Center for Computational Biology, University of California, Berkeley, Berkeley, CA, USA.
Department of Bioengineering, University of California, Berkeley, Berkeley, CA, USA.
Nat Methods. 2021 Mar;18(3):272-282. doi: 10.1038/s41592-020-01050-x. Epub 2021 Feb 15.
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.
单细胞转录组和表位测序（CITE-seq）中同时测量 RNA 和表面蛋白，是将转录组变异与细胞表型和功能联系起来的一种很有前途的方法。然而，由于每种测量方法都具有独特的技术特点，因此将这些配对的观点结合起来，形成细胞状态的统一表示形式具有挑战性。本文介绍了总变分推断（totalVI；https://scvi-tools.org ），这是一种用于 CITE-seq 数据端到端联合分析的框架，它将数据概率地表示为生物和技术因素的组合，包括蛋白质背景和批次效应。为了评估 totalVI 的性能，我们用 CITE-seq 对来自小鼠脾脏和淋巴结的免疫细胞进行了分析，共测量了 100 多种表面蛋白。结果表明，totalVI 为常见的分析任务提供了一个有凝聚力的解决方案，例如降维、整合具有不同测量蛋白的数据集、估计分子之间的相关性以及差异表达测试。
