- Adjusted tuning parameters used for ML classifier training
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Added a new machine learning random forest classifier approach to distinguish between senescent and non-senescent cells based on the markers measured (SA-B-Gal, EdU, and Nuclear Area). This additional analysis can be easily performed by modifying the input metadata as described in the app.
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Added a new tab, 'Example Data', which allows users to download example data. Such data can be used to test the FAST.R app or can serve as a benchmark to ensure your own data is structured correctly and ready for analysis.
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Fixed
FAST.R(Browser = FALSE)to correctly open the app in a new RStudio window -
Added a 'Download all graphs' button in the Data Visualization tab. This allows to download all graphs generated at once in a zip file.
- Initial CRAN submission.