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aroma.light: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types

Installation

R package aroma.light is available on Bioconductor and can be installed in R as:

source('http://bioconductor.org/biocLite.R')
biocLite('aroma.light')

Pre-release version

To install the pre-release version that is available in branch develop, use:

source('http://callr.org/install#HenrikBengtsson/aroma.light@develop')

This will install the package from source.

Software status

Resource:BioconductorTravis CIAppveyor
Platforms:MultipleLinuxWindows
R CMD check (release) (devel)
Test coverage

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Version

Version

3.2.0

License

GPL (>= 2)

Issues

0

Pull Requests

0

Stars

1

Forks

3

Maintainer

Henrik Bengtsson

Last Published

February 15th, 2017

Functions in aroma.light (3.2.0)

distanceBetweenLines

Finds the shortest distance between two lines
normalizeFragmentLength

Normalizes signals for PCR fragment-length effects
normalizeQuantileRank.matrix

Normalizes the empirical distribution of a set of samples to a common target distribution
fitXYCurve

Fitting a smooth curve through paired (x,y) data
backtransformAffine

Reverse affine transformation
wpca

Light-weight Weighted Principal Component Analysis
normalizeDifferencesToAverage

Rescales channel vectors to get the same average
normalizeQuantileRank

Normalizes the empirical distribution of one of more samples to a target distribution
likelihood.smooth.spline

Calculate the log likelihood of a smoothing spline given the data
sampleTuples

Sample tuples of elements from a set
plotMvsMPairs

Plot log-ratios vs log-ratios for all pairs of columns
findPeaksAndValleys

Finds extreme points in the empirical density estimated from data
calibrateMultiscan

Weighted affine calibration of a multiple re-scanned channel
fitPrincipalCurve

Fit a principal curve in K dimensions
aroma.light-package

Package aroma.light
normalizeQuantileSpline

Normalizes the empirical distribution of one or more samples to a target distribution
fitNaiveGenotypes

Fit naive genotype model from a normal sample
iwpca

Fits an R-dimensional hyperplane using iterative re-weighted PCA
Non-documented objects

Non-documented objects
1. Calibration and Normalization

1. Calibration and Normalization
print.SmoothSplineLikelihood

Prints an SmoothSplineLikelihood object
normalizeCurveFit

Weighted curve-fit normalization between a pair of channels
normalizeAverage

Rescales channel vectors to get the same average
fitIWPCA

Robust fit of linear subspace through multidimensional data
sampleCorrelations

Calculates the correlation for random pairs of observations
callNaiveGenotypes

Calls genotypes in a normal sample
averageQuantile

Gets the average empirical distribution
medianPolish

Median polish
robustSmoothSpline

Robust fit of a Smoothing Spline
plotMvsAPairs

Plot log-ratios/log-intensities for all unique pairs of data vectors
backtransformPrincipalCurve

Reverse transformation of principal-curve fit
normalizeTumorBoost

Normalizes allele B fractions for a tumor given a match normal
plotMvsA

Plot log-ratios vs log-intensities
pairedAlleleSpecificCopyNumbers

Calculating tumor-normal paired allele-specific copy number stratified on genotypes
plotXYCurve

Plot the relationship between two variables as a smooth curve
normalizeAffine

Weighted affine normalization between channels and arrays
plotDensity

Plots density distributions for a set of vectors