Activity Number:
|
86
- New Topics and Methodological Developments for Single-Cell Data Science
|
Type:
|
Invited
|
Date/Time:
|
Monday, August 8, 2022 : 8:30 AM to 10:20 AM
|
Sponsor:
|
Section on Statistics in Genomics and Genetics
|
Abstract #320550
|
|
Title:
|
Harnessing Multimodal Single-Cell Sequencing Data for Integrative Analysis with Cobolt
|
Author(s):
|
Elizabeth Purdom* and Boying Gong and Yun Zhao
|
Companies:
|
UC Berkeley and UC Berkeley and UC Berkeley
|
Keywords:
|
Single-cell sequencing;
Multimodal Variational Autoencoder;
Latent Dirichlet Allocation;
Integrative analysis
|
Abstract:
|
Single-cell sequencing allows for quantifying molecular traits at the single-cell level. Comparison of different cellular features (or modalities) from cells from the same biological system gives the potential for a holistic understanding of the system. A growing number of multi-modality sequencing platforms jointly measure multiple modalities from the same cells but most single-cell sequencing datasets measure a single modality, resulting in disjoint measurements of different modalities on different cells. We present Cobolt, a novel method that not only allows for analyzing data from multi-modality platforms, but also provides a coherent framework for harnessing the multi-modality data to allow for the the integration of single-modality datasets. Cobolt estimates this joint representation of all cells irrespective of modalities via a novel application of Multimodal Variational Autoencoder (MVAE) to a hierarchical generative model. We demonstrate the performance of Cobolt in two systems -- cortical brain cells in mouse and PBMC cells from humans -- where Cobolt allows for the integration of multi-modality data with single-modality datasets of scRNA-seq and ATAC-seq.
|
Authors who are presenting talks have a * after their name.