diff --git a/.gitignore b/.gitignore
index 0de5822..de22cfd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,6 +8,9 @@
 # ignore pycache
 __pycache__
 
+# ignore corrected plates if they are put into a separate folder
+Corrected_Images
+
 # ignore corrected plates images (like in download data)
 1.cellprofiler_ic/Corrected_Plate_1
 1.cellprofiler_ic/Corrected_Plate_2
@@ -17,6 +20,7 @@ __pycache__
 
 # ignore log files
 *logs
+*.logs
 
 # ignore Mac related files
 .DS_Store
diff --git a/1.cellprofiler_ic/NF1_illum_Plates_1_2.cppipe b/1.cellprofiler_ic/NF1_illum_3channel.cppipe
similarity index 88%
rename from 1.cellprofiler_ic/NF1_illum_Plates_1_2.cppipe
rename to 1.cellprofiler_ic/NF1_illum_3channel.cppipe
index 81f7a3d..ff2038e 100644
--- a/1.cellprofiler_ic/NF1_illum_Plates_1_2.cppipe
+++ b/1.cellprofiler_ic/NF1_illum_3channel.cppipe
@@ -5,12 +5,12 @@ GitHash:
 ModuleCount:11
 HasImagePlaneDetails:False
 
-Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Images module is blank since we are adding the path to the images in the CLI']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     :
     Filter images?:Images only
     Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\/]\\.")
 
-Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['No metadata is extracted during illumination correction']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Extract metadata?:No
     Metadata data type:Text
     Metadata types:{}
@@ -27,7 +27,7 @@ Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_win
     Metadata file name:None
     Does cached metadata exist?:No
 
-NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['In this pipeline, we only have three channels to correct:', '', 'DAPI', 'GFP', 'RFP']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Assign a name to:Images matching rules
     Select the image type:Grayscale image
     Name to assign these images:DNA
@@ -60,7 +60,7 @@ NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|sho
     Set intensity range from:Image metadata
     Maximum intensity:255.0
 
-Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['We do not use the Groups module']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Do you want to group your images?:No
     grouping metadata count:1
     Metadata category:None
@@ -156,7 +156,7 @@ CorrectIlluminationApply:[module_num:8|svn_version:'Unknown'|variable_revision_n
     Set output image values less than 0 equal to 0?:Yes
     Set output image values greater than 1 equal to 1?:Yes
 
-SaveImages:[module_num:9|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:['Save DAPI images as same tiff bit depth']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+SaveImages:[module_num:9|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:['Save DAPI images as same bit depth']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select the type of image to save:Image
     Select the image to save:CorrDAPI
     Select method for constructing file names:From image filename
diff --git a/1.cellprofiler_ic/NF1_illum_Plate3_Plate3prime.cppipe b/1.cellprofiler_ic/NF1_illum_4channel.cppipe
similarity index 90%
rename from 1.cellprofiler_ic/NF1_illum_Plate3_Plate3prime.cppipe
rename to 1.cellprofiler_ic/NF1_illum_4channel.cppipe
index 4b46540..4b3cc6e 100644
--- a/1.cellprofiler_ic/NF1_illum_Plate3_Plate3prime.cppipe
+++ b/1.cellprofiler_ic/NF1_illum_4channel.cppipe
@@ -5,12 +5,12 @@ GitHash:
 ModuleCount:13
 HasImagePlaneDetails:False
 
-Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Images module is blank in the GUI since we are adding the image path in CLI.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     :
     Filter images?:Images only
     Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\/]\\.")
 
-Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['The Metadata module optionally allows you to extract information describing your images (i.e, metadata) which will be stored along with your measurements. This information can be contained in the file name and/or location, or in an external file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['We do not extract metadata when performing illumination correction']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Extract metadata?:No
     Metadata data type:Text
     Metadata types:{}
@@ -27,7 +27,7 @@ Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_win
     Metadata file name:None
     Does cached metadata exist?:No
 
-NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['This pipeline is meant for correction with 4 channels specifically:', '', 'DAPI', 'GFP', 'RFP', 'CY5', '']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Assign a name to:Images matching rules
     Select the image type:Grayscale image
     Name to assign these images:DNA
@@ -66,7 +66,7 @@ NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|sho
     Set intensity range from:Image metadata
     Maximum intensity:255.0
 
-Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['We do not use the Groups module.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Do you want to group your images?:No
     grouping metadata count:1
     Metadata category:None
@@ -171,7 +171,7 @@ CorrectIlluminationCalculate:[module_num:8|svn_version:'Unknown'|variable_revisi
     Maximum number of iterations:40
     Residual value for convergence:0.001
 
-CorrectIlluminationApply:[module_num:9|svn_version:'Unknown'|variable_revision_number:5|show_window:False|notes:['Apply illumination correction functions to all channel groups']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+CorrectIlluminationApply:[module_num:9|svn_version:'Unknown'|variable_revision_number:5|show_window:False|notes:['Apply illumination correction functions to all channels']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select the input image:OrigDAPI
     Name the output image:CorrDAPI
     Select the illumination function:IllumDAPI
@@ -251,7 +251,7 @@ SaveImages:[module_num:12|svn_version:'Unknown'|variable_revision_number:16|show
     How to save the series:T (Time)
     Save with lossless compression?:Yes
 
-SaveImages:[module_num:13|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:['Save CY5 images as the same bit-deph']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+SaveImages:[module_num:13|svn_version:'Unknown'|variable_revision_number:16|show_window:False|notes:['Save CY5 images as the same bit-depth']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select the type of image to save:Image
     Select the image to save:CorrMito
     Select method for constructing file names:From image filename
diff --git a/1.cellprofiler_ic/README.md b/1.cellprofiler_ic/README.md
index 7ae411f..709f250 100644
--- a/1.cellprofiler_ic/README.md
+++ b/1.cellprofiler_ic/README.md
@@ -3,6 +3,16 @@
 In this module, we perform illumination correction (IC) on images for each plate and save the corrected images into new folders. 
 Images are saved as 16-bit depth, which is the same as the raw data.
 
+## CellProfiler Pipeline
+
+Due to there being differences in channel number between plates, we have two different illumination correction pipelines.
+
+1. [3 channel pipeline](./NF1_illum_3channel.cppipe) -> This pipeline is used only with Plates 1 and 2 since these first pilot plates only had 3 channels.
+2. [4 channel pipeline](./NF1_illum_4channel.cppipe) -> This pipeline is used for all the rest of the plates (and future plates) as we have established the protocol for staining. 
+
+**Note:** The parameters for correction between pipelines in the same channel might be slightly different. 
+But, the parameters in both perform and output the best corrected images at this point
+
 ## Run the `nf1_ic` notebook
 
