celdaGridSearch: Run Celda in parallel with multiple parameters
In compbiomed/celda: CEllular Latent Dirichlet Allocation

Description Usage Arguments Value See Also Examples

Run Celda with different combinations of parameters and multiple chains in parallel. The variable 'available_models' contains the potential models that can be utilized. Different parameters to be tested should be stored in a list and passed to the argument 'params.test'. Fixed parameters to be used in all models, such as 'sample.label', can be passed as a list to the argument 'params.fixed'. When 'verbose=TRUE', output from each chain will be sent to a log file but not be displayed in stdout.

1
2
3

celdaGridSearch(counts, model, params.test, params.fixed = NULL,
  max.iter = 200, nchains = 3, cores = 1, best.only = TRUE,
  seed = 12345, verbose = TRUE, logfile.prefix = "Celda")

`counts`	Integer matrix. Rows represent features and columns represent cells.
`model`	Celda model. Options available in 'celda::available.models'.
`params.test`	List. A list denoting the combinations of parameters to run in a celda model. For example, 'list(K=5:10, L=15:20)' will run all combinations of K from 5 to 10 and L from 15 to 20 in model 'celda_CG()'.
`params.fixed`	List. A list denoting additional parameters to use in each celda model. Default NULL.
`max.iter`	Integer. Maximum number of iterations of sampling to perform. Default 200.
`nchains`	Integer. Number of random cluster initializations. Default 3.
`cores`	Integer. The number of cores to use for parallel estimation of chains. Default 1.
`best.only`	Logical. Whether to return only the chain with the highest log likelihood per combination of parameters or return all chains. Default TRUE.
`seed`	Integer. Passed to 'set.seed()'. Default 12345.
`verbose`	Logical. Whether to print log messages during celda chain execution. Default TRUE.
`logfile.prefix`	Character. Prefix for log files from worker threads and main process. Default "Celda".

Object of class 'celda_list', which contains results for all model parameter combinations and summaries of the run parameters

'celda_G()' for feature clustering, 'celda_C()' for clustering of cells, and 'celda_CG()' for simultaneous clustering of features and cells. 'subsetCeldaList()' can subset the 'celda_list' object. 'selectBestModel()' can get the best model for each combination of parameters.

## Run various combinations of parameters with 'celdaGridSearch'
cgs = celdaGridSearch(celda.CG.sim$counts, model="celda_CG", 
                      params.test=list(K=4:6, L=9:11), 
                      params.fixed=list(sample.label=celda.CG.sim$sample.label),
                      best.only=TRUE, nchains=1)