Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models
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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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125 supertypes represented cell types that could be reliably reidentified in reference datasets (mean F1 score = 0.91) using hierarchical probabilistic Bayesian mapping.
“To map SEA-AD data to cell types consistently across all 84 donors, we first defined robust transcriptional types, named supertypes, in the BICCN reference; 125 supertypes represented cell types that could be reliably reidentified in reference datasets (mean F1 score = 0.91) using hierarchical probabilistic Bayesian mapping47,48(Extended Data Fig. 5a,b).”
From Integrated multimodal cell atlas of Alzheimer’s disease (2024) · cites this paper as [48]
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“As the scmap manuscript insists heavily on why the M3Drop (Andrews & Hemberg, 2019) gene filtering procedure is crucial to overcome batch effects and yield accurate mapping, we let scmap choose its default number of genes ( g = 500) with this method.”
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scVAE: variational auto-encoders for single-cell gene expression data
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Defining cell-type specificity at the transcriptional level in human diseasePreprint
Ju, W., Greene, C. S., Eichinger, F. H., Nair, V., Hodgin, J. B., Bitzer, M., Lee, Y.-S., Zhu, Q., Kehata, M., Li, M., et al. (2013) · Genome Research · doi:10.1101/gr.155697.113 · Cited by 254 · cited 1× · see citing works
“S100a8 is a neutrophil specific gene predicted by Nano-dissection [47] and is associated with GO processes such as leukocyte migration associated with inflammation and neutrophil aggregation.”
BackgroundFunctional interpretation of single cell similarity maps
DeTomaso, D., Jones, M. G., Subramaniam, M., Ashuach, T., Ye, C., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-12235-0 · Cited by 251 · cited 1× · see citing works
“The first is ab initio labeling of cells based on marker genes or gene signatures (DeTo-maso & Yosef, 2016; Butler et al , 2018; DeTomaso et al , 2019).”
Self-citationSingle cells make big data: New challenges and opportunities in transcriptomics
Angerer, P., Simon, L. M., Tritschler, S., Wolf, F., Fischer, D. S., & Theis, F. J. (2017) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2017.07.004 · Cited by 213 · cited 2× · see citing works
“We explore three such cases, including a bone marrow 10x and Smart-Seq2 pair from the Tabula Muris project (MarrowTM-10x, MarrowTMss2; Schaum et al , 2018), a pancreas inDrop and CEL-Seq2 pair (Pan-creas-InDrop, PancreasCEL-Seq2; Baron et al , 2016), and a dentate gyrus 10x and Fluidigm C1 pair (DentateGyrus10x, Denta…”
Wang, D., & Gu, J. (2018) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2018.08.003 · Cited by 212 · see citing works
Hierarchical Multi-Label Classification Networks
Wehrmann, J., Barros, R. C., & Cerri, R. (2018) · International Conference on Machine Learning · Cited by 195 · see citing works
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Use of the Fluidigm C1 platform for RNA sequencing of single mouse pancreatic islet cells
Xin, Y., Kim, J., Ni, M., Wei, Y. Z., Okamoto, H., Lee, J., Adler, C., Cavino, K., Murphy, A. J., Yancopoulos, G. D., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602306113 · Cited by 168 · see citing works
Benchmarking atlas-level data integration in single-cell genomicsPreprint
Luecken, M. D., Büttner, M., Chaichoompu, K., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.22.111161 · Cited by 122 · see citing works
Simulating multiple faceted variability in single cell RNA sequencing
Zhang, X., Xu, C. A., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-10500-w · Cited by 120 · see citing works
Self-citationLopez, R., Nazaret, A., Langevin, M., Samaran, J., Regier, J., Jordan, M. I., & Yosef, N. (2019) · arXiv · doi:10.48550/arxiv.1905.02269 · Cited by 105 · cited 1× · see citing works
“…Stuart et al , 2019), further work can utilize probabilistic graphical models that quantify measurement uncertainties in each assay, as well as the uncertainties of transferring information between modalities (e.g., predicting unmeasured gene expression in smFISH data as in (Lopez et al , 2019)).”
Self-citationThe human reticulocyte transcriptome
Goh, S., Josleyn, M. D., Lee, Y. T., Danner, R. L., Gherman, R. B., Cam, M. C., & Miller, J. L. (2007) · Physiological Genomics · doi:10.1152/physiolgenomics.00247.2006 · Cited by 93 · cited 1× · see citing works
“For instance, the HEMATO-Tusi unique erythroid cell population expresses Hba-a2 (hemoglobin subunit) and Alas2 (erythroid-specific mitochondrial 5-aminolevulinate synthase) that are known to be present in reticulocytes (Goh et al , 2007, MTA, 2018).”
Moana: A robust and scalable cell type classification framework for single-cell RNA-Seq dataPreprint
Wagner, F., & Yanai, I. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/456129 · Cited by 68 · cited 4× · see citing works
“…task is often complicated by factors such as differences in technology (e.g., using Smart-Seq2 data to annotate 10x Chromium data), partial overlap in cell type composition (i.e., not all labels should be transferred and not all unannotated cells should be assigned a label), complex organization…”
InfluentialHu, Q., & Greene, C. S. (2018) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813279827_0033 · Cited by 68 · see citing works
FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data
DeTomaso, D., & Yosef, N. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1176-5 · Cited by 58 · cited 1× · see citing works
“One concern in applying methods based on neural networks (Ding et al , 2018; Wang & Gu, 2018; Amodio et al , 2019; Eraslan et al , 2019; Grønbech et al , 2020) in single-cell genomics and other domains is the robustness to hyperparameters choices (Hu & Greene, 2019).”
