c(person(given   = "Michael J",
family  = "Grayling",
email   = "michael.grayling@newcastle.ac.uk",
role    = c("aut", "cre"),
comment = c(ORCID = "0000-0002-0680-6668")),
person(given   = "Gerhard",
family  = "Burger",
email   = "burger.ga@gmail.com",
role    = "ctb",
comment = c(ORCID = "0000-0003-1062-5576")),
person(given   = "Tomas",
family  = "Capretto",
role    = "ctb"),
person(given   = "Stephen P",
family  = "Ellner",
role    = "ctb"),
person(given   = "John M",
family  = "Guckenheimer",
role    = "ctb"))
Michael J Grayling [aut, cre] (<https://orcid.org/0000-0002-0680-6668>),
Gerhard Burger [ctb] (<https://orcid.org/0000-0003-1062-5576>),
Tomas Capretto [ctb],
Stephen P Ellner [ctb],
John M Guckenheimer [ctb]

PHATE - Potential of Heat-Diffusion for Affinity-Based
Transition Embedding
c(person("Krishnan", "Srinivasan", email = "krishnan.srinivasan@yale.edu", role = c("aut")),
person(given = 'Scott', family = 'Gigante', email = 'scott.gigante@yale.edu', role = 'cre', comment = c(ORCID = '0000-0002-4544-2764')))
PHATE is a tool for visualizing high dimensional single-cell data
with natural progressions or trajectories. PHATE uses a novel conceptual framework
for learning and visualizing the manifold inherent to biological systems in which
smooth transitions mark the progressions of cells from one state to another.
To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic
stem cells, human embryonic stem cells, and bone marrow samples, check out our preprint on bioRxiv
at <http://biorxiv.org/content/early/2017/03/24/120378>.
methods, stats, graphics, reticulate (>= 1.7), ggplot2
Krishnan Srinivasan [aut],
Scott Gigante [cre] (<https://orcid.org/0000-0002-4544-2764>)

methods, stats, graphics, reticulate (>= 1.4), ggplot2

methods, stats, graphics, reticulate (>= 1.8), ggplot2

PHATE is a tool for visualizing high dimensional single-cell data
with natural progressions or trajectories. PHATE uses a novel conceptual framework
for learning and visualizing the manifold inherent to biological systems in which
smooth transitions mark the progressions of cells from one state to another.
To see how PHATE can be applied to single-cell RNA-seq datasets from hematopoietic
stem cells, human embryonic stem cells, and bone marrow samples, check out our publication in Nature Biotechnology
at <doi:10.1038/s41587-019-0336-3>.