calcDatasetSpecificity: Calculate a dataset-specificity score for each factor

In rliger: Linked Inference of Genomic Experimental Relationships

Calculate a dataset-specificity score for each factor

Description

This score represents the relative magnitude of the dataset-specific components of each factor's gene loadings compared to the shared components for two datasets. First, for each dataset we calculate the norm of the sum of each factor's shared loadings (W) and dataset-specific loadings (V). We then determine the ratio of these two values and subtract from 1... TODO: finish description.

Usage

calcDatasetSpecificity(
  object,
  dataset1 = NULL,
  dataset2 = NULL,
  do.plot = TRUE
)

Arguments

object	liger object. Should run optimizeALS before calling.
dataset1	Name of first dataset (by default takes first two datasets for dataset1 and 2)
dataset2	Name of second dataset
do.plot	Display barplot of dataset specificity scores (by factor) (default TRUE).

Value

List containing three elements. First two elements are the norm of each metagene factor for each dataset. Last element is the vector of dataset specificity scores.

Examples

ligerex <- createLiger(list(ctrl = ctrl, stim = stim))
ligerex <- normalize(ligerex)
ligerex <- selectGenes(ligerex)
ligerex <- scaleNotCenter(ligerex)
# Specification for minimal example run time, not converging
ligerex <- optimizeALS(ligerex, k = 5, max.iters = 1)
calcDatasetSpecificity(ligerex)

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