Open AccessJoint probabilistic modeling of single-cell multi-omic data with totalVI
Author(s) -
Adam Gayoso,
Zoë Steier,
Romain Lopez,
Jeffrey Regier,
Kristopher L. Nazor,
Aaron Streets,
Nir Yosef
Publication year - 2021
Publication title -
nature methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 19.469
H-Index - 318
eISSN - 1548-7105
pISSN - 1548-7091
DOI - 10.1038/s41592-020-01050-x
Subject(s) - inference , computational biology , computer science , dimensionality reduction , transcriptome , probabilistic logic , biology , graphical model , data mining , artificial intelligence , gene expression , gene , genetics
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.