computeWeightedDeviations: computeWeightedDeviations

In caleblareau/gchromVAR: Compute cell-type specific enrichments with high resolution

Description

Computes weighted deviations in chromatin accessibility across sets of weights where the number of the annotation meaningful.

Usage

computeWeightedDeviations(object, weights, ...)


  ## S4 method for signature 'SummarizedExperiment,SummarizedExperiment'
computeWeightedDeviations(object,
  weights, background_peaks = getBackgroundPeaks(object),
  expectation = computeExpectations(object))

## S4 method for signature 'SummarizedExperiment,Matrix'
computeWeightedDeviations(object,
  weights, background_peaks = getBackgroundPeaks(object),
  expectation = computeExpectations(object))

## S4 method for signature 'SummarizedExperiment,matrix'
computeWeightedDeviations(object,
  weights, background_peaks = getBackgroundPeaks(object),
  expectation = computeExpectations(object))

Arguments

object	chromVARCounts object
weights	chromVARweights object
...	additional arguments
background_peaks	(optional) background peaks matrix computed using getBackgroundPeaks, computed internally with default paramaters if not provided
expectation	(optional) expectations computed using computeExpectations, computed automatically if not provided

Details

multiprocessing using bplapply

Value

chromVARDeviations-class, which inherits from SummarizedExperiment, and has two assays: deviations and deviation scores.

Methods (by class)

• object = SummarizedExperiment,weights = SummarizedExperiment: / weights are SummarizedExperiment

• object = SummarizedExperiment,weights = Matrix: object is SummarizedExperiment, weights are in a Matrix

• object = SummarizedExperiment,weights = matrix: object is SummarizedExperiment, weights are in a numeric

Author(s)

Caleb Lareau

Examples

# Register BiocParallel
BiocParallel::register(BiocParallel::SerialParam())
# Load very small example counts (already filtered)
data(mini_counts, package = "chromVAR")
# Load mini weighted counts
rdsA<-paste0(system.file('rds',package='gchromVAR'),'/mini_w.rds')
w_se <- readRDS(rdsA)

# Build weights from .bed file
files <- list.files(system.file('extdata',package='gchromVAR'),
         full.names = TRUE, pattern = "*.bed$")
data(mini_counts, package = "chromVAR")
uk_se <- importBedScore(SummarizedExperiment::rowRanges(mini_counts), files)

wdev <- computeWeightedDeviations(mini_counts, w_se)
ukdev <- computeWeightedDeviations(mini_counts, uk_se)

caleblareau/gchromVAR documentation built on Jan. 16, 2020, 3:07 p.m.