Self-citationBoosting the power of single-cell analysis
Wen, L., & Tang, F. (2018) · Nature Biotechnology · doi:10.1038/nbt.4131 · Cited by 58 · cited 3× · see citing works
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InfluentialA guide to the BRAIN Initiative Cell Census Network data ecosystem
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Exploring Single-Cell Data with Deep Multitasking Neural NetworksPreprint
Amodio, M., van Dijk, D., Srinivasan, K., Chen, W. S., Mohsen, H., Moon, K. R., Campbell, A. M., Zhao, Y., Wang, X., Venkataswamy, M. M., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/237065 · Cited by 45 · see citing works
MAGAN: Aligning Biological ManifoldsPreprint
Amodio, M., & Krishnaswamy, S. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.00385 · Cited by 33 · cited 1× · see citing works
“We further discuss work related to harmonization of scRNA-seq data, as well as machine learning research in domain adaptation (from which most of these methods, including ours, built upon) in Appendix Note A.”
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Cited by
Sorted by citations
Dictionary learning for integrative, multimodal and scalable single-cell analysis
Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., Srivastava, A., Molla, G., Madad, S., Fernandez-Granda, C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01767-y · Cited by 5,631 · see citing works
“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.”
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
“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.”
BackgroundMethodBest practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
The Tabula Sapiens: A multiple-organ, single-cell transcriptomic atlas of humans
The Tabula Sapiens Consortium*, Jones, R. C., Karkanias, J., Krasnow, M. A., Pisco, A. O., Quake, S. R., Salzman, J., Yosef, N., Bulthaup, B., Brown, P., et al. (2022) · Science · doi:10.1126/science.abl4896 · Cited by 1,056 · see citing works
“S1 to S16 Tables S1 to S9 References (40-95)”
BackgroundSelf-citationAn integrated cell atlas of the lung in health and disease
Sikkema, L., Ramírez-Suástegui, C., Strobl, D. C., Gillett, T. E., Zappia, L., Madissoon, E., Markov, N. S., Zaragosi, L., Ji, Y., Ansari, M., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02327-2 · Cited by 856 · see citing works
Self-citationA Python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789 · see citing works
Self-citationLarge-scale foundation model on single-cell transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Zhang, X., & Song, L. (2024) · Nature Methods · doi:10.1038/s41592-024-02305-7 · Cited by 628 · see citing works
“We benchmarked scFoundation against the methods CellTypist 58 , scBERT 13 , scANVI 59 , ACTINN 60 , Scanpy 61 and SingleCellNet 62 (Methods).”
Multi-omics single-cell data integration and regulatory inference with graph-linked embedding
Cao, Z.-J., & Gao, G. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01284-4 · Cited by 616 · see citing works
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602 · see citing works
Self-citationSalcher, S., Sturm, G., Horvath, L., Untergasser, G., Kuempers, C., Fotakis, G., Panizzolo, E., Martowicz, A., Trebo, M., Pall, G., et al. (2022) · Cancer Cell · doi:10.1016/j.ccell.2022.10.008 · Cited by 479 · see citing works
Integrated multimodal cell atlas of Alzheimer’s disease
Gabitto, M. I., Travaglini, K. J., Rachleff, V. M., Kaplan, E. S., Long, B., Ariza, J., Ding, Y., Mahoney, J. T., Dee, N., Goldy, J., et al. (2024) · Nature Neuroscience · doi:10.1038/s41593-024-01774-5 · Cited by 397 · see citing works
“…all 84 donors, we first defined robust transcriptional types, named supertypes, in the BICCN reference; 125 supertypes represented cell types that could be reliably reidentified in reference datasets (mean F1 score = 0.91) using hierarchical probabilistic Bayesian mapping 47,48 (Extended Data Fig.”
InfluentialThe Specious Art of Single-Cell Genomics
Chari, T., & Pachter, L. (2021) · doi:10.1101/2021.08.25.457696 · Cited by 379 · see citing works
Lineage tracing reveals the phylodynamics, plasticity, and paths of tumor evolution
Yang, D., Jones, M. G., Naranjo, S., Rideout, W. M., Min, K. H., Ho, R., Wu, W., Replogle, J. M., Page, J. L., Quinn, J. J., et al. (2022) · Cell · doi:10.1016/j.cell.2022.04.015 · Cited by 333 · see citing works
“Then, we used scANVI (Xu et al. 2021) to predict labels of cells from KPL and KPA mice using 40 latent dimensions, 2 hidden layers, and a dropout rate of 0.”
Self-citationZeng, A. G. X., Bansal, S., Jin, L., Mitchell, A., Chen, W. C., Abbas, H. A., Chan‐Seng‐Yue, M., Voisin, V., van Galen, P., Tierens, A. M., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01819-x · Cited by 336 · see citing works
Profiling cell identity and tissue architecture with single-cell and spatial transcriptomics
Gulati, G. S., D'Silva, J. P., Liu, Y., Wang, L., & Newman, A. M. (2024) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-024-00768-2 · Cited by 328 · see citing works
Automatic cell-type harmonization and integration across Human Cell Atlas datasets
Xu, C., Prete, M., Webb, S., Jardine, L., Stewart, B. J., Hoo, R., He, P., Meyer, K. B., & Teichmann, S. A. (2023) · Cell · doi:10.1016/j.cell.2023.11.026 · Cited by 289 · see citing works
Self-citationDestVI identifies continuums of cell types in spatial transcriptomics data
Lopez, R., Li, B., Keren-Shaul, H., Boyeau, P., Kedmi, M., Pilzer, D., Jelinski, A., Yofe, I., David, E., Wagner, A., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01272-8 · Cited by 276 · see citing works
“Data were processed by scVI 24 and annotated based on a public dataset 31 using scANVI 32 .”
