rsn {lumi}R Documentation

Robust Spline Normalization between chips

Description

Robust spline normalization (monotonic curves) between chips

Usage

rsn(x.lumi, targetArray = NULL, excludeFold = 2, span = 0.03, ifPlot = FALSE, ...)

Arguments

x.lumi an ExpressionSet inherited object or a data matrix with columns as samples and rows as genes
targetArray A target chip is the model for other chips for normalization. It can be a column index, a vector or a LumiBatch object with one sample.
excludeFold exclude the genes with fold change larger than "excludeFold" during fitting the curve in normalization
span the span parameter used by monoSmu
ifPlot determine whether to plot intermediate results
...

{ other parameters used by monoSmu }

Details

The robust spline normalization (RSN) algorithm combines the features of quantile and loess normalization. It is designed to normalize the variance-stabilized data. The function will check whether the data is variance stabilized (vst or log2 transform), if not, it will automatically run lumiT before run rsn. For details of the algorithm, please see the reference.

The targetArray can be a column index, a vector or a LumiBatch object with one sample, which corresponds to an external sample to be normalized with.

Value

Return an object with expression values normalized. The class of the return object is the same as the input object x.lumi. If it is a LumiBatch object, it also includes the VST transform function and its parameters as attributes: "transformFun", "parameter". See inverseVST for details.

Note

Author(s)

Pan Du, Simon Lin

References

Lin, S.M., Du, P., Kibbe, W.A., "Model-based Variance-stabilizing Transformation for Illumina Mi-croarray Data", submitted

See Also

lumiN, monoSmu

Examples



[Package lumi version 1.2.0 Index]