计算生物学
推论
计算机科学
降维
转录组
概率逻辑
生物
图形模型
数据挖掘
人工智能
基因表达
基因
遗传学
作者
Adam Gayoso,Zoë Steier,Romain Lopez,Jeffrey Regier,Kristopher L. Nazor,Aaron Streets,Nir Yosef
出处
期刊:Nature Methods
[Springer Nature]
日期:2021-02-15
卷期号:18 (3): 272-282
被引量:253
标识
DOI:10.1038/s41592-020-01050-x
摘要
The paired measurement of RNA and surface proteins in single cells with cellular indexing of transcriptomes and epitopes by sequencing (CITE-seq) is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, combining these paired views into a unified representation of cell state is made challenging by the unique technical characteristics of each measurement. Here we present Total Variational Inference (totalVI; https://scvi-tools.org ), a framework for end-to-end joint analysis of CITE-seq data that probabilistically represents the data as a composite of biological and technical factors, including protein background and batch effects. To evaluate totalVI's performance, we profiled immune cells from murine spleen and lymph nodes with CITE-seq, measuring over 100 surface proteins. We demonstrate that totalVI provides a cohesive solution for common analysis tasks such as dimensionality reduction, the integration of datasets with different measured proteins, estimation of correlations between molecules and differential expression testing.
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