Cambridge Healthtech Institute is pleased to announce MAQC Night, Sunday evening, October 22 in conjunction with the 2nd Annual Quantitative PCR, Microarrays, and Biological Validation meeting October 22-24, 2006, Providence, RI. All registered conference participants are invited to attend.

The Collaboration has Come to Fruition.
The MicroArray Quality Control (MAQC) project involves six FDA centers, major providers of microarray platforms and RNA samples, EPA, NIST, academic laboratories, and other stakeholders. The MAQC project aims to establish QC metrics and thresholds for objectively assessing the performance achievable by various microarray platforms and evaluating the advantages and disadvantages of various data analysis methods. Two RNA samples will be selected for three species: human, rat, and mouse, and differential gene expression levels between the two samples will be calibrated with microarrays and other technologies (e.g., QRT-PCR). The resulting microarray datasets will be used for assessing the precision and cross-platform/laboratory comparability of microarrays, and the QRT-PCR datasets will enable evaluation of the nature and magnitude of any systematic biases that may exist between microarrays and QRT-PCR. The availability of the calibrated RNA samples combined with the resulting microarray and QRT-PCR datasets, which will be made readily accessible to the microarray community, will allow individual laboratories to more easily identify and correct procedural failures. (website: http://edkb.fda.gov/MAQC/)

Hear the story behind the results.
The following lead authors will discuss their recently published results in an interactive community forum.

7:00 Opening Remarks
Roderick Jensen, Ph.D., Alton Brann Professor, Physics Biology & Mathematics, University of Massachusetts Boston

7:10 MS-3 MicroArray Quality Control (MAQC) Project: A Comprehensive Survey Demonstrates Concordant Results between Gene Expression Technology Platforms
Leming Shi, Ph.D., Computational Chemist, National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration (FDA)
Microarray technology has had a profound impact on gene expression research. Some studies have questioned whether similar expression results are obtained when the same RNA samples are analyzed on different platforms. The MicroArray Quality Control (MAQC) project was initiated to address these concerns, as well as other performance and analysis issues. Expression data from two distinct reference RNA samples in four titration pools were generated at multiple test sites using a variety of microarray-based and alternative technology platforms. In this article, we detail the experimental design and probe mapping efforts. We demonstrate the consistency of results within a platform across test sites as well as the high level of cross-platform concordance in terms of genes identified as differentially expressed. Additional analyses are presented in accompanying articles. Cumulatively, this study provides a rich resource that will help build consensus on the use of microarrays in research, clinical and regulatory settings. 

7:20 MS-6 The Reproducibility of Lists of Differentially Expressed Genes in Microarray Studies
Leming Shi, Ph.D., Computational Chemist, National Center for Toxicological Research (NCTR), U.S. Food and Drug Administration (FDA)
Reproducibility is a fundamental requirement in scientific experiments and clinical contexts. Recent publications raise concerns about the reliability of microarray technology because of the apparent lack of agreement between lists of differentially expressed genes (DEGs). In this study we demonstrate that (1) such discordance may stem from ranking and selecting DEGs solely by statistical significance (P) derived from widely used simple t-tests; (2) when fold change (FC) is used as the ranking criterion, the lists become much more reproducible, especially when fewer genes are selected; and (3) the instability of short DEG lists based on P cutoffs is an expected mathematical consequence of the high variability of the t-values. We recommend the use of FC ranking plus a non-stringent P cutoff as a baseline practice in order to generate more reproducible DEG lists. The FC criterion enhances reproducibility while the P criterion balances sensitivity and specificity.

7:30 MS-7 Evaluation of DNA Microarray Results with Quantitative Gene Expression Platforms
James C. Willey, MD, Professor of Medicine and Pathology, Medical College of Ohio; Chief Scientific and Medical Consultant to Gene Express, Inc.
The performance characteristics of three quantitative gene expression technologies were evaluated and their measurements were correlated to those of five commercial microarray platforms, based on the MicrroArray Quality Control (MAQC) dataset. High correlation between quantitative gene expression values and microarray platform results were observed. One cause of variability was differences in probe sequence and thus target location. Another cause was the limited and variable sensitivity of the different microarray platforms for detecting weakly expressed genes, which affected interplatform and intersite reproducibility of differentially expressed genes.

