scorePval: Making a single combined score for each gene

In GreallyLab/SMITE: Significance-based Modules Integrating the Transcriptome and Epigenome

Description

This function uses an a priori weighting scheme to combine scores for a given gene.

Usage

scorePval(pvalue_annotation, weights)

Arguments

pvalue_annotation	An S4 object of class PvalueAnnotation, for which makePvalueObject has already been run.
weights	A numeric vector of the relative importance of expression, modifications, and genomic contexts toward the final score. Names should be provided that match the "modfication_genomicfeature" format, except for expression. While the scores do not have to add up to 1, it is good practice to impose this restriction in order to track the relative importance.

Details

Because each weighting scheme generates scores from a distribution that will change depending on the analysis inputs, the function will randomly sample the final scores and compare each derived score to this simulated distribution.

If no names are given, then the function will assume the weights are in the order that it finds a particular "modification_genomicfeature" and it will print the weighting scheme so that you can verify it is correct. The total number of weights must match the total number of modifications*genomicfeatures+1 for expression.

After calculating a combined score (using a Stouffer's weighted statistic), a new p-value is derived using a non-parametric sampling approach.

Value

An S4 object of class PvalueAnnotation.

Author(s)

N. Ari Wijetunga

Examples

options(stringsAsFactors=FALSE)

data(test_annotation_score_data)

## NOTE: commented out for example.  See vignette for better explanation ##
#test_annotation<-scorePval(pvalue_annotation=test_annotation,
#weights=c(methylation_promoter=.3,methylation_body=.1,expression=.3,
#methylation_h3k4me1=.3))

GreallyLab/SMITE documentation built on May 6, 2019, 6:30 p.m.