Sometimes it is useful to serve up alternative 'shiny' UIs 
    depending on information passed in the request object, such as the value of 
    a cookie or a query parameter. This packages facilitates such switches.

Analyse single cell RNA sequencing data using entropy to
    calculate heterogeneity and homogeneity of genes amongst the cell
    population. From the work of Michael J. Casey, Ruben J. Sanchez-Garcia 
    and Ben D. MacArthur.

A pipeline for estimating the stellar
        age, mass, and radius given observational 
        effective temperature, [Fe/H], and astroseismic
	parameters. The results are obtained adopting a maximum likelihood
	technique over a grid of pre-computed stellar models, as
	described in Valle et al. (2014) <doi:10.1051/0004-6361/201322210>.

SCEPtER pipeline for estimating the stellar age for double-lined detached binary systems. The observational constraints adopted in the recovery are the effective temperature, the metallicity [Fe/H], the mass, and the radius of the two stars. The results are obtained adopting a maximum likelihood technique over a grid of pre-computed stellar models.

A common bioinformatics task in single-cell data analysis is to purify a cell type or cell population of interest from heterogeneous datasets. 'scGate' automatizes marker-based purification of specific cell populations, without requiring training data or reference gene expression profiles. Briefly, 'scGate' takes as input: i) a gene expression matrix stored in a 'Seurat' object and ii) a “gating model” (GM), consisting of a set of marker genes that define the cell population of interest. The GM can be as simple as a single marker gene, or a combination of positive and negative markers. More complex GMs can be constructed in a hierarchical fashion, akin to gating strategies employed in flow cytometry. 'scGate' evaluates the strength of signature marker expression in each cell using the rank-based method 'UCell', and then performs k-nearest neighbor (kNN) smoothing by calculating the mean 'UCell' score across neighboring cells. kNN-smoothing aims at compensating for the large degree of sparsity in scRNA-seq data. Finally, a universal threshold over kNN-smoothed signature scores is applied in binary decision trees generated from the user-provided gating model, to annotate cells as either “pure” or “impure”, with respect to the cell population of interest. See the related publication Andreatta et al. (2022) <doi:10.1093/bioinformatics/btac141>.