# Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types

## Documentation for package ‘aroma.light’ version 3.4.0

- DESCRIPTION file.
- Package NEWS.

## Help Pages

aroma.light-package | Package aroma.light
1. Calibration and Normalization | 1. Calibration and Normalization
aroma.light | Package aroma.light
averageQuantile | Gets the average empirical distribution
averageQuantile.list | Gets the average empirical distribution
averageQuantile.matrix | Gets the average empirical distribution
backtransformAffine | Reverse affine transformation
backtransformAffine.matrix | Reverse affine transformation
backtransformPrincipalCurve | Reverse transformation of principal-curve fit
backtransformPrincipalCurve.matrix | Reverse transformation of principal-curve fit
backtransformPrincipalCurve.numeric | Reverse transformation of principal-curve fit
backtransformXYCurve | Fitting a smooth curve through paired (x,y) data
backtransformXYCurve.matrix | Fitting a smooth curve through paired (x,y) data
calibrateMultiscan | Weighted affine calibration of a multiple re-scanned channel
calibrateMultiscan.matrix | Weighted affine calibration of a multiple re-scanned channel
callNaiveGenotypes | Calls genotypes in a normal sample
callNaiveGenotypes.numeric | Calls genotypes in a normal sample
distanceBetweenLines | Finds the shortest distance between two lines
distanceBetweenLines.default | Finds the shortest distance between two lines
fitIWPCA | Robust fit of linear subspace through multidimensional data
fitIWPCA.matrix | Robust fit of linear subspace through multidimensional data
fitNaiveGenotypes | Fit naive genotype model from a normal sample
fitNaiveGenotypes.numeric | Fit naive genotype model from a normal sample
fitPrincipalCurve | Fit a principal curve in K dimensions
fitPrincipalCurve.matrix | Fit a principal curve in K dimensions
fitXYCurve | Fitting a smooth curve through paired (x,y) data
fitXYCurve.matrix | Fitting a smooth curve through paired (x,y) data
iwpca | Fits an R-dimensional hyperplane using iterative re-weighted PCA
iwpca.matrix | Fits an R-dimensional hyperplane using iterative re-weighted PCA
medianPolish | Median polish
medianPolish.matrix | Median polish
normalizeAffine | Weighted affine normalization between channels and arrays
normalizeAffine.matrix | Weighted affine normalization between channels and arrays
normalizeAverage | Rescales channel vectors to get the same average
normalizeAverage.list | Rescales channel vectors to get the same average
normalizeAverage.matrix | Rescales channel vectors to get the same average
normalizeCurveFit | Weighted curve-fit normalization between a pair of channels
normalizeCurveFit.matrix | Weighted curve-fit normalization between a pair of channels
normalizeDifferencesToAverage | Rescales channel vectors to get the same average
normalizeDifferencesToAverage.list | Rescales channel vectors to get the same average
normalizeFragmentLength | Normalizes signals for PCR fragment-length effects
normalizeFragmentLength.default | Normalizes signals for PCR fragment-length effects
normalizeLoess | Weighted curve-fit normalization between a pair of channels
normalizeLoess.matrix | Weighted curve-fit normalization between a pair of channels
normalizeLowess | Weighted curve-fit normalization between a pair of channels
normalizeLowess.matrix | Weighted curve-fit normalization between a pair of channels
normalizeQuantile | Normalizes the empirical distribution of one of more samples to a target distribution
normalizeQuantile.default | Normalizes the empirical distribution of one of more samples to a target distribution
normalizeQuantileRank | Normalizes the empirical distribution of one of more samples to a target distribution
normalizeQuantileRank.list | Normalizes the empirical distribution of one of more samples to a target distribution
normalizeQuantileRank.matrix | Normalizes the empirical distribution of a set of samples to a common target distribution
normalizeQuantileRank.numeric | Normalizes the empirical distribution of one of more samples to a target distribution
normalizeQuantileSpline | Normalizes the empirical distribution of one or more samples to a target distribution
normalizeQuantileSpline.list | Normalizes the empirical distribution of one or more samples to a target distribution
normalizeQuantileSpline.matrix | Normalizes the empirical distribution of one or more samples to a target distribution
normalizeQuantileSpline.numeric | Normalizes the empirical distribution of one or more samples to a target distribution
normalizeRobustSpline | Weighted curve-fit normalization between a pair of channels
normalizeRobustSpline.matrix | Weighted curve-fit normalization between a pair of channels
normalizeSpline | Weighted curve-fit normalization between a pair of channels
normalizeSpline.matrix | Weighted curve-fit normalization between a pair of channels
normalizeTumorBoost | Normalizes allele B fractions for a tumor given a match normal
normalizeTumorBoost.numeric | Normalizes allele B fractions for a tumor given a match normal
pairedAlleleSpecificCopyNumbers | Calculating tumor-normal paired allele-specific copy number stratified on genotypes
pairedAlleleSpecificCopyNumbers.numeric | Calculating tumor-normal paired allele-specific copy number stratified on genotypes
plotDensity | Plots density distributions for a set of vectors
plotDensity.data.frame | Plots density distributions for a set of vectors
plotDensity.density | Plots density distributions for a set of vectors
plotDensity.list | Plots density distributions for a set of vectors
plotDensity.matrix | Plots density distributions for a set of vectors
plotDensity.numeric | Plots density distributions for a set of vectors
plotMvsA | Plot log-ratios vs log-intensities
plotMvsA.matrix | Plot log-ratios vs log-intensities
plotMvsAPairs | Plot log-ratios/log-intensities for all unique pairs of data vectors
plotMvsAPairs.matrix | Plot log-ratios/log-intensities for all unique pairs of data vectors
plotMvsMPairs | Plot log-ratios vs log-ratios for all pairs of columns
plotMvsMPairs.matrix | Plot log-ratios vs log-ratios for all pairs of columns
plotXYCurve | Plot the relationship between two variables as a smooth curve
plotXYCurve.matrix | Plot the relationship between two variables as a smooth curve
plotXYCurve.numeric | Plot the relationship between two variables as a smooth curve
robustSmoothSpline | Robust fit of a Smoothing Spline
robustSmoothSpline.default | Robust fit of a Smoothing Spline
sampleCorrelations | Calculates the correlation for random pairs of observations
sampleCorrelations.matrix | Calculates the correlation for random pairs of observations
sampleTuples | Sample tuples of elements from a set
sampleTuples.default | Sample tuples of elements from a set
wpca | Light-weight Weighted Principal Component Analysis
wpca.matrix | Light-weight Weighted Principal Component Analysis