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.

...

Additional parameters.

Value

The log likelihood for the given cluster assignments

See Also

'celda_CG()' for clustering features and cells

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)

[Package celda version 1.0.0 Index]