monoSmu: Monotonic smooth method

In lumi: BeadArray Specific Methods for Illumina Methylation and Expression Microarrays

Description

Fit the monotonic-constraint spline curve

Usage

monoSmu(x, y, newX = NULL, nSupport = min(200, length(x)), nKnots = 6, rotate = FALSE, ifPlot = FALSE, xlab = 'x', ylab = 'y', ...)

Arguments

x	a vector represents x values
y	a vector represents y values
newX	the new values to be transformed. If not provided, "x" will be used.
nSupport	downsampled data points
nKnots	parameter used by monoSpline
rotate	determine whether to rotate the axis with 45 degrees in clockwise, i.e., fit the curve in the MA-plot.
ifPlot	determine whether to plot intermediate results
xlab	the xlab of the plot
ylab	the ylab of the plot
...	parameters used by supsmu and plot

Details

function called by lumiN.rsn. The function first fits a monotonic spline between vector x and y, then transforms the vector newX based on the fitted spline. (After transformation the fitted spline is supposed to be a diagonal line, i.e., x=y)

Value

Return the transformed "newX" based on the smoothed curve

Author(s)

Simon Lin, Pan Du

References

Lin, S.M., Du, P., Kibbe, W.A., (2008) 'Model-based Variance-stabilizing Transformation for Illumina Microarray Data', Nucleic Acids Res. 36, e11

See Also

monoSpline

lumi documentation built on Nov. 8, 2020, 5:27 p.m.