inPAS: do estimation of alternative polyadenylation and cleavage...

In InPAS: InPAS: a bioconductor package for the identification of novel alternative PolyAdenylation Sites (PAS) using RNA-seq data

Description

do estimation of alternative polyadenylation and cleavage site in one step

Usage

inPAS(bedgraphs, genome, utr3, txdb=NA,
      tags, hugeData=FALSE, ...,
      
      gp1, gp2,
      
      window_size=100, 
      search_point_START=50, search_point_END=NA, 
      cutStart=window_size, cutEnd=0, 
      coverage_threshold=5, long_coverage_threshold=2, 
      background=c("same_as_long_coverage_threshold", 
                   "1K", "5K", "10K", "50K"), 
      adjust_distal_polyA_end=TRUE, 
      PolyA_PWM=NA, classifier=NA, classifier_cutoff=.8, 
      shift_range=window_size, 
      
      method=c("limma", "fisher.exact", 
               "singleSample", "singleGroup"),
      normalize=c("none", "quantiles", "quantiles.robust",
                  "mean", "median"),
      design, contrast.matrix, coef=1,
      
      P.Value_cutoff=0.05,
      adj.P.Val_cutoff=0.05, 
      dPDUI_cutoff=0.3, 
      PDUI_logFC_cutoff=0.59,
      
      BPPARAM=NULL)

Arguments

bedgraphs	The file names of bedgraphs generated by bedtools. eg: bedtools genomecov -bg -split -ibam $bam -g mm10.size.txt > $bedgraph
genome	an object of BSgenome
utr3	output of utr3Annotation
txdb	an object of TxDb
tags	the names for each input bedgraphs
hugeData	is this dataset consume too much memory? if it is TRUE, the coverage will be saved into tempfiles.
...	parameters can be passed into tempfile. This is useful when you submit huge dataset to cluster.
gp1	tag names involved in group 1
gp2	tag names involved in group 2
window_size	window size for noval distal position searching and adjusted polyA searching, default: 100
search_point_START	start point for searching
search_point_END	end point for searching
cutStart	how many nucleotides should be removed from the start before search, 0.1 means 10 percent.
cutEnd	how many nucleotides should be removed from the end before search, 0.1 means 10 percent.
coverage_threshold	cutoff threshold for coverage in the region of short form
long_coverage_threshold	cutoff threshold for coverage in thre region of long form
background	the range for calculating cutoff threshold of local background
adjust_distal_polyA_end	If true, adjust distal polyA end by cleanUpdTSeq
PolyA_PWM	Position Weight Matrix of polyA
classifier	An object of class "PASclassifier"
classifier_cutoff	This is the cutoff used to assign whether a putative pA is true or false. This can be any floating point number between 0 and 1. For example, classifier_cutoff = 0.5 will assign an putative pA site with prob.1 > 0.5 to the True class (1), and any putative pA site with prob.1 <= 0.5 as False (0).
shift_range	the shift range for polyA site searching
method	test method. see singleSampleAnalyze,singleGroupAnalyze, fisher.exact.test,limmaAnalyze
normalize	normalization method
design	the design matrix of the experiment, with rows corresponding to arrays and columns to coefficients to be estimated. Defaults to the unit vector meaning that the arrays are treated as replicates. see model.matrix
contrast.matrix	numeric matrix with rows corresponding to coefficients in fit and columns containing contrasts. May be a vector if there is only one contrast. see makeContrasts
coef	column number or column name specifying which coefficient or contrast of the linear model is of interest. see more topTable. default value: 1
P.Value_cutoff	cutoff of P value
adj.P.Val_cutoff	cutoff value for adjusted p.value
dPDUI_cutoff	cutoff value for differenctial PAS(polyadenylation signal) usage index
PDUI_logFC_cutoff	cutoff value for log2 fold change of PAS(polyadenylation signal) usage index
BPPARAM	An optional BiocParallelParam instance determining the parallel back-end to be used during evaluation, or a list of BiocParallelParam instances, to be applied in sequence for nested calls to bplapply.

Value

return an object of GRanges

Author(s)

Jianhong Ou

Examples

    if(interactive()){
        library(BSgenome.Mmusculus.UCSC.mm10)
        library(TxDb.Mmusculus.UCSC.mm10.knownGene)

        path <- file.path(find.package("InPAS"), "extdata")
        bedgraphs <- file.path(path, "Baf3.extract.bedgraph")
        data(utr3.mm10)
        res <- inPAS(bedgraphs=bedgraphs, tags=c("Baf3"), 
                  genome=BSgenome.Mmusculus.UCSC.mm10, 
                  utr3=utr3.mm10, gp1="Baf3", gp2=NULL,
                  txdb=TxDb.Mmusculus.UCSC.mm10.knownGene,
                  search_point_START=200,
                  short_coverage_threshold=15,
                  long_coverage_threshold=3, 
                  cutStart=0, cutEnd=.2, 
                  hugeData=FALSE)
        res
    }

InPAS documentation built on Nov. 8, 2020, 5:03 p.m.