Discrete Bayesian Modeling for Biological Systems Using Cellular Latent Dirichlet Allocation (Celda)

Wed@Hariri: Meet Our Fellows Series

Joshua Campbell, Assistant Professor, Computational Biomedicine, MED

When:
Wednesday, November 6, 2019
2:45pm-4:00pm

Where:
Hariri Institute for ComputingSeminar Room MCS 157, 111 Cummington Mall

Abstract:
Single-cell genomic technologies such as single-cell RNA-seq have emerged as powerful techniques to quantify molecular states of individual cells and can be used to elucidate the cellular building blocks of complex tissues and diseases. Given recent rapid advances in single-cell technologies, novel statistical and computational approaches are needed to efficiently analyze large-scale single-cell datasets. Discrete Bayesian hierarchical models have been widely used for unsupervised modeling of discrete data types in fields such as Nature Language Processing (NLP).

We have developed a Bayesian hierarchical model called Cellular Latent Dirichlet Allocation (Celda) that performs bi-clustering of genes into modules and cells into subpopulations. We are also developing novel models that can perform the clustering of cells into subpopulations using multi-modal genomic data. In collaboration with SAIL and RCS, our methods will be optimized and made available in a scalable cloud platform accessible to both computational and non-computational users via the R/shiny framework.

Bio: Joshua received his Ph.D. in Bioinformatics from Boston University. He performed his postdoctoral research at Dana-Farber Cancer Institute and the Broad Institute of Harvard and MIT where he worked with The Cancer Genome Atlas (TCGA) to identify novel mutational drivers of lung cancer. He is currently an assistant professor in the Department of Medicine at Boston University School of Medicine where he develops novel Bayesian approaches to analyze data from single-cell genomic technologies to analyze cellular heterogeneity in lung and prostate cancer.