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)
(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.
- Manuscript (pdf): This document introduces OLIN and OSLIN. It also includes extensive comparisons
between different normalisation schemes using several microarray data sets. The analysis presented also
includes the validation of efficient normalisation by O(S)LIN. Supplementary material can be downloaded here. Alternatively, an on-line version can be found at Genome Biology.
- Bioinformatics Application Note: This article gives a brief overview of the main features of OLIN and OLINgui.
- Presentation (ppt): This (short) presentations
outlines the problem of model selection regarding normalisation
of microarrays. It introduces the O(S)LIN scheme and elaborates a little bit on on the detection
of spatial bias in microarray data.
- Presentation II (pdf): This presentation introduces local regression by LOCFIT and
outlines the hybridisation model underlying OLIN. Additionally, it describes the some statistical tests
for bias detection.
- Chapter IV of PhD thesis (pdf): If you have time and feel energetic,
you may have a look at chapter IV
of my thesis. It includes a (now a wee bit out of date) literature review, describes the derivation of
the hybridisation model and compares different normalisation schemes. Additionally, it includes
some work regarding the identification of differentially expressed genes and location of differential
expression. A little bit more about the applied stats can be found in chapter III.
OLIN Version 1.6
Alternatively, current versions can be downloaded from the Bioconductor repository.
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.
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).
- R (>= 2.0.0). For installation of R, refer to the R project.
- R-packages: methods, stat, locfit. For installation of these add-on packages, refer to the R package archive.
- Bioconductor packages: Biobase, marray. Refer to the Bioconductor project for installation.
Back to homepage