Speaker:
Abstract: Single-cell high-throughput chromatin conformation capture methodologies (scHi-C) enable profiling long-range genomic interactions at single-cell resolution; however, data from these technologies are prone to technical noise and bias that, when unaccounted for, hinder downstream analysis. First, I will talk about our recent work that focuses on denoising and normalization of scHi-C data. Then, I will present single-cell gene associating domain (scGAD) scores as a dimension reduction and exploratory analysis tool for scHi-C data. scGAD enables summarization at the gene unit while accounting for inherent gene-level genomic biases. I will further show that scGAD facilitates the integration of scHi-C data with other single-cell data modalities by enabling its projection onto reference low-dimensional embeddings. This multi-modal data integration provides an automated and refined cell-type annotation for scHi-C data.
Bio: Dr. Keles obtained her Ph.D. in Biostatistics from UC Berkeley. She has twenty years of experience in developing statistical and computational methods for genomics, including serving as an ENCODE PI, and pioneering foundational statistical models for leveraging multi-mapping reads in high throughput sequencing data analysis. Her research interests span developing statistical and computational methods for denoising and signal extraction from sequencing data and modeling of high dimensional data. She developed widely used statistical models and software for ChIP-seq and Hi-C data. Her computational approaches led to fundamental contributions on how GATA factors mediate transcriptional regulation in HSPCs and erythroid cells.
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