YSPH Biostatistics Seminar: “A Novel Pathway-based Distance Score Enhances Assessment of Disease Heterogeneity in Gene Expression"

NOTE: BIS 525 students are required to attend in person. Others are invited to attend in person, but may also attend via Zoom.

SPEAKER: Xiting Yan, PhD, Associate Professor of Medicine (Pulmonary, Critical Care and Sleep Medicine)

TITLE: “A Novel Pathway-based Distance Score Enhances Assessment of Disease Heterogeneity in Gene Expression"

ABSTRACT: Distance based unsupervised clustering of gene expression data is commonly used to identify heterogeneity in biologic samples. However, high noise levels in gene expression data and relatively high correlation between genes are often encountered, so traditional distances such as Euclidean distance may not be effective at discriminating the biological differences between samples. An alternative method to examine disease phenotypes is to use pre-defined biological pathways. These pathways have been shown to be perturbed in different ways in different subjects who have similar clinical features. In this study, we developed a novel computational method to assess the biological differences between samples using gene expression data by assuming that ontologically defined biological pathways in biologically similar samples have similar behavior. Genes in the same pathway were used to cluster samples using the Gaussian mixture model. The clustering results across different pathways were then summarized to calculate the pathway-based distance score between samples. We applied the method to both simulated and real data sets and compared to the traditional Euclidean distance and another pathway-based clustering method, Pathifier. The results show that our method performs significantly better than the Euclidean distance, especially when the heterogeneity is low and genes in the same pathways are correlated. Compared to Pathifier, our approach achieves higher accuracy and robustness for small pathways. When the pathway size is large, by down-sampling the pathways into smaller pathways, our approach was able to achieve comparable performance.

YSPH values inclusion and access for all participants. If you have questions about accessibility or would like to request an accommodation, please contact Charmila Fernandes at Charmila.fernandes@yale.edu. We will try to provide accommodations requested by August 29, 2024.