Clem-Jos/CellScanner: CellScanner

CellScanner is a user-friendly tool that analyses the content of flow cytometry data and predicts samples' composition based on known reference information. On the one hand, based on supervised machine learning methods, the program uses the input data to create a model able to predict the composition of a coculture (in vitro or silico). It can identify the most representative parameters of each species from monoculture information called reference files. The likely species in the community must be known to train the model. The larger the community's complexity to predict, the more likely the prediction accuracy will reduce since the species profile is more likely to be similar, particularly for bacterial species. The prediction's quality is directly dependent on the reference data quality. Two main functions, visible on the main window, allow the user to assess a (1) in-silico community prediction to help the user interpret the result of a (2) in-vitro community prediction.