Add_Top_Gene_Pct_Seurat {scCustomize}

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_Seurat(
  seurat_object,
  num_top_genes = 50,
  meta_col_name = NULL,
  assay = "RNA",
  overwrite = FALSE,
  verbose = TRUE
)

Arguments

seurat_object

object name.

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.

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).

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.

Value

A Seurat Object

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.

See Also

https://bioconductor.org/packages/release/bioc/html/scuttle.html

Examples

## Not run: 
library(Seurat)
pbmc_small <- Add_Top_Gene_Pct_Seurat(seurat_object = pbmc_small, num_top_genes = 50)

## End(Not run)