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Cited by 702Single-cell and spatial transcriptomicsGene Regulatory Network AnalysisGene expression and cancer classification

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Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models

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Xu, Chenling, Lopez, Romain, Mehlman, Edouard, Regier, Jeffrey, Jordan, Michael I, Yosef, Nir·2021·Molecular Systems Biology
DOI 10.15252/msb.20209620PMC PMC7829634

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Abstract

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.

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Figure 1 Harmonization and annotation of scRNA‐seq datasets with generative models Functional overview of the methods proposed in this manuscript.Schematic diagram of the variational inference procedure in both of the scVI and scANVI models. We show the order in which random variables in the generative model are sampled and how these variables can be used to derive biological insights.The graphical models of scVI and scANVI. Vertices with black edges represent variables in both scVI and scANVI, and vertices with red edges are unique to scANVI. Shaded vertices represent observed random variables. Semi‐shaded vertices represent variables that can be either observed or random. Empty vertices represent latent random variables. Edges signify conditional dependency. Rectangles (“plates”) represent independent replication. The complete model specification and definition of internal variables is provided in the Materials and Methods
Figure 1 Harmonization and annotation of scRNA‐seq datasets with generative models Functional overview of the methods proposed in this manuscript.Schematic diagram of the variational inference procedure in both of the scVI and scANVI models. We show the order in which random variables in the generative model are sampled and how these variables can be used to derive biological insights.The graphical models of scVI and scANVI. Vertices with black edges represent variables in both scVI and scANVI, and vertices with red edges are unique to scANVI. Shaded vertices represent observed random variables. Semi‐shaded vertices represent variables that can be either observed or random. Empty vertices represent latent random variables. Edges signify conditional dependency. Rectangles (“plates”) represent independent replication. The complete model specification and definition of internal variables is provided in the Materials and MethodsCC BY · PubMed Central
Figure 2 Benchmarking of scRNA‐seq harmonization algorithms Each row is a different dataset. Each column is a metric. k‐nearest neighbors purity that ranges from 0 to 1, with higher values meaning better preservation of neighbor structure in the individual datasets after harmonization.Entropy of batch mixing where higher values means that the cells from different datasets are well mixed.The trade‐off between the kNN purity and entropy of batch mixing for a fixed K = 150. Methods on the top right corner have better performances.The trade‐off between entropy of batch mixing and the preservation of biological information using an alternative unsupervised statistic k‐means clustering preservation.
Figure 2 Benchmarking of scRNA‐seq harmonization algorithms Each row is a different dataset. Each column is a metric. k‐nearest neighbors purity that ranges from 0 to 1, with higher values meaning better preservation of neighbor structure in the individual datasets after harmonization.Entropy of batch mixing where higher values means that the cells from different datasets are well mixed.The trade‐off between the kNN purity and entropy of batch mixing for a fixed K = 150. Methods on the top right corner have better performances.The trade‐off between entropy of batch mixing and the preservation of biological information using an alternative unsupervised statistic k‐means clustering preservation.CC BY · PubMed Central
Figure 3 Harmonizing datasets with different cellular composition A–DThe case when no cell type is shared. PBMC‐8K contains all cells other than cell type c 0 while PBMC‐CITE contains only cell type c 0. Six experiments were run, each keeping one cell type from the PBMC‐CITE dataset. (A, B) UMAP visualization for the case where c 0 corresponds to natural killer cells. (C, D) entropy of batch mixing and k‐nearest neighbors purity, aggregating the six experiments (setting c 0 to a different cell type in each experiment). Data information: Red arrows indicate the desired direction for each performance measure. Low batch entropy is desirable in (C) while high k‐nearest neighbors purity is desirable in (D).E–IThe case when cell type c 0 is removed PBMC‐8K but not from PBMC‐CITE. Six experiments were run, each removing one cell type from the PBMC‐CITE dataset. (E, F) UMAP visualization for the case where c 0 corresponds to CD4+ T cells. (G) entropy of batch mixing for the removed cell type. Lower value is more desirable as indicated by the red arrow. (H) entropy of batch mixing for the remaining cell types. Higher value is more desirable as indicated by the red arrow. (I) k‐nearest neighbors purity. Higher value is more desirable as indicated by the red arrow.
Figure 3 Harmonizing datasets with different cellular composition A–DThe case when no cell type is shared. PBMC‐8K contains all cells other than cell type c 0 while PBMC‐CITE contains only cell type c 0. Six experiments were run, each keeping one cell type from the PBMC‐CITE dataset. (A, B) UMAP visualization for the case where c 0 corresponds to natural killer cells. (C, D) entropy of batch mixing and k‐nearest neighbors purity, aggregating the six experiments (setting c 0 to a different cell type in each experiment). Data information: Red arrows indicate the desired direction for each performance measure. Low batch entropy is desirable in (C) while high k‐nearest neighbors purity is desirable in (D).E–IThe case when cell type c 0 is removed PBMC‐8K but not from PBMC‐CITE. Six experiments were run, each removing one cell type from the PBMC‐CITE dataset. (E, F) UMAP visualization for the case where c 0 corresponds to CD4+ T cells. (G) entropy of batch mixing for the removed cell type. Lower value is more desirable as indicated by the red arrow. (H) entropy of batch mixing for the remaining cell types. Higher value is more desirable as indicated by the red arrow. (I) k‐nearest neighbors purity. Higher value is more desirable as indicated by the red arrow.CC BY · PubMed Central
Figure 4 Harmonizing developmental trajectories A, BUMAP visualization of the scVI latent space, with cells colored by the original labels from either the HEMATO‐Paul (A) or HEMATO‐Tusi (B) studies. The cells from the other dataset are colored in gray.CEntropy of batch mixing along 20 bins of the HEMATO‐Tusi cells, ordered by the potential of each cell. Potential is a pseudotime measure that describes the differentiation state of a cell using the population balance analysis algorithm (center: common myeloid progenitors; moving left: erythrocyte branch; moving right: granulocyte branch).D k‐nearest neighbors purity for scVI, Seurat, and scANVI.EExpression of marker genes that help determine the identity of batch‐unique cells.
