Package: aroma.light 3.35.0
Henrik Bengtsson
aroma.light: Light-Weight Methods for Normalization and Visualization of Microarray Data using Only Basic R Data Types
Methods for microarray analysis that take basic data types such as matrices and lists of vectors. These methods can be used standalone, be utilized in other packages, or be wrapped up in higher-level classes.
Authors:
aroma.light_3.35.0.tar.gz
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aroma.light.pdf |aroma.light.html✨
aroma.light/json (API)
NEWS
| # Install 'aroma.light' in R: |
| install.packages('aroma.light', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/henrikbengtsson/aroma.light/issues
On BioConductor:aroma.light-3.35.0(bioc 3.20)aroma.light-3.34.0(bioc 3.19)
Last updated 2 months agofrom:12ae795213
Exports:aroma.lightaverageQuantileaverageQuantile.listaverageQuantile.matrixbacktransformAffinebacktransformAffine.matrixbacktransformPrincipalCurvebacktransformPrincipalCurve.matrixbacktransformPrincipalCurve.numericbacktransformXYCurvebacktransformXYCurve.matrixcalibrateMultiscancalibrateMultiscan.matrixcallNaiveGenotypescallNaiveGenotypes.numericdistanceBetweenLinesdistanceBetweenLines.defaultfindPeaksAndValleysfindPeaksAndValleys.densityfindPeaksAndValleys.numericfitIWPCAfitIWPCA.matrixfitNaiveGenotypesfitNaiveGenotypes.numericfitPrincipalCurvefitPrincipalCurve.matrixfitXYCurvefitXYCurve.matrixiwpcaiwpca.matrixlikelihoodlikelihood.smooth.splinelines.XYCurveFitmedianPolishmedianPolish.matrixnormalizeAffinenormalizeAffine.matrixnormalizeAveragenormalizeAverage.listnormalizeAverage.matrixnormalizeCurveFitnormalizeCurveFit.matrixnormalizeDifferencesToAveragenormalizeDifferencesToAverage.listnormalizeFragmentLengthnormalizeFragmentLength.defaultnormalizeLoessnormalizeLoess.matrixnormalizeLowessnormalizeLowess.matrixnormalizeQuantilenormalizeQuantile.defaultnormalizeQuantileRanknormalizeQuantileRank.listnormalizeQuantileRank.matrixnormalizeQuantileRank.numericnormalizeQuantileSplinenormalizeQuantileSpline.listnormalizeQuantileSpline.matrixnormalizeQuantileSpline.numericnormalizeRobustSplinenormalizeRobustSpline.matrixnormalizeSplinenormalizeSpline.matrixnormalizeTumorBoostnormalizeTumorBoost.numericpairedAlleleSpecificCopyNumberspairedAlleleSpecificCopyNumbers.numericplotDensityplotDensity.data.frameplotDensity.densityplotDensity.listplotDensity.matrixplotDensity.numericplotMvsAplotMvsA.matrixplotMvsAPairsplotMvsAPairs.matrixplotMvsMPairsplotMvsMPairs.matrixplotXYCurveplotXYCurve.matrixplotXYCurve.numericpredict.lowessprint.SmoothSplineLikelihoodprojectUontoVrobustSmoothSplinerobustSmoothSpline.defaultrowAveragesrowAverages.matrixsampleCorrelationssampleCorrelations.matrixsampleTuplessampleTuples.defaultscalarProducttrwpcawpca.matrix
Dependencies:matrixStatsR.methodsS3R.ooR.utils
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Package aroma.light | aroma.light-package aroma.light |
| 1. Calibration and Normalization | 1. Calibration and Normalization |
| Gets the average empirical distribution | averageQuantile averageQuantile.list averageQuantile.matrix |
| Reverse affine transformation | backtransformAffine backtransformAffine.matrix |
| Reverse transformation of principal-curve fit | backtransformPrincipalCurve backtransformPrincipalCurve.matrix backtransformPrincipalCurve.numeric |
| Weighted affine calibration of a multiple re-scanned channel | calibrateMultiscan calibrateMultiscan.matrix |
| Calls genotypes in a normal sample | callNaiveGenotypes callNaiveGenotypes.numeric |
| Finds the shortest distance between two lines | distanceBetweenLines distanceBetweenLines.default |
| Robust fit of linear subspace through multidimensional data | fitIWPCA fitIWPCA.matrix |
| Fit naive genotype model from a normal sample | fitNaiveGenotypes fitNaiveGenotypes.numeric |
| Fit a principal curve in K dimensions | fitPrincipalCurve fitPrincipalCurve.matrix |
| Fitting a smooth curve through paired (x,y) data | backtransformXYCurve backtransformXYCurve.matrix fitXYCurve fitXYCurve.matrix |
| Fits an R-dimensional hyperplane using iterative re-weighted PCA | iwpca iwpca.matrix |
| Median polish | medianPolish medianPolish.matrix |
| Weighted affine normalization between channels and arrays | normalizeAffine normalizeAffine.matrix |
| Rescales channel vectors to get the same average | normalizeAverage normalizeAverage.list normalizeAverage.matrix |
| Weighted curve-fit normalization between a pair of channels | normalizeCurveFit normalizeCurveFit.matrix normalizeLoess normalizeLoess.matrix normalizeLowess normalizeLowess.matrix normalizeRobustSpline normalizeRobustSpline.matrix normalizeSpline normalizeSpline.matrix |
| Rescales channel vectors to get the same average | normalizeDifferencesToAverage normalizeDifferencesToAverage.list |
| Normalizes signals for PCR fragment-length effects | normalizeFragmentLength normalizeFragmentLength.default |
| Normalizes the empirical distribution of one of more samples to a target distribution | normalizeQuantile normalizeQuantile.default normalizeQuantileRank normalizeQuantileRank.list normalizeQuantileRank.numeric |
| Normalizes the empirical distribution of a set of samples to a common target distribution | normalizeQuantileRank.matrix |
| Normalizes the empirical distribution of one or more samples to a target distribution | normalizeQuantileSpline normalizeQuantileSpline.list normalizeQuantileSpline.matrix normalizeQuantileSpline.numeric |
| Normalizes allele B fractions for a tumor given a match normal | normalizeTumorBoost normalizeTumorBoost.numeric |
| Calculating tumor-normal paired allele-specific copy number stratified on genotypes | pairedAlleleSpecificCopyNumbers pairedAlleleSpecificCopyNumbers.numeric |
| Plots density distributions for a set of vectors | plotDensity plotDensity.data.frame plotDensity.density plotDensity.list plotDensity.matrix plotDensity.numeric |
| Plot log-ratios vs log-intensities | plotMvsA plotMvsA.matrix |
| Plot log-ratios/log-intensities for all unique pairs of data vectors | plotMvsAPairs plotMvsAPairs.matrix |
| Plot log-ratios vs log-ratios for all pairs of columns | plotMvsMPairs plotMvsMPairs.matrix |
| Plot the relationship between two variables as a smooth curve | plotXYCurve plotXYCurve.matrix plotXYCurve.numeric |
| Robust fit of a Smoothing Spline | robustSmoothSpline robustSmoothSpline.default |
| Calculates the correlation for random pairs of observations | sampleCorrelations sampleCorrelations.matrix |
| Sample tuples of elements from a set | sampleTuples sampleTuples.default |
| Light-weight Weighted Principal Component Analysis | wpca wpca.matrix |
