recursiveSplitModule: Recursive module splitting
In campbio/celda: CEllular Latent Dirichlet Allocation

recursiveSplitModule

R Documentation

Recursive module splitting

Description

Uses the celda_G model to cluster features into modules for a range of possible L's. The module labels of the previous "L-1" model are used as the initial values in the current model with L modules. The best split of an existing module is found to create the L-th module. This procedure is much faster than randomly initializing each model with a different L.

Usage

recursiveSplitModule(
  x,
  useAssay = "counts",
  altExpName = "featureSubset",
  initialL = 10,
  maxL = 100,
  tempK = 100,
  zInit = NULL,
  sampleLabel = NULL,
  alpha = 1,
  beta = 1,
  delta = 1,
  gamma = 1,
  minFeature = 3,
  reorder = TRUE,
  seed = 12345,
  perplexity = TRUE,
  doResampling = FALSE,
  numResample = 5,
  verbose = TRUE,
  logfile = NULL
)

## S4 method for signature 'SingleCellExperiment'
recursiveSplitModule(
  x,
  useAssay = "counts",
  altExpName = "featureSubset",
  initialL = 10,
  maxL = 100,
  tempK = 100,
  zInit = NULL,
  sampleLabel = NULL,
  alpha = 1,
  beta = 1,
  delta = 1,
  gamma = 1,
  minFeature = 3,
  reorder = TRUE,
  seed = 12345,
  perplexity = TRUE,
  doResampling = FALSE,
  numResample = 5,
  verbose = TRUE,
  logfile = NULL
)

## S4 method for signature 'matrix'
recursiveSplitModule(
  x,
  useAssay = "counts",
  altExpName = "featureSubset",
  initialL = 10,
  maxL = 100,
  tempK = 100,
  zInit = NULL,
  sampleLabel = NULL,
  alpha = 1,
  beta = 1,
  delta = 1,
  gamma = 1,
  minFeature = 3,
  reorder = TRUE,
  seed = 12345,
  perplexity = TRUE,
  doResampling = FALSE,
  numResample = 5,
  verbose = TRUE,
  logfile = NULL
)

Arguments

`x`	A numeric matrix of counts or a SingleCellExperiment with the matrix located in the assay slot under `useAssay`. Rows represent features and columns represent cells.
`useAssay`	A string specifying which assay slot to use if `x` is a SingleCellExperiment object. Default "counts".
`altExpName`	The name for the altExp slot to use. Default "featureSubset".
`initialL`	Integer. Initial number of modules.
`maxL`	Integer. Maximum number of modules.
`tempK`	Integer. Number of temporary cell populations to identify and use in module splitting. Only used if `zInit = NULL` Collapsing cells to a relatively smaller number of cell popluations will increase the speed of module clustering and tend to produce better modules. This number should be larger than the number of true cell populations expected in the dataset. Default `100`.
`zInit`	Integer vector. Collapse cells to cell populations based on labels in `zInit` and then perform module splitting. If NULL, no collapsing will be performed unless `tempK` is specified. Default `NULL`.
`sampleLabel`	Vector or factor. Denotes the sample label for each cell (column) in the count matrix. Default `NULL`.
`alpha`	Numeric. Concentration parameter for Theta. Adds a pseudocount to each cell population in each sample. Only used if `zInit` is set. Default `1`.
`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.
`minFeature`	Integer. Only attempt to split modules with at least this many features.
`reorder`	Logical. Whether to reorder modules using hierarchical clustering after each model has been created. If FALSE, module numbers will correspond to the split which created the module (i.e. 'L15' was created at split 15, 'L16' was created at split 16, etc.). 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.
`perplexity`	Logical. Whether to calculate perplexity for each model. If FALSE, then perplexity can be calculated later with resamplePerplexity. Default `TRUE`.
`doResampling`	Boolean. If `TRUE`, then each cell in the counts matrix will be resampled according to a multinomial distribution to introduce noise before calculating perplexity. Default `FALSE`.
`numResample`	Integer. The number of times to resample the counts matrix for evaluating perplexity if `doResampling` is set to `TRUE`. Default `5`.
`verbose`	Logical. Whether to print log messages. Default TRUE.
`logfile`	Character. Messages will be redirected to a file named "logfile". If NULL, messages will be printed to stdout. Default NULL.

Value

A SingleCellExperiment object. Function parameter settings and celda model results are stored in the metadata "celda_grid_search" slot. The models in the list will be of class celda_G if zInit = NULL or celda_CG if zInit is set.

Examples

data(sceCeldaCG)
## Create models that range from L=3 to L=20 by recursively splitting modules
## into two
moduleSplit <- recursiveSplitModule(sceCeldaCG, initialL = 3, maxL = 20)

## Example results with perplexity
plotGridSearchPerplexity(moduleSplit)

## Select model for downstream analysis
celdaMod <- subsetCeldaList(moduleSplit, list(L = 10))
data(celdaCGSim)
## Create models that range from L=3 to L=20 by recursively splitting modules
## into two
moduleSplit <- recursiveSplitModule(celdaCGSim$counts,
  initialL = 3, maxL = 20)

## Example results with perplexity
plotGridSearchPerplexity(moduleSplit)

## Select model for downstream analysis
celdaMod <- subsetCeldaList(moduleSplit, list(L = 10))

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