 To calculate and apply an IC function on each channel, run the [nf1_ic.ipynb](nf1_ic.ipynb) notebook as a python script using the code block below:
@@ -15,6 +25,20 @@ cd 1.cellprofiler_ic
 source nf1_ic.sh
 ```
 
+## CellProfiler Parallel
+
+To improve the speed for correcting the images, we have implemented `CellProfiler Parallel`, which utilizes multi-processing to run one plate per CPU core.
+
+### Speed when running CellProfiler Parallel
+
+To run **five plates** through illumination correction, it took approximately **1 hour** in total on a computer using Pop_OS! 22.04 LTS with an AMD Ryzen 7 3700X 8-Core Processor.
+
+This means that we are saving 2 hours (assuming the fifth plate running sequentially would take another hour totalling 3 hours) when running five plates.
+
+### Speed when running CellProfiler sequentially
+
+In the past, we ran one command per plate in seqential order (e.g., one plate is ran and once it finishes the next plate starts).
+
 For four plates, it took about 2 hours to calculate, apply, and save illumination corrected images on a computer using Pop_OS! 22.04 LTS with an AMD Ryzen 7 3700X 8-Core Processor.
 
 For three plates, it took about 18 hours to calculate and save illumination corrected images on a computer using MacOS Ventura 13.2.1 with the M2 chip.
diff --git a/1.cellprofiler_ic/nf1_ic.ipynb b/1.cellprofiler_ic/nf1_ic.ipynb
index 64285fc..5bf0b42 100644
--- a/1.cellprofiler_ic/nf1_ic.ipynb
+++ b/1.cellprofiler_ic/nf1_ic.ipynb
@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Correct illumination and save images for each plate using CellProfiler"
+    "# Correct illumination and save images for each plate using CellProfiler Parallel"
    ]
   },
   {
@@ -22,11 +22,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
     "import pathlib\n",
+    "import pprint\n",
+    "\n",
+    "import sys\n",
     "\n",
-    "sys.path.append(\"../\")\n",
-    "from utils import cp_utils"
+    "sys.path.append(\"../utils\")\n",
+    "import cp_parallel"
    ]
   },
   {
@@ -34,9 +36,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Set paths for each plate\n",
-    "\n",
-    "Note: Output file path does not need to be strict since the `run_cellprofiler` function can create the output folder directory if it doesn't already exist. The other paths must be strict since these files should already exist for CellProfiler to run. The output directory doesn't need to already exist."
+    "## Set paths and variables"
    ]
   },
   {
@@ -45,48 +45,88 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "plates_info_dictionary = {\n",
-    "    \"Plate_1\": {\n",
-    "        # this pipeline is specific to plates 1 and 2 (due to channel difference from plates 3 and 3 prime)\n",
-    "        \"path_to_pipeline\": pathlib.Path(\"NF1_illum_Plates_1_2.cppipe\").resolve(\n",
-    "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_images\": pathlib.Path(\"../0.download_data/Plate_1/\").resolve(\n",
-    "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_output\": pathlib.Path(\"Corrected_Plate_1\").resolve(),\n",
-    "    },\n",
-    "    \"Plate_2\": {\n",
-    "        # this pipeline is specific to plates 1 and 2 (due to channel difference from plates 3 and 3 prime)\n",
-    "        \"path_to_pipeline\": pathlib.Path(\"NF1_illum_Plates_1_2.cppipe\").resolve(\n",
-    "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_images\": pathlib.Path(\"../0.download_data/Plate_2/\").resolve(\n",
-    "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_output\": pathlib.Path(\"Corrected_Plate_2\").resolve(),\n",
-    "    },\n",
-    "    \"Plate_3\": {\n",
-    "        # this pipeline is specific to plates 3 and 3 prime (due to channel difference from plates 1 and 2)\n",
-    "        \"path_to_pipeline\": pathlib.Path(\"NF1_illum_Plate3_Plate3prime.cppipe\").resolve(\n",
-    "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_images\": pathlib.Path(\"../0.download_data/Plate_3/\").resolve(\n",
+    "# set the run type for the parallelization\n",
+    "run_name = \"illum_correction\"\n",
+    "\n",
+    "# set path for pipeline for all plates for whole image analysis\n",
+    "path_to_pipeline = pathlib.Path(\"./pipelines/nuclei_analysis.cppipe\").resolve()\n",
+    "\n",
+    "# set main output dir for all plates\n",
+    "output_dir = pathlib.Path(\"./Corrected_Images\")\n",
+    "output_dir.mkdir(exist_ok=True)\n",
+    "\n",
+    "# directory where images are located within folders\n",
+    "images_dir = pathlib.Path(\"../0.download_data/\")\n",
+    "\n",
+    "# list for plate names based on folders to use to create dictionary\n",
+    "plate_names = []\n",
+    "# iterate through 0.download_data and append plate names from folder names that contain image data from that plate\n",
+    "for file_path in pathlib.Path(\"../0.download_data/\").iterdir():\n",
+    "    if str(file_path.stem).startswith(\"Plate\"):\n",
+    "        plate_names.append(str(file_path.stem))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create dictionary with all info for each plate"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{   'Plate_1': {   'path_to_images': PosixPath('/home/jenna/nf1_cellpainting_data/0.download_data/Plate_1'),\n",
+      "                   'path_to_output': PosixPath('Corrected_Images/Corrected_Plate_1'),\n",
+      "                   'path_to_pipeline': PosixPath('/home/jenna/nf1_cellpainting_data/1.cellprofiler_ic/NF1_illum_3channel.cppipe')},\n",
+      "    'Plate_2': {   'path_to_images': PosixPath('/home/jenna/nf1_cellpainting_data/0.download_data/Plate_2'),\n",
+      "                   'path_to_output': PosixPath('Corrected_Images/Corrected_Plate_2'),\n",
+      "                   'path_to_pipeline': PosixPath('/home/jenna/nf1_cellpainting_data/1.cellprofiler_ic/NF1_illum_3channel.cppipe')},\n",
+      "    'Plate_3': {   'path_to_images': PosixPath('/home/jenna/nf1_cellpainting_data/0.download_data/Plate_3'),\n",
+      "                   'path_to_output': PosixPath('Corrected_Images/Corrected_Plate_3'),\n",
+      "                   'path_to_pipeline': PosixPath('/home/jenna/nf1_cellpainting_data/1.cellprofiler_ic/NF1_illum_4channel.cppipe')},\n",
+      "    'Plate_3_prime': {   'path_to_images': PosixPath('/home/jenna/nf1_cellpainting_data/0.download_data/Plate_3_prime'),\n",
+      "                         'path_to_output': PosixPath('Corrected_Images/Corrected_Plate_3_prime'),\n",
+      "                         'path_to_pipeline': PosixPath('/home/jenna/nf1_cellpainting_data/1.cellprofiler_ic/NF1_illum_4channel.cppipe')},\n",
+      "    'Plate_4': {   'path_to_images': PosixPath('/home/jenna/nf1_cellpainting_data/0.download_data/Plate_4'),\n",
+      "                   'path_to_output': PosixPath('Corrected_Images/Corrected_Plate_4'),\n",
+      "                   'path_to_pipeline': PosixPath('/home/jenna/nf1_cellpainting_data/1.cellprofiler_ic/NF1_illum_4channel.cppipe')}}\n"
+     ]
+    }
+   ],
+   "source": [
+    "# create plate info dictionary with all parts of the CellProfiler CLI command to run in parallel\n",
+    "plate_info_dictionary = {\n",
+    "    name: {\n",
+    "        \"path_to_images\": pathlib.Path(list(images_dir.rglob(name))[0]).resolve(\n",
     "            strict=True\n",
     "        ),\n",
-    "        \"path_to_output\": pathlib.Path(\"Corrected_Plate_3\").resolve(),\n",
-    "    },\n",
-    "    \"Plate_3_prime\": {\n",
-    "        # this pipeline is specific to plates 3 and 3 prime (due to channel difference from plates 1 and 2)\n",
-    "        \"path_to_pipeline\": pathlib.Path(\"NF1_illum_Plate3_Plate3prime.cppipe\").resolve(\n",
+    "        \"path_to_output\": pathlib.Path(f\"{output_dir}/Corrected_{name}\"),\n",
+    "    }\n",
+    "    for name in plate_names\n",
+    "}\n",
+    "\n",
+    "# iterate over the dictionary and add the path_to_pipeline specific for each plate\n",
+    "for name, info in plate_info_dictionary.items():\n",
+    "    # only plates 1 and 2 have 3 channels so these are the only plates that use this path\n",
+    "    if name == \"Plate_1\" or name == \"Plate_2\":\n",
+    "        info[\"path_to_pipeline\"] = pathlib.Path(f\"./NF1_illum_3channel.cppipe\").resolve(\n",
     "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_images\": pathlib.Path(\"../0.download_data/Plate_3_prime/\").resolve(\n",
+    "        )\n",
+    "    # all other plates have 4 channels and will use that specific pipeline\n",
+    "    else:\n",
+    "        info[\"path_to_pipeline\"] = pathlib.Path(f\"./NF1_illum_4channel.cppipe\").resolve(\n",
     "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_output\": pathlib.Path(\"Corrected_Plate_3_prime\").resolve(),\n",
-    "    },\n",
-    "}"
+    "        )\n",
+    "\n",
+    "# view the dictionary to assess that all info is added correctly\n",
+    "pprint.pprint(plate_info_dictionary, indent=4)"
    ]
   },
   {
@@ -94,30 +134,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Run illumination correction pipeline on each plate\n",
+    "## Run illumination correction pipeline on each plate in parallel\n",
     "\n",
     "In this notebook, we do not run the cells to completion as we prefer to run the notebooks as nbconverted python files due to better stability."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Correcting Plate_1\n",
-      "Starting CellProfiler run on Plate_1\n",
-      "The CellProfiler run has been completed with Plate_1. Please check log file for any errors.\n",
-      "Correcting Plate_2\n",
-      "Starting CellProfiler run on Plate_2\n",
-      "The CellProfiler run has been completed with Plate_2. Please check log file for any errors.\n",
-      "Correcting Plate_3\n",
-      "Starting CellProfiler run on Plate_3\n"
-     ]
-    },
     {
      "ename": "KeyboardInterrupt",
      "evalue": "",
@@ -125,36 +151,19 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[3], line 9\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mCorrecting \u001b[39m\u001b[39m{\u001b[39;00mplate\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      8\u001b[0m \u001b[39m# run illumination correction pipeline and save images\u001b[39;00m\n\u001b[0;32m----> 9\u001b[0m cp_utils\u001b[39m.\u001b[39;49mrun_cellprofiler(\n\u001b[1;32m     10\u001b[0m     path_to_pipeline\u001b[39m=\u001b[39;49mpath_to_pipeline,\n\u001b[1;32m     11\u001b[0m     path_to_output\u001b[39m=\u001b[39;49mpath_to_output,\n\u001b[1;32m     12\u001b[0m     path_to_images\u001b[39m=\u001b[39;49mpath_to_images,\n\u001b[1;32m     13\u001b[0m     \u001b[39m# these variables are turned off for illum pipeline\u001b[39;49;00m\n\u001b[1;32m     14\u001b[0m     sqlite_name\u001b[39m=\u001b[39;49m\u001b[39mNone\u001b[39;49;00m,\n\u001b[1;32m     15\u001b[0m     analysis_run\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m     16\u001b[0m )\n",
-      "File \u001b[0;32m~/Desktop/GitHub_Repos/nf1_cellpainting_data/1.cellprofiler_ic/../utils/cp_utils.py:98\u001b[0m, in \u001b[0;36mrun_cellprofiler\u001b[0;34m(path_to_pipeline, path_to_output, path_to_images, sqlite_name, analysis_run)\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(\n\u001b[1;32m     81\u001b[0m         pathlib\u001b[39m.\u001b[39mPath(\n\u001b[1;32m     82\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mlogs/cellprofiler_output_\u001b[39m\u001b[39m{\u001b[39;00mpathlib\u001b[39m.\u001b[39mPath(path_to_images)\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m.log\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     85\u001b[0m     ) \u001b[39mas\u001b[39;00m cellprofiler_output_file:\n\u001b[1;32m     86\u001b[0m         \u001b[39m# run CellProfiler for a illumination correction pipeline\u001b[39;00m\n\u001b[1;32m     87\u001b[0m         command \u001b[39m=\u001b[39m [\n\u001b[1;32m     88\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39mcellprofiler\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m     89\u001b[0m             \u001b[39m\"\u001b[39m\u001b[39m-c\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     96\u001b[0m             path_to_images,\n\u001b[1;32m     97\u001b[0m         ]\n\u001b[0;32m---> 98\u001b[0m         subprocess\u001b[39m.\u001b[39;49mrun(\n\u001b[1;32m     99\u001b[0m             command,\n\u001b[1;32m    100\u001b[0m             stdout\u001b[39m=\u001b[39;49mcellprofiler_output_file,\n\u001b[1;32m    101\u001b[0m             stderr\u001b[39m=\u001b[39;49mcellprofiler_output_file,\n\u001b[1;32m    102\u001b[0m             check\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m    103\u001b[0m         )\n\u001b[1;32m    104\u001b[0m         \u001b[39mprint\u001b[39m(\n\u001b[1;32m    105\u001b[0m             \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mThe CellProfiler run has been completed with \u001b[39m\u001b[39m{\u001b[39;00mpathlib\u001b[39m.\u001b[39mPath(path_to_images)\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m. Please check log file for any errors.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[1;32m    106\u001b[0m         )\n\u001b[1;32m    108\u001b[0m \u001b[39m# run CellProfiler analysis pipeline\u001b[39;00m\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/subprocess.py:495\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    493\u001b[0m \u001b[39mwith\u001b[39;00m Popen(\u001b[39m*\u001b[39mpopenargs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39mas\u001b[39;00m process:\n\u001b[1;32m    494\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 495\u001b[0m         stdout, stderr \u001b[39m=\u001b[39m process\u001b[39m.\u001b[39;49mcommunicate(\u001b[39minput\u001b[39;49m, timeout\u001b[39m=\u001b[39;49mtimeout)\n\u001b[1;32m    496\u001b[0m     \u001b[39mexcept\u001b[39;00m TimeoutExpired \u001b[39mas\u001b[39;00m exc:\n\u001b[1;32m    497\u001b[0m         process\u001b[39m.\u001b[39mkill()\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/subprocess.py:1020\u001b[0m, in \u001b[0;36mPopen.communicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m   1018\u001b[0m         stderr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstderr\u001b[39m.\u001b[39mread()\n\u001b[1;32m   1019\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstderr\u001b[39m.\u001b[39mclose()\n\u001b[0;32m-> 1020\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwait()\n\u001b[1;32m   1021\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1022\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/subprocess.py:1083\u001b[0m, in \u001b[0;36mPopen.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1081\u001b[0m     endtime \u001b[39m=\u001b[39m _time() \u001b[39m+\u001b[39m timeout\n\u001b[1;32m   1082\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1083\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_wait(timeout\u001b[39m=\u001b[39;49mtimeout)\n\u001b[1;32m   1084\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1085\u001b[0m     \u001b[39m# https://bugs.python.org/issue25942\u001b[39;00m\n\u001b[1;32m   1086\u001b[0m     \u001b[39m# The first keyboard interrupt waits briefly for the child to\u001b[39;00m\n\u001b[1;32m   1087\u001b[0m     \u001b[39m# exit under the common assumption that it also received the ^C\u001b[39;00m\n\u001b[1;32m   1088\u001b[0m     \u001b[39m# generated SIGINT and will exit rapidly.\u001b[39;00m\n\u001b[1;32m   1089\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/subprocess.py:1806\u001b[0m, in \u001b[0;36mPopen._wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1804\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturncode \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   1805\u001b[0m     \u001b[39mbreak\u001b[39;00m  \u001b[39m# Another thread waited.\u001b[39;00m\n\u001b[0;32m-> 1806\u001b[0m (pid, sts) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_try_wait(\u001b[39m0\u001b[39;49m)\n\u001b[1;32m   1807\u001b[0m \u001b[39m# Check the pid and loop as waitpid has been known to\u001b[39;00m\n\u001b[1;32m   1808\u001b[0m \u001b[39m# return 0 even without WNOHANG in odd situations.\u001b[39;00m\n\u001b[1;32m   1809\u001b[0m \u001b[39m# http://bugs.python.org/issue14396.\u001b[39;00m\n\u001b[1;32m   1810\u001b[0m \u001b[39mif\u001b[39;00m pid \u001b[39m==\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpid:\n",
