BASiCS_Summary: The BASiCS_Summary class

In catavallejos/BASiCS: Bayesian Analysis of Single-Cell Sequencing data

The BASiCS_Summary class

Description

Container of a summary of a BASiCS_Chain object. In each element of the parameters slot, first column contains posterior medians; second and third columns respectively contain the lower and upper limits of an high posterior density interval (for a given probability).

Slots

parameters

List of parameters in which each entry contains a matrix: first column contains posterior medians, second column contains the lower limits of an high posterior density interval and third column contains the upper limits of high posterior density intervals.

mu

Posterior medians (1st column), lower (2nd column) and upper (3rd column) limits of gene-specific mean expression parameters \mu_i.

delta

Posterior medians (1st column), lower (2nd column) and upper (3rd column) limits of gene-specific biological over-dispersion parameters \delta_i, biological genes only

phi

Posterior medians (1st column), lower (2nd column) and upper (3rd column) limits of cell-specific mRNA content normalisation parameters \phi_j

s

Posterior medians (1st column), lower (2nd column) and upper (3rd column) limits of cell-specific technical normalisation parameters s[j]

nu

Posterior medians (1st column), lower (2nd column) and upper (3rd column) limits of cell-specific random effects \nu_j

theta

Posterior median (1st column), lower (2nd column) and upper (3rd column) limits of technical over-dispersion parameter(s) \theta (each row represents one batch)

beta

Posterior median (first column), lower (second column) and upper (third column) limits of regression coefficients \beta

sigma2

Posterior median (first column), lower (second column) and upper (third column) limits of residual variance \sigma^2

epsilon

Posterior median (first column), lower (second column) and upper (third column) limits of gene-specific residual over-dispersion parameter \epsilon

Examples

# A BASiCS_Summary object created by the Summary method.
Data <- makeExampleBASiCS_Data()
Chain <- BASiCS_MCMC(Data, N = 100, Thin = 2, Burn = 2, Regression = FALSE)
ChainSummary <- Summary(Chain)

catavallejos/BASiCS documentation built on March 27, 2024, 12:49 a.m.