ArticleBMC bioinformatics2025
Calciumnetexplorer: an R package for network analysis of calcium imaging data.
Article in BMC bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- A human lysosomal storage disorder toolkit for decoding proteome landscapes in cortical-like and dopaminergic-like induced neurons.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Proteome Landscapes Decode Organelle Vulnerabilities in cortical and dopaminergic-like induced neurons Across Lysosomal Storage Disorders.bioRxiv : the preprint server for biology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
backgroundAnalyzing calcium imaging data to understand complex functional networks can be challenging, often requiring multiple tools, custom scripts, and some coding expertise. To address these challenges, we present CalciumNetExploreR (CNER), an R package designed to streamline and standardize the analysis of time-series data from neuronal populations.
resultsCNER integrates essential steps-normalization, binarization, population activity visualization, network construction, degree distribution analysis, principal component analysis, power spectral density evaluation, and event frequency calculations-into a single, cohesive pipeline. This comprehensive approach enables users to efficiently extract and compare network metrics, including clustering coefficients, global efficiency, community structures, and principal component variances. By offering a flexible and customizable framework, CNER simplifies the examination of functional connectivity and network topology, effectively providing the means to characterize a cellular functional network or analogous structures in other modalities.
conclusionDesigned as a user-friendly package, CNER allows both experimental and computational neuroscientists to incorporate robust statistical and graphical analyses into their workflows without extensive coding knowledge. By unifying key analytical components into one pipeline, CNER reduces barriers associated with large-scale data analyses, ultimately facilitating deeper insights into the functional organization and dynamic properties of neuronal networks across diverse recording techniques.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.