Erratum: Corrigendum: Multiple-laboratory comparison of microarray platforms

RIPOSTE: a framework for improving the design and analysis of laboratory-based research

Lack of reproducibility is an ongoing problem in some areas of the biomedical sciences. Poor experimental design and a failure to engage with experienced statisticians at key stages in the design and analysis of experiments are two factors that contribute to this problem. The RIPOSTE (Reducing IrreProducibility in labOratory STudiEs) framework has been developed to support early and regular discussions between scientists and statisticians in order to improve the design, conduct and analysis of laboratory studies and, therefore, to reduce irreproducibility. This framework is intended for use during the early stages of a research project, when specific questions or hypotheses are proposed. The essential points within the framework are explained and illustrated using three examples (a medical equipment test, a macrophage study and a gene expression study). Sound study design minimises the possibility of bias being introduced into experiments and leads to higher quality research with more reproducible results.

Removing batch effects in analysis of expression microarray data: an evaluation of six batch adjustment methods

The expression microarray is a frequently used approach to study gene expression on a genome-wide scale. However, the data produced by the thousands of microarray studies published annually are confounded by “batch effects,” the systematic error introduced when samples are processed in multiple batches. Although batch effects can be reduced by careful experimental design, they cannot be eliminated unless the whole study is done in a single batch. A number of programs are now available to adjust microarray data for batch effects prior to analysis. We systematically evaluated six of these programs using multiple measures of precision, accuracy and overall performance. ComBat, an Empirical Bayes method, outperformed the other five programs by most metrics. We also showed that it is essential to standardize expression data at the probe level when testing for correlation of expression profiles, due to a sizeable probe effect in microarray data that can inflate the correlation among replicates and unrelated samples.

Nutrient-regulated transcriptional responses in the brown tide-forming alga Aureococcus anophagefferens

Long-SAGE (serial analysis of gene expression) was used to profile the transcriptome of the brown tide-forming alga, Aureococcus anophagefferens, under nutrient replete (control), and nitrogen (N) and phosphorus (P) deficiency to understand how this organism responds at the transcriptional level to varying nutrient conditions. This approach has aided A. anophagefferens genome annotation efforts and identified a suite of genes upregulated by N and P deficiency, some of which have known roles in nutrient metabolism. Genes upregulated under N deficiency include an ammonium transporter, an acetamidase/formamidase and two peptidases. This suggests an ability to utilize reduced N compounds and dissolved organic nitrogen, supporting the hypothesized importance of these N sources in A. anophagefferens bloom formation. There are also a broad suite of P-regulated genes, including an alkaline phosphatase, and two 5'-nucleotidases, suggesting A. anophagefferens may use dissolved organic phosphorus under low phosphate conditions. These N- and P-regulated genes may be important targets for exploring nutrient controls on bloom formation in field populations.

Comparing microarray studies

We present a practical guide to some of the issues involved in comparing or integrating different microarray studies. We discuss the influence that various factors have on the agreement between studies, such as different technologies and platforms, statistical analysis criteria, protocols, and lab variability. We discuss methods to carry out or refine such comparisons, and detail several common pitfalls to avoid. Finally, we illustrate these ideas with an example case.

Systems toxicology: applications of toxicogenomics, transcriptomics, proteomics and metabolomics in toxicology

Toxicogenomics can facilitate the identification and characterization of toxicity, as illustrated in this review. Toxicogenomics, the application of the functional genomics technologies (transcriptomics, proteomics and metabolomics) in toxicology enables the study of adverse effects of xenobiotic substances in relation to structure and activity of the genome. The advantages and limitations of the different technologies are evaluated, and the prospects for integration of the technologies into a systems biology or systems toxicology approach are discussed. Applications of toxicogenomics in various laboratories around the world show that the crucial steps and sequence of events at the molecular level can be studied to provide detailed insights into mechanisms of toxic action. Toxicogenomics allowed for more sensitive and earlier detection of adverse effects in (animal) toxicity studies. Furthermore, the effects of exposure to mixtures could be studied in more detail. This review argues that in the (near) future, human health risk assessment will truly benefit from toxicogenomics (systems toxicology).

Multi-technique Comparison of Immobilized and Hybridized Oligonucleotide Surface Density on Commercial Amine-Reactive Microarray Slides

To establish a quantitative, corroborative understanding of observed correlations between immobilized probe DNA density on microarray surfaces and target hybridization efficiency in biological samples, we have characterized amine-terminated, single-stranded DNA probes attached to amine-reactive commercial microarray slides and complementary DNA target hybridization using fluorescence imaging, X-ray photoelectron spectroscopy (XPS) and 32P-radiometric assays. Importantly, we have reproduced DNA probe microarray immobilization densities in macroscopic spotted dimensions using high ionic strength, high-concentration DNA probe solutions to permit direct XPS surface analysis of DNA surface chemistry with good reliability and reproducibility. Target capture hybridization efficiency with complementary DNA exhibited an optimum value at intermediate DNA probe immobilization densities. The macroscopic array model provides a new platform for the study of DNA surface chemistry using highly sensitive, quantitative surface analytical techniques (e.g., XPS, ToF-SIMS). Sensitive 32P-DNA radiometric density measurements were calibrated with more routine XPS DNA signals, facilitating future routine DNA density determinations without the use of a hazardous radioactive assay. The objective is to provide new insight into different surface chemistry influences on immobilized DNA probe environments that affect target capture efficiency from solution to improve microarray assay performance.

Comparing independent microarray studies: the case of human embryonic stem cells

Background

Microarray studies of the same phenomenon in different labs often appear at variance because the published lists of regulated transcripts have disproportionately small intersections. We demonstrate that comparing studies by intersecting lists in this manner is methodologically flawed by reanalyzing three studies of the molecular signature of "stemness" in human embryonic stem cells. There are only 7 genes common to all three published lists, suggesting disagreement.

Results

Carefully reanalyzing all three together from the raw data we detect 111 genes upregulated and 95 downregulated in all three studies. The upregulated list was subject to rtRTPCR analysis and 75% of the genes were confirmed.

Conclusion

Our findings show that the three studies have a substantial core of common genes, which is missed if only the published lists are examined. Combined analysis of multiple experiments can be a powerful way to distil coherent conclusions.