4                NF1                WT   \n",
+       "3                B                 5                NF1                WT   \n",
+       "4                B                 6                NF1                WT   \n",
+       "\n",
+       "   Metadata_seed_density Metadata_siRNA  Metadata_RNAiMax  \\\n",
+       "0                   1000       No siRNA                 0   \n",
+       "1                   1000       Scramble                 1   \n",
+       "2                   1000       Scramble                 1   \n",
+       "3                   1000       No siRNA                 0   \n",
+       "4                   1000       Scramble                 1   \n",
+       "\n",
+       "   Metadata_Concentration Metadata_Plate Metadata_Well  ...  \\\n",
+       "0                   0.000        Plate_4            B2  ...   \n",
+       "1                   0.050        Plate_4            B3  ...   \n",
+       "2                   0.005        Plate_4            B4  ...   \n",
+       "3                   0.000        Plate_4            B5  ...   \n",
+       "4                   0.005        Plate_4            B6  ...   \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_DAPI_3_00_256  \\\n",
+       "0                                          -0.165689      \n",
+       "1                                          -0.960711      \n",
+       "2                                           1.910170      \n",
+       "3                                           2.573592      \n",
+       "4                                           0.929752      \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_DAPI_3_01_256  \\\n",
+       "0                                          -0.446846      \n",
+       "1                                          -1.438404      \n",
+       "2                                           1.887803      \n",
+       "3                                           2.273321      \n",
+       "4                                           1.058821      \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_DAPI_3_02_256  \\\n",
+       "0                                          -0.312296      \n",
+       "1                                          -0.707251      \n",
+       "2                                           2.496062      \n",
+       "3                                           2.652510      \n",
+       "4                                          -0.080929      \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_GFP_3_02_256  \\\n",
+       "0                                          -0.401454     \n",
+       "1                                           0.738356     \n",
+       "2                                          -1.858108     \n",
+       "3                                           0.884645     \n",
+       "4                                          -0.245173     \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_RFP_3_00_256  \\\n",
+       "0                                          -2.032668     \n",
+       "1                                           0.188400     \n",
+       "2                                           0.149212     \n",
+       "3                                           0.187687     \n",
+       "4                                           0.298632     \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_RFP_3_01_256  \\\n",
+       "0                                          -2.252355     \n",
+       "1                                          -0.844323     \n",
+       "2                                          -1.231724     \n",
+       "3                                          -0.199618     \n",
+       "4                                          -0.254777     \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_RFP_3_02_256  \\\n",
+       "0                                          -2.122525     \n",
+       "1                                          -0.846457     \n",
+       "2                                          -1.045144     \n",
+       "3                                          -0.262197     \n",
+       "4                                          -1.608270     \n",
+       "\n",
+       "   Nuclei_Texture_InverseDifferenceMoment_RFP_3_03_256  \\\n",
+       "0                                          -2.177496     \n",
+       "1                                          -0.342180     \n",
+       "2                                          -0.340360     \n",
+       "3                                          -0.310826     \n",
+       "4                                          -0.996690     \n",
+       "\n",
+       "   Nuclei_Texture_SumEntropy_DAPI_3_03_256  \\\n",
+       "0                                -1.448158   \n",
+       "1                                 1.306905   \n",
+       "2                                -1.806753   \n",
+       "3                                -1.312691   \n",
+       "4                                 0.151230   \n",
+       "\n",
+       "   Nuclei_Texture_SumVariance_DAPI_3_01_256  \n",
+       "0                                  0.031227  \n",
+       "1                                  0.344522  \n",
+       "2                                  0.169433  \n",
+       "3                                 -0.182742  \n",
+       "4                                  2.010409  \n",
+       "\n",
+       "[5 rows x 1182 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plate4df[\"Metadata_siRNA\"] = plate4df[\"Metadata_siRNA\"].fillna(\"No siRNA\")\n",
+    "\n",
+    "feat_cols = plate4df.columns[~plate4df.columns.str.contains(\"Metadata\")].tolist()\n",
+    "\n",
+    "plate4df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:49:54.908736Z",
+     "iopub.status.busy": "2024-11-20T20:49:54.908481Z",
+     "iopub.status.idle": "2024-11-20T20:49:54.911271Z",
+     "shell.execute_reply": "2024-11-20T20:49:54.910851Z"
+    },
+    "jukit_cell_id": "ybX13ZBK8Q"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Input Dataframe shape: (60, 1182)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\\nInput Dataframe shape: {plate4df.shape}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jukit_cell_id": "C6AaBtUOCj"
+   },
+   "source": [
+    "# siRNA Comparisons\n",
+    "In this dataset, each instance (row) corresponds to a well.\n",
