| Type: | Package |
| Title: | Accessing Intra-Tumor Heterogeneity and Tracking Longitudinal and Spatial Clonal Evolutionary History by Next-Generation Sequencing |
| Version: | 1.3.0 |
| Author: | Yuchao Jiang, Nancy R. Zhang |
| Maintainer: | Yuchao Jiang <yuchaoj@email.unc.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 (≥ 3.4), ape, fields, pheatmap, scatterplot3d |
| Imports: | grDevices, graphics, stats, utils |
| URL: | https://github.com/yuchaojiang/Canopy |
| NeedsCompilation: | no |
| Packaged: | 2017-12-18 16:00:51 UTC; yuchaojiang |
| Repository: | CRAN |
| Date/Publication: | 2017-12-18 19:12:06 UTC |
SNA input for primary tumor and relapse genome of leukemia patient from Ding et al. Nature 2012.
Description
1242 SNAs from sequencing of leukemia patient at two timepoints. All SNAs are filtered to be from copy-number-neutral region.
Usage
data(AML43)Value
List of simulated SNA input data for Canopy.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(AML43)
Dataset for project MDA231
Description
Pre-stored dataset for project MDA231. A transplantable metastasis model system was derived from a heterogeneous human breast cancer cell line MDA-MB-231. Cancer cells from the parental line MDA-MB-231 were engrafted into mouse hosts leading to organ-specific metastasis. Mixed cell populations (MCPs) were in vivo selected from either bone or lung metastasis and grew into phenotypically stable and metastatically competent cancer cell lines. The parental line as well as the MCP sublines were whole-exome sequenced with somatic SNAs and CNAs profiled.
Usage
data(MDA231)Value
List of input data for Canopy from project MDA231.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231)
List of pre-sampled trees
Description
List of sampleed trees in subtree space with different number of subclones for project MDA231.
Usage
data(MDA231_sampchain)Value
List of sampled trees from different subtree space
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_sampchain)
Most likely tree from project MDA231
Description
Most likely tree from project MDA231 as a tree example.
Usage
data(MDA231_tree)Value
Most likely tree from project MDA231
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
To determine whether the sampled tree will be accepted
Description
To determine whether the sampled tree will be accepted by comparing the
likelihood, used in canopy.sample.
Usage
addsamptree(tree,tree.new)
Arguments
tree |
input tree (current) |
tree.new |
input tree (newly sampled) |
Value
returned tree (either retain the old tree or accept the new tree).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231)
data(MDA231_tree)
sna.name = MDA231$sna.name
Y = MDA231$Y
C = MDA231$C
R = MDA231$R
X = MDA231$X
WM = MDA231$WM
Wm = MDA231$Wm
epsilonM = MDA231$epsilonM
epsilonm = MDA231$epsilonm
# sampling location of SNAs
tree.new = MDA231_tree
tree.new$sna = sampsna(MDA231_tree)
tree.new$Z = getZ(tree.new, sna.name)
tree.new$Q = getQ(tree.new, Y, C)
tree.new$H = tree.new$Q
tree.new$VAF = getVAF(tree.new, Y)
tree.new$likelihood = getlikelihood(tree.new, R, X, WM, Wm, epsilonM, epsilonm)
tree = addsamptree(MDA231_tree,tree.new)
To get BIC as a model selection criterion
Description
To get BIC as a model selection criterion from MCMC sampling results.
Usage
canopy.BIC(sampchain,projectname,K,numchain,burnin,thin,pdf)
Arguments
sampchain |
list of sampled trees returned by |
projectname |
name of project |
K |
number of subclones (vector) |
numchain |
number of MCMC chains with random initiations |
burnin |
burnin of MCMC chains |
thin |
MCMC chains thinning |
pdf |
whether a pdf plot of BIC should be generated, default to be TRUE |
Value
BIC values (vector) for model selection with plot generated (pdf format).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_sampchain)
sampchain = MDA231_sampchain
projectname = 'MD231'
K = 3:6
numchain = 20
burnin = 150
thin = 5
bic = canopy.BIC(sampchain = sampchain, projectname = projectname, K = K,
numchain = numchain, burnin = burnin, thin = thin)
EM algorithm for multivariate clustering of SNAs
Description
EM algorithm for multivariate clustering of SNAs.
