Generate overall aggregated mean expression — run_gene

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
)