run_gene_mean: Generate overall aggregated mean expression

In SydneyBioX/scFeatures: scFeatures: Multi-view representations of single-cell and spatial data for disease outcome prediction

Generate overall aggregated mean expression

Description

This function computes the mean expression of genes across samples. The user can specify the genes of interest, or let the function use the top variable genes by default. The function supports scRNA-seq, spatial proteomics, and spatial transcriptomics.

Usage

run_gene_mean(
  data,
  type = "scrna",
  genes = NULL,
  num_top_gene = NULL,
  ncores = 1
)

Arguments

data	A list object containing data matrix and celltype and sample vector.
type	The type of dataset, either "scrna", "spatial_t", or "spatial_p".
genes	Default to NULL, in which case the top variable genes will be used. If provided by user, need to be in the format of a list containing the genes of interest, eg, genes <- c(GZMA", "GZMK", "CCR7", "RPL38" )
num_top_gene	Number of top variable genes to use when genes is not provided. Defaults to 1500.
ncores	Number of cores for parallel processing.

Value

a dataframe of samples x features The features are in the form of gene 1, gene 2 ... etc, with the numbers representing averaged gene expression across all cells.

Examples

utils::data("example_scrnaseq" , package = "scFeatures")
data <- example_scrnaseq
celltype <- data$celltype
sample <- data$sample
data <- data@assays$RNA@data

alldata <- scFeatures:::formatData(data = data, celltype = celltype, sample = sample )
feature_gene_mean <- run_gene_mean(
    alldata,
    type = "scrna", num_top_gene = 150, ncores = 1
)

SydneyBioX/scFeatures documentation built on March 13, 2024, 12:36 a.m.