Differential Gene Expression (DEX) and Alternative Splicing Events (ASE) for Temporal Dynamic Processes Using HMMs and Hierarchical Bayesian Modeling Approaches.

In gene expression profile, data analysis pipeline is categorized into four levels, major downstream tasks, i.e., (1) identification of differential expression; (2) clustering co-expression patterns; (3) classification of subtypes of samples; and (4) detection of genetic regulatory networks, are performed posterior to preprocessing procedure such as normalization techniques. To be more specific, temporal dynamic gene expression data has its inherent feature, namely, two neighboring time points (previous and current state) are highly correlated with each other, compared to static expression data which samples are assumed as independent individuals. In this chapter, we demonstrate how HMMs and hierarchical Bayesian modeling methods capture the horizontal time dependency structures in time series expression profiles by focusing on the identification of differential expression. In addition, those differential expression genes and transcript variant isoforms over time detected in core prerequisite steps can be generally further applied in detection of genetic regulatory networks to comprehensively uncover dynamic repertoires in the aspects of system biology as the coupled framework.

Time series expression analyses using RNA-seq: a statistical approach.

RNA-seq is becoming the de facto standard approach for transcriptome analysis with ever-reducing cost. It has considerable advantages over conventional technologies (microarrays) because it allows for direct identification and quantification of transcripts. Many time series RNA-seq datasets have been collected to study the dynamic regulations of transcripts. However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems. These methods should explicitly account for the dependencies of expression patterns across time points. Here, we discuss several methods that can be applied to model timecourse RNA-seq data, including statistical evolutionary trajectory index (SETI), autoregressive time-lagged regression (AR(1)), and hidden Markov model (HMM) approaches. We use three real datasets and simulation studies to demonstrate the utility of these dynamic methods in temporal analysis.

Development of a noise metric for assessment of exposure risk to complex noises.

Many noise guidelines currently use A-weighted equivalent sound pressure level L(Aeq) as the noise metric and the equal energy hypothesis to assess the risk of occupational noises. Because of the time-averaging effect involved with the procedure, the current guidelines may significantly underestimate the risk associated with complex noises. This study develops and evaluates several new noise metrics for more accurate assessment of exposure risks to complex and impulsive noises. The analytic wavelet transform was used to obtain time-frequency characteristics of the noise. 6 basic, unique metric forms that reflect the time-frequency characteristics were developed, from which 14 noise metrics were derived. The noise metrics were evaluated utilizing existing animal test data that were obtained by exposing 23 groups of chinchillas to, respectively, different types of noise. Correlations of the metrics with the hearing losses observed in chinchillas were compared and the most promising noise metric was identified.