小柯机器人

华盛顿大学Su-In Lee小组近日取得一项新成果。他们使用contrastiveVI分离单细胞数据中的显著变化。这一研究成果于2023年8月7日发表在国际学术期刊《自然—方法学》上。

研究人员研发了对比变分推理（contrastiveVI），这是一个将治疗-对照单细胞RNA测序（scRNA-seq）数据集中的变异反卷积，作为共享和治疗特异性潜在变量的分析方法。使用三个治疗对照scRNA-seq数据集，研究人员应用contrastiveVI来执行各种分析任务，包括可视化、聚类和差异表达测试。研究发现，Contrastive VI始终可以获得与已知事实基本一致的结果，并且可以获得标准工作流程难以确定的细微现象。最后，研究证实了contrastiveVI也适用于联合转录组和表面蛋白测量。

研究人员表示，一般会以单细胞数据集作为研究对照细胞和相应细胞在治疗后（例如暴露于药物或病原体感染）细胞状态的变化。为了更好地了解个体对治疗反应的异质性，需要对处理细胞中富集的变异与共享对照变异进行反卷积。但是，标准的单细胞数据分析方法并不会明确区分这些变化。

附：英文原文

Title: Isolating salient variations of interest in single-cell data with contrastiveVI

Author: Weinberger, Ethan, Lin, Chris, Lee, Su-In

Issue&Volume: 2023-08-07

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.

DOI: 10.1038/s41592-023-01955-3

Source: https://www.nature.com/articles/s41592-023-01955-3

Nature Methods：《自然—方法学》，创刊于2004年。隶属于施普林格·自然出版集团，最新IF：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex