immunedeconv (version 2.0.3)

make_random_bulk: Make a random bulk sample from a single-cell dataset

Description

Take random single cells from the input expression set, given the cell_fractions vector and combine them by taking the mean.

Usage

make_random_bulk(eset, cell_fractions, n_cells = 500, combine = mean)

Arguments

eset

Biobase::ExpressionSet with a cell_type column in pData. Each sample is the gene expression of a single cell. The cell type of each cell needs to be denoted in the pData table in the cell_type column.

cell_fractions

named list indicating the fraction of each cell type which will be in the sample. The names of the list need to correspond to the cell_type column in the ExpressionSet.

n_cells

number of single cells to integrate into a sample

combine

callback function used to aggregate the counts.

Value

a single-column tibble with simulated expression for each gene. The column sum is scaled to 1 million (TPM)

Examples

# NOT RUN {
suppressPackageStartupMessages(library(Biobase))
suppressPackageStartupMessages(library(tibble))

# generate toy matrix with three marker genes and three cell types
expr = matrix(c(
    rep(c(1,0,0), 300),
    rep(c(0,1,0), 300),
    rep(c(0,0,1), 300)
), nrow=3)

# generate a featureData and phenoData data-frame. 
# row names must be consistent between expr and featureData. 
gene_names = c("CD8A", "CD4", "CD19")
rownames(expr) = gene_names
cell_types = c(rep("T cell CD8+", 300), rep("T cell CD4+", 300), rep("B cell", 300))
pdata = data.frame(cell_type=cell_types)
fdata = data.frame(gene_symbol=gene_names)
rownames(fdata) = gene_names

# tie expr, fdata and pdata together in expression set
eset = ExpressionSet(expr, 
                     phenoData=as(pdata, "AnnotatedDataFrame"),
                     featureData=as(fdata, "AnnotatedDataFrame"))
                     
# make a random bulk sample. 
make_random_bulk(eset,
                 c("T cell CD8+"=0.3, "B cell"=0.4, "T cell CD4+"=0.3),
                 n_cells=1000)

# }