TACI_python_version

This repository contains python codes and data analysis pipelines for 3D calcium imaging analysis on thermosensory neurons in drosophila.

---

Table of Contents

• Project Overview
• Data
• Installation
• Description
• File Overview
• Input and Output File Organization
• Usage Instructions
  • Generate Background
  • Individual ΔF/F₀ Calculation
  • Merging Results
• Example Directory Structures
• Troubleshooting
• License
• Contact

---

Projext Overview

Installation and python testing

1. Clone or download this repository.
2. Create a python virtrual environment.
3. Install dependencies:

   python -m pip install --upgrade pip
   python -m pip install -r requirements.txt

4. Test scripts usging demo data

   python .\CIAanalysis_120min.py -i path/demo_analysis --merge --cell_type DOWC
   python .\CITbind_dynamic.py -i path/demo_cbind -n 2
   

Description

These python scripts allows batch processing and analysis of calcium imaging datasets collected from Drosophila larvae or other small model systems. The pipline has three main stages:

1. Fluorescence extraction using TrackMate in ImageJ
   insert the user manual for the steps in Trackmate here!
2. Primary Analysis (CIAnalysis_120min.py)
   This is the main script for the processing individual calcium imaging datasets.
   It automatially: • Generates background values from background_i.xlsx
   • Extracts fluorescence change
           • DOCC: Uses the first timepoint fluorescence as F₀
           • DOWC: Uses the minimum fluorescence in the first cooling and warming cycle as Fmin
   • Merges individual neuron results into a summary CSV and plot
   Supporting modules that run internally:
   

File Name	Description
Generate_background.py	Generates background list creation.
individual_dFoverF0_1.py	ΔF/F0 calculation for DOCC.
individual_dFoverF0_DOWC.py	ΔF/Fmin calculation for DOwC.
merge_dFoverF0_1.py	Merges all neuron result files into a combined dataset.
utility.py	Shared utility functions across all modules.

3. Temperature Binding (CITbind_dynamic.py)
   After individual sample processing, this script combines temperature data and calcium imaging results and generates aligned dual-axis plots.
   • Generate summarized time-synchronized combined CSVs
   • Generate plots of ΔF/F₀ and temperature (two versions: default and y-range limited)
   

4. Summary Visualization (data_summary.py)
   Once all individual samples have been processed, this script:
   • Computes mean ± SEM
   • Outputs a stacked plot (combined_gradient_plot.png) showing:
           • Top panel: Average ΔF/F₀ or ΔF/Fmin
           • Bottom panel: Temperature curve