quantileNormalize: Quantile normalization

In HELP: Tools for HELP data analysis

Description

Apply quantile normalization to multiple bins of data, divided by a sliding window approach

Usage

quantileNormalize(x, y, ...)

Arguments

x	the vector of numerical data to be normalized. If x is a matrix it is interpreted as a vector. x can also be of class "ExpressionSet".
y	an additional vector of numerical data to be used for binning. If y is a matrix it is interpreted as a vector. y can also be of class "ExpressionSet".
...	Arguments to be passed to methods (see quantileNormalize-methods): element which element of AssayData to use for a given ExpressionSet input (default is "exprs") sample which element of sampleNames to use as data (default is 1). Can be a character matching a sample name or simply an integer indicating which sample to choose. See getSamples. feature which element of featureData to use as binning variable (default is 1). Can be a character matching varLabel or simply an integer indicating which feature to choose. See getFeatures. num.bins number of bins (default is 10) used to divide the data num.steps number of steps (default is 3) used to create bin offsets, resulting in bins of sliding windows mode the binning mode to be used. This must be either "continuous" (default) or "discrete". "continuous" mode will divide the data into density-dependent bins. "discrete" mode will divide the data uniformly by binning data values. type an integer between 1 and 9 (default is 7) selecting one of the nine quantile algorithms: see quantile. na.rm logical; if TRUE, missing values are removed from x and y. If FALSE any missing values cause an error. \dots other arguments to be passed to quantile. See quantile.

Value

Returns a vector of normalized numerical data according to input parameters.

Author(s)

Reid F. Thompson ([e-mail])

See Also

quantileNormalize-methods, quantile

Examples

#demo(pipeline,package="HELP")

x <- rep(1:100,10)+10*rep(1:10,each=100)
y <- rep(1:20,each=50)
d <- density(quantileNormalize(x,y,num.bins=20,num.steps=1,mode="discrete"))
plot(density(x))
lines(d$x,d$y/3,col="red")

#rm(x,y,d)

Example output

Loading required package: Biobase
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, sd, var, xtabs

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

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colMeans, colSums, colnames,
    dirname, 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,
    rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min

Welcome to Bioconductor

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

HELP documentation built on Nov. 8, 2020, 11:08 p.m.