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Advancement: Integrating prior biological knowledge and machine learning for single-cell transcriptomics analysis

Speaker Name: 
Lucas Seninge
Speaker Title: 
PhD Student
Speaker Organization: 
Biomolecular Engineering & Bioinformatics PhD
Start Time: 
Friday, April 9, 2021 - 3:00pm
End Time: 
Friday, April 9, 2021 - 4:00pm
Zoom - - Passcode: 153041

Abstract: Single-cell RNA sequencing (scRNA-Seq) has offered a unique window into studying cellular identity at unprecedented scale and resolution. However, the process of revealing this cellular identity remains challenging. For example, the annotation of each assayed cell with a cell type label indicating its functional identity still relies on manual examination, which is rate-limiting and poses reproducibility issues. Similarly, inferring the activity of gene regulatory pathways specifying cell state relies on methods designed for bulk RNA sequencing data and do not make use of the important amount of data generated by single-cell experiments.

Here, I propose to combine prior biological knowledge about cellular entities contained in curated databases and machine learning to shed light on the cellular identity of single cells. Specifically, I will develop a statistical framework for the automated annotation of single-cell transcriptomes with cell type labels by integrating prior cell ontology information and cell type-specific marker gene sets. Then, I propose to infer pathway activity in single cells by using recent progress in the field of deep generative modeling as well as prior knowledge from gene annotation databases. Finally, I introduce a novel generative model architecture to approach the more ambitious task of modeling targeted perturbation of these pathways to perform in-silico perturbation experiments and alter cellular state at the single-cell level. This work will be helpful in prioritizing compounds to alter the state of target cell populations, with applications to designing novel therapeutic strategies and to the field of developmental biology. 

Event Type: 
Josh Stuart
Graduate Program: 
Biomolecular Engineering & Bioinformatics PhD