OLIN webpage

OLIN webpage

Microarray measurements are affected by a variety of systematic experimental errors limiting the accuracy of data produced. Two prominent experimental biases for cDNA arrays are intensity-dependent and location-dependent bias. Although several normalization schemes have been proposed to reduce these systematic errors, an optimal adjustment of normalization models to the data has not been neglected so far. Current methods are based on default parameter values and leave it to the researchers to adjust the normalization parameters. Instructions on how to optimize parameter settings is generally not given. Optimization of parameters is, however, crucial for the normalization process. Since systematic errors in cDNA microarray data exhibit a large variability between and even within experiments. This requires an adjustment of the model parameters to the data. A set of normalization parameters of fixed value is frequently insufficient to correct experimental biases.

We have therefore introduced two normalization schemes based on iterative local regression and model selection: OLIN (Optimised Local Intensity-dependent Normalisation) and OSLIN (Optimised Scaled Local Intensity-dependent Normalisation). Both schemes aim to correct for intensity- and location-dependent dye bias in cDNA microarray data. For model selection, generalized cross-validation (GCV) was applied. GCV has computational advantages compared to standard cross-validation. It should be noted that both normalisation schemes assume random spotting. Additionally, both schemes assume that the majority of genes are not differentially expressed or that the overall up-regulation and down-regulation is balanced. Please check carefully that this is indeed the case.

Both normalisation procedures are implemented in a Bioconductor/R-package named OLIN which can be downloaded freely. Additionally, the package includes various functions for detection of intensity- or location-dependent bias which might be especially helpful for people starting microarray experiments. Note that the OLIN package underlies GPL version 2. Finally, if you have any questions or comments concerning the package, feel free to contact me.

Further information

Download

Note that the versions below are not regularly updated. Current versions can be downloaded from the Bioconductor repository: OLIN and OLINgui.

OLIN

The OLIN algorithm and additional tools for visualisation and quality control of two-colour microarray data are implemented in the R package.

OLINgui

This package provides a graphical user interface for the OLIN packag using R-TclTk interface. Most of the functionality of OLIN can be accessed via OLINgui.

Installation

Following software is required to run OLIN and OLINgui: OLINgui requires additionally the R-package tcltkt and the Bioconductor package tkWidgets. If all requirements are fulfilled, the OLIN and OLINpackage add-on R-package can be installed. To see how to install add-on R-packages on your computer system, start R and type in help(INSTALL). Once these packages are installed, you can load the package by library(OLIN) and library(OLINgui).
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Matthias Futschik
Last modified: Wed Feb 18 14:03:23 CET 2009