logLikelihoodcelda_CG: Calculate Celda_CG log likelihood

Description Usage Arguments Value See Also Examples

View source: R/celda_CG.R

Description

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

Usage

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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

See Also

'celda_CG()' for clustering features and cells

Examples

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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
)

celda documentation built on June 9, 2020, 2 a.m.