Joint probabilistic modeling of single-cell multi-omic data with totalVI

By Adam Gayoso, Zoë Steier, Romain Lopez, Jeffrey C. Regier, Kristopher L. Nazor, Aaron M. Streets, Nir Yosef. Nature Methods, 2021

513 citations recorded by the source corpus.

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. Total Variational Inference is a framework for end-to-end analysis of paired transcriptome and protein measurements such as CITE-seq data in single cells.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b135e330cc1473c8c24fa63bb9a5b64f51993f9e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

  1. Malte D. Luecken
  2. Fabian J Theis
  3. Luke Zappia

In Molecular Biology

  1. Malte D. Luecken
  2. Fabian J Theis
  3. Luke Zappia
  4. H. Schiller
  5. Anna C. Schaar
  6. F. Drost
  7. Christopher Lance
  8. Anastasia Litinetskaya

Citations in the corpus