FISSA specifications

Information


Unique identifier OMICS_28501
Name FISSA
Alternative name Fast Image Signal Separation Analysis
Software type Toolkit/Suite
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Windows
Programming languages Python, Shell (Bash)
License GNU General Public License version 3.0
Computer skills Advanced
Version 0.5.2
Stability Stable
Requirements
numpy, scipy, future, scikit-learn, scikit-image, shapely, tifffile, multiprocessing
Maintained Yes

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Versioning


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Documentation


Maintainers


  • person_outline Sander Keemink
  • person_outline Nathalie Rochefort

Publication for Fast Image Signal Separation Analysis

FISSA citation

library_books

FISSA: A neuropil decontamination toolbox for calcium imaging signals

2018
Sci Rep
PMCID: 5823956
PMID: 29472547
DOI: 10.1038/s41598-018-21640-2

[…] .7, SciPy 0.12, Matplotlib 1.2 and HoloViews 1.6 toolboxes. We implemented NMF and ICA with the Python scikit-learn NMF and fastica functions respectively.In addition to the separation algorithm, the FISSA package has two utilities, which may be used independently. First, FISSA has fast TIFF reading scripts, using the open source tifffile package. Second, FISSA has a baseline estimator which estim […]

FISSA institution(s)
Institute for Adaptive and Neural Computation, School of Informatics, University of Edinburgh, Edinburgh, UK; Bernstein Center Freiburg, Faculty of Biology, University of Freiburg, Freiburg, Germany; Centre for Discovery Brain Sciences, Biomedical Sciences, University of Edinburgh, Edinburgh, UK
FISSA funding source(s)
Supported by the BBSRC grant BB/N023161/1, by the Wellcome Trust and the Royal Society (Sir Henry Dale fellowship), the Marie Curie Actions of the European Union’s FP7 program (MC-CIG 631770 and IEF 624461), the Shirley Foundation, the Patrick Wild Center, the RS MacDonald Charitable Trust Seedcorn Grant, the Simons Initiative for the Developing Brain, and the Graduate School of Life Sciences, University of Edinburgh, the EuroSpin Erasmus Mundus program were funded by the EPSRC Doctoral Training Centre in Neuroinformatics (EP/F500386/1 and BB/F529254/1).

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