+    "When using the PairwiseCompareManager the _same_columns and _different_columns parameters must follow three conditions:\n",
+    "1. _same_columns must include at least one list element if _different_columns has less than two list elements.\n",
+    "2. _different_columns must contain one or more list elements.\n",
+    "3. _same_columns and _different_columns should not contain any of the same columns."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jukit_cell_id": "6gx14BlSwz"
+   },
+   "source": [
+    "## Compare between wells and siRNA treatments with the same concentration"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:49:54.912597Z",
+     "iopub.status.busy": "2024-11-20T20:49:54.912443Z",
+     "iopub.status.idle": "2024-11-20T20:50:18.852487Z",
+     "shell.execute_reply": "2024-11-20T20:50:18.851928Z"
+    },
+    "jukit_cell_id": "CRa8vLBRj8"
+   },
+   "outputs": [],
+   "source": [
+    "pearsons_comparator = PearsonsCorrelation()\n",
+    "\n",
+    "comparer = PairwiseCompareManager(\n",
+    "    _df=plate4df.copy(),\n",
+    "    _comparator=pearsons_comparator,\n",
+    "    _different_columns=[\"Metadata_Concentration\", \"Metadata_Well\"],\n",
+    "    _feat_cols=feat_cols,\n",
+    ")\n",
+    "\n",
+    "micdf = comparer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:18.854369Z",
+     "iopub.status.busy": "2024-11-20T20:50:18.854227Z",
+     "iopub.status.idle": "2024-11-20T20:50:18.859557Z",
+     "shell.execute_reply": "2024-11-20T20:50:18.859137Z"
+    },
+    "jukit_cell_id": "cGLOXfXNrN"
+   },
+   "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>pearsons_correlation</th>\n",
+       "      <th>Metadata_Concentration__antehoc_group0</th>\n",
+       "      <th>Metadata_Concentration__antehoc_group1</th>\n",
+       "      <th>Metadata_Well__posthoc_group0</th>\n",
+       "      <th>Metadata_Well__posthoc_group1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>-0.025999</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>E10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>-0.035167</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>E4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.148778</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>E7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.030356</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>F10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>-0.163734</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.001</td>\n",
+       "      <td>B11</td>\n",
+       "      <td>F4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pearsons_correlation  Metadata_Concentration__antehoc_group0  \\\n",
+       "0             -0.025999                                     0.0   \n",
+       "1             -0.035167                                     0.0   \n",
+       "2              0.148778                                     0.0   \n",
+       "3              0.030356                                     0.0   \n",
+       "4             -0.163734                                     0.0   \n",
+       "\n",
+       "   Metadata_Concentration__antehoc_group1 Metadata_Well__posthoc_group0  \\\n",
+       "0                                   0.001                           B11   \n",
+       "1                                   0.001                           B11   \n",
+       "2                                   0.001                           B11   \n",
+       "3                                   0.001                           B11   \n",
+       "4                                   0.001                           B11   \n",
+       "\n",
+       "  Metadata_Well__posthoc_group1  \n",
+       "0                           E10  \n",
+       "1                            E4  \n",
+       "2                            E7  \n",
+       "3                           F10  \n",
+       "4                            F4  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:18.860877Z",
+     "iopub.status.busy": "2024-11-20T20:50:18.860753Z",
+     "iopub.status.idle": "2024-11-20T20:50:18.863242Z",
+     "shell.execute_reply": "2024-11-20T20:50:18.862831Z"
+    },
+    "jukit_cell_id": "qzMvL0c8sb"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Output Dataframe shape: (1485, 5)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\\nOutput Dataframe shape: {micdf.shape}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:18.864585Z",
+     "iopub.status.busy": "2024-11-20T20:50:18.864386Z",
+     "iopub.status.idle": "2024-11-20T20:50:18.866710Z",
+     "shell.execute_reply": "2024-11-20T20:50:18.866284Z"
+    },
+    "jukit_cell_id": "7modF1v2ll"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output Dataframe Columns:\n",
+      "['pearsons_correlation', 'Metadata_Concentration__antehoc_group0', 'Metadata_Concentration__antehoc_group1', 'Metadata_Well__posthoc_group0', 'Metadata_Well__posthoc_group1']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Output Dataframe Columns:\\n{micdf.columns.tolist()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jukit_cell_id": "ucjifXHnJI"
+   },
+   "source": [
+    "## Compare between different siRNAs and Wells at the same concentrations"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:18.868054Z",
+     "iopub.status.busy": "2024-11-20T20:50:18.867853Z",
+     "iopub.status.idle": "2024-11-20T20:50:21.073683Z",
+     "shell.execute_reply": "2024-11-20T20:50:21.073012Z"
+    },
+    "jukit_cell_id": "JPLwsPbVsU"
+   },
+   "outputs": [],
+   "source": [
+    "pearsons_comparator = PearsonsCorrelation()\n",
+    "\n",
+    "comparer = PairwiseCompareManager(\n",
+    "    _df=plate4df.copy(),\n",
+    "    _comparator=pearsons_comparator,\n",
+    "    _same_columns=[\"Metadata_Concentration\"],\n",
+    "    _different_columns=[\"Metadata_siRNA\", \"Metadata_Well\"],\n",
+    "    _feat_cols=feat_cols,\n",
+    ")\n",
+    "\n",
+    "micdf = comparer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:21.075720Z",
+     "iopub.status.busy": "2024-11-20T20:50:21.075582Z",
+     "iopub.status.idle": "2024-11-20T20:50:21.082659Z",
+     "shell.execute_reply": "2024-11-20T20:50:21.082162Z"
+    },
+    "jukit_cell_id": "lFR01EXVLC"
+   },
