logLikelihoodcelda_CG {celda} | R Documentation |
Calculates the log likelihood for user-provided cell population and feature module clusters using the 'celda_CG()' model.
logLikelihoodcelda_CG(counts, sampleLabel, z, y, K, L, alpha, beta, delta, gamma)
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. |
The log likelihood for the given cluster assignments
'celda_CG()' for clustering features and cells
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)