Perform differential expression analysis on SCE object

runDEAnalysis(method = c("wilcox", "MAST", "DESeq2", "Limma", "ANOVA"), ...)

runDESeq2(
  inSCE,
  useAssay = "counts",
  useReducedDim = NULL,
  index1 = NULL,
  index2 = NULL,
  class = NULL,
  classGroup1 = NULL,
  classGroup2 = NULL,
  analysisName,
  groupName1,
  groupName2,
  covariates = NULL,
  fullReduced = TRUE,
  onlyPos = FALSE,
  log2fcThreshold = NULL,
  fdrThreshold = NULL,
  minGroup1MeanExp = NULL,
  maxGroup2MeanExp = NULL,
  minGroup1ExprPerc = NULL,
  maxGroup2ExprPerc = NULL,
  overwrite = FALSE
)

runLimmaDE(
  inSCE,
  useAssay = "logcounts",
  useReducedDim = NULL,
  index1 = NULL,
  index2 = NULL,
  class = NULL,
  classGroup1 = NULL,
  classGroup2 = NULL,
  analysisName,
  groupName1,
  groupName2,
  covariates = NULL,
  onlyPos = FALSE,
  log2fcThreshold = NULL,
  fdrThreshold = NULL,
  minGroup1MeanExp = NULL,
  maxGroup2MeanExp = NULL,
  minGroup1ExprPerc = NULL,
  maxGroup2ExprPerc = NULL,
  overwrite = FALSE
)

runANOVA(
  inSCE,
  useAssay = "logcounts",
  useReducedDim = NULL,
  index1 = NULL,
  index2 = NULL,
  class = NULL,
  classGroup1 = NULL,
  classGroup2 = NULL,
  analysisName,
  groupName1,
  groupName2,
  covariates = NULL,
  onlyPos = FALSE,
  log2fcThreshold = NULL,
  fdrThreshold = NULL,
  minGroup1MeanExp = NULL,
  maxGroup2MeanExp = NULL,
  minGroup1ExprPerc = NULL,
  maxGroup2ExprPerc = NULL,
  overwrite = FALSE
)

runMAST(
  inSCE,
  useAssay = "logcounts",
  useReducedDim = NULL,
  index1 = NULL,
  index2 = NULL,
  class = NULL,
  classGroup1 = NULL,
  classGroup2 = NULL,
  analysisName,
  groupName1,
  groupName2,
  covariates = NULL,
  onlyPos = FALSE,
  log2fcThreshold = NULL,
  fdrThreshold = NULL,
  minGroup1MeanExp = NULL,
  maxGroup2MeanExp = NULL,
  minGroup1ExprPerc = NULL,
  maxGroup2ExprPerc = NULL,
  overwrite = FALSE,
  check_sanity = TRUE
)

runWilcox(
  inSCE,
  useAssay = "logcounts",
  useReducedDim = NULL,
  index1 = NULL,
  index2 = NULL,
  class = NULL,
  classGroup1 = NULL,
  classGroup2 = NULL,
  analysisName,
  groupName1,
  groupName2,
  covariates = NULL,
  onlyPos = FALSE,
  log2fcThreshold = NULL,
  fdrThreshold = NULL,
  minGroup1MeanExp = NULL,
  maxGroup2MeanExp = NULL,
  minGroup1ExprPerc = NULL,
  maxGroup2ExprPerc = NULL,
  overwrite = FALSE
)

Arguments

method: Character. Specify which method to use when using runDEAnalysis(). Choose from "wilcox", "MAST", "DESeq2", "Limma", "ANOVA". Default "wilcox".
...: Arguments to pass to specific methods when using the generic runDEAnalysis().
inSCE: SingleCellExperiment inherited object.
useAssay: character. A string specifying which assay to use for the DE regression. Default "counts" for DESeq2, "logcounts" for other methods.
useReducedDim: character. A string specifying which reducedDim to use for DE analysis. Usually a pathway analysis result matrix. Set useAssay to NULL when using. Default NULL.
index1: Any type of indices that can subset a SingleCellExperiment inherited object by cells. Specifies which cells are of interests. Default NULL.
index2: Any type of indices that can subset a SingleCellExperiment inherited object by cells. specifies the control group against those specified by index1. If NULL when using index specification, index1 cells will be compared with all other cells. Default NULL.
class: A vector/factor with ncol(inSCE) elements, or a character scalar that specifies a column name of colData(inSCE). Default NULL.
classGroup1: a vector specifying which "levels" given in class are of interests. Default NULL.
classGroup2: a vector specifying which "levels" given in class is the control group against those specified by classGroup1. If NULL when using annotation specification, classGroup1 cells will be compared with all other cells.
analysisName: A character scalar naming the DEG analysis. Required
groupName1: A character scalar naming the group of interests. Required.
groupName2: A character scalar naming the control group. Required.
covariates: A character vector of additional covariates to use when building the model. All covariates must exist in names(colData(inSCE)). Default NULL.
fullReduced: Logical, DESeq2 only argument. Whether to apply LRT (Likelihood ratio test) with a 'full' model. Default TRUE.
onlyPos: Whether to only output DEG with positive log2_FC value. Default FALSE.
log2fcThreshold: Only out put DEGs with the absolute values of log2FC greater than this value. Default NULL.
fdrThreshold: Only out put DEGs with FDR value less than this value. Default NULL.
minGroup1MeanExp: Only out put DEGs with mean expression in group1 greater then this value. Default NULL.
maxGroup2MeanExp: Only out put DEGs with mean expression in group2 less then this value. Default NULL.
minGroup1ExprPerc: Only out put DEGs expressed in greater then this fraction of cells in group1. Default NULL.
maxGroup2ExprPerc: Only out put DEGs expressed in less then this fraction of cells in group2. Default NULL.
overwrite: A logical scalar. Whether to overwrite result if exists. Default FALSE.
check_sanity: Logical, MAST only argument. Whether to perform MAST's sanity check to see if the counts are logged. Default TRUE.

Value

The input SingleCellExperiment object with metadata(inSCE)$diffExp updated with the results: a list named by analysisName, with $groupNames containing the naming of the two conditions, $useAssay and $useReducedDim storing the matrix name that was used for calculation, $select storing the cell selection indices (logical) for each condition, $result storing a data.frame of the DEGs summary, and $method storing the character method name used.

Details

SCTK provides Limma, MAST, DESeq2, ANOVA and Wilcoxon test for differential expression analysis, where DESeq2 expects non-negtive integer assay input while others expect logcounts.

Condition specification allows two methods: 1. Index level selection. Arguments index1 and index2 will be used. 2. Annotation level selection. Arguments class, classGroup1 and classGroup2 will be used.

Examples

data(scExample, package = "singleCellTK")
sce <- subsetSCECols(sce, colData = "type != 'EmptyDroplet'")
sce <- scaterlogNormCounts(sce, assayName = "logcounts")
sce <- runDEAnalysis(method = "Limma", inSCE = sce, groupName1 = "group1",
 groupName2 = "group2", index1 = seq(20), index2 = seq(21,40),
 analysisName = "Limma")

Perform differential expression analysis on SCE object

Arguments

Value

Details

See also

Examples