+   "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>pearsons_correlation</th>\n",
+       "      <th>Metadata_Concentration__antehoc_group0</th>\n",
+       "      <th>Metadata_Concentration__antehoc_group1</th>\n",
+       "      <th>Metadata_siRNA__posthoc_group0</th>\n",
+       "      <th>Metadata_siRNA__posthoc_group1</th>\n",
+       "      <th>Metadata_Well__posthoc_group0</th>\n",
+       "      <th>Metadata_Well__posthoc_group1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.128001</td>\n",
+       "      <td>(0.001,)</td>\n",
+       "      <td>(0.001,)</td>\n",
+       "      <td>NF1 Target 1</td>\n",
+       "      <td>NF1 Target 2</td>\n",
+       "      <td>F10</td>\n",
+       "      <td>G10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.190290</td>\n",
+       "      <td>(0.001,)</td>\n",
+       "      <td>(0.001,)</td>\n",
+       "      <td>NF1 Target 1</td>\n",
+       "      <td>NF1 Target 2</td>\n",
+       "      <td>F10</td>\n",
+       "      <td>G4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.173737</td>\n",
+       "      <td>(0.001,)</td>\n",
+       "      <td>(0.001,)</td>\n",
+       "      <td>NF1 Target 1</td>\n",
+       "      <td>NF1 Target 2</td>\n",
+       "      <td>F10</td>\n",
+       "      <td>G7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.258292</td>\n",
+       "      <td>(0.001,)</td>\n",
+       "      <td>(0.001,)</td>\n",
+       "      <td>NF1 Target 1</td>\n",
+       "      <td>Scramble</td>\n",
+       "      <td>F10</td>\n",
+       "      <td>E10</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.089742</td>\n",
+       "      <td>(0.001,)</td>\n",
+       "      <td>(0.001,)</td>\n",
+       "      <td>NF1 Target 1</td>\n",
+       "      <td>Scramble</td>\n",
+       "      <td>F10</td>\n",
+       "      <td>E4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pearsons_correlation Metadata_Concentration__antehoc_group0  \\\n",
+       "0              0.128001                               (0.001,)   \n",
+       "1              0.190290                               (0.001,)   \n",
+       "2              0.173737                               (0.001,)   \n",
+       "3             -0.258292                               (0.001,)   \n",
+       "4              0.089742                               (0.001,)   \n",
+       "\n",
+       "  Metadata_Concentration__antehoc_group1 Metadata_siRNA__posthoc_group0  \\\n",
+       "0                               (0.001,)                   NF1 Target 1   \n",
+       "1                               (0.001,)                   NF1 Target 1   \n",
+       "2                               (0.001,)                   NF1 Target 1   \n",
+       "3                               (0.001,)                   NF1 Target 1   \n",
+       "4                               (0.001,)                   NF1 Target 1   \n",
+       "\n",
+       "  Metadata_siRNA__posthoc_group1 Metadata_Well__posthoc_group0  \\\n",
+       "0                   NF1 Target 2                           F10   \n",
+       "1                   NF1 Target 2                           F10   \n",
+       "2                   NF1 Target 2                           F10   \n",
+       "3                       Scramble                           F10   \n",
+       "4                       Scramble                           F10   \n",
+       "\n",
+       "  Metadata_Well__posthoc_group1  \n",
+       "0                           G10  \n",
+       "1                            G4  \n",
+       "2                            G7  \n",
+       "3                           E10  \n",
+       "4                            E4  "
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:21.084095Z",
+     "iopub.status.busy": "2024-11-20T20:50:21.083878Z",
+     "iopub.status.idle": "2024-11-20T20:50:21.086579Z",
+     "shell.execute_reply": "2024-11-20T20:50:21.086101Z"
+    },
+    "jukit_cell_id": "EiRceI8zqd"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Output Dataframe shape: (135, 7)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\\nOutput Dataframe shape: {micdf.shape}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:21.087886Z",
+     "iopub.status.busy": "2024-11-20T20:50:21.087685Z",
+     "iopub.status.idle": "2024-11-20T20:50:21.090254Z",
+     "shell.execute_reply": "2024-11-20T20:50:21.089796Z"
+    },
+    "jukit_cell_id": "uHLGwABthL"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output Dataframe Columns:\n",
+      "['pearsons_correlation', 'Metadata_Concentration__antehoc_group0', 'Metadata_Concentration__antehoc_group1', 'Metadata_siRNA__posthoc_group0', 'Metadata_siRNA__posthoc_group1', 'Metadata_Well__posthoc_group0', 'Metadata_Well__posthoc_group1']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Output Dataframe Columns:\\n{micdf.columns.tolist()}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "jukit_cell_id": "K9KWpg6A7A"
+   },
+   "source": [
+    "## Compare between different stains, conditions, and wells\n",
+    "Supplying the _drop_cols parameter excludes wells from the output dataframe, even though wells are used to compare groups."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:21.091752Z",
+     "iopub.status.busy": "2024-11-20T20:50:21.091431Z",
+     "iopub.status.idle": "2024-11-20T20:50:23.281074Z",
+     "shell.execute_reply": "2024-11-20T20:50:23.280435Z"
+    },
+    "jukit_cell_id": "OHRc2Dyid6"
+   },
+   "outputs": [],
+   "source": [
+    "pearsons_comparator = PearsonsCorrelation()\n",
+    "\n",
+    "comparer = PairwiseCompareManager(\n",
+    "    _df=plate4df.copy(),\n",
+    "    _comparator=pearsons_comparator,\n",
+    "    _same_columns=[\"Metadata_Concentration\"],\n",
+    "    _different_columns=[\"Metadata_siRNA\", \"Metadata_Well\"],\n",
+    "    _feat_cols=feat_cols,\n",
+    "    _drop_cols=[\"Metadata_Concentration\", \"Metadata_Well\"],\n",
+    ")\n",
+    "\n",
+    "micdf = comparer()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:23.282755Z",
+     "iopub.status.busy": "2024-11-20T20:50:23.282624Z",
+     "iopub.status.idle": "2024-11-20T20:50:23.288081Z",
+     "shell.execute_reply": "2024-11-20T20:50:23.287679Z"
+    },
+    "jukit_cell_id": "rGAPBpItug"
+   },
+   "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>pearsons_correlation</th>\n",