-      "File \u001b[0;32m~/opt/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/subprocess.py:1764\u001b[0m, in \u001b[0;36mPopen._try_wait\u001b[0;34m(self, wait_flags)\u001b[0m\n\u001b[1;32m   1762\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"All callers to this function MUST hold self._waitpid_lock.\"\"\"\u001b[39;00m\n\u001b[1;32m   1763\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1764\u001b[0m     (pid, sts) \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39;49mwaitpid(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpid, wait_flags)\n\u001b[1;32m   1765\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mChildProcessError\u001b[39;00m:\n\u001b[1;32m   1766\u001b[0m     \u001b[39m# This happens if SIGCLD is set to be ignored or waiting\u001b[39;00m\n\u001b[1;32m   1767\u001b[0m     \u001b[39m# for child processes has otherwise been disabled for our\u001b[39;00m\n\u001b[1;32m   1768\u001b[0m     \u001b[39m# process.  This child is dead, we can't get the status.\u001b[39;00m\n\u001b[1;32m   1769\u001b[0m     pid \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpid\n",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m cp_parallel\u001b[39m.\u001b[39;49mrun_cellprofiler_parallel(\n\u001b[1;32m      2\u001b[0m     plate_info_dictionary\u001b[39m=\u001b[39;49mplate_info_dictionary, run_name\u001b[39m=\u001b[39;49mrun_name\n\u001b[1;32m      3\u001b[0m )\n",
+      "File \u001b[0;32m~/nf1_cellpainting_data/1.cellprofiler_ic/../utils/cp_parallel.py:119\u001b[0m, in \u001b[0;36mrun_cellprofiler_parallel\u001b[0;34m(plate_info_dictionary, run_name)\u001b[0m\n\u001b[1;32m    109\u001b[0m futures: List[Future] \u001b[39m=\u001b[39m [\n\u001b[1;32m    110\u001b[0m     executor\u001b[39m.\u001b[39msubmit(\n\u001b[1;32m    111\u001b[0m         subprocess\u001b[39m.\u001b[39mrun,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[39mfor\u001b[39;00m command \u001b[39min\u001b[39;00m commands\n\u001b[1;32m    116\u001b[0m ]\n\u001b[1;32m    118\u001b[0m \u001b[39m# the list of CompletedProcesses holds all the information from the CellProfiler run\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m results: List[subprocess\u001b[39m.\u001b[39mCompletedProcess] \u001b[39m=\u001b[39m [future\u001b[39m.\u001b[39mresult() \u001b[39mfor\u001b[39;00m future \u001b[39min\u001b[39;00m futures]\n\u001b[1;32m    121\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mAll processes have been completed!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    123\u001b[0m \u001b[39m# for each process, confirm that the process completed succesfully and return a log file\u001b[39;00m\n",
+      "File \u001b[0;32m~/nf1_cellpainting_data/1.cellprofiler_ic/../utils/cp_parallel.py:119\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    109\u001b[0m futures: List[Future] \u001b[39m=\u001b[39m [\n\u001b[1;32m    110\u001b[0m     executor\u001b[39m.\u001b[39msubmit(\n\u001b[1;32m    111\u001b[0m         subprocess\u001b[39m.\u001b[39mrun,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[39mfor\u001b[39;00m command \u001b[39min\u001b[39;00m commands\n\u001b[1;32m    116\u001b[0m ]\n\u001b[1;32m    118\u001b[0m \u001b[39m# the list of CompletedProcesses holds all the information from the CellProfiler run\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m results: List[subprocess\u001b[39m.\u001b[39mCompletedProcess] \u001b[39m=\u001b[39m [future\u001b[39m.\u001b[39;49mresult() \u001b[39mfor\u001b[39;00m future \u001b[39min\u001b[39;00m futures]\n\u001b[1;32m    121\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mAll processes have been completed!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    123\u001b[0m \u001b[39m# for each process, confirm that the process completed succesfully and return a log file\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/concurrent/futures/_base.py:439\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n\u001b[1;32m    437\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n\u001b[0;32m--> 439\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_condition\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m    441\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[1;32m    442\u001b[0m     \u001b[39mraise\u001b[39;00m CancelledError()\n",
+      "File \u001b[0;32m~/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/threading.py:302\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[39mtry\u001b[39;00m:    \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    301\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 302\u001b[0m         waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[1;32m    303\u001b[0m         gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    304\u001b[0m     \u001b[39melse\u001b[39;00m:\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
    "source": [
-    "# run through each plate with each set of paths based on dictionary\n",
-    "for plate, info in plates_info_dictionary.items():\n",
-    "    path_to_pipeline = info[\"path_to_pipeline\"]\n",
-    "    path_to_output = info[\"path_to_output\"]\n",
-    "    # make output dir if needed\n",
-    "    path_to_output.mkdir(exist_ok=True)\n",
-    "    path_to_images = info[\"path_to_images\"]\n",
-    "    print(f\"Correcting {plate}\")\n",
-    "\n",
-    "    # run illumination correction pipeline and save images\n",
-    "    cp_utils.run_cellprofiler(\n",
-    "        path_to_pipeline=path_to_pipeline,\n",
-    "        path_to_output=path_to_output,\n",
-    "        path_to_images=path_to_images,\n",
-    "        # these variables are turned off for illum pipeline\n",
-    "        sqlite_name=None,\n",
-    "        analysis_run=False,\n",
-    "    )"
+    "cp_parallel.run_cellprofiler_parallel(\n",
+    "    plate_info_dictionary=plate_info_dictionary, run_name=run_name\n",
+    ")"
    ]
   }
  ],
diff --git a/1.cellprofiler_ic/nf1_ic.sh b/1.cellprofiler_ic/nf1_ic.sh
index 1161272..6119364 100644
--- a/1.cellprofiler_ic/nf1_ic.sh
+++ b/1.cellprofiler_ic/nf1_ic.sh
@@ -1,8 +1,8 @@
 #!/bin/bash
 
-# this line is needed for the sh file to properly activate a conda environment 
-eval "$(conda shell.bash hook)"
-# activate the main conda environment (if not already activated)
+# initialize the correct shell for your machine to allow conda to work (see README for note on shell names)
+conda init bash
+# activate the main conda environment
 conda activate nf1_cellpainting_data
 
 # convert the notebook into a python and run the file
diff --git a/1.cellprofiler_ic/scripts/nf1_ic.py b/1.cellprofiler_ic/scripts/nf1_ic.py
index 236ed01..2658686 100644
--- a/1.cellprofiler_ic/scripts/nf1_ic.py
+++ b/1.cellprofiler_ic/scripts/nf1_ic.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# # Correct illumination and save images for each plate using CellProfiler
+# # Correct illumination and save images for each plate using CellProfiler Parallel
 
 # ## Import libraries
 
@@ -9,84 +9,80 @@
 
 
 import pathlib
+import pprint
 
 import sys
 
-sys.path.append("../")
-from utils import cp_utils
+sys.path.append("../utils")
+import cp_parallel
 
 
-# ## Set paths for each plate
-#
-# Note: Output file path does not need to be strict since the `run_cellprofiler` function can create the output folder directory if it doesn't already exist. The other paths must be strict since these files should already exist for CellProfiler to run. The output directory doesn't need to already exist.
+# ## Set paths and variables
 
 # In[2]:
 
 
-plates_info_dictionary = {
-    "Plate_1": {
-        # this pipeline is specific to plates 1 and 2 (due to channel difference from plates 3 and 3 prime)
-        "path_to_pipeline": pathlib.Path("NF1_illum_Plates_1_2.cppipe").resolve(
-            strict=True
-        ),
-        "path_to_images": pathlib.Path("../0.download_data/Plate_1/").resolve(
-            strict=True
-        ),
-        "path_to_output": pathlib.Path("Corrected_Plate_1").resolve(),
-    },
-    "Plate_2": {
-        # this pipeline is specific to plates 1 and 2 (due to channel difference from plates 3 and 3 prime)
-        "path_to_pipeline": pathlib.Path("NF1_illum_Plates_1_2.cppipe").resolve(
-            strict=True
-        ),
-        "path_to_images": pathlib.Path("../0.download_data/Plate_2/").resolve(
-            strict=True
-        ),
-        "path_to_output": pathlib.Path("Corrected_Plate_2").resolve(),
-    },
-    "Plate_3": {
-        # this pipeline is specific to plates 3 and 3 prime (due to channel difference from plates 1 and 2)
-        "path_to_pipeline": pathlib.Path("NF1_illum_Plate3_Plate3prime.cppipe").resolve(
-            strict=True
-        ),
-        "path_to_images": pathlib.Path("../0.download_data/Plate_3/").resolve(
+# set the run type for the parallelization
+run_name = "illum_correction"
+
+# set path for pipeline for all plates for whole image analysis
+path_to_pipeline = pathlib.Path("./pipelines/nuclei_analysis.cppipe").resolve()
+
+# set main output dir for all plates
+output_dir = pathlib.Path("./Corrected_Images")
+output_dir.mkdir(exist_ok=True)
+
+# directory where images are located within folders
+images_dir = pathlib.Path("../0.download_data/")
+
+# list for plate names based on folders to use to create dictionary
+plate_names = []
+# iterate through 0.download_data and append plate names from folder names that contain image data from that plate
+for file_path in pathlib.Path("../0.download_data/").iterdir():
+    if str(file_path.stem).startswith("Plate"):
+        plate_names.append(str(file_path.stem))
+
+
+# ## Create dictionary with all info for each plate
+
+# In[3]:
+
+
+# create plate info dictionary with all parts of the CellProfiler CLI command to run in parallel
+plate_info_dictionary = {
+    name: {
+        "path_to_images": pathlib.Path(list(images_dir.rglob(name))[0]).resolve(
             strict=True
         ),
-        "path_to_output": pathlib.Path("Corrected_Plate_3").resolve(),
-    },
-    "Plate_3_prime": {
-        # this pipeline is specific to plates 3 and 3 prime (due to channel difference from plates 1 and 2)
-        "path_to_pipeline": pathlib.Path("NF1_illum_Plate3_Plate3prime.cppipe").resolve(
+        "path_to_output": pathlib.Path(f"{output_dir}/Corrected_{name}"),
+    }
+    for name in plate_names
+}
+
+# iterate over the dictionary and add the path_to_pipeline specific for each plate
+for name, info in plate_info_dictionary.items():
+    # only plates 1 and 2 have 3 channels so these are the only plates that use this path
+    if name == "Plate_1" or name == "Plate_2":
+        info["path_to_pipeline"] = pathlib.Path(f"./NF1_illum_3channel.cppipe").resolve(
             strict=True
-        ),
-        "path_to_images": pathlib.Path("../0.download_data/Plate_3_prime/").resolve(
+        )
+    # all other plates have 4 channels and will use that specific pipeline
+    else:
+        info["path_to_pipeline"] = pathlib.Path(f"./NF1_illum_4channel.cppipe").resolve(
             strict=True
-        ),
-        "path_to_output": pathlib.Path("Corrected_Plate_3_prime").resolve(),
-    },
-}
+        )
+
+# view the dictionary to assess that all info is added correctly
+pprint.pprint(plate_info_dictionary, indent=4)
 
 
-# ## Run illumination correction pipeline on each plate
+# ## Run illumination correction pipeline on each plate in parallel
 #
 # In this notebook, we do not run the cells to completion as we prefer to run the notebooks as nbconverted python files due to better stability.
 
-# In[3]:
+# In[4]:
 
 
-# run through each plate with each set of paths based on dictionary
-for plate, info in plates_info_dictionary.items():
-    path_to_pipeline = info["path_to_pipeline"]
-    path_to_output = info["path_to_output"]
-    path_to_images = info["path_to_images"]
-    print(f"Correcting {plate}")
-
-    # run illumination correction pipeline and save images
-    cp_utils.run_cellprofiler(
-        path_to_pipeline=path_to_pipeline,
-        path_to_output=path_to_output,
-        path_to_images=path_to_images,
-        # these variables are turned off for illum pipeline
-        sqlite_name=None,
-        analysis_run=False,
-    )
+cp_parallel.run_cellprofiler_parallel(
+    plate_info_dictionary=plate_info_dictionary, run_name=run_name
+)
diff --git a/2.cellprofiler_analysis/NF1_analysis_plate1_plate2.cppipe b/2.cellprofiler_analysis/NF1_analysis_3channel.cppipe
similarity index 85%
rename from 2.cellprofiler_analysis/NF1_analysis_plate1_plate2.cppipe
rename to 2.cellprofiler_analysis/NF1_analysis_3channel.cppipe
index 8449286..0d5f2b0 100644
--- a/2.cellprofiler_analysis/NF1_analysis_plate1_plate2.cppipe
+++ b/2.cellprofiler_analysis/NF1_analysis_3channel.cppipe
@@ -5,16 +5,16 @@ GitHash:
 ModuleCount:17
 HasImagePlaneDetails:False
 
-Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Images module is left blank as we are giving the path to the corrected images in the CLI']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     :
     Filter images?:Images only
     Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\/]\\.")
 
-Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Had to change the metadata regular expression to remove plate.The value it was associating as plate in the file name is arbitary and the same between all plates in the NF1 project. ', '', 'Plate is not extracted from the metadata so across-all-plate feature processing can not occur.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Extract metadata from file names and folder names using regular expressions.', '', 'The only metadata that will be outputed in the SQLite DB file are:', '', 'Plate', 'Well', 'Site', '', 'The rest of the information is useful to make sure that the expression is working, but can be removed/not necessary.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Extract metadata?:Yes
     Metadata data type:Text
     Metadata types:{"Channel": "integer", "FileLocation": "text", "Frame": "text", "Plate": "text", "Series": "text", "Site": "integer", "Stain": "float", "Well": "text"}
-    Extraction method count:1
+    Extraction method count:2
     Metadata extraction method:Extract from file/folder names
     Metadata source:File name
     Regular expression to extract from file name:(?P<Well>[A-Z]{1}[0-9]{1,2})_01_(?P<Channel>[1-3]{1})_(?P<Site>[1-4]{1})_(?P<Stain>DAPI|GFP|RFP)
@@ -26,8 +26,19 @@ Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_win
     Use case insensitive matching?:No
     Metadata file name:None
     Does cached metadata exist?:No
+    Metadata extraction method:Extract from file/folder names
+    Metadata source:Folder name
+    Regular expression to extract from file name:^(?P<Plate>.*)_(?P<Well>[A-P][0-9]{2})_s(?P<Site>[0-9])_w(?P<ChannelNumber>[0-9])
+    Regular expression to extract from folder name:Corrected_(?P<Plate>Plate_[0-9]{1})
+    Extract metadata from:All images
+    Select the filtering criteria:and (file does contain "")
+    Metadata file location:Elsewhere...|
+    Match file and image metadata:[]
+    Use case insensitive matching?:No
+    Metadata file name:None
+    Does cached metadata exist?:No
 
-NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['The NamesAndTypes module allows you to assign a meaningful name to each image by which other modules will refer to it.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['Assign files to their respective channel (only 3):', '', 'DAPI', 'GFP', 'RFP']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Assign a name to:Images matching rules
     Select the image type:Grayscale image
     Name to assign these images:DNA
@@ -60,12 +71,12 @@ NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|sho
     Set intensity range from:Image metadata
     Maximum intensity:255.0
 
-Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['We do not use the Groups module.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Do you want to group your images?:No
     grouping metadata count:1
     Metadata category:None
 
-IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:['These are the current best parameters to segment nuclei from the DAPI channel']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select the input image:DAPI
     Name the primary objects to be identified:Nuclei
     Typical diameter of objects, in pixel units (Min,Max):30,90
@@ -101,7 +112,7 @@ IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_num
     # of deviations:2.0
     Thresholding method:Minimum Cross-Entropy
 
-IdentifySecondaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:10|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+IdentifySecondaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:10|show_window:False|notes:['These are the current best parameters to segment whole cells using the RFP (actin) channel']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select the input objects:Nuclei
     Name the objects to be identified:Cells
     Select the method to identify the secondary objects:Propagation
@@ -131,7 +142,7 @@ IdentifySecondaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_n
     # of deviations:2.0
     Thresholding method:Otsu
 
-IdentifyTertiaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+IdentifyTertiaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['This module creates a third object from the first two where the nuclei is subtracted from the cells to create cytoplasm']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select the larger identified objects:Cells
     Select the smaller identified objects:Nuclei
     Name the tertiary objects to be identified:Cytoplasm
@@ -196,7 +207,7 @@ MeasureObjectNeighbors:[module_num:13|svn_version:'Unknown'|variable_revision_nu
     Name the output image:PercentTouching
     Select colormap:Oranges
 
-MeasureObjectIntensityDistribution:[module_num:14|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Measure object intensity distributions witin objects using center x,y coords for each single object.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+MeasureObjectIntensityDistribution:[module_num:14|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Measure object intensity distributions witin objects using center x,y coords for each single object.', '', 'Currently we do not measure the Zernikes.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select images to measure:DAPI, GFP, RFP
     Hidden:3
     Hidden:1
@@ -239,7 +250,7 @@ ExportToDatabase:[module_num:17|svn_version:'Unknown'|variable_revision_number:2
     Database host:
     Username:
     Password:
-    Name the SQLite database file:NF1_data.sqlite
+    Name the SQLite database file:nf1_analysis.sqlite
     Calculate the per-image mean values of object measurements?:No
     Calculate the per-image median values of object measurements?:No
     Calculate the per-image standard deviation values of object measurements?:No
diff --git a/2.cellprofiler_analysis/NF1_analysis_plate3_plate3prime.cppipe b/2.cellprofiler_analysis/NF1_analysis_4channel.cppipe
similarity index 87%
rename from 2.cellprofiler_analysis/NF1_analysis_plate3_plate3prime.cppipe
rename to 2.cellprofiler_analysis/NF1_analysis_4channel.cppipe
index 388564d..5fae06e 100644
--- a/2.cellprofiler_analysis/NF1_analysis_plate3_plate3prime.cppipe
+++ b/2.cellprofiler_analysis/NF1_analysis_4channel.cppipe
@@ -5,16 +5,16 @@ GitHash:
 ModuleCount:17
 HasImagePlaneDetails:False
 
-Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['To begin creating your project, use the Images module to compile a list of files and/or folders that you want to analyze. You can also specify a set of rules to include only the desired files in your selected folders.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Images:[module_num:1|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['Images module is left blank since we are giving the path to the images in the CLI command']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     :
     Filter images?:Images only
     Select the rule criteria:and (extension does isimage) (directory doesnot containregexp "[\\\\/]\\.")
 
-Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Like with plates 1 and 2, there is no plate metadata located in the file names for plates 3 and 3 prime. There is no plate metadata extracted and will not be avaliable in the exported SQLite file.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Metadata is extracted from the file and folder names using regular expressions.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Extract metadata?:Yes
     Metadata data type:Text
     Metadata types:{"Channel": "integer", "FileLocation": "text", "Frame": "text", "Plate": "text", "Series": "text", "Site": "integer", "Stain": "float", "Well": "text"}
-    Extraction method count:1
+    Extraction method count:2
     Metadata extraction method:Extract from file/folder names
     Metadata source:File name
     Regular expression to extract from file name:(?P<Well>[A-Z]{1}[0-9]{1,2})_01_(?P<Channel>[1-4]{1})_(?P<Site>[0-9]{1,2})_(?P<Stain>DAPI|CY5|GFP|RFP)
@@ -26,8 +26,19 @@ Metadata:[module_num:2|svn_version:'Unknown'|variable_revision_number:6|show_win
     Use case insensitive matching?:No
     Metadata file name:None
     Does cached metadata exist?:No
+    Metadata extraction method:Extract from file/folder names
+    Metadata source:Folder name
+    Regular expression to extract from file name:^(?P<Plate>.*)_(?P<Well>[A-P][0-9]{2})_s(?P<Site>[0-9])_w(?P<ChannelNumber>[0-9])
+    Regular expression to extract from folder name:Corrected_(?P<Plate>Plate_[0-9]{1})
+    Extract metadata from:All images
+    Select the filtering criteria:and (file does contain "")
+    Metadata file location:Elsewhere...|
+    Match file and image metadata:[]
+    Use case insensitive matching?:No
+    Metadata file name:None
+    Does cached metadata exist?:No
 
-NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['Plate 3 and 3’ need to have their own pipeline from the first two plates due to difference in channels (added mito channel)']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|show_window:False|notes:['This pipeline assigns names to 4 channels specifically:', '', 'DAPI', 'GFP', 'CY5', 'RFP']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Assign a name to:Images matching rules
     Select the image type:Grayscale image
     Name to assign these images:DNA
@@ -66,12 +77,12 @@ NamesAndTypes:[module_num:3|svn_version:'Unknown'|variable_revision_number:8|sho
     Set intensity range from:Image metadata
     Maximum intensity:255.0
 
-Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['The Groups module optionally allows you to split your list of images into image subsets (groups) which will be processed independently of each other. Examples of groupings include screening batches, microtiter plates, time-lapse movies, etc.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+Groups:[module_num:4|svn_version:'Unknown'|variable_revision_number:2|show_window:False|notes:['We do not use the Groups module.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Do you want to group your images?:No
     grouping metadata count:1
     Metadata category:None
 
-IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:['The parameters are the same as the analysis pipeline for plates 1 and 2.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_number:15|show_window:False|notes:['The parameters are the same as the analysis pipeline for 3 channels.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select the input image:DAPI
     Name the primary objects to be identified:Nuclei
     Typical diameter of objects, in pixel units (Min,Max):30,90
@@ -107,7 +118,7 @@ IdentifyPrimaryObjects:[module_num:5|svn_version:'Unknown'|variable_revision_num
     # of deviations:2.0
     Thresholding method:Minimum Cross-Entropy
 
-IdentifySecondaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:10|show_window:False|notes:['The parameters are the same as the analysis pipeline for plates 1 and 2.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+IdentifySecondaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_number:10|show_window:False|notes:['The parameters are the same as the analysis pipeline for 3 channels.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select the input objects:Nuclei
     Name the objects to be identified:Cells
     Select the method to identify the secondary objects:Propagation
@@ -137,7 +148,7 @@ IdentifySecondaryObjects:[module_num:6|svn_version:'Unknown'|variable_revision_n
     # of deviations:2.0
     Thresholding method:Otsu
 
-IdentifyTertiaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['The parameters are the same as the analysis pipeline for plates 1 and 2.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+IdentifyTertiaryObjects:[module_num:7|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['The parameters are the same as the analysis pipeline for 3 channels.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select the larger identified objects:Cells
     Select the smaller identified objects:Nuclei
     Name the tertiary objects to be identified:Cytoplasm
@@ -202,7 +213,7 @@ MeasureObjectNeighbors:[module_num:13|svn_version:'Unknown'|variable_revision_nu
     Name the output image:PercentTouching
     Select colormap:Oranges
 
-MeasureObjectIntensityDistribution:[module_num:14|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Measure object intensity distributions witin objects using center x,y coords for each single object.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+MeasureObjectIntensityDistribution:[module_num:14|svn_version:'Unknown'|variable_revision_number:6|show_window:False|notes:['Measure object intensity distributions witin objects using center x,y coords for each single object.', '', 'We do not calculate Zernikes currently.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select images to measure:CY5, DAPI, GFP, RFP
     Hidden:3
     Hidden:1
@@ -222,7 +233,7 @@ MeasureObjectIntensityDistribution:[module_num:14|svn_version:'Unknown'|variable
     Number of bins:4
     Maximum radius:100
 
-MeasureObjectSizeShape:[module_num:15|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Measure object size and shape']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+MeasureObjectSizeShape:[module_num:15|svn_version:'Unknown'|variable_revision_number:3|show_window:False|notes:['Measure object size and shape.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Select object sets to measure:Cells, Cytoplasm, Nuclei
     Calculate the Zernike features?:Yes
     Calculate the advanced features?:No
@@ -235,7 +246,7 @@ MeasureTexture:[module_num:16|svn_version:'Unknown'|variable_revision_number:7|s
     Measure whole images or objects?:Both
     Texture scale to measure:3
 
-ExportToDatabase:[module_num:17|svn_version:'Unknown'|variable_revision_number:28|show_window:False|notes:[]|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
+ExportToDatabase:[module_num:17|svn_version:'Unknown'|variable_revision_number:28|show_window:False|notes:['Output the morphology features into an SQLite database.']|batch_state:array([], dtype=uint8)|enabled:True|wants_pause:False]
     Database type:SQLite
     Database name:DefaultDB
     Add a prefix to table names?:No
@@ -245,7 +256,7 @@ ExportToDatabase:[module_num:17|svn_version:'Unknown'|variable_revision_number:2
     Database host:
     Username:
     Password:
-    Name the SQLite database file:NF1_data.sqlite
+    Name the SQLite database file:nf1_analysis.sqlite
     Calculate the per-image mean values of object measurements?:No
     Calculate the per-image median values of object measurements?:No
     Calculate the per-image standard deviation values of object measurements?:No
diff --git a/2.cellprofiler_analysis/README.md b/2.cellprofiler_analysis/README.md
index 196020b..7b2f26d 100644
--- a/2.cellprofiler_analysis/README.md
+++ b/2.cellprofiler_analysis/README.md
@@ -16,9 +16,31 @@ cd 2.cellprofiler_analysis
 source nf1_analysis.sh
 ```
 
