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Container of an MCMC sample of the BASiCS' model parameters (see Vallejos et al, 2015) as generated
by the function BASiCS_MCMC
.
mu
MCMC chain for gene-specific expression levels q
columns, technical genes located at the end of the matrix, all elements must be positive numbers)
delta
MCMC chain for gene-specific biological cell-to-cell heterogeneity hyper-parameters q.bio
columns, all elements must be positive numbers)
phi
MCMC chain for cell-specific mRNA content normalising constants n
columns, all elements must be positive numbers and the sum of its elements must be equal to n
)
s
MCMC chain for cell-specific capture efficiency (or amplification biases if not using UMI based counts) normalising constants n
columns, all elements must be positive numbers)
nu
MCMC chain for cell-specific random effects n
columns, all elements must be positive numbers)
theta
MCMC chain for technical variability hyper-parameter(s)
Vallejos, Marioni and Richardson (2015). Bayesian Analysis of Single-Cell Sequencing data.
# NOT RUN {
# A BASiCS_Chain object created by the BASiCS_MCMC function.
Data = makeExampleBASiCS_Data()
MCMC_Output <- BASiCS_MCMC(Data, N = 100, Thin = 2, Burn = 2)
# }
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