+       "      <th>Metadata_siRNA__posthoc_group0</th>\n",
+       "      <th>Metadata_siRNA__posthoc_group1</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.128001</td>\n",
+       "      <td>(NF1 Target 1, F10)</td>\n",
+       "      <td>(NF1 Target 2, G10)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.190290</td>\n",
+       "      <td>(NF1 Target 1, F10)</td>\n",
+       "      <td>(NF1 Target 2, G4)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.173737</td>\n",
+       "      <td>(NF1 Target 1, F10)</td>\n",
+       "      <td>(NF1 Target 2, G7)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>-0.258292</td>\n",
+       "      <td>(NF1 Target 1, F10)</td>\n",
+       "      <td>(Scramble, E10)</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.089742</td>\n",
+       "      <td>(NF1 Target 1, F10)</td>\n",
+       "      <td>(Scramble, E4)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   pearsons_correlation Metadata_siRNA__posthoc_group0  \\\n",
+       "0              0.128001            (NF1 Target 1, F10)   \n",
+       "1              0.190290            (NF1 Target 1, F10)   \n",
+       "2              0.173737            (NF1 Target 1, F10)   \n",
+       "3             -0.258292            (NF1 Target 1, F10)   \n",
+       "4              0.089742            (NF1 Target 1, F10)   \n",
+       "\n",
+       "  Metadata_siRNA__posthoc_group1  \n",
+       "0            (NF1 Target 2, G10)  \n",
+       "1             (NF1 Target 2, G4)  \n",
+       "2             (NF1 Target 2, G7)  \n",
+       "3                (Scramble, E10)  \n",
+       "4                 (Scramble, E4)  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "micdf.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:23.289299Z",
+     "iopub.status.busy": "2024-11-20T20:50:23.289187Z",
+     "iopub.status.idle": "2024-11-20T20:50:23.291803Z",
+     "shell.execute_reply": "2024-11-20T20:50:23.291348Z"
+    },
+    "jukit_cell_id": "zZHKZyRQcw"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Output Dataframe shape: (135, 3)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"\\nOutput Dataframe shape: {micdf.shape}\\n\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2024-11-20T20:50:23.293240Z",
+     "iopub.status.busy": "2024-11-20T20:50:23.292861Z",
+     "iopub.status.idle": "2024-11-20T20:50:23.295566Z",
+     "shell.execute_reply": "2024-11-20T20:50:23.295072Z"
+    },
+    "jukit_cell_id": "8G7ZiPRvd8"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Output Dataframe Columns:\n",
+      "['pearsons_correlation', 'Metadata_siRNA__posthoc_group0', 'Metadata_siRNA__posthoc_group1']\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"Output Dataframe Columns:\\n{micdf.columns.tolist()}\")"
+   ]
+  }
+ ],
+ "metadata": {
+  "anaconda-cloud": {},
+  "kernelspec": {
+   "display_name": "python",
+   "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": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/poetry.lock b/poetry.lock
new file mode 100644
index 0000000..67cc940
--- /dev/null
+++ b/poetry.lock
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+]
+
+[package.extras]
+docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
+optional = ["python-socks", "wsaccel"]
+test = ["websockets"]
+
+[metadata]
+lock-version = "2.0"
+python-versions = ">=3.10,<=3.11"
+content-hash = "76f5c92854154a1603f7ea72947f9c97f9d8a9461695c10d9a877e38f4111fa6"
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 0000000..a399796
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,33 @@
+[tool.poetry]
+name = "pairwise-compare"
+version = "0.1.0"
+description = ""
+authors = ["Your Name <you@example.com>"]
+license = "CC0 1.0 Universal"
+readme = "README.md"
+
+packages = [
+    { include = "comparison_tools", from = "src" },
+    { include = "comparators", from = "src" },
+]
+
+[tool.poetry.dependencies]
+python = ">=3.10,<=3.11"
+pathlib2 = "^2.3.7.post1"
+pandas = "^2.2.3"
+setuptools = ">=14.0"
+numpy = "^2.1.3"
+pyarrow = "^18.0.0"
+
+
+[tool.poetry.group.dev.dependencies]
+pytest = "^8.3.3"
+pynvim = "^0.5.1"
+ipython = "^8.29.0"
+matplotlib = "^3.9.2"
+pylint = "^3.3.1"
+notebook = "^7.2.2"
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"
diff --git a/src/comparators/Comparator.py b/src/comparators/Comparator.py
new file mode 100644
index 0000000..f8ca65e
--- /dev/null
+++ b/src/comparators/Comparator.py
@@ -0,0 +1,41 @@
+from abc import ABC, abstractmethod
+from collections import defaultdict
+
+import pandas as pd
+
+
+class Comparator(ABC):
+    """
+    This is a template class for how comparator modules should be structured to make comparisons.
+    When inheriting from this template class ensure this class's constructor is called.
+    """
+
+    def __init__(self):
+        """
+        Store comparisons in self._comparisons to eventually be converted to a pandas datafram, where:
+        1. Each key is a column name.
+        2. The index at each position in each list corresponds to a row.
+        """
+
+        self._comparisons = defaultdict(list)
+
+    @property
+    def comparisons(self):
+        return self._comparisons
+
+    @abstractmethod
+    def save_groups(self, _group_cols: list[str], **_groups: dict[str, pd.DataFrame]):
+        """
+        Save tracked column values defining comparison groups.
+        Tracked columns include columns used to compare groups.
+        """
+        pass
+
+    @abstractmethod
+    def __call__(self, _group0: pd.DataFrame, _group1: pd.DataFrame):
+        """
+        This function is intended for:
+        1. Comparing the two dataframes.
+        2. Saving the result in the self._comparisons data structure, which is later converted to a pandas dataframe.
+        """
+        pass
diff --git a/src/comparators/PearsonsCorrelation.py b/src/comparators/PearsonsCorrelation.py
new file mode 100644
index 0000000..58e5a42
--- /dev/null
+++ b/src/comparators/PearsonsCorrelation.py
@@ -0,0 +1,58 @@
+from collections import defaultdict
+from typing import Optional
+
+import numpy as np
+import pandas as pd
+from comparators.Comparator import Comparator
+
+
+class PearsonsCorrelation(Comparator):
+    """Computes and stores group and Pearsons Correlation data between paired groups."""
