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Joint probabilistic modeling of single-cell multi-omic data with totalVI
The paired measurement of RNA and surface proteins in single cells with 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 techni...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7954949/ https://www.ncbi.nlm.nih.gov/pubmed/33589839 http://dx.doi.org/10.1038/s41592-020-01050-x |
Sumario: | The paired measurement of RNA and surface proteins in single cells with 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 like dimensionality reduction, the integration of datasets with different measured proteins, estimation of correlations between molecules, and differential expression testing. |
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