FindAllMarkers.bygroup: Gene expression markers shared by multiple groups of cells

In carmonalab/ProjecTILs: Reference-based analysis of scRNA-seq data

Gene expression markers shared by multiple groups of cells

Description

This function expands FindAllMarkers to find markers that are differentially expressed across multiple datasets or samples. Given a Seurat object with identity classes (for example annotated clusters) and a grouping variable (for example a Sample ID), it calculate differentially expressed genes (DEGs) individually for each sample. Then it determines the fraction of samples for which the gene was found to be differentially expressed.

Usage

FindAllMarkers.bygroup(
  object,
  split.by = NULL,
  only.pos = TRUE,
  features = NULL,
  min.cells.group = 10,
  min.freq = 0.5,
  ...
)

Arguments

object	A Seurat object
split.by	A metadata column name - the data will be split by this column to calculate FindAllMarkers separately for each data split
only.pos	Only return positive markers (TRUE by default)
features	Genes to test. Default is to use all genes
min.cells.group	Minimum number of cells in the group - if lower the group is skipped
min.freq	Only return markers which are differentially expressed in at least this fraction of datasets.
...	Additional paramters to FindAllMarkers

Details

This function can be useful to find marker genes that are specific for individual cell types, and that are found to be so consistently across multiple samples.

Value

A list of marker genes for each identity class (typically clusters), with two associated numerical values: i) the fraction of datasets for which the marker was found to be differentially expressed; ii) the average log-fold change for the genes across datasets

Examples

ref <- load.reference.map(ref = "https://figshare.com/ndownloader/files/38921366")
Idents(ref) <- "functional.cluster"
FindAllMarkers.bygroup(ref, split.by = "Sample", min.cells.group=30, min.freq=0.8)

carmonalab/ProjecTILs documentation built on April 13, 2025, 10:04 p.m.