compute_markers: Compute differential expression statistics for given dataset...

In gillislab/MetaMarkers: Extract robust markers from multiple datasets

Description

Compute differential expression statistics for given dataset and cell types, stratified by groups.

Usage

compute_markers(
  expression,
  cell_type_labels,
  group_labels = rep("all", length(cell_type_labels)),
  two_tailed = FALSE,
  tie_correction = FALSE,
  genes_are_rows = TRUE
)

Arguments

expression	Expression matrix (may be sparse).
cell_type_labels	Character vector providing cell type names for each sample in the expression matrix.
group_labels	Character vector providing hierarchical grouping for cell types (one group name for each sample in the expression matrix).
two_tailed	Boolean. If FALSE, only upregulated genes are considered significant (ROC test).
tie_correction	Boolean. For the ROC test, should tie correction be applied? Note that skipping tie correction is slightly conservative.
genes_are_rows	Boolean. In the expression matrix, were genes provided as rows (as in SingleCellExperiment or Seurat objects)?

Value

A tibble containing basic differential expression statistics for all cell types and genes. All statistics are 1-vs-rest within groups. NOTE: genes with duplicate names will be removed.

gillislab/MetaMarkers documentation built on April 24, 2021, 9:25 p.m.