MethodSelf-citationDental cell type atlas reveals stem and differentiated cell types in mouse and human teeth
Křivánek, J., Soldatov, R., Kastriti, M. E., Chontorotzea, T., Herdina, A. N., Petersen, J., Szarowská, B., Landová, M., Matejova, V. K., Hollá, L. I., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-18512-7 · Cited by 269 · see citing works
Functional interpretation of single cell similarity maps
DeTomaso, D., Jones, M. G., Subramaniam, M., Ashuach, T., Ye, C., & Yosef, N. (2019) · Nature Communications · doi:10.1038/s41467-019-12235-0 · Cited by 251 · see citing works
Self-citationPathogenic variants damage cell composition and single cell transcription in cardiomyopathies
Reichart, D., Lindberg, E. L., Maatz, H., Miranda, A. M. A., Viveiros, A., Shvetsov, N., Gärtner, A., Nadelmann, E. R., Lee, M., Kanemaru, K., et al. (2022) · Science · doi:10.1126/science.abo1984 · Cited by 240 · see citing works
Automated methods for cell type annotation on scRNA-seq data
Pasquini, G., Rojo Arias, J. E., Schäfer, P., & Busskamp, V. (2021) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.01.015 · Cited by 249 · see citing works
“scANVI [43] Capybara U U [44] scID U U [45] scNym U U U U [46] G.”
BackgroundMethodEfficient and precise single-cell reference atlas mapping with Symphony
Kang, J. B., Nathan, A., Weinand, K., Zhang, F., Millard, N., Rumker, L., Moody, D. B., Korsunsky, I., & Raychaudhuri, S. (2021) · Nature Communications · doi:10.1038/s41467-021-25957-x · Cited by 228 · see citing works
A Review of Single-Cell RNA-Seq Annotation, Integration, and Cell–Cell Communication
Cheng, C., Chen, W., Jin, H., & Chen, X. '. (2023) · Cells · doi:10.3390/cells12151970 · Cited by 237 · see citing works
“The mapping method gimVI [52] uses either NB distribution or zero-inflated NB (ZINB) distribution depending on the data sets; scANVI [53] uses the larger model ZINB to accommodate data sets from different platforms.”
MethodAn integrated single cell and spatial transcriptomic map of human white adipose tissue
Massier, L., Jalkanen, J., Elmastas, M., Zhong, J., Wang, T., Nono Nankam, P. A., Frendo-Cumbo, S., Bäckdahl, J., Subramanian, N., Sekine, T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-36983-2 · Cited by 195 · see citing works
Wu, Y., & Xie, L. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2024.12.030 · Cited by 205 · see citing works
Liu, Z., Kong, X., Long, Y., Liu, S., Zhang, H., Jia, J., Cui, W., Zhang, Z., Song, X., Qiu, L., et al. (2023) · Nature Plants · doi:10.1038/s41477-023-01387-z · Cited by 171 · see citing works
An integrated transcriptomic cell atlas of human neural organoids
He, Z., Dony, L., Fleck, J. S., Szałata, A., Li, K. X., Slišković, I., Lin, H., Santel, M., Atamian, A., Quadrato, G., et al. (2024) · Nature · doi:10.1038/s41586-024-08172-8 · Cited by 169 · see citing works
“We applied scANVI 41 to the primary reference atlas, and used 42 to project the HNOCA to the same latent space. latent space allowed us to reconstruct a bipartite weighted k-nearest neighbor (wKNN) graph between cells in the HNOCA and the primary reference atlas.”
Searching large-scale scRNA-seq databases via unbiased cell embedding with Cell BLAST
Cao, Z., Lin, W., Shen, L., Yang, D.-C., & Gao, G. (2020) · Nature Communications · doi:10.1038/s41467-020-17281-7 · Cited by 148 · see citing works
Hu, J., Li, X., Hu, G., Lyu, Y., Suszták, K., & Li, M. (2020) · Nature Machine Intelligence · doi:10.1038/s42256-020-00233-7 · Cited by 145 · see citing works
“We selected scVI 8 as the semi-supervised method due to its popularity.”
Dib, L. H., Koneva, L. A., Edsfeldt, A., Zurke, Y.-X., Sun, J., Nitulescu, M., Attar, M., Lutgens, E., Schmidt, S., Lindholm, M., et al. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-023-00295-x · Cited by 150 · see citing works
A cell atlas foundation model for scalable search of similar human cells
Heimberg, G., Kuo, T. C.-Y., DePianto, D. J., Salem, O., Heigl, T., Diamant, N., Scalia, G., Biancalani, T., Turley, S. J., Rock, J. R., et al. (2024) · Nature · doi:10.1038/s41586-024-08411-y · Cited by 179 · see citing works
Krausgruber, T., Redl, A., Barreca, D., Doberer, K., Romanovskaia, D., Dobnikar, L., Guarini, M., Unterluggauer, L., Kleißl, L., Atzmüller, D., et al. (2023) · Immunity · doi:10.1016/j.immuni.2023.01.014 · Cited by 126 · see citing works
Population-level integration of single-cell datasets enables multi-scale analysis across samples
Donno, C. D., Hediyeh-zadeh, S., Moinfar, A. A., Wagenstetter, M., Zappia, L., Lotfollahi, M., & Theis, F. J. (2023) · Nature Methods · doi:10.1038/s41592-023-02035-2 · Cited by 125 · see citing works
“Among current reference building 60 methods [10, 12, 24, 26] only scANVI and Seurat v3 offer built-in cell-type classification coupled 61 with a reference mapping algorithm [19, 21].”
