rliger: calcDatasetSpecificity – R documentation

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calcDatasetSpecificity

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

## Not run: 
# ligerex (liger object), factorization complete
# generate H.norm by quantile normalizig factor loadings
ligerex <- quantile_norm(ligerex)
dataset_spec <- calcDatasetSpecificity(ligerex, do.plot = F)

## End(Not run)

rliger

Linked Inference of Genomic Experimental Relationships

v1.0.0

GPL-3

Authors

Joshua Welch [aut, ctb], Chao Gao [aut, ctb, cre], Jialin Liu [aut, ctb], Joshua Sodicoff [aut, ctb], Velina Kozareva [aut, ctb], Evan Macosko [aut, ctb], Paul Hoffman [ctb], Ilya Korsunsky [ctb], Robert Lee [ctb]

Initial release

2021-04-18