InPAS (version 1.4.4)

inPAS: do estimation of alternative polyadenylation and cleavage site in one step

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"), gcCompensation=NA, mappabilityCompensation=NA,  FFT=FALSE, fft.sm.power=20,  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

gcCompensation

GC content compensation vector

mappabilityCompensation

mappability compensation vector

FFT

use Fast Fourier Transform Algorithm to smooth the data or not. default: FALSE

fft.sm.power

if FFT is TRUE, the frequency should be removed

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

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

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
    }