Learn R Programming

Canopy (version 1.3.0)

Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal and Spatial Clonal Evolutionary History by Next-Generation Sequencing

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.

Copy Link

Version

Install

install.packages('Canopy')

Monthly Downloads

223

Version

1.3.0

License

GPL-2

Issues

Pull Requests

Stars

Forks

Maintainer

Last Published

December 18th, 2017

Functions in Canopy (1.3.0)

canopy.cluster

EM algorithm for multivariate clustering of SNAs
canopy.sample.cluster.nocna

MCMC sampling in tree space with pre-clustering of SNAs
canopy.output

To generate a posterior tree
canopy.sample.nocna

MCMC sampling in tree space
getCMCm

To get major and minor copy per clone
MDA231_sampchain

List of pre-sampled trees
getCZ

To get CNA genotyping matrix CZ
MDA231_tree

Most likely tree from project MDA231
canopy.plottree

To plot tree inferred by Canopy
canopy.post

Posterior evaluation of MCMC sampled trees
addsamptree

To determine whether the sampled tree will be accepted
getQ

To get SNA-CNA genotyping matrix
canopy.BIC

To get BIC as a model selection criterion
getVAF

To get variant allele frequency (VAF)
canopy.sample

MCMC sampling in tree space
AML43

SNA input for primary tumor and relapse genome of leukemia patient from Ding et al. Nature 2012.
canopy.sample.cluster

MCMC sampling in tree space with pre-clustering of SNAs
MDA231

Dataset for project MDA231
initialcnacopy

To initialize major and minor copies of CNAs
canopy.cluster.Estep

E-step of EM algorithm for multivariate clustering of SNAs
initialsna

To initialize positions of SNAs
toy

Toy dataset for Canopy
canopy.cluster.Mstep

M-step of EM algorithm for multivariate clustering of SNAs
toy2

Toy dataset 2 for Canopy
sampP

To sample clonal frequency
getlikelihood

To get likelihood of the tree
sampcna

To sample CNA positions
getlikelihood.sna

To get SNA likelihood of the tree
initialP

To initialize clonal frequency matrix
sampsna.cluster

To sample positions of SNA clusters
initialcna

To initialize positions of CNAs
sortcna

To sort identified overlapping CNAs.
sampcnacopy

To sample major and minor copies of CNAs
sampsna

To sample SNA positions
getZ

To get SNA genotyping matrix \(Z\)
toy3

Toy dataset 3 for Canopy
getclonalcomposition

To get clonal composition