Package 'SMASH'

A collection of pre-defined subclone configurations.

Description

A R list containing subclone configurations in matrix form for 1 to 5 subclones. For each matrix, each column corresponds to a subclone and each row corresponds to a variant's allocation across all subclones. For example, the first row of each matrix is a vector of 1's to represent clonal variants, variants present in all subclones.

Usage

eS

Format

An object of class list of length 5.

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gen_ITH_RD

Description

Simulates observed alternate and reference read counts

Usage

gen_ITH_RD(DATA, RD)

Arguments

DATA	The output data.frame from gen_subj_truth
RD	A positive integer for the mean read depth generated from the negative binomial distribution

Value

A matrix of simulated alternate and reference read counts.

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gen_subj_truth

Description

Simulates copy number states, multiplicities, allocations

Usage

gen_subj_truth(mat_eS, maxLOCI, nCN = NULL)

Arguments

mat_eS	A subclone configuration matrix pre-defined in R list eS
maxLOCI	A positive integer number of simulated somatic variant calls
nCN	A positive integer for the number of allelic copy number pairings to sample from. If NULL, it will be randomly sampled between 1 and 5.

Value

A list containing the following components:

subj_truth

dataframe of each variant's simulated minor (CN_1) and major (CN_2) copy number states, total copy number (tCN), subclone allocation (true_A), multiplicity (true_M), mutant allele frequency (true_MAF), and cellular prevalence (true_CP)

purity

tumor purity

eta

the product of tumor purity and subclone proportions

q

vector of subclone proportions

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grid_ITH_optim

Description

This function performs a grid search over enumerated configurations within the pre-defined list eS

Usage

grid_ITH_optim(
  my_data,
  my_purity,
  list_eS,
  pi_eps0 = NULL,
  trials = 20,
  max_iter = 4000,
  my_epsilon = 1e-06
)

Arguments

my_data	A R dataframe containing the following columns: tAD tumor alternate read counts tRD tumor reference read counts CN_1 minor allele count CN_2 major allele count, where CN_1 <= CN_2 tCN CN_1 + CN_2
my_purity	A single numeric value of known/estimated purity
list_eS	A nested list of subclone configuration matrices
pi_eps0	A user-specified parameter denoting the proportion of loci not explained by the combinations of purity, copy number, multiplicity, and allocation. If NULL, it is initialized at 1e-3. If set to 0.0, the parameter is not estimated.
trials	Positive integer, number of random initializations of subclone proportions
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

A R list containing two objects. GRID is a dataframe where each row denotes a feasible subclone configuration with corresponding subclone proportion estimates q and somatic variant allocations alloc. INFER is a list where INFER[[i]] corresponds to the i-th row or model of GRID.

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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: tAD tumor alternate read counts tRD tumor reference read counts CN_1 minor allele count CN_2 major allele count, where CN_1 <= CN_2 tCN CN_1 + CN_2
my_purity	A single numeric value of known/estimated purity
init_eS	A subclone configuration matrix pre-defined in R list eS
pi_eps0	A user-specified parameter denoting the proportion of loci not explained by the combinations of purity, copy number, multiplicity, and allocation. If NULL, it is initialized at 1e-3. If set to 0.0, the parameter is not estimated.
my_unc_q	An optimal initial vector for the unconstrained q vector, useful after running grid_ITH_optim. If this variable is NULL, then the subclone proportions, q, are randomly initialized. For instance, if my_unc_q = ( x1 , x2 ), then q = ( exp(x1) / (1 + exp(x1) + exp(x2)) , exp(x2) / (1 + exp(x1) + exp(x2)) , 1 / (1 + exp(x1) + exp(x2)).
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.

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vis_GRID

Description

A simple visualization of SMASH's grid of solutions

Usage

vis_GRID(GRID)

Arguments

GRID	The GRID object output from grid_ITH_optim.

Value

A ggplot object for data visualization

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