Description Usage Arguments Details Value Examples
View source: R/hbfm_functions.R
Function used to implement the stochastic EM algorithm defined in the "A sparse Bayesian factor model for the construction of gene co-expression networks from single-cell RNA sequencing count data" manuscript.
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Y |
data.frame or matrix of gene expression counts where the rows correspond to genes and columns correspond to cells; Y must contain integers and have row names |
Fac |
number of factors to consider in the model; only a single number is accepted |
M.stoc |
total number of stochastic EM iterations |
M.int |
initial number of MCMC draws before maximization |
M.eval |
number of iterations to be used for parameter estimation; the final |
M.ll.seq |
intervals for calculating marginal log-likelihood before final |
H |
number of lambda draws for marginal log-likelihood calculation |
seed |
seed for random number generation |
verbose |
if TRUE, |
This algorithm should be used before hbfm.fit
as it generates initial parameter values
for use in hbfm.fit
.
hbfm.par-class object containing:
Y: data.frame or matrix of gene expression counts
Fac: number of factors considered in the model
beta: initial beta parameter vector for input into hbfm.fit
theta: initial theta parameter vector for input into hbfm.fit
alpha: initial alpha parameter matrix for input into hbfm.fit
lambda: initial lambda parameter matrix for input into hbfm.fit
phi: initial phi parameter vector for input into hbfm.fit
h1: location hyperparameter of lognormal prior for phi to input into hbfm.fit
h2: scale hyperparameter of lognormal prior for phi to input into hbfm.fit
mll: calculated marginal log-likelihoods at given iterations; used for convergence diagnostics
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