fit.li.wong: Fit Li and Wong Model to a Probe Set

In affy: Methods for Affymetrix Oligonucleotide Arrays

Description

Fits the model described in Li and Wong (2001) to a probe set with I chips and J probes.

Usage

fit.li.wong(data.matrix, remove.outliers=TRUE, normal.array.quantile=0.5,
            normal.resid.quantile=0.9, large.threshold=3, large.variation=0.8,
            outlier.fraction=0.14, delta=1e-06, maxit=50,
            outer.maxit=50,verbose=FALSE, ...)

li.wong(data.matrix,remove.outliers=TRUE, normal.array.quantile=0.5,
            normal.resid.quantile=0.9, large.threshold=3, large.variation=0.8,
            outlier.fraction=0.14, delta=1e-06, maxit=50,
            outer.maxit=50,verbose=FALSE)

Arguments

data.matrix	an I x J matrix containing the probe set data. Typically the i,j entry will contain the PM-MM value for probe pair j in chip i. Another possible use, is to use PM instead of PM-MM.
remove.outliers	logical value indicating if the algorithm will remove outliers according to the procedure described in Li and Wong (2001).
large.threshold	used to define outliers.
normal.array.quantile	quantile to be used when determining what a normal SD is. probes or chips having estimates with SDs bigger than the quantile normal.array.quantile of all SDs x large.threshold.
normal.resid.quantile	any residual bigger than the normal.resid.quantile quantile of all residuals x large.threshold is considered an outlier.
large.variation	any probe or chip describing more than this much total variation is considered an outlier.
outlier.fraction	this is the maximum fraction of single outliers that can be in the same probe or chip.
delta	numerical value used to define the stopping criterion.
maxit	maximum number of iterations when fitting the model.
outer.maxit	maximum number of iterations of defined outliers.
verbose	logical value. If TRUE information is given of the status of the algorithm.
...	additional arguments.

Details

This is Bioconductor's implementation of the Li and Wong algorithm. The Li and Wong PNAS 2001 paper was followed. However, you will not get the same results as you would get with dChip. dChip is not open source so it is not easy to reproduce.

Notice that this iterative algorithm will not always converge. If you run the algorithm on thousands of probes expect some non-convergence warnings. These are more likely when few arrays are used. We recommend using this method only if you have 10 or more arrays.

Please refer to references for more details.

Value

li.wong returns a vector of expression measures (or column effects) followed by their respective standard error estimates. It was designed to work with express which is no longer part of the package.

fit.li.wong returns much more. Namely, a list containing the fitted parameters and relevant information.

theta	fitted thetas.
phi	fitted phis.
sigma.eps	estimated standard deviation of the error term.
sigma.theta	estimated standard error of theta.
sigma.phi	estimated standard error of phis.
theta.outliers	logical vector describing which chips (thetas) are considered outliers (TRUE).
phi.outliers	logical vector describing which probe sets (phis) are considered outliers (TRUE)
convergence1	logical value. If FALSE the algorithm did not converge when fitting the phis and thetas.
convergence2	logical value. If FALSE the algorithm did not converge in deciding what are outliers.
iter	number of iterations needed to achieve convergence.
delta	difference between thetas when iteration stopped.

Author(s)

Rafael A. Irizarry, Cheng Li, Fred A. Wright, Ben Bolstad

References

Li, C. and Wong, W.H. (2001) Genome Biology 2, 1–11.

Li, C. and Wong, W.H. (2001) Proc. Natl. Acad. Sci USA 98, 31–36.

See Also

li.wong, expresso

Examples

    x <- sweep(matrix(2^rnorm(600),30,20),1,seq(1,2,len=30),FUN="+")
    fit1 <- fit.li.wong(x)
    plot(x[1,])
    lines(fit1$theta)

affy documentation built on Nov. 8, 2020, 8:18 p.m.