celda_C.Rdcelda Cell Clustering Model
celda_C(counts, sample.label = NULL, K, alpha = 1, beta = 1, algorithm = c("EM", "Gibbs"), 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, z.init = NULL, logfile = NULL, verbose = TRUE)
| counts | Integer matrix. Rows represent features and columns represent cells. |
|---|---|
| sample.label | Vector or factor. Denotes the sample label for each cell (column) in the count matrix. |
| K | Integer. Number of cell populations. |
| 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 in each cell population. Default 1. |
| algorithm | String. Algorithm to use for clustering cell subpopulations. One of 'EM' or 'Gibbs'. Default 'EM'. |
| 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 cell population should be reassigned and another cell population should be split into two clusters. To disable splitting, set to -1. Default 10. |
| split.on.last | Integer. After the the chain has converged, according to `stop.iter`, 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 1. |
| initialize | Chararacter. One of 'random' or 'split'. With 'random', cells are randomly assigned to a clusters. With 'split' cell clusters will be recurssively split into two clusters using `celda_C` until the specified K is reached. Default 'random'. |
| count.checksum | "Character. An MD5 checksum for the `counts` matrix. Default NULL. |
| z.init | Integer vector. Sets initial starting values of z. If NULL, starting values for each cell will be randomly sampled from 1:K. 'z.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_C with clustering results and various sampling statistics.
celda.sim = simulateCells(model="celda_C") celda.mod = celda_C(celda.sim$counts, K=celda.sim$K, sample.label=celda.sim$sample.label)#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>#>