Detailed Usage

Page Contents

• Starting SCC
• Batch Mode and Preview Mode Parameters
  • Spreads Only
  • Make Output Images
  • Selection Strength
  • Fluorescent
  • Size
  • Minimum and Maximum Area
  • Circularity Threshold
  • Channel Identifier
• Co-expression Parameters
  • Select Contour Files
  • Add Contours to Output Images

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Starting SCC

• Run Anaconda Prompt

• Run conda activate <environment name>

• Navigate to the location of scc.py, in the SimpylCellCounter directory

  • Use the cd (change directory) command
    • The format is cd <path to directory>
    • Absolute paths (e.g., cd C:\Users\ReevesC\Documents\ArguelloLab\SimpylCellCounter) and relative paths (e.g., if I were in the "Documents" directory, cd ArguelloLab\SimpylCellCounter) are both valid
    • Use cd ~ to navigate to your home directory (i.e., "C:\Users\ReevesC" for me)
    • Use cd .. to navigate up one directory if need be
    • Pressing Tab will autocomplete entries
    • The ls command will display a list of directories and files in the current directory
  • Tip: You can copy the address of scc.py in File Explorer or Finder and paste it into the "cd" command to avoid typing

• Run python scc.py

  • After a few seconds, SCC's GUI should pop up

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Editing and Saving Parameters

Parameters can all be adjusted in the current instance of SCC, but their states will not be saved across instances. However, parameters can be exported and stored in a .ini config file which can then be imported into another instance.

To export the current parameters, select the Config dropdown at the top left of the GUI. Then select Export and choose a file name and location.

To import a past set of parameters, select Import in the Config dropdown and navigate to the desired file.

• Note: Old config files may not work after you update SCC. However, parameters can still be manually retrieved from config files by opening them in a text editor such as NotePad or TextEdit.

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Batch Mode and Preview Mode Parameters

Preview Mode allows the user to select a single image which SCC will quickly analyze and give a detailed visual output. Use to determine whether your parameters are well tuned to your data set.

Batch Mode allows the user to select a folder within which SCC will analyze all images and output cell counts and summary data for each.

Spreads Only

• Checkbox
• Batch mode will not count cells and will only report the background brightness of each image and the difference in brightness between the background and cells in the image. Use to quickly get an overview of the data set and an idea of how strong selection strength should be.

Make Output Images

• Checkbox
• Batch mode will output copies of each image it analyzes with cells and filtered contours outlined. A legend with counts is displayed at the top left of each image.

Selection Strength

• Float [0.0, 50.0]
• How strongly tissue must contrast the tissue around it to become a contour. Higher values will restrict counts to more contrasting cells.

Fluorescent

• Checkbox
• Whether the images being analyzed are fluorescent. Note: SCC will not work on bright-field and fluorescent images simultaneously.

Size

• Integer [1, 100]
• The minimum radius, in pixels regions of contrasting tissue must be to become a contour. This will approximately be the pixel radius of the smallest cells counted.

Minimum and Maximum Area

• Integer [0, 4,000], Integer [0, 10,000]
• The range of areas contained within contours which will pass the area filter. Non-Spreads-Only Batch Mode outputs the average area of counted cells, which can help in tuning this parameter.

Circularity Threshold

• Float [0.00, 1.00]
• The minimum degree of circularity a contour must have to pass the circularity filter. Higher values will restrict counts to more circular cells.

Channel Identifier

• String
• The string that is part of the name of each image in the channel currently being analyzed. Only fill in if analyzing a channel for future coexpression analysis. Leaving blank will cause SCC to not store the location of each counted cell.
• E.g., if images of the same tissue on different channels were named subj45_vHPP_4_L_cFOS.png and subj45_vHPP_4_L_CTB.png, their channel identifiers would be cFOS and CTB, respectively

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Co-expression Parameters

The Co-expression tab serves to identify cells that appear in the same locations on previously analyzed datasets run with the Channel Identifier parameter filled in.

IMPORTANT: When analyzing tissue for co-expression, images of the same tissue must have the exact same name, other than the Channel Identifier for each.

• E.g., if analyzing a dataset of cFos and CTB images, file names could look like subj45_vHPP_4_L_cFOS.png and subj45_vHPP_4_L_CTB.png.

Select Contour Files

• .npz files are the contour files saved from previous Batch Mode instances, and are saved under the same name as the .csv output file for each such instance.
• SCC will look for cells expressed on all channels in files; i.e., if three files are selected, SCC will only count cells appearing on all three channels.

Add Contours to Output Images

• Checkbox
• Co-expression Analysis will create new output images with co-expressing cells outlined. If output images already exist from Batch Mode runs, co-expression will add on to them; otherwise, the new output images will have only co-expressing cells outlined.

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For help interpreting SCC's outputs, check out SCC Outputs