Usage
canopy.cluster(R, X, num_cluster, num_run, Mu.init = NULL, Tau_Kplus1 = NULL)
Arguments
R |
alternative allele read depth matrix |
X |
total read depth matrix |
num_cluster |
number of mutation clusters (BIC as model selection metric) |
num_run |
number of EM runs for estimation for each specific number of clusters (to avoid EM being stuck in local optima) |
Mu.init |
(optional) initial value of the VAF centroid for each mutation cluster in each sample |
Tau_Kplus1 |
(optional) pre-specified proportion of noise component in clustering, uniformly distributed between 0 and 1 |
Value
Matrix of posterior probability of cluster assignment for each mutation.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(AML43)
R = AML43$R
X = AML43$X
Mu = AML43$Mu
Tau = AML43$Tau
pG = canopy.cluster.Estep(Tau, Mu, R, X)
E-step of EM algorithm for multivariate clustering of SNAs
Description
E-step of EM algorithm for multivariate clustering of SNAs. Used in
canopy.cluster.
Usage
canopy.cluster.Estep(Tau, Mu, R, X)
Arguments
Tau |
prior for proportions of mutation clusters |
Mu |
MAF centroid for each mutation cluster in each sample |
R |
alternative allele read depth matrix |
X |
total read depth matrix |
Value
Matrix of posterior probability of cluster assignment for each mutation.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(AML43)
R = AML43$R
X = AML43$X
Mu = AML43$Mu
Tau = AML43$Tau
pG = canopy.cluster.Estep(Tau, Mu, R, X)
M-step of EM algorithm for multivariate clustering of SNAs
Description
M-step of EM algorithm for multivariate clustering of SNAs. Used in
canopy.cluster.
Usage
canopy.cluster.Mstep(pG, R, X, Tau_Kplus1)
Arguments
pG |
matrix of posterior probability of cluster assignment for each mutation |
R |
alternative allele read depth matrix |
X |
total read depth matrix |
Tau_Kplus1 |
proportion mutation cluster that is uniformly distributed to capture noise |
Value
List of bic, converged Mu, Tau, and SNA cluster assignment.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(AML43)
R = AML43$R; X = AML43$X
num_cluster = 4 # Range of number of clusters to run
num_run = 6 # How many EM runs per clustering step
Tau_Kplus1=0.05 # Proportion of noise component
Mu.init=cbind(c(0.01,0.15,0.25,0.45),c(0.2,0.2,0.01,0.2)) # initial value
# of centroid
canopy.cluster=canopy.cluster(R = R, X = X, num_cluster = num_cluster,
num_run = num_run, Mu.init = Mu.init,
Tau_Kplus1=Tau_Kplus1)
To generate a posterior tree
Description
To generate a posterior tree from the sub-tree space of trees with the same configurations.
Usage
canopy.output(post, config.i, C)
Arguments
post |
list returned by |
config.i |
configuration of sub-tree space to be output |
C |
CNA and CNA-region overlapping matrix, only needed if overlapping CNAs are used as input |
Value
posterior tree from the sub-tree space of trees with the same configurations.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_sampchain)
data(MDA231)
sampchain = MDA231_sampchain
projectname = 'MD231'
K = 3:6
numchain = 20
burnin = 150
thin = 5
optK = 4
C = MDA231$C
post = canopy.post(sampchain = sampchain, projectname = projectname, K = K,
numchain = numchain, burnin = burnin, thin = thin,
optK = optK, C = C)
config.i = 3
output.tree = canopy.output(post = post, config.i = config.i, C = C)
To plot tree inferred by Canopy
Description
To plot Canopy's reconstructed phylogeny. Major plotting function of Canopy.
Usage
canopy.plottree(tree, pdf, pdf.name, txt, txt.name)
Arguments
tree |
input tree to be plotted |
pdf |
whether a pdf plot should be generated, default to be FALSE |
pdf.name |
name of pdf to be generated, has to be provided if pdf is to be generated |
txt |
whether a txt file should be generated with information on mutations along the tree branches, default to be FALSE |
txt.name |
name of txt to be generated, has to be provided if txt is to be generated |
Value
Plot of tree structure, clonal frequency and mutation legends (pdf format).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
canopy.plottree(MDA231_tree, pdf = TRUE, pdf.name = 'MDA231_tree.pdf')
Posterior evaluation of MCMC sampled trees
Description
Burnin, thinning, and posterior evaluation of MCMC sampled trees.
