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.

The goal of 'scfetch' is to access and format single-cell RNA-seq datasets. It can be used to download single-cell RNA-seq
    datasets from widely used public resources, including GEO <https://www.ncbi.nlm.nih.gov/geo/>, Zenodo <https://zenodo.org/>, 
    CELLxGENE <https://cellxgene.cziscience.com/>, Human Cell Atlas <https://www.humancellatlas.org/>, PanglaoDB <https://panglaodb.se/index.html> 
    and UCSC Cell Browser <https://cells.ucsc.edu/>. And, it can also be used to perform object conversion between SeuratObject <https://satijalab.org/seurat/>, 
    loom <http://loompy.org/>, h5ad <https://scanpy.readthedocs.io/en/stable/>, SingleCellExperiment <https://bioconductor.org/packages/release/bioc/html/scran.html>, 
    CellDataSet <http://cole-trapnell-lab.github.io/monocle-release/> and cell_data_set <https://cole-trapnell-lab.github.io/monocle3/>.

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>.