Add_Top_Gene_Pct: Add Percent of High Abundance Genes

Description

Add the percentage of counts occupied by the top XX most highly expressed genes in each cell.

Usage

Add_Top_Gene_Pct(object, ...)
# S3 method for liger
Add_Top_Gene_Pct(
  object,
  num_top_genes = 50,
  meta_col_name = NULL,
  overwrite = FALSE,
  verbose = TRUE,
  ...
)
# S3 method for Seurat
Add_Top_Gene_Pct(
  object,
  num_top_genes = 50,
  meta_col_name = NULL,
  assay = "RNA",
  overwrite = FALSE,
  verbose = TRUE,
  ...
)

Value

A liger Object

A Seurat Object

Arguments

object: Seurat or LIGER object.
...: Arguments passed to other methods
num_top_genes: An integer vector specifying the size(s) of the top set of high-abundance genes. Used to compute the percentage of library size occupied by the most highly expressed genes in each cell.
meta_col_name: name to use for new meta data column. Default is "percent_topXX", where XX is equal to the value provided to num_top_genes.
overwrite: Logical. Whether to overwrite existing an meta.data column. Default is FALSE meaning that function will abort if column with name provided to meta_col_name is present in meta.data slot.
verbose: logical, whether to print messages with status updates, default is TRUE.
assay: assay to use in calculation. Default is "RNA". Note This should only be changed if storing corrected and uncorrected assays in same object (e.g. outputs of both Cell Ranger and Cell Bender).

References

This function uses scuttle package (license: GPL-3) to calculate the percent of expression coming from top XX genes in each cell. Parameter description for num_top_genes also from scuttle. If using this function in analysis, in addition to citing scCustomize, please cite scuttle: McCarthy DJ, Campbell KR, Lun ATL, Willis QF (2017). “Scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in R.” Bioinformatics, 33, 1179-1186. doi:10.1093/bioinformatics/btw777.

Examples

Run this code

if (FALSE) {
liger_object <- Add_Top_Gene_Pct(object = liger_object, num_top_genes = 50)
}

if (FALSE) {
library(Seurat)
pbmc_small <- Add_Top_Gene_Pct(seurat_object = pbmc_small, num_top_genes = 50)
}