Briefings in Bioinformatics Advance Access originally published online on June 23, 2009
Briefings in Bioinformatics 2009 10(5):547-555; doi:10.1093/bib/bbp033
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Optimized detection of differential expression in global profiling experiments: case studies in clinical transcriptomic and quantitative proteomic datasets
Corresponding author. Laura L. Elo, Department of Mathematics, University of Turku, FI-20014 Turku, Finland. Tel: +358 2 333 6027; Fax: +358 2 333 6595; E-mail: laliel{at}utu.fi
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.
Keywords: clinical material, DNA microarray, mass-spectrometry, gene expression, quantitative proteomics, differential expression, reproducibility
Submitted: March 30, 2009. Received (in revised form): June 3, 2009.
CiteULike 
Connotea 
Del.icio.us What's this?
This article has been cited by other articles:
|
|
 |
|
  T. Aittokallio Dealing with missing values in large-scale studies: microarray data imputation and beyond Brief Bioinform, December 4, 2009; (2009) bbp059v1. [Abstract] [Full Text] [PDF]
|
|