+## CellProfiler Parallel
+
+To improve the speed for analysis, we have implemented `CellProfiler Parallel`, which utilizes multi-processing to run one plate per CPU core.
+
+All analysis is performed on a Linux-based machine running Pop_OS! LTS 22.04 with an AMD Ryzen 7 3700X 8-Core Processor, 16 CPU cores, and 64GB of RAM.
+
+### Speed when running CellProfiler Parallel
+
+When using CellProfiler Parallel to run analysis on 5 plates, it took approximately 28 hours to finish.
+
+### Speed when running CellProfiler Sequential
+
 To run analysis on plates 1 and 2, it took approximately one hour. 
-To run analysis on plates 3 and 3 prime, it took approximately 31 hours.
-The analysis was run on a Linux-based machine running Pop_OS! LTS 22.04 with an AMD Ryzen 7 3700X 8-Core Processor.
+To run analysis on plates 3 and 3 prime, it took approximately 31 hours (~ 16 hours each).
+
+#### Difference in speed
+
+When looking at the log files, a fifth plate (e.g., `Plate_4``) took about **24 hours** to processes. 
+Added on to the CellProfiler Sequential, which in total took 32 hours for 4 plates, it would likely have added on another 10-14 hours (given how long plates 3 and 3 prime took), totalling **over 45 hours**.
+
+Even though plates individually ran faster, the computational time saved by running them in parallel **saves over 20 hours of time**. 
+
+What might be able to improve the individual processing time per plate could be to increase the number of workers (even though CellProfiler CLI should only be using one core per processes). 
+Currently, CellProfiler Parallel will automatically set the number of workers based on the number of commands (e.g., plates to be processed).
+Depending on needs, a parameter can be added to manually set the max_workers.
 
