As the number of single‐cell transcriptomics datasets grows, the natural next step is to integrate the accumulating data to achieve a common ontology of cell types and states. However, it is not ...straightforward to compare gene expression levels across datasets and to automatically assign cell type labels in a new dataset 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 scRNA‐seq data, 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 existing cell state annotations. We demonstrate that scVI and scANVI compare favorably to state‐of‐the‐art methods for data integration and cell state annotation in terms of accuracy, scalability, and adaptability to challenging settings. In contrast to existing methods, scVI and scANVI integrate multiple datasets with a single generative model that can be directly used for downstream tasks, such as differential expression. Both methods are easily accessible through scvi‐tools.
SYNOPSIS
This study demonstrates the ability of scVI to integrate single‐cell RNA‐seq datasets in a variety of settings and presents scANVI, a new development based on scVI for automated annotation of cell types and states.
In scVI, datasets from different labs and technologies are integrated in a joint latent space.
In scANVI, cell type annotations are transferred between datasets and across different scenarios.
Uncertainties of differential gene expression in multiple samples are quantified.
The performance of scVI and scANVI in data integration and cell state annotation is superior to other related methods.
This study demonstrates the ability of scVI to integrate single‐cell RNA‐seq datasets in a variety of settings and presents scANVI, a new development based on scVI for automated annotation of cell types and states.
Class labels are often imperfectly observed, due to mistakes and to genuine ambiguity among classes. We propose a new semi-supervised deep generative model that explicitly models noisy labels, called ...the Mislabeled VAE (M-VAE). The M-VAE can perform better than existing deep generative models which do not account for label noise. Additionally, the derivation of M-VAE gives new theoretical insights into the popular M1+M2 semi-supervised model.
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