Description Usage Arguments Details Value Author(s) See Also Examples
The DeepCNV
class is used to fit a Bayesian model to targeted
sequencing data from one or a few genes in order to draw inferences
about possible copy number changes. It includes routines to simulate
read-counts with known copy number state and known fraction of normal
'contaminating' cells.
1 2 3 |
nMut |
integer; the number of somatic mutations in a gene |
nVar |
integer; the number of variant SNPs in a gene |
nu |
numeric between 0 and 1; the fr4action of normal cells in the sample |
cnstate |
the copy number state of the gene; must be one of
"Deleted", "Normal", or "Gained", as enumerated in |
depth |
integer; the average read depth at the gene |
sdepth |
integer; the stnadard deviation of the read depth across varaints within a gene |
... |
extra parameters to pass from |
S |
The copy number state, as enumerated in |
V |
The variant type. Must be one of "Mutation" or "SNP" as
enumerated in |
M |
the total number of replicate copies of a (allelic) gene. The default value of 2 corresponds to a gain of one copy |
K |
Number of variant reads |
N |
Number of total reads (both variant and refernce) |
The DeepCNV
class is used to fit a Bayesian model to targeted
sequencing data from one or a few genes in order to draw inferences
about possible copy number changes. Basically, we assume that the
observed data consists of a list of triples (K, N, V), one for each
variant in a gene. Here K is the number of variant reads, N is the
total number of reads, and V is the type of each variant (either a
known SNP or a somatic mutation). We model (K, N) using a binomial
distribution, where the 'success' parameter φ depends (in a
deterministic way) on the unknown parameters of interest: the fraction
ν of normal cells in the sample and the copy number state (Normal,
Deleted, or Gained).
The functions cnvLikelihood
and CNVariant
are used to
compute the log-likelihood of the unknown parameters given the
observed data. CNVariant
computes the success parameter
φ as a function of the observed data (K, N, V), and this
parameter is then used to compute the binomial log-likelihood.
The simReads
function generates simulated read-count data based
on the underlying theoretical binomial model. More details can be
found in the vignettes d01-cnvTheory
and
d02-oneGeneSims
.
The simReads
function returns a data frame suitable for use by
the function makeCNVPosterior
.
The CNVariant
function returns a real number between zero and
one, corresponding to the fraction of reads that are expected to be
variants given the variant type (V
), the copy number state
(S
), and the fraction of normal cells (nu
).
The cnvLikelihood
function returns a real number representing
the log-likelihood (yes, I know; it probably should be renamed) of the
parameters (S, ν) given the observed data (K, N).
Kevin R. Coombes krc@silicovore.com
1 2 3 4 5 6 7 | simReads(nMut=2, nVar=7, nu=0.17, "Norm", depth=130 )
# check log-likelihhoods of different copy number states
# for the same observed data
obs <- data.frame(K=c(69, 48), N=c(153, 167))
cnvLikelihood(0.22, obs)
cnvLikelihood(0.22, obs)
cnvLikelihood(0.22, obs)
|
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