Optimized detection of differential expression in global profiling experiments: case studies in clinical transcriptomic and quantitative proteomic datasets

• Published in: Briefings in bioinformatics Vol. 10; no. 5; pp. 547 - 555
• Main Authors: Elo, Laura L., Hiissa, Jukka, Tuimala, Jarno, Kallio, Aleksi, Korpelainen, Eija, Aittokallio, Tero
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 01.09.2009; Oxford Publishing Limited (England)
• Subjects: Bioinformatics; Biological and medical sciences; Biomarkers - metabolism; Fundamental and applied biological sciences. Psychology; Gene expression; Gene Expression Profiling - methods; General aspects; Humans; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Statistical; Molecular biology; Oligonucleotide Array Sequence Analysis - methods; Proteomics; Proteomics - methods; Reproducibility of Results; Sensitivity and Specificity; Software; Statistical methods; DNA microarray; mass-spectrometry; reproducibility; gene expression, quantitative proteomics, differential expression; clinical material; DNA chip; differential expression; Microarray; Optimization; Case study; Proteomics; Detection; Mass spectrometry; gene expression; quantitative proteomics; Quantitative analysis
• ISSN: 1467-5463; 1477-4054
• DOI: 10.1093/bib/bbp033

Identification of reliable molecular markers that show differential expression between distinct groups of samples has remained a fundamental research problem in many large-scale profiling studies, such as those based on DNA microarray or mass-spectrometry technologies. Despite the availability of a wide spectrum of statistical procedures, the users of the high-throughput platforms are still facing the crucial challenge of deciding which test statistic is best adapted to the intrinsic properties of their own datasets. To meet this challenge, we recently introduced an adaptive procedure, named ROTS (Reproducibility-Optimized Test Statistic), which learns an optimal statistic directly from the given data, and whose relative benefits have previously been shown in comparison with state-of-the-art procedures for detecting differential expression. Using gene expression microarray and mass-spectrometry (MS)-based protein expression datasets as case studies, we illustrate here the practical usage and advantages of ROTS toward detecting reliable marker lists in clinical transcriptomic and proteomic studies. In a public leukemia microarray dataset, the procedure could improve the sensitivity of the gene marker lists detected with high specificity. When applied to a recent LC-MS dataset, involving plasma samples from severe burn patients, the procedure could identify several peptide markers that remained undetected in the conventional analysis, thus demonstrating the effectiveness of ROTS also for global quantitative proteomic studies. To promote its widespread usage, we have made freely available efficient implementations of ROTS, which are easily accessible either as a stand-alone R-package or as integrated in the open-source data analysis software Chipster.