Smooth Winsor Normalization

Description

Function performs Winsor normalization (see winsor() function) of each window of specified window_size, sliding in a given vector by 1 position, and reports a list of (1) mean Winsorized values for each vector position (mean of Winsorized value for a given position as calculated within each overlapping window) and (2) standard deviation of those Winsorized values.

Usage

swinsor_vector(input_vector, window_size, winsor_level = 0.9,
  only_top = FALSE)

Arguments

Value

Author(s)

Lukasz Jan Kielpinski

References

"Analysis of sequencing based RNA structure probing data" Kielpinski, Sidiropoulos, Vinther. Chapter in "Methods in Enzymology" (in preparation)

Examples

data_set <- runif(1:100)*100
plot(swinsor_vector(data_set, window_size=71,
                    winsor_level=0.8)[[1]] ~ data_set)

Results

R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RNAprobR)
Loading required package: GenomicFeatures
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, cbind, colnames, do.call, duplicated, eval, evalq,
    get, grep, grepl, intersect, is.unsorted, lapply, lengths, mapply,
    match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank,
    rbind, rownames, sapply, setdiff, sort, table, tapply, union,
    unique, unsplit

Loading required package: S4Vectors
Loading required package: stats4

Attaching package: 'S4Vectors'

The following objects are masked from 'package:base':

    colMeans, colSums, expand.grid, rowMeans, rowSums

Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: GenomicRanges
Loading required package: AnnotationDbi
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: plyr

Attaching package: 'plyr'

The following object is masked from 'package:IRanges':

    desc

The following object is masked from 'package:S4Vectors':

    rename

> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/RNAprobR/swinsor_vector.Rd_%03d_medium.png", width=480, height=480)
> ### Name: swinsor_vector
> ### Title: Smooth Winsor Normalization
> ### Aliases: swinsor_vector
> 
> ### ** Examples
> 
> data_set <- runif(1:100)*100
> plot(swinsor_vector(data_set, window_size=71,
+                     winsor_level=0.8)[[1]] ~ data_set)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>