celda_G: Feature clustering with Celda
In compbiomed/celda: CEllular Latent Dirichlet Allocation

Description Usage Arguments Value See Also Examples

Clusters the rows of a count matrix containing single-cell data into L modules.

celda_G(counts, L, beta = 1, delta = 1, gamma = 1, stop.iter = 10,
  max.iter = 200, split.on.iter = 10, split.on.last = TRUE,
  seed = 12345, nchains = 3, initialize = c("random", "split"),
  count.checksum = NULL, y.init = NULL, logfile = NULL, verbose = TRUE)

`counts`	Integer matrix. Rows represent features and columns represent cells.
`L`	Integer. Number of feature modules.
`beta`	Numeric. Concentration parameter for Phi. Adds a pseudocount to each feature module in each cell. 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.
`stop.iter`	Integer. Number of iterations without improvement in the log likelihood to stop inference. Default 10.
`max.iter`	Integer. Maximum number of iterations of Gibbs sampling to perform. Default 200.
`split.on.iter`	Integer. On every 'split.on.iter' iteration, a heuristic will be applied to determine if a feature module should be reassigned and another feature module should be split into two clusters. To disable splitting, set to -1. Default 10.
`split.on.last`	Integer. After 'stop.iter' iterations have been performed without improvement, a heuristic will be applied to determine if a cell population should be reassigned and another cell population should be split into two clusters. If a split occurs, then 'stop.iter' will be reset. Default TRUE.
`seed`	Integer. Passed to 'set.seed()'. Default 12345.
`nchains`	Integer. Number of random cluster initializations. Default 3.
`initialize`	Chararacter. One of 'random' or 'split'. With 'random', features are randomly assigned to a clusters. With 'split' cell and feature clusters will be recurssively split into two clusters using ‘celda_G()' until the specified L is reached. Default ’random'.
`count.checksum`	Character. An MD5 checksum for the 'counts' matrix. Default NULL.
`y.init`	Integer vector. Sets initial starting values of y. If NULL, starting values for each feature will be randomly sampled from ‘1:L'. 'y.init' can only be used when 'initialize = ’random''. Default NULL.
`logfile`	Character. Messages will be redirected to a file named 'logfile'. If NULL, messages will be printed to stdout. Default NULL.
`verbose`	Logical. Whether to print log messages. Default TRUE.

An object of class 'celda_G' with the feature module clusters stored in 'y'.

'celda_C()' for cell clustering and 'celda_CG()' for simultaneous clustering of features and cells. 'celdaGridSearch()' can be used to run multiple values of L and multiple chains in parallel.