Usage
canopy.post(sampchain, projectname, K, numchain, burnin, thin, optK,
C, post.config.cutoff)
Arguments
sampchain |
list of sampled trees returned by |
projectname |
name of project |
K |
number of subclones (vector) |
numchain |
number of MCMC chains with random initiations |
burnin |
burnin of MCMC chains |
thin |
MCMC chain thinning. |
optK |
optimal number of subclones determined by |
C |
CNA and CNA-region overlapping matrix, only needed if overlapping CNAs are used as input |
post.config.cutoff |
cutoff value for posterior probabilities of tree configurations, default is set to be 0.05 (only tree configurations with greater than 0.05 posterior probabilities will be reported by Canopy) |
Value
samptreethin |
list of sampled posterior trees |
samptreethin.lik |
vector of likelihood of sampled posterior trees |
config |
vector of configuration of sampled posterior trees (integer values) |
config.summary |
summary of configurations of sampled posterior trees |
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_sampchain)
data(MDA231)
sampchain = MDA231_sampchain
projectname = 'MD231'
K = 3:6
numchain = 20
burnin = 150
thin = 5
optK = 4
C = MDA231$C
post = canopy.post(sampchain = sampchain, projectname = projectname, K = K,
numchain = numchain, burnin = burnin, thin = thin,
optK = optK, C = C)
MCMC sampling in tree space
Description
To sample the posterior trees. Major function of Canopy.
Usage
canopy.sample(R, X, WM, Wm, epsilonM, epsilonm, C=NULL,
Y, K, numchain, max.simrun, min.simrun, writeskip, projectname,
cell.line=NULL, plot.likelihood=NULL)
Arguments
R |
alternative allele read depth matrix |
X |
total read depth matrix |
WM |
observed major copy number matrix |
Wm |
observed minor copy number matrix |
epsilonM |
observed standard deviation of major copy number (scalar input is transformed into matrix) |
epsilonm |
observed standard deviation of minor copy number (scalar input is transformed into matrix) |
C |
CNA and CNA-region overlapping matrix, only needed if overlapping CNAs are used as input |
Y |
SNA and CNA-region overlapping matrix |
K |
number of subclones (vector) |
numchain |
number of MCMC chains with random initiations |
max.simrun |
maximum number of simutation iterations for each chain |
min.simrun |
minimum number of simutation iterations for each chain |
writeskip |
interval to store sampled trees |
projectname |
name of project |
cell.line |
default to be FALSE, TRUE if input sample is cell line (no normal cell contamination) |
plot.likelihood |
default to be TRUE, posterior likelihood plot generated for check of
convergence and selection of burnin and thinning in
|
Value
List of sampleed trees in subtree space with different number of subclones; plot of posterior likelihoods in each subtree space generated (pdf format).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231)
R = MDA231$R; X = MDA231$X
WM = MDA231$WM; Wm = MDA231$Wm
epsilonM = MDA231$epsilonM; epsilonm = MDA231$epsilonm
C = MDA231$C
Y = MDA231$Y
K = 3:6
numchain = 20
projectname = 'MDA231'
# sampchain = canopy.sample(R = R, X = X, WM = WM, Wm = Wm, epsilonM = epsilonM,
# epsilonm = epsilonm, C = C, Y = Y, K = K, numchain = numchain,
# max.simrun = 50000, min.simrun = 10000, writeskip = 200,
# projectname = projectname, cell.line = TRUE, plot.likelihood = TRUE)
MCMC sampling in tree space with pre-clustering of SNAs
Description
To sample the posterior trees with pre-clustering step of SNAs. Major function of Canopy.