BackgroundMethodInfluentialAnnotation of spatially resolved single-cell data with STELLAR
Brbić, M., Cao, K., Hickey, J. W., Tan, Y., Snyder, M. P., Nolan, G. P., & Leskovec, J. (2022) · Nature Methods · doi:10.1038/s41592-022-01651-8 · Cited by 115 · see citing works
“Single-cell baselines Seurat V4 and scNym achieved lower performance than other baselines so we additionally compared to another single-cell baseline scANVI [32].”
ResultGottschlich, A., Thomas, M., Grünmeier, R., Lesch, S., Rohrbacher, L., Igl, V., Briukhovetska, D., Benmebarek, M.-R., Vick, B., Dede, S., et al. (2023) · Nature Biotechnology · doi:10.1038/s41587-023-01684-0 · Cited by 121 · see citing works
“To ensure CAR efficacy, a suitable candidate is target identification approach, we used scANVI, a semisupervised variational autoencoder 40 , to map the data from Petti et al. 39 onto a newly generated reference map of van Galen et al. 21 (Extended Data Fig.”
MethodSingle-cell integration reveals metaplasia in inflammatory gut diseases
Oliver, A. J., Huang, N., Bartolomé-Casado, R., Li, R., Koplev, S., Nilsen, H. R., Moy, M. R., Çakır, B., Polański, K., Gudiño, V., et al. (2024) · Nature · doi:10.1038/s41586-024-07571-1 · Cited by 117 · see citing works
Semisupervised adversarial neural networks for single-cell classificationPreprint
Kimmel, J. C., & Kelley, D. R. (2021) · Genome Research · doi:10.1101/gr.268581.120 · Cited by 112 · see citing works
“…a linear SVM (Abdelaal et al. 2019), scPred (Alquicira-Hernandez et al. 2019), SingleCellNet (Tan and Cahan 2021), CHETAH (de Kanter et al. 2019), Harmony followed by an SVM (Korsunsky et al. 2019), LIGER followed by an SVM (Stuart et al. 2019), and scANVI (Lopez et al. 2018; Xu et al. 2021).”
Large Scale Foundation Model on Single-cell Transcriptomics
Hao, M., Gong, J., Zeng, X., Liu, C., Guo, Y., Cheng, X., Wang, T., Ma, J., Song, L., & Zhang, X. (2023) · doi:10.1101/2023.05.29.542705 · Cited by 639 · see citing works
A brain cell atlas integrating single-cell transcriptomes across human brain regions
Chen, X., Huang, Y., Huang, L., Huang, Z., Hao, Z.-Z., Xu, L., Xu, N., Li, Z., Mou, Y., Ye, M., et al. (2024) · Nature Medicine · doi:10.1038/s41591-024-03150-z · Cited by 117 · see citing works
Miyoshi, E., Morabito, S., Henningfield, C. M., Das, S., Rahimzadeh, N., Shabestari, S. K., Michael, N., Emerson, N., Reese, F., Shi, Z., et al. (2024) · Nature Genetics · doi:10.1038/s41588-024-01961-x · Cited by 115 · see citing works
“25–29; Methods).”
InfluentialExplainable multi-task learning for multi-modality biological data analysis
Tang, X., Zhang, J., He, Y., Zhang, X., Lin, Z., Partarrieu, S., Hanna, E. B., Ren, Z., Shen, H., Yang, Y., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37477-x · Cited by 113 · see citing works
“We compared UnitedNet with a state-of-the-art annotation transfer method 42 and an ablated version of UnitedNet that only involved one task.”
Benchmarking strategies for cross-species integration of single-cell RNA sequencing data
Song, Y., Miao, Z., Brāzma, A., & Papatheodorou, I. (2023) · Nature Communications · doi:10.1038/s41467-023-41855-w · Cited by 106 · see citing works
Concordance of MERFISH spatial transcriptomics with bulk and single-cell RNA sequencing
Liu, J., Tran, V., Vemuri, V. N. P., Byrne, A., Borja, M., Kim, Y. J., Agarwal, S., Wang, R., Awayan, K., Murti, A., et al. (2022) · Life Science Alliance · doi:10.26508/lsa.202201701 · Cited by 105 · see citing works
“…our MERFISH measurements with Tabula Muris Senis using a few computational methods (Lopez et al, 2018; Korsunsky et al, 2019; Kang et al, 2021; Xu et al, 2021) to automatically predict cell type annotations and investigate what additional insights were offered via computational integration.”
Spatial transcriptomic characterization of pathologic niches in IPF
Mayr, C. H., Santacruz, D., Jarosch, S., Bleck, M., Dalton, J., McNabola, A., Lempp, C., Neubert, L., Rath, B., Kamp, J. C., et al. (2024) · Science Advances · doi:10.1126/sciadv.adl5473 · Cited by 119 · see citing works
“Using the highly variable genes as input, we used scvi-tools ( 57 , 66 ) to model the latent variables that were then used for Uniform Manifold Ap-proximation and Projection and Leiden clustering, using 30 neighbors, which then was both transferred back to the full-gene dataset.”
