pseudobulk_de: Run pseudobulk differential expression methods

In neurorestore/Libra: Differential expression utilizing biological replicates in single-cell data

Run pseudobulk differential expression methods

Description

Run pseudobulk differential expression methods on single-cell data

Usage

pseudobulk_de(
  input,
  meta = NULL,
  replicate_col = "replicate",
  cell_type_col = "cell_type",
  label_col = "label",
  min_cells = 3,
  min_reps = 2,
  min_features = 0,
  de_family = "pseudobulk",
  de_method = "edgeR",
  de_type = "LRT"
)

Arguments

input	a single-cell matrix to be converted, with features (genes) in rows and cells in columns. Alternatively, a Seurat, monocle3, or or SingleCellExperiment object can be directly input.
meta	the accompanying meta data whereby the rownames match the column names of input.
replicate_col	the vector in meta containing the replicate information. Defaults to replicate.
cell_type_col	the vector in meta containing the cell type information. Defaults to cell_type.
label_col	the vector in meta containing the experimental label. Defaults to label.
min_cells	the minimum number of cells in a cell type to retain it. Defaults to 3.
min_reps	the minimum number of replicates in a cell type to retain it. Defaults to 2.
min_features	the minimum number of expressing cells (or replicates) for a gene to retain it. Defaults to 0.
de_method	the specific differential expression testing method to use. Defaults to edgeR.
de_type	the specific parameter of the differential expression testing method. Defaults to LRT for edgeR, LRT for DESeq2, and trend for limma.
input_type	refers to either scRNA or scATAC

Value

a data frame containing differential expression results.

neurorestore/Libra documentation built on Aug. 31, 2024, 8:53 p.m.