+
+    def __init__(self, _comparison_name: str = "pearsons_correlation"):
+
+        super().__init__()
+
+        self._comparison_name = _comparison_name
+
+    def _preprocess_data(self):
+        self._group0, self._group1 = self._group0.values, self._group1.values
+
+    def save_groups(self, _group_cols: list[str], **_groups: dict[str, pd.DataFrame]):
+        """Save column values defining comparison groups"""
+
+        comparison_count = self._group0.shape[0] * self._group1.shape[0]
+
+        for idx, col in enumerate(_group_cols):
+            for group_name, group in _groups.items():
+                if len(_group_cols) > 1:
+                    self._comparisons[f"{col}__{group_name}"].extend(
+                        [group[idx]] * comparison_count
+                    )
+                else:
+                    self._comparisons[f"{col}__{group_name}"].extend(
+                        [group] * comparison_count
+                    )
+
+    @property
+    def comparisons(self):
+        return self._comparisons
+
+    def __call__(self, _group0: pd.DataFrame, _group1: pd.DataFrame):
+
+        self._group0, self._group1 = _group0, _group1
+        self._preprocess_data()
+
+        group0_centered = self._group0 - self._group0.mean(axis=1, keepdims=True)
+        group1_centered = self._group1 - self._group1.mean(axis=1, keepdims=True)
+
+        dot_products = np.dot(group0_centered, group1_centered.T)
+
+        group0_norms = np.linalg.norm(group0_centered, axis=1, keepdims=True)
+        group1_norms = np.linalg.norm(group1_centered, axis=1, keepdims=True)
+
+        norm_products = np.dot(group0_norms, group1_norms.T)
+
+        correlations = (dot_products / norm_products).flatten()
+
+        self._comparisons[self._comparison_name].extend(correlations.tolist())
diff --git a/src/comparators/__init__.py b/src/comparators/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/src/comparison_tools/PairwiseCompare.py b/src/comparison_tools/PairwiseCompare.py
new file mode 100644
index 0000000..11a9eba
--- /dev/null
+++ b/src/comparison_tools/PairwiseCompare.py
@@ -0,0 +1,273 @@
+import warnings
+from collections.abc import Iterable
+from itertools import combinations, product
+from typing import Optional
+
+import pandas as pd
+from comparators.Comparator import Comparator
+
+
+class PairwiseCompare:
+    """
+    Compute inter or intra group comparisons between objects.
+    """
+
+    def __init__(
+        self,
+        _df: pd.DataFrame,
+        _comparator: Comparator,
+        _antehoc_group_cols: list[str],
+        _posthoc_group_cols: list[str],
+        _feat_cols: list[str],
+        _one_different_comparison: bool = False,
+        _drop_cols: Optional[list[str]] = None,
+    ):
+        """
+        Perform Input validation and initializations.
+
+        Parameters
+        ----------
+        _df:
+            Contains the features and group columns to use for comparing paired data.
+
+        _comparator:
+            Defines how to perform and save comparisons.
+
+        _antehoc_group_cols:
+            Columns to groupby before organizing by _posthoc_group_cols.
+
+        _feat_cols:
+            Feature columns for creating comparisons.
+
+        _posthoc_group_cols:
+            Columns to groupby after organizing by _antehoc_group_cols.
+
+        _one_different_comparison:
+            Type of comparison. If True, only one column value needs to be different between two groups for both group to be considered different.
+            Otherwise, all column values need to be different for both groups to be considered different.
+
+        _drop_cols:
+            Columns to not save in the final output.
+        """
+
+        if not isinstance(_df, pd.DataFrame):
+            raise TypeError("Expected a Pandas DataFrame.")
+
+        if not isinstance(_comparator, Comparator):
+            raise TypeError("Expected type Comparator.")
+
+        self.__is_iterable_with_strings(_antehoc_group_cols)
+        self.__is_iterable_with_strings(_posthoc_group_cols)
+        self.__is_iterable_with_strings(_feat_cols)
+
+        if _df.empty:
+            raise ValueError("DataFrame is empty")
+
+        missing_cols = (
+            set(_antehoc_group_cols) | set(_posthoc_group_cols) | set(_feat_cols)
+        ) - set(_df.columns)
+
+        if missing_cols:
+            raise ValueError(f"Missing columns {missing_cols}")
+
+        if any(not pd.api.types.is_numeric_dtype(_df[col].dtype) for col in _feat_cols):
+            raise TypeError(
+                "At least one of the feature columns is not a numerical data type."
+            )
+
+        if _df.isna().any().any():
+            warnings.warn("DataFrame contains NaNs")
+
+        if not _drop_cols:
+            self.__drop_cols = []
+
+        else:
+            self.__is_iterable_with_strings(_drop_cols)
+            self.__drop_cols = _drop_cols
+
+        self.__df = _df
+        self.__comparator = _comparator
+        self.__feat_cols = _feat_cols
+        self.__one_different_comparison = _one_different_comparison
+
+        group_suffixes = ("_group0", "_group1")
+
+        self.__posthoc_group_names = [f"posthoc{suffix}" for suffix in group_suffixes]
+        self.__antehoc_group_names = [f"antehoc{suffix}" for suffix in group_suffixes]
+
+        self.__antehoc_group_cols = _antehoc_group_cols
+        self.__posthoc_group_cols = _posthoc_group_cols
+
+        self.__filtered_antehoc_group_cols = self.__get_group_fields(
+            _group_cols=self.__antehoc_group_cols,
+        )
+
+        self.__filtered_posthoc_group_cols = self.__get_group_fields(
+            _group_cols=self.__posthoc_group_cols,
+        )
+
+    def __warn_empty_comparisons(self, _comparison_type_name):
+
+        warnings.warn(f"{_comparison_type_name} were empty", UserWarning)
+
+    def __is_iterable_with_strings(self, _data_structure):
+
+        prefix_msg = "Expected an Iterable of Strings."
