BASiCS (version 0.7.30)

BASiCS_Chain-class: The BASiCS_Chain class

Description

Container of an MCMC sample of the BASiCS' model parameters (see Vallejos et al, 2015) as generated by the function BASiCS_MCMC.

Slots

mu

MCMC chain for gene-specific expression levels \(\mu[i]\), defined as true input molecules in case of technical genes (matrix with 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 \(\delta[i]\), biological genes only (matrix with q.bio columns, all elements must be positive numbers)

phi

MCMC chain for cell-specific mRNA content normalising constants \(\phi[j]\) (matrix with 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 \(s[j]\) (matrix with n columns, all elements must be positive numbers)

nu

MCMC chain for cell-specific random effects \(\nu[j]\) (matrix with n columns, all elements must be positive numbers)

theta

MCMC chain for technical variability hyper-parameter(s) \(\theta\) (matrix, all elements must be positive, each colum represents 1 batch)

References

Vallejos, Marioni and Richardson (2015). Bayesian Analysis of Single-Cell Sequencing data. PLoS Computational Biology.

Examples

# 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)

# }