12pm: Emily Maciejewski (Ernst), Grad Student, Computer Science
TITLE: “Cross-species and tissue imputation of species-level DNA methylation samples”
ABSTRACT: DNA methylation data is highly informative to study a variety of aspects of mammalian biology. The availability of such data for many mammals at conserved sites was recently vastly enhanced by the development and large-scale application of the mammalian methylation array. For instance, we consider here 13,245 samples profiled on this array representing 348 species and 59 tissues from 746 species-tissue combinations. While having some coverage of many different species and tissue types, this data only captures 3.6% of potential species-tissue combinations. We thus developed CMImpute (Cross-species Methylation Imputation) which uses a Conditional Variational Autoencoder to impute DNA methylation of non-profiled species-tissue combinations. In cross-validation, we show that CMImpute yields high correlation with held-out observed values, outperforming multiple baselines. We then train a model on all the data to impute 19,786 new species-tissue combinations. We expect CMImpute and our imputed data resource will be useful for DNA methylation analyses across mammalian species.
12:30pm: Chenlu Di (Lohmueller), Postdoc, Ecology & Evolutionary Biology
TITLE: “Inference of fitness effects of mutations in noncoding regions of the human genome”
1:30pm: Qingyang Wang (Li JJ), Grad Student, Statistics
TITLE “Review of computational methods for estimating cell potency from single-cell RNA-seq data”
ABSTRACT: In single-cell RNA sequencing (scRNA-seq) data analysis, a critical challenge is to infer hidden dynamic cellular processes from measured static cell snapshots. To tackle this challenge, many computational methods have been developed from distinct perspectives. Besides the common perspectives of inferring trajectories (or pseudotime) and RNA velocity, another important perspective is to estimate the differentiation potential of cells, which is commonly referred to as “cell potency.” In this review, we provide a comprehensive summary of 11 computational methods that estimate cell potency from scRNA-seq data under different assumptions, some of which are even conceptually contradictory. We divide these methods into three categories: mean-based, entropy-based, and correlation-based methods, depending on how a method summarizes gene expression levels of a cell or cell type into a potency measure. Our review focuses on the key similarities and differences of the methods within each category and between the categories, providing a high-level intuition of each method. Moreover, we use a unified set of mathematical notations to detail the 11 methods’ methodologies and summarize their usage complexities, including the number of ad-hoc parameters, the number of required inputs, and the existence of discrepancies between the method description in publications and the method implementation in software packages. Realizing the conceptual contradictions of existing methods and the difficulty of fair benchmarking without single-cell-level ground truths, we conclude that accurate estimation of cell potency from scRNA-seq data remains an open challenge.
2:00pm: Alex Bermudez (Lin), Grad Student, Bioengineering
TITLE: “TCell Morphology Impacts Chromatin States During Crowding”
ABSTRACT: Variability is an inherent characteristic of all biological systems, exemplified by the diverse shapes, sizes, and gene expression profiles of cells comprising tissues. Despite its ubiquity, our understanding of how such a phenotypic heterogeneity plays a role in regulating cell biology remains incomplete. In this talk, I will discuss how cell shape heterogeneity arises and its impacts on chromatin organization of each cell during epithelial crowding, a canonical process where cells proliferate until a densely packed monolayer forms. Our findings suggest that cell morphological heterogeneity is not mere noise, but a crucial factor driving chromatin state and gene expression, directing tissue development and remodeling.