find_markers: A function for finding markers for hidden factors

In UcarLab/IA-SVA: Iteratively Adjusted Surrogate Variable Analysis

Description

Function takes a read counts matrix of entire gene set and a matrix of surrogate variables estimated by IA-SVA as input, identifies marker genes highly correlated with each surrogate variable and returns a read counts matrix of the markers.

Usage

find_markers(
  Y,
  iasva.sv,
  method = "BH",
  sig.cutoff = 0.05,
  rsq.cutoff = 0.3,
  verbose = FALSE
)

Arguments

Y	A SummarizedExperiment or SingleCellExperiment class containing read counts where rows represent genes and columns represent samples.
iasva.sv	matrix of estimated surrogate variables, one column for each surrogate variable.
method	multiple testing adjustment method, default = "BH".
sig.cutoff	significance cutoff.
rsq.cutoff	R squared cutoff.
verbose	If verbose = TRUE, the function outputs detailed messages.

Value

marker.counts read counts matrix of markers, one column for each cell.

Examples

counts_file <- system.file("extdata", "iasva_counts_test.Rds",
 package = "iasva")
counts <- readRDS(counts_file)
anns_file <- system.file("extdata", "iasva_anns_test.Rds",
 package = "iasva")
 anns <- readRDS(anns_file)
Geo_Lib_Size <- colSums(log(counts + 1))
Patient_ID <- anns$Patient_ID
mod <- model.matrix(~Patient_ID + Geo_Lib_Size)
summ_exp <- SummarizedExperiment::SummarizedExperiment(assays = counts)
iasva.res <- iasva(summ_exp, mod[, -1], num.sv = 5, permute = FALSE)
markers <- find_markers(summ_exp, iasva.res$sv)

UcarLab/IA-SVA documentation built on Sept. 3, 2021, 1:38 p.m.