Figure 4 Harmonizing developmental trajectories A, BUMAP visualization of the scVI latent space, with cells colored by the original labels from either the HEMATO‐Paul (A) or HEMATO‐Tusi (B) studies. The cells from the other dataset are colored in gray.CEntropy of batch mixing along 20 bins of the HEMATO‐Tusi cells, ordered by the potential of each cell. Potential is a pseudotime measure that describes the differentiation state of a cell using the population balance analysis algorithm (center: common myeloid progenitors; moving left: erythrocyte branch; moving right: granulocyte branch).D k‐nearest neighbors purity for scVI, Seurat, and scANVI.EExpression of marker genes that help determine the identity of batch‐unique cells.CC BY · PubMed Central
Figure 5 Validation of cell type annotations using additional metadata A, BUMAP plot of the scANVI latent space inferred for three harmonized datasets: PBMC‐CITE, PBMC‐sorted, and PBMC‐68K. Cells are colored by the dataset of origin (A) and the PBMC‐sorted labels (B). Cells from the PBMC‐CITE and PBMC‐68K are colored in gray in (B).CThe consistency of the harmonized PBMC‐CITE mRNA data with the respective protein measurements, evaluated by mean squared error and for different neighborhood size. Lower values indicate higher consistency.DUMAP plot of the scANVI latent space, where cells are colored by normalized protein measurement. Only PBMC‐CITE cells are displayed.EUMAP plot of the scANVI latent space, with cells from the PBMC‐68k dataset colored according to their original label. For clarity of presentation, only cells originally labeled as dendritic cells or natural killer cells are colored. Evidently, a large number of these cells are mapped to a cluster of T cells (right side of the plot).
Figure 5 Validation of cell type annotations using additional metadata A, BUMAP plot of the scANVI latent space inferred for three harmonized datasets: PBMC‐CITE, PBMC‐sorted, and PBMC‐68K. Cells are colored by the dataset of origin (A) and the PBMC‐sorted labels (B). Cells from the PBMC‐CITE and PBMC‐68K are colored in gray in (B).CThe consistency of the harmonized PBMC‐CITE mRNA data with the respective protein measurements, evaluated by mean squared error and for different neighborhood size. Lower values indicate higher consistency.DUMAP plot of the scANVI latent space, where cells are colored by normalized protein measurement. Only PBMC‐CITE cells are displayed.EUMAP plot of the scANVI latent space, with cells from the PBMC‐68k dataset colored according to their original label. For clarity of presentation, only cells originally labeled as dendritic cells or natural killer cells are colored. Evidently, a large number of these cells are mapped to a cluster of T cells (right side of the plot).CC BY · PubMed Central
Figure 6 Cell type annotation in a single dataset using “seed” labeling ADiscrepancies between marker genes that can be used to confidently label cells and highly variable genes in scRNA‐seq analysis.B–DUMAP plot of the scVI latent space. (B) Seed cells are colored by their annotation (using known marker genes). (C) PBMC‐sorted cell type labels from the original study based on marker‐based sorting. (D) The posterior probability of each cell being one of the four T cell subtypes obtained with scANVI.
Figure 6 Cell type annotation in a single dataset using “seed” labeling ADiscrepancies between marker genes that can be used to confidently label cells and highly variable genes in scRNA‐seq analysis.B–DUMAP plot of the scVI latent space. (B) Seed cells are colored by their annotation (using known marker genes). (C) PBMC‐sorted cell type labels from the original study based on marker‐based sorting. (D) The posterior probability of each cell being one of the four T cell subtypes obtained with scANVI.CC BY · PubMed Central
Figure 7 Differential Expression on multiple datasets with scVI Evaluation of consistency with Spearman rank correlation and Kendall‐Tau is shown for comparisons of multiple pairs of cell types in the simulated data. For each comparison, we subsampled 30 cells from each group, and repeated the subsampling 10 times to evaluate the uncertainty in our result.Distribution of true log fold change between all pairs of cell types for the simulated data. The pairs of cells are chosen to represent different levels of distance on the tree as in Appendix Fig S18A. The pairs of population from most distant to least distant are “12”, “24”, “23”, “45”.Evaluation of consistency with the AUROC and Kendal Tau metric is shown for comparisons of CD4 vs CD8 T cells and B cells vs dendritic cells on the PBMC‐8K only (A), the PBMC‐68k only (B) and the merged PBMC‐8K / PBMC‐68K (A + B) for scVI and edgeR. For each comparison, we subsampled 30 cells from each group, and repeated the subsampling 10 times to evaluate the uncertainty in our result.Mislabeling experiment in differential expression in both the SymSim simulated datasets and in the PBMC8K and PBMC68K dataset. The top row shows differential expression results for the correctly labeled population pair (Population 1 vs. Population 2 in simulated dataset and CD4 T cells vs. CD8 T cells in PBMC dataset. The bottom row shows differential expression results for the mislabelled population pair (Population 2 vs. Population 3 in simulated dataset and dendritic cells vs. B cells in PBMC dataset). For all, x‐axis represents the proportion of flipped labels. Data information: The boxplots are standard Tukey boxplots where the box is delineated by the first and third quartile and the whisker lines are the first and third quartile plus minus 1.5 times the box height. The dots are outliers that fall above or below the whisker lines. The center band indicates the median.
Figure 7 Differential Expression on multiple datasets with scVI Evaluation of consistency with Spearman rank correlation and Kendall‐Tau is shown for comparisons of multiple pairs of cell types in the simulated data. For each comparison, we subsampled 30 cells from each group, and repeated the subsampling 10 times to evaluate the uncertainty in our result.Distribution of true log fold change between all pairs of cell types for the simulated data. The pairs of cells are chosen to represent different levels of distance on the tree as in Appendix Fig S18A. The pairs of population from most distant to least distant are “12”, “24”, “23”, “45”.Evaluation of consistency with the AUROC and Kendal Tau metric is shown for comparisons of CD4 vs CD8 T cells and B cells vs dendritic cells on the PBMC‐8K only (A), the PBMC‐68k only (B) and the merged PBMC‐8K / PBMC‐68K (A + B) for scVI and edgeR. For each comparison, we subsampled 30 cells from each group, and repeated the subsampling 10 times to evaluate the uncertainty in our result.Mislabeling experiment in differential expression in both the SymSim simulated datasets and in the PBMC8K and PBMC68K dataset. The top row shows differential expression results for the correctly labeled population pair (Population 1 vs. Population 2 in simulated dataset and CD4 T cells vs. CD8 T cells in PBMC dataset. The bottom row shows differential expression results for the mislabelled population pair (Population 2 vs. Population 3 in simulated dataset and dendritic cells vs. B cells in PBMC dataset). For all, x‐axis represents the proportion of flipped labels. Data information: The boxplots are standard Tukey boxplots where the box is delineated by the first and third quartile and the whisker lines are the first and third quartile plus minus 1.5 times the box height. The dots are outliers that fall above or below the whisker lines. The center band indicates the median.CC BY · PubMed Central