Usage
canopy.sample.cluster(R, X, sna_cluster, WM, Wm, epsilonM, epsilonm, C=NULL,
Y, K, numchain, max.simrun, min.simrun, writeskip, projectname,
cell.line=NULL, plot.likelihood=NULL)
Arguments
R |
alternative allele read depth matrix |
X |
total read depth matrix |
sna_cluster |
cluster assignment for each mutation from the EM Binomial clustering algorithm |
WM |
observed major copy number matrix |
Wm |
observed minor copy number matrix |
epsilonM |
observed standard deviation of major copy number (scalar input is transformed into matrix) |
epsilonm |
observed standard deviation of minor copy number (scalar input is transformed into matrix) |
C |
CNA and CNA-region overlapping matrix, only needed if overlapping CNAs are used as input |
Y |
SNA and CNA-region overlapping matrix |
K |
number of subclones (vector) |
numchain |
number of MCMC chains with random initiations |
max.simrun |
maximum number of simutation iterations for each chain |
min.simrun |
minimum number of simutation iterations for each chain |
writeskip |
interval to store sampled trees |
projectname |
name of project |
cell.line |
default to be FALSE, TRUE if input sample is cell line (no normal cell contamination) |
plot.likelihood |
default to be TRUE, posterior likelihood plot generated for check of
convergence and selection of burnin and thinning in
|
Value
List of sampleed trees in subtree space with different number of subclones; plot of posterior likelihoods in each subtree space generated (pdf format).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231)
R = MDA231$R; X = MDA231$X
WM = MDA231$WM; Wm = MDA231$Wm
epsilonM = MDA231$epsilonM; epsilonm = MDA231$epsilonm
C = MDA231$C
Y = MDA231$Y
K = 3:6
numchain = 20
projectname = 'MDA231'
# sampchain = canopy.sample.cluster(R = R, X = X, sna_cluster=c(1,2,3,4),
# WM = WM, Wm = Wm, epsilonM = epsilonM,
# epsilonm = epsilonm, C = C, Y = Y, K = K, numchain = numchain,
# max.simrun = 50000, min.simrun = 10000, writeskip = 200,
# projectname = projectname, cell.line = TRUE, plot.likelihood = TRUE)
MCMC sampling in tree space with pre-clustering of SNAs
Description
To sample the posterior trees with pre-clustering step of SNAs. Major function of Canopy.
Usage
canopy.sample.cluster.nocna(R, X, sna_cluster, K, numchain,
max.simrun, min.simrun, writeskip, projectname,
cell.line=NULL, plot.likelihood=NULL)
Arguments
R |
alternative allele read depth matrix |
X |
total read depth matrix |
sna_cluster |
cluster assignment for each mutation from the EM Binomial clustering algorithm |
K |
number of subclones (vector) |
numchain |
number of MCMC chains with random initiations |
max.simrun |
maximum number of simutation iterations for each chain |
min.simrun |
minimum number of simutation iterations for each chain |
writeskip |
interval to store sampled trees |
projectname |
name of project |
cell.line |
default to be FALSE, TRUE if input sample is cell line (no normal cell contamination) |
plot.likelihood |
default to be TRUE, posterior likelihood plot generated for check of
convergence and selection of burnin and thinning in
|
Value
List of sampleed trees in subtree space with different number of subclones; plot of posterior likelihoods in each subtree space generated (pdf format).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(toy3)
R = toy3$R; X = toy3$X
sna_cluster = toy3$sna_cluster
K = 3:5
numchain = 10
projectname = 'toy3'
# sampchain = canopy.sample.cluster.nocna(R = R, X = X,
# sna_cluster=sna_cluster, K = K, numchain = numchain,
# max.simrun = 40000, min.simrun = 10000, writeskip = 200,
# projectname = projectname,
# cell.line = TRUE, plot.likelihood = TRUE)
MCMC sampling in tree space
Description
To sample the posterior trees without CNA input. Major function of Canopy.
Usage
canopy.sample.nocna(R, X, K, numchain, max.simrun, min.simrun, writeskip,
projectname, cell.line=NULL, plot.likelihood=NULL)
Arguments
R |
alternative allele read depth matrix |
X |
total read depth matrix |
K |
number of subclones (vector) |
numchain |
number of MCMC chains with random initiations |
max.simrun |
maximum number of simutation iterations for each chain |
min.simrun |
minimum number of simutation iterations for each chain |
writeskip |
interval to store sampled trees |
projectname |
name of project |
cell.line |
default to be FALSE, TRUE if input sample is cell line (no normal cell contamination) |
plot.likelihood |
default to be TRUE, posterior likelihood plot generated for check of
convergence and selection of burnin and thinning in
|
Value
List of sampleed trees in subtree space with different number of subclones; plot of posterior likelihoods in each subtree space generated (pdf format).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(toy3)
R = toy3$R; X = toy3$X
K = 3:5
numchain = 10
projectname = 'toy3'
# sampchain = canopy.sample.nocna(R = R, X = X, K = K, numchain = numchain,
# max.simrun = 50000, min.simrun = 10000, writeskip = 200,
# projectname = projectname,
# cell.line = TRUE, plot.likelihood = TRUE)
To get major and minor copy per clone
Description
To get major and minor copy per clone. Used in canopy.sample.
