Quantile Normalization using a specified target distribution vector
Using a normalization based upon quantiles, these function normalizes the columns of a matrix based upon a specified normalization distribution
A matrix of intensities where each column corresponds to a chip and each row is a probe.
Make a copy of matrix before normalizing. Usually safer to work with a copy
A vector containing datapoints from the distribution to be normalized to
number of datapoints to return in target
distribution vector. If
A logical variable indexing whether corresponding row should be used in reference distribution determination
This method is based upon the concept of a quantile-quantile
plot extended to n dimensions. No special allowances are made for
outliers. If you make use of quantile normalization either through
please cite Bolstad et al, Bioinformatics (2003).
These functions will handle missing data (ie NA values), based on the assumption that the data is missing at random.
normalize.quantiles.use.target a normalized
Ben Bolstad, firstname.lastname@example.org
Bolstad, B (2001) Probe Level Quantile Normalization of High Density Oligonucleotide Array Data. Unpublished manuscript http://bmbolstad.com/stuff/qnorm.pdf
Bolstad, B. M., Irizarry R. A., Astrand, M, and Speed, T. P. (2003) A Comparison of Normalization Methods for High Density Oligonucleotide Array Data Based on Bias and Variance. Bioinformatics 19(2) ,pp 185-193. http://bmbolstad.com/misc/normalize/normalize.html
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