Ding, J., & Regev, A. (2021) · Nature Communications · doi:10.1038/s41467-021-22851-4 · Cited by 103 · see citing works
“When cell type annotations or cell type marker genes for some of the analyzed cells are available, we can include semi-supervised learning to annotate cell types (Xu et al., 2019; Zhang et al., 2019).”
BackgroundIntegrated analysis of multimodal single-cell data with structural similarity
Cao, Y., Fu, L., Wu, J., Peng, Q., Nie, Q. H., Zhang, J., & Xie, X. S. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac781 · Cited by 106 · see citing works
“Other compared reference embeddings are generated with scVI (35), scANVI (13) and Scanaroma (11) using scIB package (36).”
Lange, M., Granados, A. A., VijayKumar, S., Bragantini, J., Ancheta, S., Kim, Y. J., Santhosh, S., Borja, M., Kobayashi, H., McGeever, A., et al. (2024) · Cell · doi:10.1016/j.cell.2024.09.047 · Cited by 154 · see citing works
Harmonized single-cell landscape, intercellular crosstalk and tumor architecture of glioblastomaPreprint
Ruiz-Moreno, C., Salas, S. M., Samuelsson, E. R., Brandner, S., Kranendonk, M. E. G., Nilsson, M. F., & Stunnenberg, H. G. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.08.27.505439 · Cited by 100 · see citing works
“To overcome the technical challenge of data integration, we employed a semi-supervised neural network model (scANVI) (Xu et al., 2021) implemented in the transfer-learning framework of the scArches algorithm (Lotfollahi et al., 2021), which takes advantage of the uniform prior cell type labeling to…”
MethodNetskar, H., Pfefferle, A., Goodridge, J. P., Sohlberg, E., Dufva, O., Teichmann, S. A., Brownlie, D., Michaëlsson, J., Marquardt, N., Clancy, T., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01884-z · Cited by 112 · see citing works
“https://doi.org/10.1038/s41590-024-01884-z our transcriptomes with five publicly available donor datasets 2,18 using scVI (single-cell Variational Inference) 19 (Supplementary Table 1).”
InfluentialBiologically informed deep learning to query gene programs in single-cell atlases
Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2023) · Nature Cell Biology · doi:10.1038/s41556-022-01072-x · Cited by 100 · see citing works
Application of Deep Learning on Single-Cell RNA Sequencing Data Analysis: A Review
Brendel, M., Su, C., Bai, Z., Zhang, H., Elemento, O., & Wang, F. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.11.011 · Cited by 102 · see citing works
“Single-cell annotation using variational inference (scANVI) [131] is an extension of scVI [55] by incorporating semi-supervised learning to address cell type annotation with partial label information. scSemiCluster [132] learned cell labels using the combination of unlabeled data and labeled data…”
MethodEnhancing scientific discoveries in molecular biology with deep generative models
Lopez, R., Gayoso, A., & Yosef, N. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199198 · Cited by 82 · see citing works
“In a certain number of real-world instances, using a linear model might result in an important loss of performance and the input data might not have spatial structure such as molecular information.”
BackgroundMethodInfluentialSelf-citationLiu, Z., Yang, J., Long, Y., Zhang, C., Wang, D., Zhang, X., Dong, W., Zhao, L., Liu, C., Zhai, J., et al. (2023) · Nature Plants · doi:10.1038/s41477-023-01524-8 · Cited by 83 · see citing works
A suite of enhancer AAVs and transgenic mouse lines for genetic access to cortical cell types
Ben‐Simon, Y., Hooper, M. J., Narayan, S., Daigle, T. L., Dwivedi, D., Way, S. W., Oster, A., Stafford, D. A., Mich, J. K., Taormina, M. J., et al. (2025) · Cell · doi:10.1016/j.cell.2025.05.002 · Cited by 81 · see citing works
“39,40 We compared the human taxonomy and a previously published mouse cortical taxonomy 9 to generate a common list of cell types that occur in both species.”
Ge, S., Sun, S., Xu, H., Cheng, Q. S., & Ren, Z. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf136 · Cited by 89 · see citing works
“[136], indicating that scANVI [151], Scanorama [94], scVI [105], and scGen [152] perform well on complex integration tasks.”
Batch alignment of single-cell transcriptomics data using deep metric learning
Yu, X., Xu, X., Zhang, J., & Li, X. (2023) · Nature Communications · doi:10.1038/s41467-023-36635-5 · Cited by 81 · see citing works
“As for lung dataset and macaque retina dataset, Algorithm 3 suggested the numbers of clusters to be [5,10,13,16,18] and [8,12,18], respectively.”