Usage
getCMCm(tree, C)
Arguments
tree |
input tree |
C |
CNA regions and CNA overlapping matrix |
Value
CM |
Matrix of major copy per clone. |
Cm |
Matrix of minor copy per clone. |
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
data(MDA231)
C = MDA231$C
getCMCm(MDA231_tree, C)
To get CNA genotyping matrix CZ
Description
To get CNA genotyping matrix CZ from location of CNAs on the tree. Used in
canopy.sample.
Usage
getCZ(tree)
Arguments
tree |
input tree |
Value
CNA genotyping matrix CZ.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
getCZ(MDA231_tree)
To get SNA-CNA genotyping matrix
Description
To get SNA-CNA genotyping matrix Q, which specifies whether an SNA
precedes a CNA. Used in canopy.sample.
Usage
getQ(tree, Y, C)
Arguments
tree |
input tree |
Y |
SNA CNA overlapping matrix |
C |
CNA and CNA region overlapping matrix |
Value
Genotyping matrix Q.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
data(MDA231)
Y = MDA231$Y
C = MDA231$C
getQ(MDA231_tree, Y, C)
To get variant allele frequency (VAF)
Description
To get variant allele frequency (VAF) matrix, which contains percentage of
mutant SNA alleles across samples. Used in canopy.sample.
Usage
getVAF(tree,Y)
Arguments
tree |
input tree |
Y |
SNA CNA overlapping matrix |
Value
Variant allele frequency matrix VAF.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
data(MDA231)
Y = MDA231$Y
getVAF(MDA231_tree, Y)
To get SNA genotyping matrix Z
Description
To get SNA genotyping matrix Z from location of SNAs on the tree. Used in
canopy.sample.
Usage
getZ(tree, sna.name)
Arguments
tree |
input tree |
sna.name |
vector of SNA names |
Value
Genotyping matrix Z.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
data(MDA231)
sna.name = rownames(MDA231$R)
getZ(MDA231_tree, sna.name)
To get clonal composition
Description
To get clonal composition (mutational profile of each clone) of tree. Used in
canopy.post.
Usage
getclonalcomposition(tree)
Arguments
tree |
input tree |
Value
List of each clone's mutational profile.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
getclonalcomposition(MDA231_tree)
To get likelihood of the tree
Description
To get likelihood of the tree given tree struture and data input. Used in
canopy.sample.
Usage
getlikelihood(tree,R,X,WM,Wm,epsilonM,epsilonm)
Arguments
tree |
input tree |
R |
alternative allele read depth matrix |
X |
total read depth matrix |
WM |
observed major copy number matrix |
Wm |
observed minor copy number matrix |
epsilonM |
observed standard deviation of major copy number (scalar input is transformed into matrix) |
epsilonm |
observed standard deviation of minor copy number (scalar input is transformed into matrix) |
Value
Likelihood of sampled tree.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231)
data(MDA231_tree)
R = MDA231$R
X = MDA231$X
WM = MDA231$WM
Wm = MDA231$Wm
epsilonM = MDA231$epsilonM
epsilonm = MDA231$epsilonm
getlikelihood(MDA231_tree, R, X, WM, Wm, epsilonM, epsilonm)
To get SNA likelihood of the tree
Description
To get SNA likelihood of the tree given tree struture and data input. Used in
canopy.sample.nocna and canopy.sample.cluster.nocna.
Usage
getlikelihood.sna(tree, R, X)
Arguments
tree |
input tree |
R |
alternative allele read depth matrix |
X |
total read depth matrix |
Value
Likelihood of sampled tree.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231)
data(MDA231_tree)
R = MDA231$R
X = MDA231$X
getlikelihood.sna(MDA231_tree, R, X)
To initialize clonal frequency matrix
Description
To initialize clonal frequency matris P. Used in initialization step of
canopy.sample.
