11/8 – Jalees Rehman, University of Illinois Chicago
CBQB Seminar
November 8, 2021
12:00 PM - 1:00 PM
Address
Chicago, IL
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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
Abstract:
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 https://github.com/jaleesr/BITFAM) 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 https://github.com/jaleesr/TrendCatcher_1.0.0) 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