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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Glia. 2022 Mar 17;70(8):1554–1580. doi: 10.1002/glia.24168

TABLE 4.

Computational tools for quantifying astrocyte or neuronal activitya

Select tools for quantifying astrocyte excitation
Name Properties References
AQuA

  • Java- and MATLAB-based

  • Probabilistically principled, unbiased, data-driven framework

 Wang et al., 2019
CaSCaDe

  • MATLAB-based

  • Developed for multi-photon data

 Agarwal et al., 2017
GECIquant

  • Java-based

  • Semi-automated, region of interest (ROI)-based framework

 Venugopal et al., 2019
N/A

  • MATLAB-based

  • Automated region of activity (ROA)-based analysis algorithm

 Bojarskaite et al., 2020
Select tools for quantifying neuronal activity
CalmAn

  • Python-based

  • Suitable for one- and multi-photon imaging data

  • Enables real-time analysis on streaming data

 Giovannucci et al., 2019
CASCADE

  • Python-based

  • Developed for multi-photon data

  • Enables spike inference from calcium imaging data

 Rupprecht et al., 2021
EXTRACT

  • MATLAB-based

  • Estimation theory-based

 Inan et al., 2021
EZcalcium

  • MATLAB-based

  • Developed for multi-photon data

 Cantu et al., 2020
Minian

  • Python-based

  • Developed for one-photon data

 Dong, Mau, et al., 2021
MIN1PIPE

  • MATLAB-based

  • Developed for one-photon data

 Lu et al., 2018
OnACID-E

  • Python-based

  • Designed for online analysis of one-photon data

 Friedrich et al., 2021
Suite2p

  • Python-based

  • Developed for multi-photon data

 Pachitariu et al., 2017
a

This table does not cover denoising algorithms (e.g., Deep Interpolation) (Lecoq et al., 2021), which might enhance signal extraction from noisy data.