differentialExpression: Differential expression for cell subpopulations using MAST
In celda: CEllular Latent Dirichlet Allocation

Description Usage Arguments Value Examples

Uses MAST to find differentially expressed features for specified cell subpopulations.

differentialExpression(x, ...)

## S4 method for signature 'SingleCellExperiment'
differentialExpression(
  x,
  useAssay = "counts",
  altExpName = "featureSubset",
  c1,
  c2 = NULL,
  onlyPos = FALSE,
  log2fcThreshold = NULL,
  fdrThreshold = 1
)

## S4 method for signature 'matrix'
differentialExpression(
  x,
  celdaMod,
  c1,
  c2 = NULL,
  onlyPos = FALSE,
  log2fcThreshold = NULL,
  fdrThreshold = 1
)

`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. Must contain cluster labels in `celdaClusters(x, altExpName = altExpName)` if `x` is a SingleCellExperiment object.
`...`	Ignored. Placeholder to prevent check warning.
`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".
`c1`	Integer vector. Cell populations to include in group 1 for the differential expression analysis.
`c2`	Integer vector. Cell populations to include in group 2 for the differential expression analysis. If NULL, the clusters in the c1 group are compared to all other clusters. Default NULL.
`onlyPos`	Logical. Whether to only return markers with positive log2 fold change. Default FALSE.
`log2fcThreshold`	Numeric. A number greater than 0 that specifies the absolute log2 fold change threshold. Only features with absolute value above this threshold will be returned. If NULL, this filter will not be applied. Default NULL.
`fdrThreshold`	Numeric. A number between 0 and 1 that specifies the false discovery rate (FDR) threshold. Only features below this threshold will be returned. Default 1.
`celdaMod`	Celda object of class 'celda_C' or 'celda_CG'.

Data frame containing MAST results including statistics such as p-value, log2 fold change, and FDR.

data(sceCeldaCG)
clusterDiffexpRes <- differentialExpression(sceCeldaCG, c1 = c(1, 2))
data(celdaCGSim, celdaCGMod)
clusterDiffexpRes <- differentialExpression(celdaCGSim$counts,
  celdaCGMod,
  c1 = c(1, 2))