アブストラクト
| Date | 14:00-15:00 Jun 11, 2024 |
| Speaker | Tsung Fei Khang University of Malaya, Malaysia |
| Title | SIEVE: One-stop differential expression, variability, and skewness analyses using RNA-Seq data |
| Abstract |
RNA-Seq data analysis is commonly biased towards detecting genes that show
significant differences in mean. As a result, the complexity of gene expression
changes between biological conditions, such as those involving changes in variance
and skewness, are frequently ignored. SIEVE is a novel statistical methodology that
embraces a compositional data analysis framework that transforms discrete RNA-Seq
counts to a continuous form with a distribution that is well-fitted by a skew-normal
distribution. Simulation results show that, with respect to the false discovery rate and
probability of Type II error, SIEVE has comparable or superior performance than its
competitors for testing differences in mean and variance. Analysis of the Mayo
RNASeq dataset for Alzheimer’s disease using SIEVE reveals that a gene set with
significant expression difference in mean, variance and skewness between the control
and the Alzheimer’s disease group strongly predicts a subject’s disease state.
Furthermore, functional enrichment analysis shows that incorporating genes that show
differential variability and skewness reveals a richer spectrum of biological aspects
associated with Alzheimer’s disease. Thus, SIEVE may be a useful tool to gain
systems biology understanding of the intricate changes in gene expressions in
complex diseases.
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