Light-weight methods for normalization and visualization of microarray data using only basic R data types

[Package List] [Top]

Documentation for package ‘aroma.light’ version 1.8.1

Help Pages

aroma.light-package Package aroma.light
1. Calibration and Normalization 1. Calibration and Normalization
aroma.light Package aroma.light
averageQuantile.list Gets the average empirical distribution
backtransformAffine.matrix Reverse affine transformation
calibrateMultiscan.matrix Weighted affine calibration of a multiple re-scanned channel
distanceBetweenLines Finds the shortest distance between two lines
fitIWPCA.matrix Robust fit of linear subspace through multidimensional data
iwpca.matrix Fits an R-dimensional hyperplane using iterative re-weighted PCA
likelihood.smooth.spline Calculate the log likelihood of a smoothing spline given the data
list.normalizeQuantile Normalizes the empirical distribution of a set of samples to a target distribution
matrix.normalizeQuantile Weighted sample quantile normalization
medianPolish.matrix Median polish
normalizeAffine.matrix Weighted affine normalization between channels and arrays
normalizeAverage.list Rescales channel vectors to get the same average
normalizeAverage.matrix Rescales channel vectors to get the same average
normalizeCurveFit.matrix Weighted curve-fit normalization between a pair of channels
normalizeFragmentLength Normalizes signals for PCR fragment-length effects
normalizeLoess.matrix Weighted curve-fit normalization between a pair of channels
normalizeLowess.matrix Weighted curve-fit normalization between a pair of channels
normalizeQuantile,list-method Normalizes the empirical distribution of a set of samples to a target distribution
normalizeQuantile,matrix-method Weighted sample quantile normalization
normalizeQuantile,numeric-method Normalizes the empirical distribution of a single sample to a target distribution
normalizeQuantile.list Normalizes the empirical distribution of a set of samples to a target distribution
normalizeQuantile.matrix Weighted sample quantile normalization
normalizeQuantile.numeric Normalizes the empirical distribution of a single sample to a target distribution
normalizeQuantileRank.list Normalizes the empirical distribution of a set of samples to a target distribution
normalizeQuantileRank.matrix Weighted sample quantile normalization
normalizeQuantileRank.numeric Normalizes the empirical distribution of a single sample to a target distribution
normalizeQuantileSpline.list Normalizes the empirical distribution of a set of samples to a target distribution
normalizeQuantileSpline.matrix Weighted sample quantile normalization
normalizeQuantileSpline.numeric Normalizes the empirical distribution of a single sample to a target distribution
normalizeRobustSpline.matrix Weighted curve-fit normalization between a pair of channels
normalizeSpline.matrix Weighted curve-fit normalization between a pair of channels
numeric.normalizeQuantile Normalizes the empirical distribution of a single sample to a target distribution
plotDensity.data.frame Plots density distributions for a set of vector
plotDensity.list Plots density distributions for a set of vector
plotDensity.matrix Plots density distributions for a set of vector
plotDensity.numeric Plots density distributions for a set of vector
plotMvsA.matrix Plot log-ratios vs log-intensities
plotMvsAPairs.matrix Plot log-ratios/log-intensities for all unique pairs of data vectors
plotMvsMPairs.matrix Plot log-ratios vs log-ratios for all pairs of columns
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
sampleCorrelations.matrix Calculates the correlation for random pairs of observations
sampleTuples Sample tuples of elements from a set
weightedMedian Weighted Median Value
wpca.matrix Light-weight Weighted Principal Component Analysis