한빛사논문
- 조회169
Ethan Weinberger1,2, Chris Lin1,2 & Su-In Lee1,*
1Paul G. Allen School of Computer Science & Engineering, University of Washington, Seattle, WA, USA.
2These authors contributed equally: Ethan Weinberger, Chris Lin.
*Corresponding author: correspondence to Su-In Lee
Abstract
Single-cell datasets are routinely collected to investigate changes in cellular state between control cells and the corresponding cells in a treatment condition, such as exposure to a drug or infection by a pathogen. To better understand heterogeneity in treatment response, it is desirable to deconvolve variations enriched in treated cells from those shared with controls. However, standard computational models of single-cell data are not designed to explicitly separate these variations. Here, we introduce contrastive variational inference (contrastiveVI; https://github.com/suinleelab/contrastiveVI), a framework for deconvolving variations in treatment–control single-cell RNA sequencing (scRNA-seq) datasets into shared and treatment-specific latent variables. Using three treatment–control scRNA-seq datasets, we apply contrastiveVI to perform a variety of analysis tasks, including visualization, clustering and differential expression testing. We find that contrastiveVI consistently achieves results that agree with known ground truths and often highlights subtle phenomena that may be difficult to ascertain with standard workflows. We conclude by generalizing contrastiveVI to accommodate joint transcriptome and surface protein measurements.
논문정보
- Research article
- 2023년 08월 (BRIC 등록일 2024-03-07)
- 국외 연구진
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