Title: Differential expression analysis of Digital Gene Expression data: RNA-tag filtering, comparison of t-type tests and their genome-wide co-expression based adjustments

Authors: Yinglei Lai

Addresses: Department of Statistics and Biostatistics Center, The George Washington University, 2140 Pennsylvania Avenue, N.W., Washington DC 20052, USA

Abstract: Deep sequencing techniques have shown a promising impact on biomedical studies. Based on a recently published two-sample Digital Gene Expression (DGE) data set, we compared three widely used t-type tests for the differential expression analysis. Both the |soft| and |hard| filtering strategies were considered. For the |hard| filtering strategy, we also considered a genome-wide co-expression based adjustment for each t-type test. Our results suggest that excluding RNA-tags at an appropriate level of data variability can improve the control of false positives. Furthermore, the genome-wide co-expression based adjustments consistently provide comparably low levels of false positive control for different exclusion criteria.

Keywords: co-expression; data filtering; deep sequencing; differential expression analysis; DGE; digital gene expression; t-tests; RNA tag filtering; bioinformatics; filtering strategies; false positives.

DOI: 10.1504/IJBRA.2010.035999

International Journal of Bioinformatics Research and Applications, 2010 Vol.6 No.4, pp.353 - 365

Published online: 11 Oct 2010 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article