Biomedical Data Science Seminar Series

Five decades have passed in the evolution of Artificial Intelligence in Medicine (AIM), a field that has evolved substantially while tracking the corresponding changes in computer science, hardware technology, communications, and biomedicine. Emerging from medical schools and computer science departments in its early years, the AIM field is now more visible and influential than ever before, paralleling the enthusiasm and accomplishments of AI more generally. This talk will briefly summarize some of AIM history, providing an update on the status of the field as we enter our second half-century. My remarks on this subject will emphasize the role that Stanford played in the emergence of the field. They will also offer the perspective of an informatics journal editor-in-chief who has seen many state-of-the-art AIM papers and thereby recognizes the tension between applying existing methods to new problems and developing new science that advances the field in a generalizable way. In addition, the inherent complexity of medicine and of clinical care necessitates that we address not only decision-making performance but also issues of usability, workflow, transparency, safety, and the pursuit of persuasive results from formal clinical trials. These requirements contribute to an ongoing investigative agenda that means fundamental AIM research will continue to be crucial and will define our accomplishments in the decades ahead.

Quantification of gene expression using single cell RNA-sequencing of brain tissues, can be a critical step in the understanding of cell development and differences between cells sampled from case and control subjects.   We describe statistical challenges encountered analyzing expression of brain cells in the context of two projects. First, over-correction has been one of the main concerns in employing various data integration methods that risk removing the biological distinctions, which is harmful for cell type identification. Here, we present a simple yet surprisingly effective transfer learning model named cFIT for removing batch effects across experiments, technologies, subjects, and even species. Second, gene co-expression networks yield critical insights into biological processes, and single-cell RNA sequencing provides an opportunity to target inquiries at the cellular level.  However, due to the sparsity and heterogeneity of transcript counts, it is challenging to construct accurate gene co-expression networks.  We develop an alternative approach that estimates cell-specific networks for each single cell. We use this method to identify differential network genes in a comparison of cells from brains of individuals with autism spectrum disorder and those without.

Over 100,000 variants have been implicated in human traits through genome-wide association studies (GWAS).  Virtually all reported GWAS identify associations by measuring the correlation between a single variant and a phenotype of interest. Recently, several studies reported that at many risk loci, there may exist multiple causal variants, a phenomenon referred to as allelic heterogeneity. Naturally, for a locus with multiple causal variants with small effect sizes, the standard association test is underpowered to detect the associations. Alternatively, an approach considering effects of multiple variants simultaneously may increase statistical power. Counterintuitively, most approaches that consider multiple variants in association studies find fewer associations than the single SNP association test.  This is due to the fact that most multiple variant methods assume a structure of allelic heterogeneity which is very different from what is observed in genetic studies.  In this work, we propose a new statistical method, Model-based Association test Reflecting causal Status (MARS), that tries to find an association between variants in risk loci and a phenotype, considering multiple variants at each locus. One of the main advantages of MARS is that it only requires the existing summary statistics to detect associated risk loci. Thus, MARS is applicable to any association study with summary statistics, even though individual level data is not available for the study. Utilizing extensive simulated data sets, we show that MARS increases the power of detecting true associated risk loci compared to previous approaches that consider multiple variants, while robustly controls the type I error. Applied to data of 44 tissues provided by the Genotype- Tissue Expression (GTEx) consortium, we show that MARS identifies more eGenes compared to previous approaches in most of the tissues; e.g. MARS identified 16% more eGenes than the ones reported by the GTEx consortium. Moreover, applied to Northern Finland Birth Cohort (NFBC) data, we demonstrate that MARS effectively identifies association loci with improved power (56% of more loci found by MARS) in GWAS studies compared to the standard association test.