Journal
NATURE METHODS
Volume 18, Issue 3, Pages 272-+Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41592-020-01050-x
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Funding
- NIGMS of the National Institutes of Health [R35GM124916]
- Chan Zuckerberg Foundation Network [2019-02452]
- National Institutes of Mental Health [U19MH114821]
- National Institutes of Health Training Grant [5T32HG000047-19]
- National Science Foundation Graduate Research Fellowship
- Koret-Berkeley-Tel Aviv Initiative in Computational Biology
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totalVI is a framework for end-to-end joint analysis of CITE-seq data which probabilistically represents data as a composite of biological and technical factors, providing a cohesive solution for common analysis tasks. It demonstrates strong performance in tasks such as dimensionality reduction, dataset integration, correlation estimation, and differential expression testing.
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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