Bludger: Run Double Binomial GLM differential analyses

In fursham-h/quafle: Quantifying Usage of Alternative First and Last Exons

Run Double Binomial GLM differential analyses

Description

'Bludger' compares the usage of each AFL event between two groups, using a double binomial GLM model.

Usage

Bludger(se, group, contrast = c(1, 2))

Arguments

se	RangedSummarizedExperiment object from 'Quaffle' output
group	Can be name of variable from 'colData(se)' that contain groupings of samples. Can also be a vector (length equal to number of samples) that describes the sample groupings
contrast	numeric vector of length 2 specifying which levels of the "groups" factor should be compared.

Value

a dataframe describing the output of differential AFL usage with the folloinwing column names:

• gene_idID of parent gene

• gene_nameName of parent gene

• effectivecoordCoordinate of AFL event

• typeType of event; Alternative First (AF) or Last (AL)

• MLE_*Maximum likelihood estimate of event usage in each sample

• deltaMLEChange in event usage between comparisons

• pvalUnadjusted p-value of differential analysis

• adj_pvalAdjusted p-value of differential analysis

• MeanTotCt_*Mean total read count in each sample

Examples

#' library(quaffle)
gtf <- system.file("extdata/wtap.gtf", package = "quaffle")
bams <- system.file("extdata/bams", package = "quaffle")

se <- Quaffle(bams, gtf)

Bludger(se, rep(c("A","B"), each = 3))

fursham-h/quafle documentation built on Oct. 9, 2022, 4:49 p.m.