FcsIT: An Open-Source, Cross-Platform Tool for Correlation and Analysis of Fluorescence Correlation Spectroscopy Data
arXiv:2603.29684v1 Announce Type: cross Abstract: FcsIT is a platform-independent, open-source tool for calculating the correlation and fitting fluorescence correlation spectroscopy data. The software is written in Python and uses a powerful Dear PyGUI engine for its interface. It provides reading and correlating the TTTR data, as well as TCSPC filtering of the photon time-trace data. The circular-block bootstrap method applied to the calculation of correlation data and its variance results in data quality comparable to that obtained with commercially available software. An intuitive fitting interface provides efficient analysis of large datasets and includes nine predefined mathematical models for fitting correlation curves. Moreover, it allows users to add their own models in a user-frie
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Abstract:FcsIT is a platform-independent, open-source tool for calculating the correlation and fitting fluorescence correlation spectroscopy data. The software is written in Python and uses a powerful Dear PyGUI engine for its interface. It provides reading and correlating the TTTR data, as well as TCSPC filtering of the photon time-trace data. The circular-block bootstrap method applied to the calculation of correlation data and its variance results in data quality comparable to that obtained with commercially available software. An intuitive fitting interface provides efficient analysis of large datasets and includes nine predefined mathematical models for fitting correlation curves. Moreover, it allows users to add their own models in a user-friendly manner. Validation of the FcsIT tool against simulated FCS data and real FCS experiments confirms its usability and potential appeal to a wide variety of FCS users.
Comments: 15 pages, 6 figures, 1 table
Subjects:
Quantitative Methods (q-bio.QM); Data Analysis, Statistics and Probability (physics.data-an)
Cite as: arXiv:2603.29684 [q-bio.QM]
(or arXiv:2603.29684v1 [q-bio.QM] for this version)
https://doi.org/10.48550/arXiv.2603.29684
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tomasz Kalwarczyk [view email] [v1] Tue, 31 Mar 2026 12:37:40 UTC (1,777 KB)
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