BackgroundPrecise identification of cell states altered in disease using healthy single-cell references
Dann, E., Cujba, A.-M., Oliver, A. J., Meyer, K. B., Teichmann, S. A., & Marioni, J. C. (2023) · Nature Genetics · doi:10.1038/s41588-023-01523-7 · Cited by 82 · see citing works
“For latent embedding with the CR design, we trained a scANVI model 77 on the concatenated control and disease replicating the parameters used to train the HLCA model (according to the notebooks in https://github.com/ LungCellAtlas/HLCA_reproducibility), using dataset ID as the batch covariate and…”
MethodThe future of rapid and automated single-cell data analysis using reference mapping
Lotfollahi, M., Hao, Y., Theis, F. J., & Satija, R. (2024) · Cell · doi:10.1016/j.cell.2024.03.009 · Cited by 77 · see citing works
Guimarães, G. R., Maklouf, G. R., Teixeira, C. E., Santos, L. D. O., Tessarollo, N. G., de Toledo, N. E., Serain, A. F., de Lanna, C. A., Pretti, M. A. M., da Cruz, J. G. V., et al. (2024) · Nature Communications · doi:10.1038/s41467-024-49916-4 · Cited by 85 · see citing works
Liu, X., Wang, X. M., Yang, Q., Luo, L., Liu, Z., Ren, X., Lei, K., Li, S., Xie, Z., Zheng, G., et al. (2024) · Cancer Research · doi:10.1158/0008-5472.can-23-2123 · Cited by 77 · see citing works
Steele, L., Olabi, B., Roberts, K., Mazin, P., Koplev, S., Tudor, C. L., Rumney, B. M., Admane, C., Jiang, T., Correa‐Gallegos, D., et al. (2025) · Nature Immunology · doi:10.1038/s41590-025-02267-8 · Cited by 88 · see citing works
Deep learning tackles single-cell analysis—a survey of deep learning for scRNA-seq analysis
Flores, M. A., Liu, Z., Zhang, T., Hasib, M. M., Chiu, Y., Ye, Z., Paniagua, K., Jo, S., Zhang, J., Gao, S., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab531 · Cited by 72 · see citing works
CellPLM: Pre-training of Cell Language Model Beyond Single CellsPreprint
Wen, H., Tang, W., Dai, X., Ding, J., Jin, W., Xie, Y., & Tang, J. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.10.03.560734 · Cited by 85 · see citing works
“Prior studies have employed variational autoencoders for single-cell analysis, which typically assumes an isotropic Gaussian distribution as the prior distribution of the latent space (Lopez et al., 2018; Xu et al., 2021).”
BackgroundSanborn, M. A., Wang, X., Gao, S., Dai, Y., & Rehman, J. (2025) · Nature Communications · doi:10.1038/s41467-025-57047-7 · Cited by 82 · see citing works
“Since the Human Cell Landscape contained many tissues and cell types, we transferred the annotations from Tabula Sapiens using scANVI 44 after processing the raw data with scVI-tools 45 .”
A diverse fibroblastic stromal cell landscape in the spleen directs tissue homeostasis and immunity
Alexandre, Y. O., Schienstock, D., Lee, H. J., Gandolfo, L. C., Williams, C. G., Devi, S., Pal, B., Groom, J. R., Cao, W. H. J., Christo, S. N., et al. (2022) · Science Immunology · doi:10.1126/sciimmunol.abj0641 · Cited by 75 · see citing works
“We corrected for batch effects and integrated all datasets together using single-cell annotation using variational inference (scANVI) (46).”
MethodInterindividual variation in human cortical cell type abundance and expression
Johansen, N., Somasundaram, S., Travaglini, K. J., Yanny, A. M., Shumyatcher, M., Casper, T., Cobbs, C., Dee, N., Ellenbogen, R. G., Ferreira, M. L., et al. (2023) · Science · doi:10.1126/science.adf2359 · Cited by 73 · see citing works
“7 on held-out reference data (29) (Methods).”
Semi-supervised integration of single-cell transcriptomics data
Andreatta, M., Hérault, L., Gueguen, P., Gfeller, D., Berenstein, A. J., & Carmona, S. (2023) · bioRxiv · doi:10.1038/s41467-024-45240-z · Cited by 65 · see citing works
Charting the single-cell and spatial landscape of IDH-wild-type glioblastoma with GBmap
Ruiz-Moreno, C., Salas, S. M., Samuelsson, E. R., Minaeva, M., Ibarra, I. L., Grillo, M., Brandner, S., Roy, A., Forsberg‐Nilsson, K., Kranendonk, M. E. G., et al. (2025) · Neuro-Oncology · doi:10.1093/neuonc/noaf113 · Cited by 74 · see citing works
“Following benchmarking of several integration tools, we selected scANVI, 19 which showed the best balance in removing batch effects while conserving biological variation.”
Hautz, T., Salcher, S., Fodor, M., Sturm, G., Ebner, S., Mair, A., Trebo, M., Untergasser, G., Sopper, S., Cardini, B., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-37674-8 · Cited by 71 · see citing works
“Cell transcriptomes were embedded into a batch-corrected low-dimensional latent space using scVI 88,89 , treating each sample as a separate batch.”
