celda_CG: Cell and feature clustering with Celda

In campbio/celda: CEllular Latent Dirichlet Allocation

Cell and feature clustering with Celda

Description

Clusters the rows and columns of a count matrix containing single-cell data into L modules and K subpopulations, respectively. The useAssay assay slot in altExpName altExp slot will be used if it exists. Otherwise, the useAssay assay slot in x will be used if x is a SingleCellExperiment object.

Usage

celda_CG(
  x,
  useAssay = "counts",
  altExpName = "featureSubset",
  sampleLabel = NULL,
  K,
  L,
  alpha = 1,
  beta = 1,
  delta = 1,
  gamma = 1,
  algorithm = c("EM", "Gibbs"),
  stopIter = 10,
  maxIter = 200,
  splitOnIter = 10,
  splitOnLast = TRUE,
  seed = 12345,
  nchains = 3,
  zInitialize = c("split", "random", "predefined"),
  yInitialize = c("split", "random", "predefined"),
  countChecksum = NULL,
  zInit = NULL,
  yInit = NULL,
  logfile = NULL,
  verbose = TRUE
)

## S4 method for signature 'SingleCellExperiment'
celda_CG(
  x,
  useAssay = "counts",
  altExpName = "featureSubset",
  sampleLabel = NULL,
  K,
  L,
  alpha = 1,
  beta = 1,
  delta = 1,
  gamma = 1,
  algorithm = c("EM", "Gibbs"),
  stopIter = 10,
  maxIter = 200,
  splitOnIter = 10,
  splitOnLast = TRUE,
  seed = 12345,
  nchains = 3,
  zInitialize = c("split", "random", "predefined"),
  yInitialize = c("split", "random", "predefined"),
  countChecksum = NULL,
  zInit = NULL,
  yInit = NULL,
  logfile = NULL,
  verbose = TRUE
)

## S4 method for signature 'ANY'
celda_CG(
  x,
  useAssay = "counts",
  altExpName = "featureSubset",
  sampleLabel = NULL,
  K,
  L,
  alpha = 1,
  beta = 1,
  delta = 1,
  gamma = 1,
  algorithm = c("EM", "Gibbs"),
  stopIter = 10,
  maxIter = 200,
  splitOnIter = 10,
  splitOnLast = TRUE,
  seed = 12345,
  nchains = 3,
  zInitialize = c("split", "random", "predefined"),
  yInitialize = c("split", "random", "predefined"),
  countChecksum = NULL,
  zInit = NULL,
  yInit = NULL,
  logfile = NULL,
  verbose = TRUE
)

Arguments

x	A SingleCellExperiment with the matrix located in the assay slot under useAssay. Rows represent features and columns represent cells. Alternatively, any matrix-like object that can be coerced to a sparse matrix of class "dgCMatrix" can be directly used as input. The matrix will automatically be converted to a SingleCellExperiment object.
useAssay	A string specifying the name of the assay slot to use. Default "counts".
altExpName	The name for the altExp slot to use. Default "featureSubset".
sampleLabel	Vector or factor. Denotes the sample label for each cell (column) in the count matrix.
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.
algorithm	String. Algorithm to use for clustering cell subpopulations. One of 'EM' or 'Gibbs'. The EM algorithm for cell clustering is faster, especially for larger numbers of cells. However, more chains may be required to ensure a good solution is found. Default 'EM'.
stopIter	Integer. Number of iterations without improvement in the log likelihood to stop inference. Default 10.
maxIter	Integer. Maximum number of iterations of Gibbs sampling to perform. Default 200.
splitOnIter	Integer. On every splitOnIter iteration, a heuristic will be applied to determine if a cell population or feature module should be reassigned and another cell population or feature module should be split into two clusters. To disable splitting, set to -1. Default 10.
splitOnLast	Integer. After stopIter iterations have been performed without improvement, a heuristic will be applied to determine if a cell population or feature module should be reassigned and another cell population or feature module should be split into two clusters. If a split occurs, then 'stopIter' will be reset. Default TRUE.
seed	Integer. Passed to with_seed. For reproducibility, a default value of 12345 is used. If NULL, no calls to with_seed are made.
nchains	Integer. Number of random cluster initializations. Default 3.
zInitialize	Chararacter. One of 'random', 'split', or 'predefined'. With 'random', cells are randomly assigned to a populations. With 'split', cells will be split into sqrt(K) populations and then each population will be subsequently split into another sqrt(K) populations. With 'predefined', values in zInit will be used to initialize z. Default 'split'.
yInitialize	Character. One of 'random', 'split', or 'predefined'. With 'random', features are randomly assigned to a modules. With 'split', features will be split into sqrt(L) modules and then each module will be subsequently split into another sqrt(L) modules. With 'predefined', values in yInit will be used to initialize y. Default 'split'.
countChecksum	Character. An MD5 checksum for the counts matrix. Default NULL.
zInit	Integer vector. Sets initial starting values of z. 'zInit' is only used when ‘zInitialize = ’predfined''. Default NULL.
yInit	Integer vector. Sets initial starting values of y. 'yInit' is only be used when 'yInitialize = "predefined"'. 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.

Value

A SingleCellExperiment object. Function parameter settings are stored in metadata "celda_parameters" in altExp slot. In altExp slot, columns celda_sample_label and celda_cell_cluster in colData contain sample labels and celda cell population clusters. Column celda_feature_module in rowData contains feature modules.

See Also

celda_G for feature clustering and celda_C for clustering cells. celdaGridSearch can be used to run multiple values of K/L and multiple chains in parallel.

Examples

data(celdaCGSim)
sce <- celda_CG(celdaCGSim$counts,
    K = celdaCGSim$K,
    L = celdaCGSim$L,
    sampleLabel = celdaCGSim$sampleLabel,
    nchains = 1)

campbio/celda documentation built on April 5, 2024, 11:47 a.m.