Differential expression analysis of Digital Gene Expression data: RNA-tag filtering, comparison of t-type tests and their genome-wide co-expression based adjustments.
Differential expression analysis of Digital Gene Expression data: RNA-tag filtering, comparison of t-type tests and their genome-wide co-expression based adjustments.
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DOI：
10.1504/ijbra.2010.035999
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发表时间：
2010
影响因子：
--
通讯作者：
Lai Y
中科院分区：
文献类型：
--
作者：
Lai Y
深度测序技术已经在生物医学研究中显示出有希望的影响。基于最近发表的两样本数字基因表达（DGE）数据集，我们比较了三种广泛使用的t型检验用于差异表达分析。考虑了“软”和“硬”过滤策略。对于“硬”过滤策略，我们还考虑了每个t型检验的全基因组共表达调整。我们的研究结果表明，在适当的数据变异性水平上排除rna标签可以改善对假阳性的控制。此外，基于全基因组共表达的调整一致地为不同的排除标准提供了相对较低水平的假阳性控制。
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.