InfluentialHe, S., Jin, Y., Nazaret, A., Shi, L., Chen, X., Rampersaud, S., Dhillon, B. S., Valdez, I. K., Friend, L. E., Fan, J. L., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-024-02173-8 · Cited by 76 · see citing works
“…implementations of deep generative models in single-cell omics analysis (scvi-tools 19 , scVI 20 , totalVI 21 , scArches 22 , trVAE 23 , scANVI 24 , MrVI 25 ), Starfysh jointly models ST and histology as data observed from a shared low-dimensional latent mixing of signals from…”
Integration of Single-Cell RNA-Seq Datasets: A Review of Computational Methods
Ryu, Y., Han, G. H., Jung, E., & Hwang, D. (2023) · Molecules and Cells · doi:10.14348/molcells.2023.0009 · Cited by 68 · see citing works
Bailey, J. I., Puritz, C. H., Senkow, K. J., Markov, N. S., Diaz, E., Jonasson, E., Yu, Z., Swaminathan, S., Lu, Z., Fenske, S. W., et al. (2024) · Nature Immunology · doi:10.1038/s41590-024-01975-x · Cited by 70 · see citing works
Advances and applications in single-cell and spatial genomics
Wang, J., Ye, F., Chai, H., Jiang, Y., Wang, T., Ran, X., Xia, Q., Xu, Z., Fu, Y., Zhang, G., et al. (2024) · Science China Life Sciences · doi:10.1007/s11427-024-2770-x · Cited by 69 · see citing works
Cross-tissue multicellular coordination and its rewiring in cancer
Shi, Q., Chen, Y., Li, Y., Qin, S., Yang, Y., Gao, Y., Zhu, L., Wang, D., & Zhang, Z. (2025) · Nature · doi:10.1038/s41586-025-09053-4 · Cited by 72 · see citing works
Integrated single-cell atlas of human atherosclerotic plaques
Traeuble, K., Munz, M., Pauli, J., Sachs, N., Vafadarnejad, E., Carrillo‐Roa, T., Mäegdefessel, L., Kastner, P., & Heinig, M. (2025) · Nature Communications · doi:10.1038/s41467-025-63202-x · Cited by 72 · see citing works
“To select the best method for this task, we performed an extensive benchmark with scib-metrics 31 of commonly used integration methods: scVI 32 , Harmony 33 , LIGER 34 , scANVI 35 , scGen 36 , scPoli (with negative binomial and mean squared error loss) 37 and baseline PCA .”
Considerations for building and using integrated single-cell atlases
Hrovatin, K., Sikkema, L., Shitov, V. A., Heimberg, G., Shulman, M., Oliver, A. J., Mueller, M. F., Ibarra, I. L., Wang, H., Ramírez-Suástegui, C., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02532-y · Cited by 67 · see citing works
“Similarly, the availability of cell-level metadata, mainly cell-type labels, can aid in the atlas building process in several ways, including during quality control (for example, labels of doublets), integration (for methods using cell-type labels 17,43,45,46 ) and atlas evaluation and use.”
Mapping the topography of spatial gene expression with interpretable deep learning
Chitra, U., Arnold, B. J., Sarkar, H., Sanno, K., Ma, C., Lopez-Darwin, S. L., & Raphael, B. J. (2025) · Nature Methods · doi:10.1038/s41592-024-02503-3 · Cited by 63 · see citing works
“6d) by using scANVI 85 to integrate the SRT data with a separate mouse olfactory bulb scRNA-seq dataset 60 ; for the scRNA-seq data, we followed the preprocessing steps in ref.”
Evaluating the Utilities of Foundation Models in Single-cell Data Analysis
Liu, T., Li, K., Wang, Y., Li, H., & Zhao, H. (2023) · doi:10.1101/2023.09.08.555192 · Cited by 62 · see citing works
Brown, D. A., Altermatt, M., Dobreva, T., Chen, S., Wang, A. Z., Thomson, M. W., & Gradinaru, V. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.730825 · Cited by 58 · see citing works
“subtype annotations are predicted by a model learned from the Allen Institute reference dataset using scANVI (94, 95).”
MethodEvaluating the Utilities of Foundation Models in Single‐Cell Data Analysis
Liu, T., Li, K., Wang, Y., Li, H., & Zhao, H. (2026) · Advanced Science · doi:10.1002/advs.202514490 · Cited by 76 · see citing works
scTab: Scaling cross-tissue single-cell annotation models
Fischer, F., Fischer, D. S., Mukhin, R. S., Isaev, A. V., Biederstedt, E., Villani, A., & Theis, F. J. (2024) · Nature Communications · doi:10.1038/s41467-024-51059-5 · Cited by 67 · see citing works
“…largely treat the cell type labels as mutually exclusive and ignore label relations that were previously exploited in organ-centric classi fi cation tasks 14,24 , thus questioning if the resulting benchmarking metrics are faithful evaluations of the performance on these heterogeneous datasets.”
Yang, L.-X., Qi, C., Lu, S., Ye, X.-S., Merikhian, P., Zhang, D.-Y., Yao, T., Zhao, J.-S., Wu, Y., Jia, Y., et al. (2025) · Nature Communications · doi:10.1038/s41467-024-55738-1 · Cited by 57 · see citing works
Transcriptomes and metabolism define mouse and human MAIT cell populations
Chandra, S., Ascui, G., Riffelmacher, T., Chawla, A., Ramírez-Suástegui, C., Castelan, V. O. C., Seumois, G., Simon, H. A., Murray, M. P., Seo, G., et al. (2023) · Science Immunology · doi:10.1126/sciimmunol.abn8531 · Cited by 56 · see citing works
Dopp, J., Ortega, A., Davie, K., Poovathingal, S. K., Baz, E., & Liu, S. (2024) · Nature Neuroscience · doi:10.1038/s41593-023-01549-4 · Cited by 55 · see citing works
“3); it was performed as described previously 48 , with the top ten marker genes (determined according to z -score) per cluster from resolution 0.8 being used as the gene subset.”
InfluentialMethods and Insights from Single-Cell Expression Quantitative Trait Loci
Kang, J. B., Raveane, A., Nathan, A., Soranzo, N., & Raychaudhuri, S. (2023) · Annual Review of Genomics and Human Genetics · doi:10.1146/annurev-genom-101422-100437 · Cited by 53 · see citing works
“…clustering and annotation can be subjective, an alternative is to perform label transfer from an external well-annotated reference dataset, using automated cell-type classification (3, 6, 26, 78, 141, 159, 173) or reference mapping (71, 90, 138), which offers speed and reproducibility advantages.”