Usage
initialP(tree,sampname,cell.line)
Arguments
tree |
input tree |
sampname |
vector of input sample names |
cell.line |
default to be FALSE, TRUE if input sample is cell line (no normal cell contamination) |
Value
Clonal frequency matrix P.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
data(MDA231)
sampname = colnames(MDA231$R)
initialP(MDA231_tree, sampname, cell.line = TRUE)
To initialize positions of CNAs
Description
To initialize positions of CNAs on the tree. Used in initialization step of
canopy.sample.
Usage
initialcna(tree,cna.name)
Arguments
tree |
input tree |
cna.name |
vector of input CNA names |
Value
Matrix specifying positions of CNAs (start and end node).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
data(MDA231)
cna.name = rownames(MDA231$WM)
initialcna(MDA231_tree, cna.name)
To initialize major and minor copies of CNAs
Description
To initialize major and minor copies of CNAs. Used in initialization step of
canopy.sample.
Usage
initialcnacopy(tree)
Arguments
tree |
input tree |
Value
Matrix specifying major and minor copies of CNAs.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
initialcnacopy(MDA231_tree)
To initialize positions of SNAs
Description
To initialize positions of SNAs on the tree. Used in initialization step of
canopy.sample.
Usage
initialsna(tree,sna.name)
Arguments
tree |
input tree |
sna.name |
vector of input SNA names |
Value
Matrix specifying positions of SNAs (start and end node).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
data(MDA231)
sna.name = rownames(MDA231$R)
initialsna(MDA231_tree, sna.name)
To sample clonal frequency
Description
To sample clonal frequency matrix P. Used in canopy.sample.
Usage
sampP(tree, cell.line)
Arguments
tree |
input tree |
cell.line |
default to be FALSE, TRUE if input sample is cell line (no normal cell contamination) |
Value
Newly sampled clonal frequency matrix P.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
sampP(MDA231_tree, cell.line = TRUE)
To sample CNA positions
Description
To sample CNA positions along the tree. Used in canopy.sample.
Usage
sampcna(tree)
Arguments
tree |
input tree |
Value
Newly sampled matrix specifying positions of CNAs (start and end node).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
sampcna(MDA231_tree)
To sample major and minor copies of CNAs
Description
To sample major and minor copies of CNAs. Used in canopy.sample.
Usage
sampcnacopy(tree)
Arguments
tree |
input tree |
Value
Newly sampled matrix specifying major and minor copies of CNAs.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
sampcnacopy(MDA231_tree)
To sample SNA positions
Description
To sample SNA positions along the tree. Used in canopy.sample.
Usage
sampsna(tree)
Arguments
tree |
input tree |
Value
Newly sampled matrix specifying positions of SNAs (start and end node).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
sampsna(MDA231_tree)
To sample positions of SNA clusters
Description
To sample SNA cluster positions along the tree. Used in
canopy.sample.cluster and canopy.sample.cluster.nocna.
Usage
sampsna.cluster(tree)
Arguments
tree |
input tree |
Value
Newly sampled matrix specifying positions of SNA clusters (start and end node).
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
MDA231_tree$sna.cluster=initialsna(MDA231_tree,paste('cluster',1:4,sep=''))
sampsna.cluster(MDA231_tree)
To sort identified overlapping CNAs.
Description
To sort identified overlapping CNAs by their major and minor copy numbers.
Used in canopy.post.
Usage
sortcna(tree,C)
Arguments
tree |
input tree |
C |
CNA and CNA-region overlapping matrix |
Value
Tree whose overlapping CNAs are sorted by major and minor copy numbers.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(MDA231_tree)
data(MDA231)
C = MDA231$C
sortcna(MDA231_tree, C)
Toy dataset for Canopy
Description
Pre-stored simulated toy dataset.
Usage
data(toy)Value
List of simulated input data for Canopy.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(toy)
Toy dataset 2 for Canopy
Description
Pre-stored simulated toy dataset.
Usage
data(toy2)Value
List of simulated input data for Canopy.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(toy2)
Toy dataset 3 for Canopy
Description
Pre-stored simulated toy dataset. 200 simulated SNAs from a tree with 4 branches. No CNA events at play.
Usage
data(toy3)Value
List of simulated SNA input data for Canopy.
Author(s)
Yuchao Jiang yuchaoj@wharton.upenn.edu
Examples
data(toy3)