DESCRIPTION

Package: scTenifoldNet
Type: Package
Title: Construct and Compare scGRN from Single-Cell Transcriptomic Data
Version: 1.3
Authors@R: c(person(given = "Daniel", family = "Osorio", email = "dcosorioh@utexas.edu", role = c("aut","cre"), comment = c(ORCID = '0000-0003-4424-8422')),
             person(given = "Yan", family = "Zhong", role = c("aut","ctb")),
             person(given = "Guanxun", family = "Li", role = c("aut","ctb")),
             person(given = "Jianhua", family = "Huang", role = c("aut","ctb")),
             person(given = "James", family = "Cai", role = c("aut", "ctb", "ths"), comment = c(ORCID = '0000-0002-8081-6725'))
             )
Description: A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
URL: https://github.com/cailab-tamu/scTenifoldNet
BugReports: https://github.com/cailab-tamu/scTenifoldNet/issues
License: GPL (>=2)
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.1.2
biocViews:
Imports: pbapply,
         RSpectra,
         Matrix,
         methods,
         stats,
         utils,
         MASS,
         RhpcBLASctl
Suggests: 
    testthat (>= 2.1.0)