Seeking Clones {CloneSeeker}  R Documentation 
Starting with copy number segmentation data and/or sequencing mutation data for a tumor, seek the number of clones, the fraction of cells belonging to each clone, and the likely set of abnormalities in each clone.
seekClones(cndata, vardata, cnmodels, psiset, pars, imputedCN = NULL) runAlg(...)
cndata 
A data frame with seven columns; can also be NULL. The
names of the required columns are enumerated in the man page for

vardata 
A data frame with eight columns; can also be NULL. The
names of the required columns are enumerated in the man page for

cnmodels 
A matrix. Each row represents a model to be considered; each column represents a clone. The entries are integers specifying the number of DNA copies present in that clone. See details. 
psiset 
A matrix. Each column represents a clone, and each row represents a different possible model of the fraction of cells per clone. See details. 
pars 
A list of algorithm parameters; see details. 
imputedCN 
a logical value; if missing, should the copy number be imputed from the mutation data. 
... 
additional variables 
The algorithm starts with an initial set of 'psi' parameters (representing the fraction of tumor belonging to each clone). It computes the best (maximum a posteriori) clonal copy number and/or number of mutated alleles for each clone for each segment/mutation, conditional on the data and each of the initial psi vectors. It then computes the posterior probability for each psivector and its computed copy number and mutation parameters. It uses these posterior probabilities to resample new possible psivectors. The process repeats iteratively, and with each iterations obtains a better estimate of psi and the clonal segment copy number and mutation assignments until it terminates.
The set of copy number models that we use is typically
generated using the following command:
as.matrix(expand.grid(lapply(1:5, function(i){0:5})))
This setup considers all (7776) possible models with up to five clones,
where the copy number for each clone ranges from 0 to 5. (In the future,
we are likely to make this the default; right now, you have to generate
these models yourself.)
The set of possible psivectors (that is, the fraction of cells allocated
to each clone) that we use is typically generated using the following
command:
psis.20 < generateSimplex(20,5)
This setup considers all (192) possible divisions of the tumor into up to
five clones, where the fraction of cells per clone is any possible
multiple of 0.05. Each row is sorted to put the most abundant clones first,
which makes it easier to identify specific clones, except in the rare case
when two clones contain exactly the same fraction of cells. (In the future,
we are likely to make this the default; right now, you have to generate
these models yourself.)
The object pars
is a list of numerical algorithm parameters. The
elements are:
The standard deviation of measured allelic copy number at the SNP level.
The probability parameter of the geometric prior
distribution on K
, the number of clones.
The probability parameter of the geometric prior distribution on genomic copy number.
The probability parameter of the geometric prior distribution on the occurence of point mutations.
The (repeated) alpha parameter of a symmetric Dirichlet distributed prior on the fractions of cells belong to each clone; default value is 0.5, giving a Jeffreys Prior.
The threshold determining the smallest possible detectable clone.
SNP array segments with fewer markers than this are excluded.
Determines the number of new psi
vectors resampled
from the estimated posterior probability distribution at each
iteration of the algorithm
The number of iterations in the algorithm.
The default settings we used are from commonly used unfinformative priors (e.g., alpha=0.5 for the Dirichlet distribution is the Jeffreys Prior) or based on empirical assessments of the variation in data (sigma0, for example, which describes variation in SNP array data).
Note that runAlg
(an alias for seekClones
) is DEPRECATED.
The seekClones
function returns a (rather long) list containing:
psi 
The most likely posterior psivector, given the data. The number of nonzero entries is the number of clones found, and the nonzero entries are the fraction of cells per clone 
A 
The most likely copy numbers for the A allele in each segment in each clone. 
B 
The most likely copy numbers for the B allele in each segment in each clone. 
psibank 
A matrix, where each row is one of the psivectors considered during the analysis. 
psiPosts 
A numeric vector, the (marginal) posterior probability of each psivector considered during the analysis. 
indices 
??? 
data 
a list with two dataframe components containing the data used during the analysis. 
filtered.data 
a list with two dataframe components containing the filtered data used during the analysis. Filtering removes noninformative segments that have normal copy number or contain only germline mutations. 
etaA 
A vector of the weighted average allelic copy number for the 'AAllele' at each segment (that is, the sum of the clonal Aallelic copy number values multiplied by the fraction of the tumor made up by each clone) 
etaB 
A vector of the weighted average allelic copy number for the 'BAllele' at each segment 
etaM 
A vector of the weighted average number of copies of the mutated allele at each mutation 
mutated 
A matrix of the number of mutated alleles at each locus in each clone, where the number of rows is the number of somatic mutations in the data and the number of columns is the number of clones 
Kevin R. Coombes krc@silicovore.com, Mark Zucker zucker.64@buckeyemail.osu.edu
Zucker MR, Abruzzo LV, Herling CD, Barron LL, Keating MJ, Abrams ZB, Heerema N, Coombes KR. Inferring Clonal Heterogeneity in Cancer using SNP Arrays and Whole Genome Sequencing. Bioinformatics. To appear. doi: 10.1093/bioinformatics/btz057.
# set up models psis.20 < generateSimplex(20,5) cnmodels < as.matrix(expand.grid(lapply(1:5, function(i){ 0:5 }))) # set up algortihm parameters pars < list(sigma0=5, theta = 0.9, ktheta = 0.3, mtheta = 0.9, alpha = 0.5, thresh = 0.04, cutoff = 100, Q = 100, iters = 4) # create a tumor psis < c(0.6, 0.3, 0.1) # three clones tumor < Tumor(psis, rounds = 100, nu = 0, pcnv = 1, norm.contam = FALSE) # simulate a dataset dataset < generateTumorData(tumor, 10000, 600000, 70, 25, 0.15, 0.03, 0.1) result < seekClones(dataset$cn.data, dataset$seq.data, cnmodels, psis.20, pars = pars, imputedCN = NULL)