RT Journal Article
SR Electronic
T1 Joint probabilistic modeling of paired transcriptome and proteome measurements in single cells
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2020.05.08.083337
DO 10.1101/2020.05.08.083337
A1 Adam Gayoso
A1 Zoë Steier
A1 Romain Lopez
A1 Jeffrey Regier
A1 Kristopher L Nazor
A1 Aaron Streets
A1 Nir Yosef
YR 2020
UL http://biorxiv.org/content/early/2020/05/10/2020.05.08.083337.abstract
AB The paired measurement of RNA and surface protein abundance in single cells with CITE-seq is a promising approach to connect transcriptional variation with cell phenotypes and functions. However, each data modality exhibits unique technical biases, making it challenging to conduct a joint analysis and combine these two views into a unified representation of cell state. Here we present Total Variational Inference (totalVI), a framework for the joint probabilistic analysis of paired RNA and protein data from single cells. totalVI probabilistically represents the data as a composite of biological and technical factors such as limited sensitivity of the RNA data, background in the protein data, and batch effects. To evaluate totalVI, we performed CITE-seq on immune cells from murine spleen and lymph nodes with biological replicates and with different antibody panels measuring over 100 surface proteins. With this dataset, we demonstrate that totalVI provides a cohesive solution for common analysis tasks like the integration of datasets with matched or unmatched protein panels, dimensionality reduction, clustering, evaluation of correlations between molecules, and differential expression testing. totalVI enables scalable, end-to-end analysis of paired RNA and protein data from single cells and is available as open-source software.Competing Interest StatementThe authors have declared no competing interest.