inPAS {InPAS} | R Documentation

## 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"),
          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
        }

[Package InPAS version 1.18.0 Index]