Copyright © 2007 Nicola L. Dawes and Jarka Glassey. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background. Normalisation is a critical step in obtaining meaningful information from the high-dimensional DNA array data. This is particularly important when complex biological hypotheses/questions, such a functional analysis and regulatory interactions within biological systems, are investigated. A nonparametric, intensity-dependent normalisation method based on global identification of self-consistent set (SCS) of genes is proposed here for such systems. Results. The SCS normalisation is introduced and its behaviour demonstrated for a range of user-defined parameters affecting sits performance. It is compared to a standard global normalisation method in terms of noise reduction and signal retention. Conclusions. The SCS normalisation results using 16 macroarray data sets from a Bacillus subtilis experiment confirm that the method is capable of reducing undesirable experimental variation whilst retaining important biological information. The ease and speed of implementation mean that this method can be easily adapted to other multicondition time/strain series single colour array data.