AQuA2 (Activity Quantification and Analysis) is a tool for quantifying spatiotemporal signals across biosensors, cell types, organs, animal models and imaging modalities of biological fluorescent imaging data.
If you have any feedback or issue, you are welcome to either post issue in Issues section or send email to yug@tsinghua.edu.cn (Guoqiang Yu at Tsinghua University).
- In vivo and ex vivo
- Neuron, astrocyte, oligodendrocyte
- Calcium, ATP, NE, GABA, dopamine
- 2D and 3D
- Single-color and dual-color
- And more
- Preprocessing - dF
- Statistical test - Active region
- Temporal segmentation - Super events
- Spatial segmentation - Events
- Consensus Functional Unit (CFU) definition:
- If one spatial region generates repeated signal events, it is more likely to be a functional unit, and we refer to such a region as a CFU. This concept offers greater flexibility compared to ROIs, allowing each occurrence of signals to have different sizes, shapes, and propagation patterns while maintaining consistent spatial foundations. The derived CFU could be single cell, cell group, tissue, or organ.
- CFU identification
- Interaction analysis between CFUs
- CFU grouping
- Event-level features
- Location of events
- Basic features of individual events, including voxel set, duration, area size, average curve of event's spatial footprint, average dF curve of spatial footprint, rising time, peak p-value, area under the curve (AUC), and others.
- Propagation-related features, including propagation speed, propagation map, and propagation trend in various directions.
- Network features, which involve the distances between events and user-defined regions (e.g., cell regions or landmarks), as well as assessing the co-occurrence of events in spatial or temporal dimensions.
- CFU-level features
- Individual CFU information, including the spatial map, event sequence, average curve, and average dF curve.
- The dependency between every pair of CFUs, as well as the relative delay between two CFUs.
- The information of CFU groups, including CFU indexes and the relative delay of each CFU.
- Similar GUI as AQuA
- Step by step guide
- Event viewer
- Feature visualizer
- Proofreading and filtering
- Side by side view
- Region and landmark tool
- And more
- Step by step guide
- Event viewer
- Feature visualizer
- Proofreading and filtering
- Side by side view
- Region and landmark tool
- And more
- Download latest version here.
- Unzip the downloaded file.
- Start MATLAB.
- Switch the current folder to AQuA2's folder.
- Double click
aqua_gui.m, or typeaqua_guiin MATLAB command line.
We recommend MATLAB versions later than 2022b. For 3D imaging data, we recommend to use MATLAB 2022b.
- Double click
aqua_batch.mfile. - Set the folder path 'pIn', and for each target dataset, set the parameters in
AQuA2/cfg/parameters_for_batch.csv. Each dataset is corresponding to one parameter setting. - Run the file.
- The output files will be saved in subfolders of 'pOut'.
- Fiji plugin version of AQuA can be found here.
If you are using AQuA2 for the first time, please read the step by step user guide.
Or you can check the details on output files, extracted features, and parameter settings.
You can try these real data sets in AQuA2.
Xuelong Mi, Alex Bo-Yuan Chen, Daniela Duarte, Erin Carey, Charlotte R. Taylor, Philipp N. Braaker, Mark Bright, Rafael G. Almeida, Jing-Xuan Lim, Virginia M. Rutten, Wei Zheng, Mengfan Wang, Michael E. Reitman, Yizhi Wang, Kira E. Poskanzer, David A. Lyons, Axel Nimmerjahn, Misha B. Ahrens, Guoqiang Yu, Fast, Accurate, and Versatile Data Analysis Platform for the Quantification of Molecular Spatiotemporal Signals, BioRxiv 592259; doi: https://doi.org/10.1101/2024.05.02.592259. [Link to BioRxiv]
06/05/2024:
Enable aqua_cmd_batch.m to output more features.
05/22/2024:
Modify aqua_cmd_batch.m to enable the extraction of network features.
05/21/2024:
Add post_cfu_cmd_batch.m to enable the CFU identification and analysis based on the saved files of aqua_cmd_batch.m.