+
+        if isinstance(_data_structure, str):
+            raise TypeError(f"{prefix_msg} Data is of type String.")
+
+        elif not isinstance(_data_structure, Iterable):
+            raise TypeError(f"{prefix_msg} Data is not an Iterable.")
+
+        elif isinstance(_data_structure, Iterable):
+            if any(not isinstance(element, str) for element in _data_structure):
+                raise TypeError(f"{prefix_msg} Data in Iterable is not of type String.")
+
+    def __get_group_fields(self, _group_cols):
+        """Get group fields after removing dropped columns."""
+
+        return [
+            group_col for group_col in _group_cols if group_col not in self.__drop_cols
+        ]
+
+    def __contains_match(self, _groups):
+        """Check if the same features between both groups are the same value."""
+
+        if not self.__one_different_comparison:
+            if len(self.__posthoc_group_cols) == 1:
+                if _groups[0] == _groups[1]:
+                    return True
+
+            else:
+                if any(_groups[0][i] == _groups[1][i] for i in range(len(_groups[0]))):
+                    return True
+
+        return False
+
+    def inter_comparisons(self):
+        """
+        Computes comparisons between two pandas rows using both post hoc and ante groups.
+        This is accomplished by computing comparisons between these rows in different ante groups (inter comparisons) and between all possible post hoc comparisons between those groups.
+        """
+
+        groupdf = self.__df.groupby(self.__antehoc_group_cols)
+
+        # Retrieve keys for the ante group
+        gkeys = groupdf.groups.keys()
+
+        # Find all possible combinations of size 2 ante group keys
+        apairs = list(combinations(gkeys, 2))
+
+        # Iterate through each ante group combination
+        for apair in apairs:
+
+            if self.__contains_match(apair):
+                continue
+
+            apair0 = apair[0]
+            apair1 = apair[1]
+
+            # Avoids a future deprecation in the pandas get_group method
+            if not isinstance(apair0, tuple) or not isinstance(apair1, tuple):
+                apair0 = (apair0,)
+                apair1 = (apair1,)
+
+            # Extract the keys for the first post hoc group
+            group0df = groupdf.get_group(apair0).copy()
+            group0df = group0df.groupby(self.__posthoc_group_cols)[self.__feat_cols]
+            group0_keys = group0df.groups.keys()
+
+            # Extract the keys for the second post hoc group
+            group1df = groupdf.get_group(apair1).copy()
+            group1df = group1df.groupby(self.__posthoc_group_cols)[self.__feat_cols]
+            group1_keys = group1df.groups.keys()
+
+            comparison_key_product = list(product(group0_keys, group1_keys))
+
+            if not comparison_key_product:
+                self.__warn_empty_comparisons(_comparison_type_name="Inter Comparisons")
+                continue
+
+            # Iterate through each well group cartesian product and save the data
+            for ppair in comparison_key_product:
+
+                if self.__contains_match(ppair):
+                    continue
+
+                ppair0 = ppair[0]
+                ppair1 = ppair[1]
+
+                # Avoids a future deprecation in the pandas get_group method
+                if not isinstance(ppair0, tuple) or not isinstance(ppair1, tuple):
+                    ppair0 = (ppair0,)
+                    ppair1 = (ppair1,)
+
+                self.__comparator(
+                    group0df.get_group(ppair0), group1df.get_group(ppair1)
+                )
+
+                self.__comparator.save_groups(
+                    self.__antehoc_group_cols,
+                    **dict(zip(self.__antehoc_group_names, apair)),
+                )
+
+                self.__comparator.save_groups(
+                    self.__posthoc_group_cols,
+                    **dict(zip(self.__posthoc_group_names, ppair)),
+                )
+
+    def intra_comparisons(self):
+        """
+        Computes comparisons between two pandas rows using both post hoc and ante groups.
+        This is accomplished by computing comparisons between these rows only in the same ante group (intra comparisons), but between different post hoc groups.
+        """
+
+        groupdf = self.__df.groupby(self.__antehoc_group_cols)
+
+        # Retrieve keys for the ante group
+        akeys = groupdf.groups.keys()
+
+        # Iterate through each ante group combination
+        for agroup in akeys:
+
+            # Avoids a future deprecation in the pandas get_group method
+            if not isinstance(agroup, tuple):
+                agroup = (agroup,)
+
+            # Extract keys for poshoc group columns
+            group = groupdf.get_group(agroup).copy()
+            group = group.groupby(self.__posthoc_group_cols)[self.__feat_cols]
+            group_keys = group.groups.keys()
+
+            comparison_key_combinations = list(combinations(group_keys, 2))
+
+            if not comparison_key_combinations:
+                self.__warn_empty_comparisons(_comparison_type_name="Intra Comparisons")
+                continue
+
+            # Iterate through the combinations pairs of the groups
+            for ppair in comparison_key_combinations:
+
+                if self.__contains_match(ppair):
+                    continue
+
+                ppair0 = ppair[0]
+                ppair1 = ppair[1]
+
+                # Avoids a future deprecation in the pandas get_group method
+                if not isinstance(ppair0, tuple) or not isinstance(ppair1, tuple):
+                    ppair0 = (ppair0,)
+                    ppair1 = (ppair1,)
+
+                self.__comparator(group.get_group(ppair0), group.get_group(ppair1))
+
+                self.__comparator.save_groups(
+                    self.__filtered_antehoc_group_cols,
+                    **dict(zip(self.__antehoc_group_names, (agroup, agroup))),
+                )
+
+                self.__comparator.save_groups(
+                    self.__filtered_posthoc_group_cols,
+                    **dict(zip(self.__posthoc_group_names, ppair)),
+                )
diff --git a/src/comparison_tools/PairwiseCompareManager.py b/src/comparison_tools/PairwiseCompareManager.py
new file mode 100644
index 0000000..1f595ec
--- /dev/null
+++ b/src/comparison_tools/PairwiseCompareManager.py
@@ -0,0 +1,89 @@
+from typing import Optional
+
+import pandas as pd
+from comparators.Comparator import Comparator
+
+from comparison_tools.PairwiseCompare import PairwiseCompare
+
+
+class PairwiseCompareManager:
+    """
+    Allows the user to make comparisons between groups in a pandas dataframe as specified by the columns defining the comparison groups.
