RoarDatasetMultipleAPA-class: Class '"RoarDatasetMultipleAPA"'

In vodkatad/roar: Identify differential APA usage from RNA-seq alignments

Description

RoarDataset - a class to perform 3'UTR shortening analyses

Objects from the Class

Objects of thiss class should be created using the functions RoarDatasetMultipleAPA or RoarDatasetMultipleAPAFromFiles, ideally the raw new method should never be invoked by end users. Then to perform the analysis the user should call, in order: countPrePost, computeRoars, computePvals and one of the methods to format results. This class is used to allow efficient analyses that allow to study more than one APA site for each gene: internally it uses a RoarDataset object that stores PRE/POST counts for all possible alternative APA choices for each gene.

Slots

treatmentBams:

Object of class "list" - a list of GappedAlignment objects for the first condition (by convention it is considered the “treated” condition) in analysis.

controlBams:

Object of class "list" - a list of GappedAlignment objects for the second condition (by convention it is considered the “control” condition) in analysis.

geneCoords:

Object of class "GRangesList" - private object that represents the exon structures of genes in study.

apaCoords:

Object of class "GRangesList" - private object that represents the APA fallin on genes in study.

fragments:

Object of class "GRangesList" - private object used to efficiently count reads falling on short and long isoforms.

prePostDef:

Object of class "list" - private object representing all possible short and long isoforms.

roars:

Object of class "list" - private object with a list of RoarDataset objects, each one representing all possible PRE/POST choices for a single gene.

corrTreatment:

"numeric" slot - private, integer representing the mean length of reads for the treatment samples.

corrControl:

"numeric" slot - private, integer representing the mean length of reads for the control samples.

paired:

"logical" slot - private.

step:

"numeric" slot - private.

cores:

"numeric" slot - private.

Methods

countPrePost

signature(rds = "RoarDatasetMultipleAPA", stranded = "logical"): Counts reads falling over all the possible PRE/POST portions of the given transcripts. WARNING: stranded = TRUE is still unsupported and could give unpredictable results.

computeRoars

signature(rds = "RoarDatasetMultipleAPA"): Computes m/M and roar values for this RoarDatasetMultipleAPA object.

computePvals

signature(rds = "RoarDatasetMultipleAPA"): Computes pvalues (Fisher test) for this RoarDatasetMultipleAPA object.

totalResults

signature(rds = "RoarDatasetMultipleAPA"): Returns a dataframe with results of the analysis for a RoarDatasetMultipleAPA object.

fpkmResults

signature(rds = "RoarDatasetMultipleAPA"): The last step of a classical Roar analyses: it returns a dataframe containing m/M values, roar values, pvalues and estimates of expression (a measure recalling FPKM).

countResults

signature(rds = "RoarDatasetMultipleAPA"): The last step of a classical Roar analyses: it returns a dataframe containing m/M values, roar values, pvalues and estimates of expression (counts of reads falling over a gene).

standardFilter

signature(rds = "RoarDatasetMultipleAPA", fpkmCutoff = "double"): Returns a dataframe with results of the analysis for a RoarDatasetMultipleAPA object.

pvalueFilter

signature(rds = "RoarDatasetMultipleAPA", fpkmCutoff = "double", pvalCutoff = "double"): ...

cores

signature(rds = "RoarDatasetMultipleAPA"): returns the number of cores used for computation, right now always 1.

Author(s)

Elena Grassi, PhD student in Biomedical Sciences and Oncology - Dept. of Molecular Biotechnologies and Health Sciences, Molecular Biotechnology Center, Torino

Examples

showClass("RoarDatasetMultipleAPA")

vodkatad/roar documentation built on March 30, 2020, 2:56 p.m.