getFactorMarkers: Find shared and dataset-specific markers

In rliger: Linked Inference of Genomic Experimental Relationships

Find shared and dataset-specific markers

Description

Applies various filters to genes on the shared (W) and dataset-specific (V) components of the factorization, before selecting those which load most significantly on each factor (in a shared or dataset-specific way).

Usage

getFactorMarkers(
  object,
  dataset1 = NULL,
  dataset2 = NULL,
  factor.share.thresh = 10,
  dataset.specificity = NULL,
  log.fc.thresh = 1,
  pval.thresh = 0.05,
  num.genes = 30,
  print.genes = FALSE,
  verbose = TRUE
)

Arguments

object	liger object. Should call optimizeALS before calling.
dataset1	Name of first dataset (default first dataset by order)
dataset2	Name of second dataset (default second dataset by order)
factor.share.thresh	Use only factors with a dataset specificity less than or equalt to threshold (default 10).
dataset.specificity	Pre-calculated dataset specificity if available. Will calculate if not available.
log.fc.thresh	Lower log-fold change threshold for differential expression in markers (default 1).
pval.thresh	Upper p-value threshold for Wilcoxon rank test for gene expression (default 0.05).
num.genes	Max number of genes to report for each dataset (default 30).
print.genes	Print ordered markers passing logfc, umi and frac thresholds (default FALSE).
verbose	Print messages (TRUE by default)

Value

List of shared and specific factors. First three elements are dataframes of dataset1- specific, shared, and dataset2-specific markers. Last two elements are tables indicating the number of factors in which marker appears.

Examples

ligerex <- createLiger(list(ctrl = ctrl, stim = stim))
ligerex <- normalize(ligerex)
ligerex <- selectGenes(ligerex)
ligerex <- scaleNotCenter(ligerex)
ligerex <- optimizeALS(ligerex, k = 5, max.iter = 2)
ligerex <- quantile_norm(ligerex)
fm <- getFactorMarkers(ligerex, dataset1 = "stim", dataset2 = "ctrl")

rliger documentation built on Nov. 9, 2023, 1:07 a.m.