Multimodal characterization of cell states at single-cell resolution
Galip Gürkan Yardimci; Assistant Professor, CEDAR, OHSU Knight Cancer Institute, School of Medicine.
Bio: Dr. Yardimci develops machine learning and statistical methods to better understand chromatin organization in cancerous and healthy cells. He aims to build models that improve our understanding of chromatin organization in regulating essential processes like transcriptional regulation and methods that will enable detection of aberration of chromatin organization in cancerous cells. In building such methods, he leverages a variety of population and single-cell genomics assays and high resolution microscopy images. Our side interests involve developing computational methods that aid experimental design of high throughput genomics assays, and assessing the quality and reproducibility of datasets from such experiments.
Abstract:
Advent of single-cell genomics has enhanced our ability to study heterogeneous cell populations (1) to track course of temporal processes, such as cellular differentiation, (2) to identify novel and rare cell states, and (3) characterize the heterogeneity of complex tissues, such as tumors. In this talk, I will present two methods to study such cell populations using multimodal single-cell omics assays. Our first algorithm, Epiconfig, is an interpretable multimodal topic model that learns unsupervised clustering of single-cells while modeling cross modality relationships. We applied EpiConfig to a collection of sc-RNA+ATAC-seq assays that jointly measure transcriptomic and chromatin accessibility of single cells from healthy and cancerous cell populations. Epi-Config is as accurate as widely used sc-multiomics clustering methods; it learns sets of RNA,ATAC and cross modality features , called topics, that correspond to specific cell types and states. We developed a shiny app for interpretation of these topics to obtain biological insights into different cell states; we show that cross modality features reflect 3D genome interactions. Our second method, RIDDLER, can identify copy number variation (CNV) events in single-cell datasets. CNV is a widely studied structural variation seen in the genomes of cancerous and other dysfunctional cells. CNVs can have direct and indirect effects on gene dosage, and may drive cancer progression and other disorders. RIDDLER is a single-cell resolution CNV detection algorithm based on outlier aware generalized linear modeling. We demonstrate the effectiveness of our algorithm on cancer cell line models where it achieves better agreement with sc-WGS derived CNVs than competing methods. RIDDLER is able to accurately reconstruct clonal heterogeneity of the cell population, in accordance with sc-WGS derived clones, and can be applied to both sc-ATAC-seq, sc-WGS and sc-methylation datasets. Furthermore, we show that RIDDLER can enable CNV similarity based integration of different modalities.