R: Calculate Celda_CG log likelihood

logLikelihoodcelda_CG {celda}

R Documentation

Calculate Celda_CG log likelihood

Description

Calculates the log likelihood for user-provided cell population and feature module clusters using the 'celda_CG()' model.

Usage

logLikelihoodcelda_CG(
  counts,
  sampleLabel,
  z,
  y,
  K,
  L,
  alpha,
  beta,
  delta,
  gamma
)

Arguments

`counts`	Integer matrix. Rows represent features and columns represent cells.
`sampleLabel`	Vector or factor. Denotes the sample label for each cell (column) in the count matrix.
`z`	Numeric vector. Denotes cell population labels.
`y`	Numeric vector. Denotes feature module labels.
`K`	Integer. Number of cell populations.
`L`	Integer. Number of feature modules.
`alpha`	Numeric. Concentration parameter for Theta. Adds a pseudocount to each cell population in each sample. Default 1.
`beta`	Numeric. Concentration parameter for Phi. Adds a pseudocount to each feature module in each cell population. Default 1.
`delta`	Numeric. Concentration parameter for Psi. Adds a pseudocount to each feature in each module. Default 1.
`gamma`	Numeric. Concentration parameter for Eta. Adds a pseudocount to the number of features in each module. Default 1.

Value

The log likelihood for the given cluster assignments

Examples

data(celdaCGSim)
loglik <- logLikelihoodcelda_CG(celdaCGSim$counts,
    sampleLabel = celdaCGSim$sampleLabel,
    z = celdaCGSim$z,
    y = celdaCGSim$y,
    K = celdaCGSim$K,
    L = celdaCGSim$L,
    alpha = celdaCGSim$alpha,
    beta = celdaCGSim$beta,
    gamma = celdaCGSim$gamma,
    delta = celdaCGSim$delta)

loglik <- logLikelihood(celdaCGSim$counts,
    model = "celda_CG",
    sampleLabel = celdaCGSim$sampleLabel,
    z = celdaCGSim$z,
    y = celdaCGSim$y,
    K = celdaCGSim$K,
    L = celdaCGSim$L,
    alpha = celdaCGSim$alpha,
    beta = celdaCGSim$beta,
    gamma = celdaCGSim$gamma,
    delta = celdaCGSim$delta)