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Research works printed but not linked: 4

  1. Goldberger J, Ben‐Reuven E (2017) Training deep neural‐networks using a noise adaptation layer. In Proceedings of the International Conference on Learning Representations, Toulon, France
  2. Kingma DP, Mohamed S, Jimenez Rezende D, Welling M (2014) Semisupervised learning with deep generative models. Adv Neural Inf Process Syst 27: 3581–3589
  3. Kingma DP, Welling M (2014) Auto‐encoding variational Bayes. In Proceedings of the International Conference on Learning Representations, Banff, Canada
  4. Louizos C, Swersky K, Li Y, Welling M, Zemel R (2016) The variational fair autoencoder. In Proceedings of the International Conference on Learning Representations, San Juan, Puerto Rico

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Metadata

Authors
Xu, C., Lopez, R., Mehlman, E., Regier, J., Jordan, M. I., & Yosef, N.
Affiliations
  • University of California, Berkeley (US)
  • École Polytechnique (FR)
  • University of Michigan (US)
  • Harvard University (US)
Corresponding author
Yosef, Nir
ORCID
Venue
Molecular Systems Biology
Publisher
Springer Science and Business Media LLC
Published
2021-01-01
Volume
17
Issue
1
Pages
e9620-e9620
ISSN
1744-4292 (ISSN-L)
DOI
10.15252/msb.20209620
PMC
PMC7829634
OpenAlex
W3121168830
Semantic Scholar
58c22899af553a611bbd3acfd8d2b43f86d2b3fe
Cited by
691
References
83
Citations by year
2026: 206 · 2025: 188 · 2024: 126 · 2023: 91 · 2022: 44 · 2021: 29
FWCI
23.55
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular Systems Biology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • HHS | NIH | National Institute of Allergy and Infectious Diseases (U19 AI090023)
  • NIH | National Institute of Mental Health (U19 MH114821)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Keywords
single-cell transcriptomics, data integration, cell type annotation, scVI, scANVI, variational inference, deep generative models, stochastic modeling, uncertainty quantification, semi-supervised learning, differential gene expression
MeSH terms
Supervised Machine Learning, Humans, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Databases, Genetic, Molecular Sequence Annotation, Single-Cell Analysis

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