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BASiCS (version 0.3.1)

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.

Arguments

Slots

mu

MCMC chain for gene-specific expression levels μ[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 δ[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 ϕ[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 ν[j] (matrix with n columns, all elements must be positive numbers)

theta

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

References

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

Examples

Run this code
# 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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