+    See the function parameter descriptions of the constructor, and the README for more info.
+    """
+
+    def __init__(
+        self,
+        _df: pd.DataFrame,
+        _different_columns: list[str],
+        _feat_cols: list[str],
+        _comparator: Comparator,
+        _same_columns: Optional[list[str]] = None,
+        _drop_cols: Optional[list[str]] = None,
+    ):
+        """
+        _df: Data to compare, which must contain the specified columns passed as arguments. All rows in this dataframe will be compared at least once.
+        _different_columns: The values of these columns must all be different when comparing any two groups.
+        _feat_cols: Feature columns to be used in comparisons from _df.
+        _comparator: The module to use for comparing data.
+        _same_columns: The values of these columns will all be the same when comparing any two groups.
+        _drop_cols: Columns in the final output datarame, which are excluded.
+        """
+
+        # Ensure no duplicate columns
+        _feat_cols = list(set(_feat_cols))
+
+        if _same_columns:
+            _same_columns = list(set(_same_columns))
+
+        if _different_columns:
+            _different_columns = list(set(_different_columns))
+
+        if _drop_cols:
+            _drop_cols = list(set(_drop_cols))
+
+        if _same_columns and _different_columns:
+            if len(set(_same_columns) & set(_different_columns)) > 0:
+                raise ValueError(
+                    "_same_columns and _different_columns cannot have any columns in common."
+                )
+
+        if not _same_columns and len(_different_columns) == 1:
+            raise ValueError(
+                "Must specify at least two different columns or at least one different column and at least one same column."
+            )
+
+        if len(_feat_cols) == 0:
+            raise ValueError(
+                "You must specify at least one feature to compare between samples"
+            )
+
+        self._comparator = _comparator
+
+        if len(_different_columns) >= 2 and not _same_columns:
+            pairwise_compare_obj = PairwiseCompare(
+                _df=_df,
+                _comparator=_comparator,
+                _antehoc_group_cols=_different_columns[:1],
+                _posthoc_group_cols=_different_columns[1:],
+                _feat_cols=_feat_cols,
+                _drop_cols=_drop_cols,
+            )
+
+            pairwise_compare_obj.inter_comparisons()
+
+        elif len(_different_columns) >= 1 and len(_same_columns) >= 1:
+            pairwise_compare_obj = PairwiseCompare(
+                _df=_df,
+                _comparator=_comparator,
+                _antehoc_group_cols=_same_columns,
+                _posthoc_group_cols=_different_columns,
+                _feat_cols=_feat_cols,
+                _drop_cols=_drop_cols,
+            )
+
+            pairwise_compare_obj.intra_comparisons()
+
+    def __call__(self):
+        """Final comparisons dataframe."""
+        return pd.DataFrame(self._comparator.comparisons)
diff --git a/src/comparison_tools/__init__.py b/src/comparison_tools/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/tests/test_correctness_pairwise_compare_manager.py b/tests/test_correctness_pairwise_compare_manager.py
new file mode 100644
index 0000000..04cac6d
--- /dev/null
+++ b/tests/test_correctness_pairwise_compare_manager.py
@@ -0,0 +1,149 @@
+import pathlib
+import sys
+
+import pandas as pd
+import pytest
+
+from comparators.PearsonsCorrelation import PearsonsCorrelation
+from comparison_tools.PairwiseCompareManager import PairwiseCompareManager
+
+# Paths to original nuclear speckle data
+data_dir = pathlib.Path(
+    "data"
+).resolve(strict=True)
+
+for file in data_dir.iterdir():
+    if file.is_file():
+        print(file)
+
+plate4df = pd.read_parquet(data_dir / "Plate_4_bulk_feature_selected.parquet")
+
+plate4df["Metadata_siRNA"] = plate4df["Metadata_siRNA"].fillna("No siRNA")
+
+feat_cols = plate4df.columns[~plate4df.columns.str.contains("Metadata")].tolist()
+
+
+# Compute comparison outputs with the PairwiseCompareManager
+@pytest.fixture
+def test_data(request):
+
+    case = request.param
+
+    comparer = PairwiseCompareManager(
+        _df=plate4df.copy(),
+        _comparator=case["_comparator"],
+        _same_columns=case["_same_columns"],
+        _different_columns=case["_different_columns"],
+        _feat_cols=feat_cols,
+        _drop_cols=case["_drop_cols"],
+    )
+
+    case["_comparer"] = comparer()
+
+    return case
+
+
+# Specify test cases
+@pytest.mark.parametrize(
+    "test_data",
+    [
+        (
+            {
+                "_comparator": PearsonsCorrelation(),
+                "_same_columns": ["Metadata_Concentration"],
+                "_different_columns": ["Metadata_siRNA", "Metadata_Well"],
+                "_drop_cols": ["Metadata_Concentration", "Metadata_Well"],
+            }
+        ),
+        (
+            {
+                "_comparator": PearsonsCorrelation(),
+                "_same_columns": None,
+                "_different_columns": ["Metadata_siRNA", "Metadata_Well"],
+                "_drop_cols": None,
+            }
+        ),
+        (
+            {
+                "_comparator": PearsonsCorrelation(),
+                "_same_columns": ["Metadata_Concentration"],
+                "_different_columns": ["Metadata_siRNA"],
+                "_drop_cols": ["Metadata_Concentration"],
+            }
+        ),
+        (
+            {
+                "_comparator": PearsonsCorrelation(),
+                "_same_columns": ["Metadata_Concentration"],
+                "_different_columns": ["Metadata_siRNA"],
+                "_drop_cols": None,
+            }
+        ),
+    ],
+    indirect=True,
+)
+def test_dataframe_shape_correct(test_data: dict):
+    """Tests if the output dataframe contains the correct number of rows and columns."""
