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Calciumnetexplorer: an R package for network analysis of calcium imaging data
1Centre for Discovery Brain Sciences, University of Edinburgh, 49 Little France Crescent, Edinburgh, EH16 4SB, UK. s.lenci@ed.ac.uk.
BMC Bioinformatics
|August 26, 2025
Summary
CalciumNetExploreR (CNER) is a new R package that simplifies calcium imaging data analysis. It provides a user-friendly pipeline for exploring neuronal functional networks and their dynamic properties.
Area of Science:
- Neuroscience
- Computational Biology
- Data Science
Background:
- Analyzing complex functional networks from calcium imaging data is challenging.
- Requires multiple tools, custom scripts, and coding expertise.
- Need for streamlined and standardized analysis of neuronal population time-series data.
Purpose of the Study:
- Introduce CalciumNetExploreR (CNER), an R package.
- Streamline and standardize the analysis of neuronal population time-series data.
- Facilitate deeper insights into functional organization and dynamic properties of neuronal networks.
Main Methods:
- CNER integrates normalization, binarization, and population activity visualization.
- Includes network construction, degree distribution, and principal component analysis.
- Evaluates power spectral density and event frequency calculations within a cohesive pipeline.
Main Results:
- Enables efficient extraction and comparison of network metrics (clustering coefficients, global efficiency, community structures).
- Simplifies examination of functional connectivity and network topology.
- Provides means to characterize cellular functional networks and analogous structures.
Conclusions:
- CNER is a user-friendly package for neuroscientists.
- Allows incorporation of robust statistical and graphical analyses without extensive coding.
- Reduces barriers in large-scale data analysis for deeper insights into neuronal networks.