 ## Accessing the CellProfiler output - SQLite files
 
diff --git a/2.cellprofiler_analysis/analysis_output/Plate_1.sqlite b/2.cellprofiler_analysis/analysis_output/Plate_1.sqlite
deleted file mode 100644
index 7ba0520..0000000
--- a/2.cellprofiler_analysis/analysis_output/Plate_1.sqlite
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:a3336fcf89ea296832c4f3ca7a532f2c3124e7aba5249f30a4899e5eda2d67e4
-size 4001792
diff --git a/2.cellprofiler_analysis/analysis_output/Plate_1/nf1_analysis.sqlite b/2.cellprofiler_analysis/analysis_output/Plate_1/nf1_analysis.sqlite
new file mode 100644
index 0000000..331bff3
--- /dev/null
+++ b/2.cellprofiler_analysis/analysis_output/Plate_1/nf1_analysis.sqlite
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:13de543486df0a97344ef40b2dec46562d9d5190515136251020481bfd91fd66
+size 4005888
diff --git a/2.cellprofiler_analysis/analysis_output/Plate_2.sqlite b/2.cellprofiler_analysis/analysis_output/Plate_2.sqlite
deleted file mode 100644
index 166d1a0..0000000
--- a/2.cellprofiler_analysis/analysis_output/Plate_2.sqlite
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:66c14464eb2da55c37d0bdb11792a344775511db9b9dfdb330b8168044e16107
-size 26222592
diff --git a/2.cellprofiler_analysis/analysis_output/Plate_2/nf1_analysis.sqlite b/2.cellprofiler_analysis/analysis_output/Plate_2/nf1_analysis.sqlite
new file mode 100644
index 0000000..747ad16
--- /dev/null
+++ b/2.cellprofiler_analysis/analysis_output/Plate_2/nf1_analysis.sqlite
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e0beb41c53315d84af9818526eac1a7e393fa2feb44e9c1eae6f940360a03771
+size 26226688
diff --git a/2.cellprofiler_analysis/analysis_output/Plate_3.sqlite b/2.cellprofiler_analysis/analysis_output/Plate_3.sqlite
deleted file mode 100644
index bc2d42f..0000000
--- a/2.cellprofiler_analysis/analysis_output/Plate_3.sqlite
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:97463933d94ca926a7ac5b358f73c3454dc4c309ea38e7b4d44c743a1159728b
-size 329084928
diff --git a/2.cellprofiler_analysis/analysis_output/Plate_3/nf1_analysis.sqlite b/2.cellprofiler_analysis/analysis_output/Plate_3/nf1_analysis.sqlite
new file mode 100644
index 0000000..960a7e9
--- /dev/null
+++ b/2.cellprofiler_analysis/analysis_output/Plate_3/nf1_analysis.sqlite
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:0688c07ac7c3ed6b4f2fccefd3bd4553252005c11ebd184660c498e452474252
+size 329482240
diff --git a/2.cellprofiler_analysis/analysis_output/Plate_3_prime.sqlite b/2.cellprofiler_analysis/analysis_output/Plate_3_prime.sqlite
deleted file mode 100644
index a54b835..0000000
--- a/2.cellprofiler_analysis/analysis_output/Plate_3_prime.sqlite
+++ /dev/null
@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:3308860ebffdcdec3a9d709da0890f58becf947a8a218faf430178833afe8982
-size 266272768
diff --git a/2.cellprofiler_analysis/analysis_output/Plate_3_prime/nf1_analysis.sqlite b/2.cellprofiler_analysis/analysis_output/Plate_3_prime/nf1_analysis.sqlite
new file mode 100644
index 0000000..7afcb65
--- /dev/null
+++ b/2.cellprofiler_analysis/analysis_output/Plate_3_prime/nf1_analysis.sqlite
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
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+size 266534912
diff --git a/2.cellprofiler_analysis/analysis_output/Plate_4/nf1_analysis.sqlite b/2.cellprofiler_analysis/analysis_output/Plate_4/nf1_analysis.sqlite
new file mode 100644
index 0000000..0e9e27a
--- /dev/null
+++ b/2.cellprofiler_analysis/analysis_output/Plate_4/nf1_analysis.sqlite
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:954032099b6969f32241cde62588090ddd314255001505c44b29fa7d45d71d92
+size 134832128
diff --git a/2.cellprofiler_analysis/nf1_analysis.ipynb b/2.cellprofiler_analysis/nf1_analysis.ipynb
index 7873442..84fb6ca 100644
--- a/2.cellprofiler_analysis/nf1_analysis.ipynb
+++ b/2.cellprofiler_analysis/nf1_analysis.ipynb
@@ -5,7 +5,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Perform segmentation and feature extraction for each plate using CellProfiler"
+    "# Perform segmentation and feature extraction for each plate using CellProfiler Parallel"
    ]
   },
   {
@@ -22,11 +22,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import sys\n",
     "import pathlib\n",
+    "import pprint\n",
+    "\n",
+    "import sys\n",
     "\n",
-    "sys.path.append(\"../\")\n",
-    "from utils import cp_utils"
+    "sys.path.append(\"../utils\")\n",
+    "import cp_parallel"
    ]
   },
   {
@@ -34,9 +36,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Set paths for each plate\n",
-    "\n",
-    "Note: Due to the different channel numbers between plates 1 + 2 (3 channels) and plates 3 + 3 prime (4 channels), there needs to be two different cppipe files (like in the IC module). "
+    "## Set paths and variables"
    ]
   },
   {
@@ -45,59 +45,29 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# create output directory for SQLite files if needed\n",
-    "path_to_output = pathlib.Path(\"./analysis_output\").resolve()\n",
-    "path_to_output.mkdir(exist_ok=True)\n",
+    "# set the run type for the parallelization\n",
+    "run_name = \"analysis\"\n",
     "\n",
-    "# dictionary with paths for each plate\n",
-    "plates_info_dictionary = {\n",
-    "    \"Plate_1\": {\n",
-    "        # this pipeline is specific to plates 1 and 2\n",
-    "        \"path_to_pipeline\": pathlib.Path(\"NF1_analysis_plate1_plate2.cppipe\").resolve(\n",
-    "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_images\": pathlib.Path(\n",
-    "            \"../1.cellprofiler_ic/Corrected_Plate_1/\"\n",
-    "        ).resolve(strict=True),\n",
-    "    },\n",
-    "    \"Plate_2\": {\n",
-    "        # this pipeline is specific to plates 1 and 2\n",
-    "        \"path_to_pipeline\": pathlib.Path(\"NF1_analysis_plate1_plate2.cppipe\").resolve(\n",
-    "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_images\": pathlib.Path(\n",
-    "            \"../1.cellprofiler_ic/Corrected_Plate_2/\"\n",
-    "        ).resolve(strict=True),\n",
-    "    },\n",
-    "    \"Plate_3\": {\n",
-    "        # this pipeline is specific to plates 3 and 3'\n",
-    "        \"path_to_pipeline\": pathlib.Path(\"NF1_analysis_plate3_plate3prime.cppipe\").resolve(\n",
-    "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_images\": pathlib.Path(\n",
-    "            \"../1.cellprofiler_ic/Corrected_Plate_3/\"\n",
-    "        ).resolve(strict=True),\n",
-    "    },\n",
-    "    \"Plate_3_prime\": {\n",
-    "        # this pipeline is specific to plates 3 and 3'\n",
-    "        \"path_to_pipeline\": pathlib.Path(\"NF1_analysis_plate3_plate3prime.cppipe\").resolve(\n",
-    "            strict=True\n",
-    "        ),\n",
-    "        \"path_to_images\": pathlib.Path(\n",
-    "            \"../1.cellprofiler_ic/Corrected_Plate_3_prime/\"\n",
-    "        ).resolve(strict=True),\n",
-    "    },\n",
-    "}"
+    "# set main output dir for all plates\n",
+    "output_dir = pathlib.Path(\"./analysis_output\")\n",
+    "output_dir.mkdir(exist_ok=True)\n",
+    "\n",
+    "# directory where images are located within folders\n",
+    "images_dir = pathlib.Path(\"../1.cellprofiler_ic/Corrected_Images/\")\n",
+    "\n",
+    "# list for plate names based on folders to use to create dictionary\n",
+    "plate_names = []\n",
+    "# iterate through 0.download_data and append plate names from folder names that contain image data from that plate\n",
+    "for file_path in pathlib.Path(\"../0.download_data/\").iterdir():\n",
+    "    if str(file_path.stem).startswith(\"Plate\"):\n",
+    "        plate_names.append(str(file_path.stem))"
    ]
   },
   {
-   "attachments": {},
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Run analysis pipeline on each plate\n",
-    "\n",
-    "This cell is not finished to completion due to how long it would take. It is ran in the python file instead."
+    "## Create dictionary with all info for each plate"
    ]
   },
   {
@@ -109,13 +79,68 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Running analysis on Plate_1!\n",
-      "Starting CellProfiler run on Corrected_Plate_1\n",
-      "The file is renamed to Plate_1.sqlite!\n",
-      "Running analysis on Plate_2!\n",
-      "Starting CellProfiler run on Corrected_Plate_2\n"
+      "{   'Plate_1': {   'path_to_images': PosixPath('/home/jenna/nf1_cellpainting_data/1.cellprofiler_ic/Corrected_Images/Corrected_Plate_1'),\n",
+      "                   'path_to_output': PosixPath('analysis_output/Plate_1'),\n",
+      "                   'path_to_pipeline': PosixPath('/home/jenna/nf1_cellpainting_data/2.cellprofiler_analysis/NF1_analysis_3channel.cppipe')},\n",
+      "    'Plate_2': {   'path_to_images': PosixPath('/home/jenna/nf1_cellpainting_data/1.cellprofiler_ic/Corrected_Images/Corrected_Plate_2'),\n",
+      "                   'path_to_output': PosixPath('analysis_output/Plate_2'),\n",
+      "                   'path_to_pipeline': PosixPath('/home/jenna/nf1_cellpainting_data/2.cellprofiler_analysis/NF1_analysis_3channel.cppipe')},\n",
+      "    'Plate_3': {   'path_to_images': PosixPath('/home/jenna/nf1_cellpainting_data/1.cellprofiler_ic/Corrected_Images/Corrected_Plate_3'),\n",
+      "                   'path_to_output': PosixPath('analysis_output/Plate_3'),\n",
+      "                   'path_to_pipeline': PosixPath('/home/jenna/nf1_cellpainting_data/2.cellprofiler_analysis/NF1_analysis_4channel.cppipe')},\n",
+      "    'Plate_3_prime': {   'path_to_images': PosixPath('/home/jenna/nf1_cellpainting_data/1.cellprofiler_ic/Corrected_Images/Corrected_Plate_3_prime'),\n",
+      "                         'path_to_output': PosixPath('analysis_output/Plate_3_prime'),\n",
+      "                         'path_to_pipeline': PosixPath('/home/jenna/nf1_cellpainting_data/2.cellprofiler_analysis/NF1_analysis_4channel.cppipe')},\n",
+      "    'Plate_4': {   'path_to_images': PosixPath('/home/jenna/nf1_cellpainting_data/1.cellprofiler_ic/Corrected_Images/Corrected_Plate_4'),\n",
+      "                   'path_to_output': PosixPath('analysis_output/Plate_4'),\n",
+      "                   'path_to_pipeline': PosixPath('/home/jenna/nf1_cellpainting_data/2.cellprofiler_analysis/NF1_analysis_4channel.cppipe')}}\n"
      ]
-    },
+    }
+   ],
+   "source": [
+    "# create plate info dictionary with all parts of the CellProfiler CLI command to run in parallel\n",
+    "plate_info_dictionary = {\n",
+    "    name: {\n",
+    "        \"path_to_images\": pathlib.Path(\n",
+    "            list(images_dir.rglob(f\"Corrected_{name}\"))[0]\n",
+    "        ).resolve(strict=True),\n",
+    "        \"path_to_output\": pathlib.Path(f\"{output_dir}/{name}\"),\n",
+    "    }\n",
+    "    for name in plate_names\n",
+    "}\n",
+    "\n",
+    "# iterate over the dictionary and add the path_to_pipeline specific for each plate\n",
+    "for name, info in plate_info_dictionary.items():\n",
+    "    # only plates 1 and 2 have 3 channels so these are the only plates that use this path\n",
+    "    if name == \"Plate_1\" or name == \"Plate_2\":\n",
+    "        info[\"path_to_pipeline\"] = pathlib.Path(\n",
+    "            f\"./NF1_analysis_3channel.cppipe\"\n",
+    "        ).resolve(strict=True)\n",
+    "    # all other plates have 4 channels and will use that specific pipeline\n",
+    "    else:\n",
+    "        info[\"path_to_pipeline\"] = pathlib.Path(\n",
+    "            f\"./NF1_analysis_4channel.cppipe\"\n",
+    "        ).resolve(strict=True)\n",
+    "\n",
+    "# view the dictionary to assess that all info is added correctly\n",
+    "pprint.pprint(plate_info_dictionary, indent=4)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Run analysis pipeline on each plate in parallel\n",
+    "\n",
+    "This cell is not finished to completion due to how long it would take. It is ran in the python file instead."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
      "ename": "KeyboardInterrupt",
      "evalue": "",
@@ -123,33 +148,19 @@
      "traceback": [