7:40 MS-8 The Use of RNA Sample Titrations for Assessing Microarray Platform Performance and Normalization Techniques
Richard Shippy, MS, Senior Scientist, Genomic Sciences, GE Healthcare
As part of the MAQC project, I will describe the performance of five commercial microarray platforms using two independent RNA samples and two titration mixtures of these samples. Focusing on 12,091 genes common across all platforms, the MAQC MS-8 manuscript team investigated the ability of each platform to detect the correct titration response across the samples. Overall, both the qualitative and quantitative correspondence from different platforms is very high. During this talk I will demonstrate the utility of titration samples for evaluating microarray platform performance and the impact of different normalization methods. Overall, the titration samples are a valuable tool, not only for assessing microarray platform performance and different analysis methods, but also for determining some underlying biological features of the samples.

7:50 MS-11: Performance Comparison of One-Color and Two-Color Platforms within the Microarray Quality Control (MAQC) Project
Tucker A. Patterson, Ph.D., Research Biologist, Division of Neurotoxicology, National Center for Toxicological Research, U.S. Food & Drug Administration
This study represents the first comparison of results from one- and two-color approaches on each of three different microarray platforms tested, using two independent RNA samples from the MicroArray Quality Control (MAQC) project. Microarray experimental design and performance characteristics of both one-color and two-color approaches were examined and researchers are now provided insight and guidance for properly selecting the best approach (one- or two-color) to meet their research needs. The results indicate that the two-color assay may have an advantage with regard to power (sensitivity) and the detection of small fold changes, however, one-color data appear to be less compressed than two-color data. Performance of the two approaches was approximately equal when assessing the reproducibility of the biology across the two approaches by comparing the concordance of differentially expressed gene lists. Cumulatively, these results indicate that data generated from both one- and two-color assays are approximately equivalent and provide similar levels of biological insight. 

8:o0 MS-12: Evaluation of External RNA Controls for the Assessment of Microarray Performance in the MAQC Study
Anne Bergstrom Lucas, BS, Research Scientist, Microarray Division, Life Sciences Solutions Unit, Agilent Technologies, Inc.
External RNA controls are RNA species that can be added at known quantities to various steps of the analytical process to aid in the quality control of the assay. A number of commercial microarray platforms in the MAQC study used external RNA controls in their assays. When multiple external RNA controls are added at various concentrations to an assay, a concentration-response curve can be constructed and modeled with linear regression to determine assay outliers. One interesting finding was that external RNA controls that are added at the initial (total RNA) step of the assay were found to be sample-dependent, and these controls uncovered underlying biological differences between the RNA samples tested in the MAQC study.

8:10 MS-13: Biological Response is Preserved Across Microarray Platforms
Lei Guo Ph.D., Research Biologist, National Center for Toxicological Research, (NCTR), U.S. Food and Drug Administration (FDA)
The MAQC project, using distinct reference RNA samples, has demonstrated a high level of concordance in inter-site and cross-platform comparisons and the dramatic impact of gene selection methods on the reproducibility of differentially expressed genes. To validate and extend these findings, a biologically focused toxicogenomics data set was generated using 36 RNA samples from rats treated with three chemicals (aristolochic acid, riddelliine, and comfrey) and each sample was hybridized to four microarray platforms. Highly concordant results for inter-site and cross-platform comparisons were obtained and gene lists identified by fold-change were more reproducible than those by t-test P-value or Significance Analysis of Microarrays (SAM). Moreover, non-reproducible gene lists were shown to result in disparate Gene Ontology (GO) terms and pathways, and thus differing biological interpretation. When genes were selected by fold-change ranking with a non-stringent P-value cutoff, consistent and novel biological findings resulting from chemical exposure were reported by all platforms.

8:20 Interactive Panel Discussion

8:45 International Coffees and Desserts