Biostatistics Weekly Seminar


Statistical Learning and Analysis of Single-Cell Multi-Omics Data

Xinjun Wang, PhD Candidate in Biostatistics
University of Pittsburgh

Droplet-based single cell transcriptome sequencing (scRNA-seq) technology, largely represented by the 10x Genomics Chromium system, is able to measure the gene expression from tens of thousands of single cells simultaneously. More recently, coupled with the cutting-edge Cellular Indexing of Transcriptomes and Epitopes by Sequencing (CITE-seq), the droplet-based system has allowed for immunophenotyping of single cells based on cell surface expression of specific proteins together with simultaneous transcriptome profiling in the same cell. Although CITE-seq and other similar technologies have gained lots of popularity, novel methods for analyzing this type of single cell multi-omics data are in urgent need. In this talk, I will focus on two statistical methods I have developed for analyzing CITE-seq data, namely BREM-SC and SECANT. In BREM-SC, I propose a model-based data-driven approach for joint clustering CITE-seq data. Specifically, cell-specific random effects are introduced in the model to integrate two data modalities and MCMC is utilized for optimization. In SECANT, I propose a model-based biology-driven approach to analyze CITE-seq data or jointly analyze paired CITE-seq and scRNA-seq data, where we consider surface protein data provide general guidance for cell clustering with RNA data. A novel statistical model in SECANT is proposed under semi-supervised learning framework, and is optimized using gradient-based method. I will demonstrate the usefulness of BREM-SC and SECANT via real data applications using human PBMC datasets. We expect our new methods will be complementary to existing tools for characterizing novel cell types and make new biological discoveries using single cell multi-omics data.


Zoom (link to follow)
21 January 2022
11am


Speaker Itinerary

Topic revision: r4 - 14 Jan 2022, QiL
 

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