Introduction to Calcium Imaging Analysis
Analyze calcium imaging data from TIFF stacks to neuronal activity using trace extraction, spike inference, and tools like CaImAn and Suite2P.
Authors
This course introduces key methods for analyzing calcium imaging data, from handling TIFF and OME-TIFF image stacks to interpreting fluorescence signals as indicators of neuronal activity. It covers trace extraction, spike inference through deconvolution and machine learning, and the identification of regions of interest. The content also highlights software tools such as CaImAn and Suite2P, which provide efficient, reproducible workflows for large-scale imaging experiments.
Credits
Installation
To run the course materials on your own machine, it is recommended that you:
- Install VSCode as your editor
- Install pixi or alternatively conda to create virtual Python environments (see the lessons on environment and package management)
- Create a dedicated folder for this course and install the virtual environment:
Download the pixi.toml file and install the environment:
pixi install --manifest-path pixi.toml
pixi shellDownload the environment.yml file and install the environment:
conda env create -f environment.yml
conda activate calcium_imagingCourse Contents
Calcium Imaging Dataset Formats
Going From Calcium Traces to Spikes
Fundamentals of Spike-Train Analysis
Spike train analysis in Python using NumPy and matplotlib
Single-Pixel Fluorescence in Calcium Imaging
Intuition for extraction of calcium Fluorescence from raw data
Spike Inference from Calcium Traces
ROI Selection
Multi-Pixel Data Extraction to 1D Calcium Trace
Averaging fluorescence from all the pixels in the ROI
Multi-Cell Manual ROI Selection in Napari
Manual selection of multiple ROIs
Automated Cell Segmentation with scikit-image
Automated ROI selection