spliceR (version 1.14.0)

SpliceRList: Transcript data and annotation object for spliceR

Description

Creates a SpliceRList object from two GRanges objects, an assembly id, and a source id. The first GRanges, transcript_features, containing a list of transcripts, and including the columns gene_id for gene id, tx_id for transcript id, sample_1 and sample_2 for sample identifiers, expression_1 and expression_2 for expression values for sample 1 and sample 2, respectively (typically FPKM values or some other normalized count values), and additional optional columns (see prepareCuff). The second, exon_features, containing a list of exons, and including the columns gene_id for gene id and tx_id for transcript id. Assembly id, denoting genome assembly ('hg19', 'hg18', 'mm9', etc.) Source id, denoting source of transcript assembly (currently 'cufflinks' or 'other') Note, that the cromosome identifiers should match the assembly. For experiments

Usage

SpliceRList(transcript_features, exon_features, assembly_id, source_id, conditions, transcripts_plot=NULL,filter_params=NULL)

Arguments

transcript_features

GRanges object containing transcript features.

exon_features

GRanges object containing transcript features.

assembly_id

character, giving genome assemlby.

source_id

A character, either "cufflinks" or "granges", stating source of transcript assembly.

conditions

A character vector, giving the samples or conditions for the RNA-seq experiment.

transcripts_plot

A dataframe, reserved for plotting functions

filter_params

A character vector, reserved for plotting functions.

Value

A SpliceRList object.

Details

For cufflinks data, call prepareCuff to prepare a SpliceRList. For other RNA-seq assemblies, use this constructor to create a SpliceRList.

See the spliceR vignette for an example of creating a spliceRList from another source than Cufflinks.

References

Vitting-Seerup K , Porse BT, Sandelin A, Waage J. (2014) spliceR: an R package for classification of alternative splicing and prediction of coding potential from RNA-seq data. BMC Bioinformatics 15:81.