Table of Contents Author Guidelines Submit a Manuscript
Journal of Biomedicine and Biotechnology
Volume 2011 (2011), Article ID 860732, 15 pages
Research Article

A New Normalizing Algorithm for BAC CGH Arrays with Quality Control Metrics

1Department of Biostatistics, University at Buffalo, Buffalo, NY 14214, USA
2Department of Biostatistics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
3Department of Bioinformatics, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
4Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY 14263, USA
5Department of Biochemistry, University at Buffalo, Buffalo, NY 14214, USA

Received 1 July 2010; Revised 23 November 2010; Accepted 18 December 2010

Academic Editor: Adewale Adeyinka

Copyright © 2011 Jeffrey C. Miecznikowski et al. 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.


The main focus in pin-tip (or print-tip) microarray analysis is determining which probes, genes, or oligonucleotides are differentially expressed. Specifically in array comparative genomic hybridization (aCGH) experiments, researchers search for chromosomal imbalances in the genome. To model this data, scientists apply statistical methods to the structure of the experiment and assume that the data consist of the signal plus random noise. In this paper we propose “SmoothArray”, a new method to preprocess comparative genomic hybridization (CGH) bacterial artificial chromosome (BAC) arrays and we show the effects on a cancer dataset. As part of our R software package “aCGHplus,” this freely available algorithm removes the variation due to the intensity effects, pin/print-tip, the spatial location on the microarray chip, and the relative location from the well plate. removal of this variation improves the downstream analysis and subsequent inferences made on the data. Further, we present measures to evaluate the quality of the dataset according to the arrayer pins, 384-well plates, plate rows, and plate columns. We compare our method against competing methods using several metrics to measure the biological signal. With this novel normalization algorithm and quality control measures, the user can improve their inferences on datasets and pinpoint problems that may arise in their BAC aCGH technology.