MethodMaden, S. K., Kwon, S. H., Huuki-Myers, L. A., Collado‐Torres, L., Hicks, S. C., & Maynard, K. R. (2023) · Genome biology · doi:10.1186/s13059-023-03123-4 · Cited by 58 · see citing works
“An individual sc/snRNA-seq experiment can use either manual [42, 56], computational [155–157, 164], or combined [ 165 ]”
MethodCellcano: supervised cell type identification for single cell ATAC-seq data
Ma, W., Lu, J., & Wu, H. (2023) · Nature Communications · doi:10.1038/s41467-023-37439-3 · Cited by 51 · see citing works
Worley, M. I., Everetts, N. J., Yasutomi, R., Chang, R. J., Saretha, S., Yosef, N., & Hariharan, I. K. (2022) · Current Biology · doi:10.1016/j.cub.2022.06.040 · Cited by 48 · see citing works
Self-citationBoyeau, P., Regier, J., Gayoso, A., Jordan, M. I., Lopez, R., & Yosef, N. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2209124120 · Cited by 56 · see citing works
Self-citationChen, L., Zhai, Y., He, Q., Wang, W., & Deng, M. (2020) · Genes · doi:10.3390/genes11070792 · Cited by 46 · see citing works
Consensus prediction of cell type labels in single-cell data with popV
Ergen, C., Xing, G. K., Xu, C. A., Kim, M., Jayasuriya, M., McGeever, A., Pisco, A. O., Streets, A., & Yosef, N. (2024) · Nature Genetics · doi:10.1038/s41588-024-01993-3 · Cited by 47 · see citing works
Self-citationChu, T., Zhu, G., Tang, Z., Qu, W., Yang, R., Pan, H., Wáng, Y., Tian, R., Chen, L., Guan, Z., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-62132-y · Cited by 54 · see citing works
“To enable integrated analysis of single-cell multi-omics data, we utilized scANVI 21 for batch correction, which has been shown to perform wellonintegratingstrongbatchwhilepreservingunderlyingbiological information 22 (Illustrated in Supplementary Fig.”
Human hippocampal neurogenesis in adulthood, ageing and Alzheimer’s disease
Disouky, A., Sanborn, M. A., Sabitha, K. R., MOSTAFA, M., Ayala, I., Bennett, D. A. A., Lu, Y., Zhou, Y. F., Keene, C. D., Weıntraub, S., et al. (2026) · Nature · doi:10.1038/s41586-026-10169-4 · Cited by 53 · see citing works
“To identify primary cell types, we used a transfer-learning approach based on scVI 52 and scANVI 28 , in which reference dataset annotations are transferred to annotate subpopulations in a query dataset.”
InfluentialAdversarial domain translation networks for integrating large-scale atlas-level single-cell datasets
Zhao, J., Wang, G., Ming, J., Lin, Z., Wang, Y., Agarwal, S., Agrawal, A., Al‐Moujahed, A., Alam, A. A., Albertelli, M. A., et al. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00251-y · Cited by 42 · see citing works
Scanorama: integrating large and diverse single-cell transcriptomic datasets
Hie, B., Kim, S., Rando, T. A., Bryson, B. D., & Berger, B. (2024) · Nature Protocols · doi:10.1038/s41596-024-00991-3 · Cited by 46 · see citing works
“…several methods such as batch-balanced k-nearest neighbors (BBKNN) 8 and deep learning models such as single-cell variational inference (scVI) 9 and single-cell annotation using variational inference (scANVI) 10 , Scanorama has emerged as one of the leading tools for scRNA-seq data integration.”
An integrated transcriptomic cell atlas of human endoderm-derived organoids
Xu, Q., Halle, L., Hediyeh-zadeh, S., Kuijs, M., Riedweg, R., Kilik, U., Recaldin, T., Yu, Q., Rall, I., Frum, T., et al. (2025) · Nature Genetics · doi:10.1038/s41588-025-02182-6 · Cited by 43 · see citing works
“We applied the atlas as a diverse cohort to assess organoid protocols, perturbations and disease models.”
Spatiotemporal transcriptomic maps of mouse intracerebral hemorrhage at single-cell resolution
Xiang, R., Wang, J., Chen, Z., Tao, J. Z., Peng, Q., Ding, R., Zhou, T., Tu, Z., Yang, T., Yang, T., et al. (2025) · Neuron · doi:10.1016/j.neuron.2025.04.026 · Cited by 48 · see citing works
Building the mega single-cell transcriptome ocular meta-atlas
Swamy, V. S., Fufa, T., Hufnagel, R. B., & McGaughey, D. M. (2021) · GigaScience · doi:10.1093/gigascience/giab061 · Cited by 44 · see citing works
Inferring single-cell transcriptomic dynamics with structured latent gene expression dynamics
Farrell, S., Mani, M., & Goyal, S. (2023) · Cell Reports Methods · doi:10.1016/j.crmeth.2023.100581 · Cited by 47 · see citing works
CellFlow enables generative single-cell phenotype modeling with flow matchingPreprint
Klein, D., Fleck, J. S., Bobrovskiy, D. M., Zimmermann, L., Becker, S., Palma, A., Dony, L., Tejada-Lapuerta, A., Huguet, G., Lin, H., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.04.11.648220 · Cited by 52 · see citing works
- 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
- Venue
- Molecular Systems Biology
- Published
- 2021-01-01
- Volume
- 17
- Issue
- 1
- Pages
- e9620-e9620
- ISSN
- 1744-4292 (ISSN-L)
- 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 · 2020: 6 · 2019: 1
- FWCI
- 23.55
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- 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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