Finds the most likely originating tissue(s) and developmental stage(s) of tissue-specific RNA sequencing data. The package identifies both pure transcriptomes and mixtures of transcriptomes. The most likely identity is found through comparisons of the sequencing data with high-throughput in situ hybridisation patterns. Typical uses are the identification of cancer cell origins, validation of cell culture strain identities, validation of single-cell transcriptomes, and validation of identity and purity of flow-sorting and dissection sequencing products.

Annotate single-cell RNA sequencing data manually based on
    marker gene thresholds.
    Find cell type rules (gene+threshold) through exploration,
    use the popular piping operator '%>%' to reconstruct complex
    cell type hierarchies.
    'cellpypes' models technical noise to find positive and negative cells for
    a given expression threshold and returns cell type labels or pseudobulks.
    Cite this package as Frauhammer (2022) <doi:10.5281/zenodo.6555728> and
    visit <https://github.com/FelixTheStudent/cellpypes> for tutorials and newest
    features.

Helper functions to work with spreadsheets and the "A1:D10" style
    of cell range specification.

Methods for analyzing (cell) motion in two or three dimensions.
	Available measures include displacement, confinement ratio, autocorrelation,
	straightness, turning angle, and fractal dimension. Measures can be applied to entire tracks,
	steps, or subtracks with varying length. While the methodology has been developed for
	cell trajectory analysis, it is applicable to anything that moves including animals,
	people, or vehicles.
	Some of the methodology implemented in this packages was described by: 
	Beauchemin, Dixit, and Perelson (2007) <doi:10.4049/jimmunol.178.9.5505>, 
	Beltman, Maree, and de Boer (2009) <doi:10.1038/nri2638>,
	Gneiting and Schlather (2004) <doi:10.1137/S0036144501394387>,
	Mokhtari, Mech, Zitzmann, Hasenberg, Gunzer, and Figge (2013) <doi:10.1371/journal.pone.0080808>,
	Moreau, Lemaitre, Terriac, Azar, Piel, Lennon-Dumenil, and Bousso (2012) <doi:10.1016/j.immuni.2012.05.014>,
	Textor, Peixoto, Henrickson, Sinn, von Andrian, and Westermann (2011) <doi:10.1073/pnas.1102288108>,
  Textor, Sinn, and de Boer (2013) <doi:10.1186/1471-2105-14-S6-S10>,
  Textor, Henrickson, Mandl, von Andrian, Westermann, de Boer, and Beltman (2014) <doi:10.1371/journal.pcbi.1003752>.