Cell and feature clustering with Celda

Source: R/celda_CG.R

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.

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 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 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)

#> --------------------------------------------------
#> Starting Celda_CG: Clustering cells and genes.
#> --------------------------------------------------
#> Mon Jul 19 10:59:36 2021 .. Initializing 'z' in chain 1 with 'split' 
#> Mon Jul 19 10:59:36 2021 .. Initializing 'y' in chain 1 with 'split' 
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 1 | logLik: -1215542.98684529
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 2 | logLik: -1215541.0958389
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 3 | logLik: -1215541.0958389
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 4 | logLik: -1215542.98684529
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 5 | logLik: -1215541.0958389
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 6 | logLik: -1215541.0958389
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 7 | logLik: -1215541.0958389
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 8 | logLik: -1215541.0958389
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 9 | logLik: -1215541.0958389
#> Mon Jul 19 10:59:39 2021 .... Determining if any gene clusters should be split.
#> Mon Jul 19 10:59:39 2021 .... No additional splitting was performed.
#> Mon Jul 19 10:59:39 2021 .... Determining if any cell clusters should be split.
#> Mon Jul 19 10:59:39 2021 .... No additional splitting was performed.
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 10 | logLik: -1215541.0958389
#> Mon Jul 19 10:59:39 2021 .... Determining if any cell clusters should be split.
#> Mon Jul 19 10:59:39 2021 .... No additional splitting was performed.
#> Mon Jul 19 10:59:39 2021 .... Completed iteration: 11 | logLik: -1215541.0958389
#> Mon Jul 19 10:59:39 2021 .. Finished chain 1
#> --------------------------------------------------
#> Completed Celda_CG. Total time: 3.388525 secs
#> --------------------------------------------------