SICKO - Systematic Imaging of C. elegans Killing Organisms

Developed by Samuel Freitas, Luis Espejo, and George Sutphin (PI) at the University of Arizona

This repository is a complete data computation and analysis package for the SICKO -- Patent pending -- system.

SICKO was initially developed to quantitatively analyze infection progression of fluorescently tagged E. coli and P. aeruginosa in a C. elegans system, consequently SICKO can analyze any fluorescent-type signal in a longitudinal analysis of C. elegans

For the post processing scripts please see the Full statistical analysis and Figure production -- https://github.com/lespejo1990/SICKO_Analysis

Preprint

Read our preprint here -- https://www.biorxiv.org/content/10.1101/2023.02.17.529009v2

Features

• Invidiualized longitudinal analysis of infected C. elegans
• Automatic comparison and analysis
• Automatic Heatmap creation

Usage

The basic steps of using SICKO are:

1. SICKO ------- SICKO_2022.m

   • Explanation: A Full GUI for selecting and censoring the fluorescent data
   • Usage: Full usage breakdown in the SICKO "Protocol.pdf" in the scripts folder
     • Output(s): many csv(s) in each individual folder each representing a single days worth of data for each of the experiment subsections (plates) or groups of images

2. combine ----- combine_csv.m

   • Explanation: Combines the many csv(s) from all(any of) the replicates into a single csv file for further processing; furthermore, associates all metadata (area,intensity,dead,fled) with the specific animal
   • Usage: When ran, select the experiments folder that contains the experiemntal replicate(s)
     • Output(s): a single csv in the 'outputs' folder titled 'experiment_name_N.csv'

3. compile ----- compile_data.m

   • Explanation: Compiles the data from the 'experiment_name_N.csv' into a single longitudinal based array for each specific animal
   • Usage: When ran, select the 'experiment_name_N.csv'
     • Output(s): a single csv in the 'outputs' folder titled 'experiment_name_N_compiled.csv'

4. analyze ----- analyze_data_heatmap.m

   • Explanation: Data analysis for the compiled data, and implementation of the 'SICKO coefficient' for deatn incorporation in logitudinal studies
   • Usage: When ran, select the 'experiment_name_N_compiled.csv'
   • if SICKO coefficient is to be used, select 'yes' on the next window and enter the associated numbers with the experiment
     • Output(s): a single csv in the 'outputs' folder titled 'experiment_name_N_compiled_analyzed.csv'
     • the cumulative sum and heatmap data graphs (with and without SICKO coefficient if 'yes' selected, otherwise just standard outputs)

File setup

Required Packages

• MATLAB 2020+
• Image processing toolbox

Installation (github desktop)

• copy this URL

https://github.com/Sam-Freitas/SICKO

• Go to File>Clone repository (Ctrl+Shift+O)
• On the top bar click on the URL tab
• Paste the previously copied URL and click 'Clone'

Installation (git)

cd Documents
git clone https://github.com/Sam-Freitas/SICKO

Common errors that crop up

• "erase" or any error during combine_csv.m
  • Make sure that there are the correct amount of csvs (6) for the associated conditions*days
  • Delete the extra csvs or make sure that you ran the SICKO script on all the data
  • Extra csvs can crop up when incorrect folders are present
• Heatmap only showing one (or just a few) of the data points
  • One (or a few) of the data points might have not been censored when necessary
  • You can manually find the non-matching data using the compiled csv
  • Once identified find the corresponding censor.csv and rerun the combine/compile/analyze scripts

Example data (publication coming soon)