+
+    total_number_of_comparisons = 0
+
+    def number_of_comparisons(_df: pd.DataFrame):
+        """Calculate the number of expected comparisons."""
+
+        number_of_comparisons = 0
+
+        numberdf_samples = _df.shape[0]
+
+        for first_row in range(numberdf_samples - 1):
+            for second_row in range(first_row + 1, numberdf_samples):
+                groupsdf = _df.iloc[[first_row, second_row]].copy()
+                if (
+                    not groupsdf[test_data["_different_columns"]]
+                    .apply(lambda col: col.duplicated(keep=False))
+                    .any()
+                    .any()
+                ):
+                    number_of_comparisons += 1
+
+        return number_of_comparisons
+
+    # Calculate the expected number of comparisons if _same_columns is specified
+    if test_data["_same_columns"]:
+        for _, urow in plate4df.drop_duplicates(
+            subset=test_data["_same_columns"]
+        ).iterrows():
+            same_df = plate4df[
+                (
+                    plate4df[test_data["_same_columns"]]
+                    == urow[test_data["_same_columns"]]
+                ).all(axis=1)
+            ]
+            total_number_of_comparisons += number_of_comparisons(same_df)
+
+    else:
+        total_number_of_comparisons += number_of_comparisons(plate4df)
+
+    assert test_data["_comparer"].shape[0] == total_number_of_comparisons
+
+    if not test_data["_drop_cols"]:
+        number_drop_cols = 0
+
+    else:
+        number_drop_cols = len(test_data["_drop_cols"])
+
+    if not test_data["_same_columns"]:
+        number_same_columns = 0
+
+    else:
+        number_same_columns = len(test_data["_same_columns"])
+
+    # There are 2 of each column, but only one comparison column
+    assert (
+        2
+        * (
+            number_same_columns
+            + len(test_data["_different_columns"])
+            - number_drop_cols
+        )
+        == test_data["_comparer"].shape[1] - 1
+    )
diff --git a/tests/test_fault_injections_compare_manager.py b/tests/test_fault_injections_compare_manager.py
new file mode 100644
index 0000000..2d615a9
--- /dev/null
+++ b/tests/test_fault_injections_compare_manager.py
@@ -0,0 +1,66 @@
+import pathlib
+import sys
+
+import pandas as pd
+import pytest
+
+from comparators.PearsonsCorrelation import PearsonsCorrelation
+from comparison_tools.PairwiseCompareManager import PairwiseCompareManager
+
+# Paths to original nuclear speckle data
+data_dir = pathlib.Path(
+    "data"
+).resolve(strict=True)
+
+plate4df = pd.read_parquet(data_dir / "Plate_4_bulk_feature_selected.parquet")
+
+plate4df["Metadata_siRNA"] = plate4df["Metadata_siRNA"].fillna("No siRNA")
+
+feat_cols = plate4df.columns[~plate4df.columns.str.contains("Metadata")].tolist()
+
+
+# Specify tests cases
+@pytest.mark.parametrize(
+    "case",
+    [
+        {
+            "_comparator": PearsonsCorrelation(),
+            "_same_columns": ["Metadata_Concentration"],
+            "_different_columns": None,
+            "_feat_cols": feat_cols,
+            "_drop_cols": ["Metadata_Concentration", "Metadata_Well"],
+        },
+        {
+            "_comparator": PearsonsCorrelation(),
+            "_same_columns": None,
+            "_different_columns": ["Metadata_siRNA"],
+            "_feat_cols": feat_cols,
+            "_drop_cols": None,
+        },
+        {
+            "_comparator": PearsonsCorrelation(),
+            "_same_columns": ["Metadata_Concentration"],
+            "_different_columns": ["Metadata_siRNA"],
+            "_feat_cols": None,
+            "_drop_cols": None,
+        },
+        {
+            "_comparator": PearsonsCorrelation(),
+            "_same_columns": ["Metadata_Concentration"],
+            "_different_columns": ["Metadata_siRNA", "Metadata_Concentration"],
+            "_feat_cols": feat_cols,
+            "_drop_cols": None,
+        },
+    ],
+)
+def test_dataframe_shape_correct(case):
+    # Pass the test if an exception is raised
+    with pytest.raises(Exception):
+        comparer = PairwiseCompareManager(
+            _df=plate4df.copy(),
+            _comparator=case["_comparator"],
+            _same_columns=case["_same_columns"],
+            _different_columns=case["_different_columns"],
+            _feat_cols=case["_feat_cols"],
+            _drop_cols=case["_drop_cols"],
+        )