datasets. Reference: Loza M. et al. (NAR Genomics and Bioinformatics, 2020) <doi:10.1093/nargab/lqac022>.

Title: Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal
and Spatial Clonal Evolutionary History by Next-Generation
Description: A statistical framework and computational procedure for identifying the sub-populations within a tumor, determining the mutation profiles of each subpopulation, and inferring the tumor's phylogenetic history. The input are variant allele frequencies (VAFs) of somatic single nucleotide alterations (SNAs) along with allele-specific coverage ratios between the tumor and matched normal sample for somatic copy number alterations (CNAs). These quantities can be directly taken from the output of existing software. Canopy provides a general mathematical framework for pooling data across samples and sites to infer the underlying parameters. For SNAs that fall within CNA regions, Canopy infers their temporal ordering and resolves their phase.  When there are multiple evolutionary configurations consistent with the data, Canopy outputs all configurations along with their confidence assessment.
Packaged: 2015-10-29 17:07:21 UTC; yuchaojiang

Package: Canopy
Type: Package
Title: Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal
and Spatial Clonal Evolutionary History by Next-Generation
Sequencing
Version: 1.1.1
Author: Yuchao Jiang, Nancy R. Zhang
Maintainer: Yuchao Jiang <yuchaoj@wharton.upenn.edu>
Description: A statistical framework and computational procedure for identifying
the sub-populations within a tumor, determining the mutation profiles of each
subpopulation, and inferring the tumor's phylogenetic history. The input are
variant allele frequencies (VAFs) of somatic single nucleotide alterations
(SNAs) along with allele-specific coverage ratios between the tumor and matched
normal sample for somatic copy number alterations (CNAs). These quantities can
be directly taken from the output of existing software. Canopy provides a
general mathematical framework for pooling data across samples and sites to
infer the underlying parameters. For SNAs that fall within CNA regions, Canopy
infers their temporal ordering and resolves their phase.  When there are
multiple evolutionary configurations consistent with the data, Canopy outputs
all configurations along with their confidence assessment.
License: GPL-2
Depends: R (>= 3.2), ape, fields
Imports: grDevices, graphics, stats
URL: https://github.com/yuchaojiang/Canopy
NeedsCompilation: no
Packaged: 2016-09-02 19:48:37 UTC; yuchaojiang
Repository: CRAN
Date/Publication: 2016-09-02 22:26:50