make_bulk_eset: Make a random expression set from a single-cell dataset

In grst/immunedeconv: Methods for immune cell deconvolution

Make a random expression set from a single-cell dataset

Description

Make a random expression set from a single-cell dataset

Usage

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

Arguments

eset	Biobase::ExpressionSet with a cell_type column in pData.
cell_fractions	n x n_cell_types dataframe with the fraction for each sample in each row.
n_cells	number of single cells to use in each sample
combine	callback function used to aggregate the counts.

Value

matrix with genes in rows and samples in columns. The column sum is scaled to 1 million (TPM).

See Also

make_random_bulk

Examples

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")
)

# create data frame with cell fractions (one row per sample)
desired_cell_fractions <- tibble(
  "T cell CD8+" = c(0.1, 0.2, 0.3),
  "T cell CD4+" = c(0.9, 0.7, 0.5),
  "B cell" = c(0, 0.1, 0.2)
)
new_eset <- make_bulk_eset(eset, desired_cell_fractions, n_cells = 500)
exprs(new_eset)

grst/immunedeconv documentation built on Nov. 10, 2023, 1:33 a.m.