Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models
By Chenling A. Xu, Romain Lopez, Edouard Mehlman, Jeffrey C. Regier, Michael I. Jordan, Nir Yosef. bioRxiv, 2019
591 citations recorded by the source corpus.
As single-cell transcriptomics becomes a mainstream technology, the natural next step is to integrate the accumulating data in order to achieve a common ontology of cell types and states. However, owing to various nuisance factors of variation, it is not straightforward how to compare gene expression levels across data sets and how to automatically assign cell type labels in a new data set based on existing annotations. In this manuscript, we demonstrate that our previously developed method, scVI, provides an effective and fully probabilistic approach for joint representation and analysis of cohorts of single-cell RNA-seq data sets, while accounting for uncertainty caused by biological and measurement noise. We also introduce single-cell ANnotation using Variational Inference (scANVI), a semi-supervised variant of scVI designed to leverage any available cell state annotations — for instance when only one data set in a cohort is annotated, or when only a few cells in a single data set can be labeled using marker genes. We demonstrate that scVI and scANVI compare favorably to the existing methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings such as a hierarchical structure of cell state labels. We further show that different from existing methods, scVI and scANVI represent the integrated datasets with a single generative model that can be directly used for any probabilistic decision making task, using differential expression as our case study. scVI and scANVI are available as open source software and can be readily used to facilitate cell state annotation and help ensure consistency and reproducibility across studies.
Subfields: Biophysics, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/58c22899af553a611bbd3acfd8d2b43f86d2b3fe
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In Biophysics
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Citations in the corpus
- The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans (2021, background): “S1 to S16 Tables S1 to S9 References (40-95)”
- Dictionary learning for integrative, multimodal, and massively scalable single-cell analysis (2022, unknown): “This strategy is enabled by the curation and public release of reference datasets as well as the development of new computational tools, including statistical learning 4–7 and deep learning-based approaches 8,9 , that have been successfully applied toward this goal.”
- Large Scale Foundation Model on Single-cell Transcriptomics (2023, unknown): “We benchmarked scFoundation against the methods CellTypist 58 , scBERT 13 , scANVI 59 , ACTINN 60 , Scanpy 61 and SingleCellNet 62 (Methods).”
- Benchmarking atlas-level data integration in single-cell genomics (2020, methodology): “2–15), we found that the varying complexity of tasks affects the ranking of integration methods: while Seurat v3 and Harmony perform well on simpler real data tasks and some simulations, Scanorama and scVI performed particularly well on more complex real data.”