derive_GeoMx_background: Derive background at the scale of the normalized data for...

In SpatialDecon: Deconvolution of mixed cells from spatial and/or bulk gene expression data

Description

Estimates per-datapoint background levels from a GeoMx experiment. In studies with two or more probe pools, different probes will have different background levels. This function provides a convenient way to account for this phenomenon.

Usage

derive_GeoMx_background(norm, probepool, negnames)

Arguments

norm	Matrix of normalized data, genes in rows and segments in columns. Must include negprobes, and must have rownames.
probepool	Vector of probe pool names for each gene, aligned to the rows of "norm".
negnames	Names of all negProbes in the dataset. Must be at least one neg.name within each probe pool.

Value

A matrix of expected background values, in the same scale and dimensions as the "norm" argument.

Examples

data(mini_geomx_dataset)
# estimate background:
mini_geomx_dataset$bg <- derive_GeoMx_background(
  norm = mini_geomx_dataset$normalized,
  probepool = rep(1, nrow(mini_geomx_dataset$normalized)),
  negnames = "NegProbe"
)

SpatialDecon documentation built on Nov. 8, 2020, 6 p.m.