Imports: ggplot2, xts, zoo, R2HTML, grid, tools, tsoutliers,
htmlwidgets, forecast, rJava, timeDate, dygraphs, gridExtra,

Title: Discretization-based Direct Random Sample Generation
Maintainer: Chel Hee Lee <gnustats@gmail.com>
Description: The package provides two discretization-based Monte Carlo
algorithms, namely the Fu-Wang algorithm and the Wang-Lee
algorithm, for random sample generation from a high dimensional
distribution of complex structure. The normalizing constant of
the target distribution needs not to be known.

Title: Discretization-Based Direct Random Sample Generation
Authors@R: c(person(given = "Chel Hee", family = "Lee", role = c("aut", "cre"), email="gnustats@gmail.com"),
person(given = "Liqun", family = "Wang", role = c("aut"), email="liqun.wang@umanitoba.ca"))
Contact: Chel Hee Lee <chl948@mail.usask.ca>
Description: Two discretization-based Monte Carlo algorithms, namely the Fu-Wang algorithm and the Wang-Lee algorithm, are provided for random sample generation from a high dimensional distribution of complex structure.  The normalizing constant of the target distribution needs not to be known.

Title: Normalize & Denoise Droplet Single Cell Protein Data (CITE-seq)
Description: This lightweight R package provides a method for normalizing and denoising protein expression data from droplet based single cell experiments. Raw protein Unique Molecular Index (UMI) counts from sequencing DNA-conjugated antibodies in droplets (e.g. 'CITE-seq') have substantial measurement noise. Our experiments and computational modeling revealed two major components of this noise: 1) protein-specific noise originating from ambient, unbound antibody encapsulated in droplets that can be accurately inferred via the expected protein counts detected in empty droplets, and 2) droplet/cell-specific noise revealed via the shared variance component associated with isotype antibody controls and background protein counts in each cell. This package normalizes and removes both of these sources of noise from raw protein data derived from methods such as 'CITE-seq', 'REAP-seq', and data from the Mission Bio 'Tapestri' platform. See the vignette for tutorials on how to integrate dsb with 'Seurat', 'Bioconductor' and the AnnData class in 'Python'. Please also see our preprint Mulè M.P., Martins A.J., and Tsang J.S. (2020) <https://www.biorxiv.org/content/10.1101/2020.02.24.963603v1> for more details on the dsb method.
License: BSD_3_clause + file LICENSE | file LICENSE
Packaged: 2021-02-01 23:49:58 UTC; matthewmule
Author: Matthew Mulè [aut, cre] (<https://orcid.org/0000-0001-8457-2716>),