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Nov 8 2021

11/8 – Jalees Rehman, University of Illinois Chicago

CBQB Seminar

November 8, 2021

12:00 PM - 1:00 PM


Chicago, IL

Watch this webinar live with Zoom>>

Speaker: Jalees Rehman

Associate Head, Department of Pharmacology and Regenerative Medicine
Professor of Medicine, Pharmacology and Biomedical Engineering, University of Illinois Chicago

Title: Leveraging Prior Knowledge and Temporal Analysis to Analyze Single Cell RNA-Sequencing Data – Cellular Heterogeneity, Inflammation and Aging

One of the key challenges in the analysis of single cell RNA-seq data is the interpretation of the cell-type specific results in a manner that allows us to understand cellular functions and the biological context of the gene expression profiles. One of our approaches to facilitate the biological interpretation of single cell RNA-seq data is to leverage prior biological knowledge. We have developed the novel open source analytical platform BITFAM (Bayesian inference of transcription factor activity model; Github which allows us to infer the activity of transcription factors in individual cells based on the prior knowledge of transcription factor targets available through publicly available ChiP-seq databases. By applying BITFAM to new datasets, we can reduce the dimensionality of single cell RNA-seq data and cluster cells by inferred transcription factor activities. Importantly, the identified transcription factor activities provide biological insights into the transcription factors that drive cellular identities as well as pathogenic responses in disease. We are now building on this work by studying time course shifts in gene expression data and have created the open source R-package TrendCatcher (Github which allows for the analysis and visualization of dynamic gene expression shifts. Applying TrendCatcher to immune cells in COVID-19 identifies distinct inflammatory processes that predict the development of severe COVID-19. We are also using known markers of cellular senescence to establish cell-type specific and universal cellular senescence signatures in single cell RNA-seq data.



Date posted

Nov 1, 2021

Date updated

Nov 1, 2021