STATEMENT OF RECENT WORK – HAIM BAR

My research interests include statistical modeling for high-throughput data, variable selection, and network analysis. I develop statistical methods for the so-called ’Omics data, which includes microarray data, RNA-seq, metabolomics, and proteomics. New techniques which enabled the sequencing of the genome (or proteome, or microbiome, etc.) introduced fantastic opportunities in medical research. However, the abundance of data at the gene, protein, or metabolite level created multiple challenges from the statistical point of view. For example, quite often the number of response variables (e.g. gene expression levels) is very large, and analyzing each one separately in the traditional way (e.g. via t-test or linear regression) may result in insufficient statistical power, since it is essential to account for (possibly massive) multiple testing. In addition, although the cost of experiments involving sequencing is decreasing, obtaining data from a large sample is still not affordable to most organizations. Typical experiments involve fairly small sample sizes, which again adversely affects the statistical power. Another challenge in these applications is that response variables are not likely to be independent. Many traditional, and even modern statistical methods rely on the convenient, but unrealistic assumption that response variables are independent. I develop powerful and computationally efficient methods to address these challenges, and I collaborate extensively with multiple research groups. Generally, my approach to addressing the aforementioned challenges has been to develop hierarchical mixture models with random effects. In my models I assume that the data are drawn from a parametric distribution in which the number of parameters is not only small, but remains constant as the dimensionality of the problem increases. Using this approach yields parsimonious models and ‘shrinkage estimation’, which not only increases power by borrowing information across responses, but also increases computational efficiency and scalability. 1. High Throughput Data Analysis – ’Omics Applications My work in the area of high throughput data analysis has been motivated by problems in genetics and bioinformatics. My earlier contribution was to develop a powerful method to detect differentially expressed genes in microarray data, in the two group setting [3]. The power of the method stems from the parsimonious nature of the mathematical model. More specifically, when comparing gene expression levels in two populations, I assume that genes may belong to one of three components, namely, genes which are not differentially expressed, genes which are more expressed in population 1, and genes which are more expressed in population 2. This is implemented as a three component mixture model such that each component is modeled to be normally distributed with different means and variances. This mixture model random-effect approach leads to ‘borrowing information’ across all genes in a component, thus increasing the power to detect differentially expressed genes, even when the number of genes is very large (tens of thousands) and the sample sizes are small. I implemented it as an R package [2] using an empirical Bayes approach. I also implemented it using a fully Bayesian approach [4]. The method was applied in a number of collaborations with biologists. For example, in [5] and [6] we were interested in finding which genes in the genome of the Shewanella oneidensis bacteria are associated with an increased ability of the bacteria to convert waste water to electricity. In another collaboration, with the Caudill group at Cornell University, we investigated the association between pregnancy status, choline intake levels, and gene expression [7, 8, 9, 10]. This work led to the interesting observation that in some situations the difference between the populations may be in the variance of the gene expression levels, rather than the mean. I developed a model for parallel testing for unequal variances [11]. This approach has proven to be quite robust to deviations from the normality assumption, and it substantially increases the power to detect differences in variance over the more traditional approaches. I applied this method in [12] where we compared metabolic levels across different strains of Staphylococcus aureus, in order to understand their Intermediate-Type Vancomycin Resistance (VISA). Recently, I developed a bivariate model in which it is possible to test for differential dispersion and differential expression, simultaneously [13]. I have also developed an extension to the model to the k groups scenario, and to account for additional fixed-effects in the model. I am currently in the final stages of the implementation of the extended model as a free and user-friendly software (using the ‘Shiny’ package in R).