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

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.

SUBMITTER: Lai Y

PROVIDER: S-EPMC3133627 | biostudies-literature | 2010

REPOSITORIES: biostudies-literature

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

Lai Yinglei Y

International journal of bioinformatics research and applications 20100101 4

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 ...[more]

PMID: 20940123

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