       "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[3], line 7\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mRunning analysis on \u001b[39m\u001b[39m{\u001b[39;00mplate\u001b[39m}\u001b[39;00m\u001b[39m!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m      6\u001b[0m \u001b[39m# run analysis pipeline\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m cp_utils\u001b[39m.\u001b[39;49mrun_cellprofiler(\n\u001b[1;32m      8\u001b[0m     path_to_pipeline\u001b[39m=\u001b[39;49mpath_to_pipeline,\n\u001b[1;32m      9\u001b[0m     path_to_output\u001b[39m=\u001b[39;49mpath_to_output,\n\u001b[1;32m     10\u001b[0m     path_to_images\u001b[39m=\u001b[39;49mpath_to_images,\n\u001b[1;32m     11\u001b[0m     \u001b[39m# name each SQLite file after plate name\u001b[39;49;00m\n\u001b[1;32m     12\u001b[0m     sqlite_name\u001b[39m=\u001b[39;49mplate,\n\u001b[1;32m     13\u001b[0m     analysis_run\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m,\n\u001b[1;32m     14\u001b[0m )\n",
-      "File \u001b[0;32m~/nf1_cellpainting_data/2.cellprofiler_analysis/../utils/cp_utils.py:68\u001b[0m, in \u001b[0;36mrun_cellprofiler\u001b[0;34m(path_to_pipeline, path_to_output, path_to_images, sqlite_name, analysis_run)\u001b[0m\n\u001b[1;32m     65\u001b[0m \u001b[39mwith\u001b[39;00m \u001b[39mopen\u001b[39m(pathlib\u001b[39m.\u001b[39mPath(\u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mlogs/cellprofiler_output_\u001b[39m\u001b[39m{\u001b[39;00mpathlib\u001b[39m.\u001b[39mPath(path_to_images)\u001b[39m.\u001b[39mname\u001b[39m}\u001b[39;00m\u001b[39m.log\u001b[39m\u001b[39m\"\u001b[39m), \u001b[39m\"\u001b[39m\u001b[39mw\u001b[39m\u001b[39m\"\u001b[39m) \u001b[39mas\u001b[39;00m cellprofiler_output_file:\n\u001b[1;32m     66\u001b[0m     \u001b[39m# run CellProfiler for a illumination correction pipeline\u001b[39;00m\n\u001b[1;32m     67\u001b[0m     command \u001b[39m=\u001b[39m \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mcellprofiler -c -r -p \u001b[39m\u001b[39m{\u001b[39;00mpath_to_pipeline\u001b[39m}\u001b[39;00m\u001b[39m -o \u001b[39m\u001b[39m{\u001b[39;00mpath_to_output\u001b[39m}\u001b[39;00m\u001b[39m -i \u001b[39m\u001b[39m{\u001b[39;00mpath_to_images\u001b[39m}\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m---> 68\u001b[0m     subprocess\u001b[39m.\u001b[39;49mrun(command, shell\u001b[39m=\u001b[39;49m\u001b[39mTrue\u001b[39;49;00m, stdout\u001b[39m=\u001b[39;49mcellprofiler_output_file, stderr\u001b[39m=\u001b[39;49mcellprofiler_output_file)\n\u001b[1;32m     69\u001b[0m cellprofiler_output_file\u001b[39m.\u001b[39mclose()\n\u001b[1;32m     71\u001b[0m \u001b[39mif\u001b[39;00m analysis_run:\n\u001b[1;32m     72\u001b[0m     \u001b[39m# runs through any files that are in the output path\u001b[39;00m\n",
-      "File \u001b[0;32m~/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/subprocess.py:495\u001b[0m, in \u001b[0;36mrun\u001b[0;34m(input, capture_output, timeout, check, *popenargs, **kwargs)\u001b[0m\n\u001b[1;32m    493\u001b[0m \u001b[39mwith\u001b[39;00m Popen(\u001b[39m*\u001b[39mpopenargs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs) \u001b[39mas\u001b[39;00m process:\n\u001b[1;32m    494\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m--> 495\u001b[0m         stdout, stderr \u001b[39m=\u001b[39m process\u001b[39m.\u001b[39;49mcommunicate(\u001b[39minput\u001b[39;49m, timeout\u001b[39m=\u001b[39;49mtimeout)\n\u001b[1;32m    496\u001b[0m     \u001b[39mexcept\u001b[39;00m TimeoutExpired \u001b[39mas\u001b[39;00m exc:\n\u001b[1;32m    497\u001b[0m         process\u001b[39m.\u001b[39mkill()\n",
-      "File \u001b[0;32m~/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/subprocess.py:1020\u001b[0m, in \u001b[0;36mPopen.communicate\u001b[0;34m(self, input, timeout)\u001b[0m\n\u001b[1;32m   1018\u001b[0m         stderr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstderr\u001b[39m.\u001b[39mread()\n\u001b[1;32m   1019\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstderr\u001b[39m.\u001b[39mclose()\n\u001b[0;32m-> 1020\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mwait()\n\u001b[1;32m   1021\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1022\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/subprocess.py:1083\u001b[0m, in \u001b[0;36mPopen.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1081\u001b[0m     endtime \u001b[39m=\u001b[39m _time() \u001b[39m+\u001b[39m timeout\n\u001b[1;32m   1082\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1083\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_wait(timeout\u001b[39m=\u001b[39;49mtimeout)\n\u001b[1;32m   1084\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mKeyboardInterrupt\u001b[39;00m:\n\u001b[1;32m   1085\u001b[0m     \u001b[39m# https://bugs.python.org/issue25942\u001b[39;00m\n\u001b[1;32m   1086\u001b[0m     \u001b[39m# The first keyboard interrupt waits briefly for the child to\u001b[39;00m\n\u001b[1;32m   1087\u001b[0m     \u001b[39m# exit under the common assumption that it also received the ^C\u001b[39;00m\n\u001b[1;32m   1088\u001b[0m     \u001b[39m# generated SIGINT and will exit rapidly.\u001b[39;00m\n\u001b[1;32m   1089\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n",
-      "File \u001b[0;32m~/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/subprocess.py:1806\u001b[0m, in \u001b[0;36mPopen._wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m   1804\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mreturncode \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   1805\u001b[0m     \u001b[39mbreak\u001b[39;00m  \u001b[39m# Another thread waited.\u001b[39;00m\n\u001b[0;32m-> 1806\u001b[0m (pid, sts) \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_try_wait(\u001b[39m0\u001b[39;49m)\n\u001b[1;32m   1807\u001b[0m \u001b[39m# Check the pid and loop as waitpid has been known to\u001b[39;00m\n\u001b[1;32m   1808\u001b[0m \u001b[39m# return 0 even without WNOHANG in odd situations.\u001b[39;00m\n\u001b[1;32m   1809\u001b[0m \u001b[39m# http://bugs.python.org/issue14396.\u001b[39;00m\n\u001b[1;32m   1810\u001b[0m \u001b[39mif\u001b[39;00m pid \u001b[39m==\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpid:\n",
-      "File \u001b[0;32m~/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/subprocess.py:1764\u001b[0m, in \u001b[0;36mPopen._try_wait\u001b[0;34m(self, wait_flags)\u001b[0m\n\u001b[1;32m   1762\u001b[0m \u001b[39m\"\"\"All callers to this function MUST hold self._waitpid_lock.\"\"\"\u001b[39;00m\n\u001b[1;32m   1763\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[0;32m-> 1764\u001b[0m     (pid, sts) \u001b[39m=\u001b[39m os\u001b[39m.\u001b[39;49mwaitpid(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpid, wait_flags)\n\u001b[1;32m   1765\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mChildProcessError\u001b[39;00m:\n\u001b[1;32m   1766\u001b[0m     \u001b[39m# This happens if SIGCLD is set to be ignored or waiting\u001b[39;00m\n\u001b[1;32m   1767\u001b[0m     \u001b[39m# for child processes has otherwise been disabled for our\u001b[39;00m\n\u001b[1;32m   1768\u001b[0m     \u001b[39m# process.  This child is dead, we can't get the status.\u001b[39;00m\n\u001b[1;32m   1769\u001b[0m     pid \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpid\n",
+      "Cell \u001b[0;32mIn[4], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m cp_parallel\u001b[39m.\u001b[39;49mrun_cellprofiler_parallel(\n\u001b[1;32m      2\u001b[0m     plate_info_dictionary\u001b[39m=\u001b[39;49mplate_info_dictionary, run_name\u001b[39m=\u001b[39;49mrun_name\n\u001b[1;32m      3\u001b[0m )\n",
+      "File \u001b[0;32m~/nf1_cellpainting_data/2.cellprofiler_analysis/../utils/cp_parallel.py:119\u001b[0m, in \u001b[0;36mrun_cellprofiler_parallel\u001b[0;34m(plate_info_dictionary, run_name)\u001b[0m\n\u001b[1;32m    109\u001b[0m futures: List[Future] \u001b[39m=\u001b[39m [\n\u001b[1;32m    110\u001b[0m     executor\u001b[39m.\u001b[39msubmit(\n\u001b[1;32m    111\u001b[0m         subprocess\u001b[39m.\u001b[39mrun,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[39mfor\u001b[39;00m command \u001b[39min\u001b[39;00m commands\n\u001b[1;32m    116\u001b[0m ]\n\u001b[1;32m    118\u001b[0m \u001b[39m# the list of CompletedProcesses holds all the information from the CellProfiler run\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m results: List[subprocess\u001b[39m.\u001b[39mCompletedProcess] \u001b[39m=\u001b[39m [future\u001b[39m.\u001b[39mresult() \u001b[39mfor\u001b[39;00m future \u001b[39min\u001b[39;00m futures]\n\u001b[1;32m    121\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mAll processes have been completed!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    123\u001b[0m \u001b[39m# for each process, confirm that the process completed succesfully and return a log file\u001b[39;00m\n",
+      "File \u001b[0;32m~/nf1_cellpainting_data/2.cellprofiler_analysis/../utils/cp_parallel.py:119\u001b[0m, in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    109\u001b[0m futures: List[Future] \u001b[39m=\u001b[39m [\n\u001b[1;32m    110\u001b[0m     executor\u001b[39m.\u001b[39msubmit(\n\u001b[1;32m    111\u001b[0m         subprocess\u001b[39m.\u001b[39mrun,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    115\u001b[0m     \u001b[39mfor\u001b[39;00m command \u001b[39min\u001b[39;00m commands\n\u001b[1;32m    116\u001b[0m ]\n\u001b[1;32m    118\u001b[0m \u001b[39m# the list of CompletedProcesses holds all the information from the CellProfiler run\u001b[39;00m\n\u001b[0;32m--> 119\u001b[0m results: List[subprocess\u001b[39m.\u001b[39mCompletedProcess] \u001b[39m=\u001b[39m [future\u001b[39m.\u001b[39;49mresult() \u001b[39mfor\u001b[39;00m future \u001b[39min\u001b[39;00m futures]\n\u001b[1;32m    121\u001b[0m \u001b[39mprint\u001b[39m(\u001b[39m\"\u001b[39m\u001b[39mAll processes have been completed!\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m    123\u001b[0m \u001b[39m# for each process, confirm that the process completed succesfully and return a log file\u001b[39;00m\n",
+      "File \u001b[0;32m~/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/concurrent/futures/_base.py:439\u001b[0m, in \u001b[0;36mFuture.result\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39m==\u001b[39m FINISHED:\n\u001b[1;32m    437\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m__get_result()\n\u001b[0;32m--> 439\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_condition\u001b[39m.\u001b[39;49mwait(timeout)\n\u001b[1;32m    441\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_state \u001b[39min\u001b[39;00m [CANCELLED, CANCELLED_AND_NOTIFIED]:\n\u001b[1;32m    442\u001b[0m     \u001b[39mraise\u001b[39;00m CancelledError()\n",
+      "File \u001b[0;32m~/anaconda3/envs/nf1_cellpainting_data/lib/python3.8/threading.py:302\u001b[0m, in \u001b[0;36mCondition.wait\u001b[0;34m(self, timeout)\u001b[0m\n\u001b[1;32m    300\u001b[0m \u001b[39mtry\u001b[39;00m:    \u001b[39m# restore state no matter what (e.g., KeyboardInterrupt)\u001b[39;00m\n\u001b[1;32m    301\u001b[0m     \u001b[39mif\u001b[39;00m timeout \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m--> 302\u001b[0m         waiter\u001b[39m.\u001b[39;49macquire()\n\u001b[1;32m    303\u001b[0m         gotit \u001b[39m=\u001b[39m \u001b[39mTrue\u001b[39;00m\n\u001b[1;32m    304\u001b[0m     \u001b[39melse\u001b[39;00m:\n",
       "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
      ]
     }
    ],
    "source": [
-    "# run through each plate with each set of paths based on dictionary\n",
-    "for plate, info in plates_info_dictionary.items():\n",
-    "    path_to_pipeline = info[\"path_to_pipeline\"]\n",
-    "    path_to_images = info[\"path_to_images\"]\n",
-    "    print(f\"Running analysis on {plate}!\")\n",
-    "\n",
-    "    # run analysis pipeline\n",
-    "    cp_utils.run_cellprofiler(\n",
-    "        path_to_pipeline=path_to_pipeline,\n",
-    "        path_to_output=path_to_output,\n",
-    "        path_to_images=path_to_images,\n",
-    "        # name each SQLite file after plate name\n",
-    "        sqlite_name=plate,\n",
-    "        analysis_run=True,\n",
-    "    )"
+    "cp_parallel.run_cellprofiler_parallel(\n",
+    "    plate_info_dictionary=plate_info_dictionary, run_name=run_name\n",
+    ")"
    ]
   }
  ],
diff --git a/2.cellprofiler_analysis/scripts/nf1_analysis.py b/2.cellprofiler_analysis/scripts/nf1_analysis.py
index d124894..e08b8d9 100644
--- a/2.cellprofiler_analysis/scripts/nf1_analysis.py
+++ b/2.cellprofiler_analysis/scripts/nf1_analysis.py
@@ -1,92 +1,86 @@
 #!/usr/bin/env python
 # coding: utf-8
 
