MarkerBasedDecomposition {BisqueRNA}

Performs marker-based decomposition of bulk expression using marker genes

Description

Estimates relative abundances of cell types from PCA-based decomposition. Uses a list of marker genes to subset the expression data, and returns the first PC of each sub-matrix as the cell type fraction estimates. Optionally, weights for each marker gene can be used to prioritize genes that are highly expressed in the given cell type.

Usage

MarkerBasedDecomposition(
  bulk.eset,
  markers,
  ct_col = "cluster",
  gene_col = "gene",
  min_gene = 5,
  max_gene = 200,
  weighted = FALSE,
  w_col = "avg_logFC",
  unique_markers = TRUE,
  verbose = TRUE
)

Arguments

bulk.eset

Expression Set. Normalized bulk expression data.

markers

Data frame with columns specifying cluster and gene, and optionally a column for weights, typically the fold-change of the gene. Important that the genes for each cell type are row-sorted by signficance.

ct_col

Character string. Column name specifying cluster/cell type corresponding to each marker gene in markers.

gene_col

Character string. Column name specifying gene names in markers.

min_gene

Numeric. Min number of genes to use for each cell type.

max_gene

Numeric. Max number of genes to use for each cell type.

weighted

Boolean. Whether to use weights for gene prioritization

w_col

Character string. Column name for weights, such as "avg_logFC", in markers

unique_markers

Boolean. If TRUE, subset markers to include only genes that are markers for only one cell type

verbose

Boolean. Whether to print log info during decomposition. Errors will be printed regardless.

Details

Note that this method expects the input bulk data to be normalized, unlike the reference-based method.

Value

A List. Slot bulk.props contains estimated relative cell type abundances. Slot var.explained contains variance explained by first 20 PCs for cell type marker genes. Slot genes.used contains vector of genes used for decomposition.

Examples

library(Biobase)
sim.data <- SimulateData(n.ind=10, n.genes=100, n.cells=100,
                         cell.types=c("Neurons", "Astrocytes", "Microglia"),
                         avg.props=c(.5, .3, .2))
res <- MarkerBasedDecomposition(sim.data$bulk.eset, sim.data$markers, weighted=FALSE)
estimated.cell.proportions <- res$bulk.props