vb_factorize: Bayesian NMF inference of count matrix

In hjunwoo/ccfindR: Cancer Clone Finder

Description

Perform variational Bayes NMF and store factor matrices in object

Usage

vb_factorize(object, ranks = 2, nrun = 1, verbose = 2,
  progress.bar = TRUE, initializer = "random", Itmax = 10000,
  hyper.update = rep(TRUE, 4), gamma.a = 1, gamma.b = 1,
  Tol = 1e-05, hyper.update.n0 = 10, hyper.update.dn = 1,
  connectivity = TRUE, fudge = NULL, ncores = 1, useC = TRUE,
  unif.stop = TRUE)

Arguments

object	scNMFSet object containing count matrix.
ranks	Rank for factorization; can be a vector of multiple values.
nrun	No. of runs with different initial guesses.
verbose	The verbosity level: 3, each iteration output printed; 2, each run output printed; 1, each randomized sample output printed; 0, silent.
progress.bar	Display progress bar with verbose = 1 for multiple runs.
initializer	If 'random', randomized initial conditions; 'svd2' for singular value decomposed initial condition.
Itmax	Maximum no. of iteration.
hyper.update	Vector of four logicals, each indcating whether hyperparameters c(aw, bw, ah, bh) should be optimized.
gamma.a	Gamma distribution shape parameter.
gamma.b	Gamma distribution mean. These two parameters are used for fixed hyperparameters with hyper.update elements FALSE.
Tol	Tolerance for terminating iteration.
hyper.update.n0	Initial number of steps in which hyperparameters are fixed.
hyper.update.dn	Step intervals for hyperparameter updates.
connectivity	If TRUE, connectivity and dispersion will be calculated after each run. Can be turned off to save memory.
fudge	Small positive number used as lower bound for factor matrix elements to avoid singularity. If fudge = NULL (default), it will be replaced by .Machine$double.eps. Can be set to 0 to skip regularization.
ncores	Number of processors (cores) to run. If ncores > 1, parallelization is attempted.
useC	Use C++ version of updates for speed.
unif.stop	Terminate if any of columns in basis matrix is uniform.

Details

The main input is the scNMFSet object with count matrix. This function performs non-negative factorization using Bayesian algorithm and gamma priors. Slots basis, coeff, and ranks are filled.

When run with multiple values of ranks, factorization is repeated for each rank and the slot measure contains log evidence and optimal hyperparameters for each rank. With nrun > 1, the solution with the maximum log evidence is stored for a given rank.

Value

Object of class scNMFSet with factorization slots filled.

Examples

set.seed(1)
x <- simulate_whx(nrow=50,ncol=100,rank=5)
s <- scNMFSet(x$x)
s <- vb_factorize(s,ranks=seq(2,8),nrun=5)
plot(s)

hjunwoo/ccfindR documentation built on Oct. 4, 2019, 10:31 a.m.