-# # Perform segmentation and feature extraction for each plate using CellProfiler
+# # Perform segmentation and feature extraction for each plate using CellProfiler Parallel
 
 # ## Import libraries
 
 # In[1]:
 
 
-import sys
 import pathlib
+import pprint
 
-sys.path.append("../")
-from utils import cp_utils
+import sys
 
+sys.path.append("../utils")
+import cp_parallel
 
-# ## Set paths for each plate
-# 
-# Note: Due to the different channel numbers between plates 1 + 2 (3 channels) and plates 3 + 3 prime (4 channels), there needs to be two different cppipe files (like in the IC module). 
+
+# ## Set paths and variables
 
 # In[2]:
 
 
-# create output directory for SQLite files if needed
-path_to_output = pathlib.Path("./analysis_output").resolve()
-path_to_output.mkdir(exist_ok=True)
+# set the run type for the parallelization
+run_name = "analysis"
 
-# dictionary with paths for each plate
-plates_info_dictionary = {
-    "Plate_1": {
-        # this pipeline is specific to plates 1 and 2
-        "path_to_pipeline": pathlib.Path("NF1_analysis_plate1_plate2.cppipe").resolve(
-            strict=True
-        ),
-        "path_to_images": pathlib.Path(
-            "../1.cellprofiler_ic/Corrected_Plate_1/"
-        ).resolve(strict=True),
-    },
-    "Plate_2": {
-        # this pipeline is specific to plates 1 and 2
-        "path_to_pipeline": pathlib.Path("NF1_analysis_plate1_plate2.cppipe").resolve(
-            strict=True
-        ),
-        "path_to_images": pathlib.Path(
-            "../1.cellprofiler_ic/Corrected_Plate_2/"
-        ).resolve(strict=True),
-    },
-    "Plate_3": {
-        # this pipeline is specific to plates 3 and 3'
-        "path_to_pipeline": pathlib.Path("NF1_analysis_plate3_plate3prime.cppipe").resolve(
-            strict=True
-        ),
-        "path_to_images": pathlib.Path(
-            "../1.cellprofiler_ic/Corrected_Plate_3/"
-        ).resolve(strict=True),
-    },
-    "Plate_3_prime": {
-        # this pipeline is specific to plates 3 and 3'
-        "path_to_pipeline": pathlib.Path("NF1_analysis_plate3_plate3prime.cppipe").resolve(
-            strict=True
-        ),
+# set main output dir for all plates
+output_dir = pathlib.Path("./analysis_output")
+output_dir.mkdir(exist_ok=True)
+
+# directory where images are located within folders
+images_dir = pathlib.Path("../1.cellprofiler_ic/Corrected_Images/")
+
+# list for plate names based on folders to use to create dictionary
+plate_names = []
+# iterate through 0.download_data and append plate names from folder names that contain image data from that plate
+for file_path in pathlib.Path("../0.download_data/").iterdir():
+    if str(file_path.stem).startswith("Plate"):
+        plate_names.append(str(file_path.stem))
+
+
+# ## Create dictionary with all info for each plate
+
+# In[3]:
+
+
+# create plate info dictionary with all parts of the CellProfiler CLI command to run in parallel
+plate_info_dictionary = {
+    name: {
         "path_to_images": pathlib.Path(
-            "../1.cellprofiler_ic/Corrected_Plate_3_prime/"
+            list(images_dir.rglob(f"Corrected_{name}"))[0]
         ).resolve(strict=True),
-    },
+        "path_to_output": pathlib.Path(f"{output_dir}/{name}"),
+    }
+    for name in plate_names
 }
 
+# iterate over the dictionary and add the path_to_pipeline specific for each plate
+for name, info in plate_info_dictionary.items():
+    # only plates 1 and 2 have 3 channels so these are the only plates that use this path
+    if name == "Plate_1" or name == "Plate_2":
+        info["path_to_pipeline"] = pathlib.Path(
+            f"./NF1_analysis_3channel.cppipe"
+        ).resolve(strict=True)
+    # all other plates have 4 channels and will use that specific pipeline
+    else:
+        info["path_to_pipeline"] = pathlib.Path(
+            f"./NF1_analysis_4channel.cppipe"
+        ).resolve(strict=True)
+
+# view the dictionary to assess that all info is added correctly
+pprint.pprint(plate_info_dictionary, indent=4)
 
-# ## Run analysis pipeline on each plate
+
+# ## Run analysis pipeline on each plate in parallel
 # 
 # This cell is not finished to completion due to how long it would take. It is ran in the python file instead.
 
-# In[3]:
+# In[4]:
 
 
-# run through each plate with each set of paths based on dictionary
-for plate, info in plates_info_dictionary.items():
-    path_to_pipeline = info["path_to_pipeline"]
-    path_to_images = info["path_to_images"]
-    print(f"Running analysis on {plate}!")
-
-    # run analysis pipeline
-    cp_utils.run_cellprofiler(
-        path_to_pipeline=path_to_pipeline,
-        path_to_output=path_to_output,
-        path_to_images=path_to_images,
-        # name each SQLite file after plate name
-        sqlite_name=plate,
-        analysis_run=True,
-    )
+cp_parallel.run_cellprofiler_parallel(
+    plate_info_dictionary=plate_info_dictionary, run_name=run_name
+)
 
diff --git a/utils/cp_parallel.py b/utils/cp_parallel.py
new file mode 100644
index 0000000..d2acfe6
--- /dev/null
+++ b/utils/cp_parallel.py
@@ -0,0 +1,134 @@
+"""
+This collection of functions runs CellProfiler in parallel and can convert the results into log files
+for each process.
+"""
+
+import multiprocessing
+from typing import List
+import os
+import subprocess
+import pathlib
+from concurrent.futures import ProcessPoolExecutor, Future
+from errors.exceptions import MaxWorkerError
+
+
+def results_to_log(
+    results: List[subprocess.CompletedProcess], log_dir: pathlib.Path, run_name: str
+) -> None:
+    """
+    This function will take the list of subprocess.results from a CellProfiler parallelization run and
+    convert into a log file for each process.
+
+    Args:
+        results (List[subprocess.CompletedProcess]): the outputs from a subprocess.run
+        log_dir (pathlib.Path): directory for log files
+        run_name (str): a given name for the type of CellProfiler run being done on the plates (example: whole image features)
+    """
+    # Access the command (args) and stderr (output) for each CompletedProcess object
+    for result in results:
+        # assign plate name and decode the CellProfiler output to use in log file
+        plate_name = result.args[6].name
+        output_string = result.stderr.decode("utf-8")
+
+        # set log file name as plate name from command
+        log_file_path = pathlib.Path(f"{log_dir}/{plate_name}_{run_name}_run.log")
+        # print output to a log file for each plate to view after the run
+        with open(log_file_path, "w") as log_file:
+            log_file.write(plate_name + "\n")
+            log_file.write(output_string + "\n")
+
+
+def run_cellprofiler_parallel(
+    plate_info_dictionary: dict,
+    run_name: str,
+) -> None:
+    """
+    This function utilizes multi-processing to run CellProfiler pipelines in parallel.
+
+    Args:
+        plate_info_dictionary (dict): dictionary with all paths for CellProfiler to run a pipeline
+        run_name (str): a given name for the type of CellProfiler run being done on the plates (example: whole image features)
+
+    Raises:
+        FileNotFoundError: if paths to pipeline and images do not exist
+    """
+    # create a list of commands for each plate with their respective log file
+    commands = []
+
+    # make logs directory
+    log_dir = pathlib.Path("./logs")
+    os.makedirs(log_dir, exist_ok=True)
+
+    # iterate through each plate in the dictionary
+    for _, info in plate_info_dictionary.items():
+        # set paths for CellProfiler
+        path_to_pipeline = info["path_to_pipeline"]
+        path_to_images = info["path_to_images"]
+        path_to_output = info["path_to_output"]
+
+        # check to make sure paths to pipeline and directory of images are correct before running the pipeline
+        if not pathlib.Path(path_to_pipeline).resolve(strict=True):
+            raise FileNotFoundError(
+                f"The file '{pathlib.Path(path_to_pipeline).name}' does not exist"
+            )
+        if not pathlib.Path(path_to_images).is_dir():
+            raise FileNotFoundError(
+                f"Directory '{pathlib.Path(path_to_images).name}' does not exist or is not a directory"
+            )
+        # make output directory if it is not already created
+        pathlib.Path(path_to_output).mkdir(exist_ok=True)
+
+        # creates a command for each plate in the list
+        command = [
+            "cellprofiler",
+            "-c",
+            "-r",
+            "-p",
+            path_to_pipeline,
+            "-o",
+            path_to_output,
+            "-i",
+            path_to_images,
+        ]
+        # creates a list of commands
+        commands.append(command)
+
+    # set the number of CPUs/workers as the number of commands
+    num_processes = len(commands)
+
+    # make sure that the number of workers does not exceed the maximum number of workers for the machine
+    if num_processes > multiprocessing.cpu_count():
+        raise MaxWorkerError(
+            "Exception occurred: The number of commands exceeds the number of CPUs/workers. Please reduce the number of commands."
+        )
+
+    # set parallelization executer to the number of commands
+    executor = ProcessPoolExecutor(max_workers=num_processes)
+
+    # creates a list of futures that are each CellProfiler process for each plate
+    futures: List[Future] = [
+        executor.submit(
+            subprocess.run,
+            args=command,
+            capture_output=True,
+        )
+        for command in commands
+    ]
+
+    # the list of CompletedProcesses holds all the information from the CellProfiler run
+    results: List[subprocess.CompletedProcess] = [future.result() for future in futures]
+
+    print("All processes have been completed!")
+
+    # for each process, confirm that the process completed succesfully and return a log file
+    for result in results:
+        plate_name = result.args[6].name
+        # convert the results into log files
+        results_to_log(results=results, log_dir=log_dir, run_name=run_name)
+        if result.returncode == 1:
+            print(
+                f"A return code of {result.returncode} was returned for {plate_name}, which means there was an error in the CellProfiler run."
+            )
+
+    # to avoid having multiple print statements due to for loop, confirmation that logs are converted is printed here
+    print("All results have been converted to log files!")
diff --git a/utils/cp_utils.py b/utils/cp_sequential.py
similarity index 100%
rename from utils/cp_utils.py
rename to utils/cp_sequential.py
diff --git a/utils/errors/exceptions.py b/utils/errors/exceptions.py
new file mode 100644
index 0000000..71b1426
--- /dev/null
+++ b/utils/errors/exceptions.py
@@ -0,0 +1,9 @@
+"""
+This class defines a custom exception class for exceeding the max workers on a machine.
+"""
+
+class MaxWorkerError(Exception):
+    """
+    Raised when the number of workers assigned to `max_workers` exceeds the number of CPU/workers on the machine. 
+    """
+    pass