ITH_optim {SMASH} | R Documentation |
ITH_optim
Description
Performs EM algorithm for a given configuration matrix
Usage
ITH_optim(
my_data,
my_purity,
init_eS,
pi_eps0 = NULL,
my_unc_q = NULL,
max_iter = 4000,
my_epsilon = 1e-06
)
Arguments
my_data |
A R dataframe containing the following columns:
|
my_purity |
A single numeric value of known/estimated purity |
init_eS |
A subclone configuration matrix pre-defined in R
list |
pi_eps0 |
A user-specified parameter denoting the proportion
of loci not explained by the combinations of purity, copy number,
multiplicity, and allocation. If |
my_unc_q |
An optimal initial vector for the unconstrained
|
max_iter |
Positive integer, preferably 1000 or more, setting the maximum number of iterations |
my_epsilon |
Convergence criterion threshold for changes in the log likelihood, preferably 1e-6 or smaller |
Value
If the EM algorithm converges, the output will be a list containing
iter
number of iterations
converge
convergence status
unc_q0
initial unconstrained subclone proportions parameter
unc_q
unconstrained estimate of
q
q
estimated subclone proportions among cancer cells
CN_MA_pi
estimated mixture probabilities of multiplicities and allocations given copy number states
eta
estimated subclone proportion among tumor cells
purity
user-inputted tumor purity
entropy
estimated entropy
infer
A R dataframe containing inferred variant allocations (
infer_A
), multiplicities (infer_M
), cellular prevalences (infer_CP
).ms
model size, number of parameters within parameter space
LL
The observed log likelihood evaluated at maximum likelihood estimates.
AIC = 2 * LL - 2 * ms
Negative AIC, used for model selection
BIC = 2 * LL - ms * log(LOCI)
Negative BIC, used for model selection
LOCI
The number of inputted somatic variants.