singlecell_de: Run single-cell differential expression

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

Run single-cell differential expression

Description

Run differential expression using traditional single-cell methods Note that this is effectively a wrapper of the FindMarkers function in Seurat.

Usage

singlecell_de(
  input,
  meta = NULL,
  cell_type_col = "cell_type",
  label_col = "label",
  de_method = "wilcox",
  min_cells = 3,
  min_features = 0,
  normalization = "log_tp10k",
  binarization = FALSE,
  latent_vars = NULL,
  input_type = "scRNA"
)

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.
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.
de_method	the mixed model type to use. Defaults to wilcox.
min_cells	the minimum number of cells in a cell type to retain it. Defaults to 3.
min_features	the minimum number of expressing cells (or replicates) for a gene to retain it. Defaults to 0.
normalization	normalization for Seurat/Signac methods
binarization	binarization for single-cell ATAC-seq only
latent_vars	latent variables for Seurat/Signac methods
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.