Joint inference of discrete cell types and continuous type-specific variability in single-cell datasets with MMIDAS

Fishell, G. & Heintz, N. The neuron identity problem: form meets function. Neuron 80, 602–612 (2013).Article 

Google Scholar 
Seung, H. S. & Sümbül, U. Neuronal cell types and connectivity: lessons from the retina. Neuron 83, 1262–1272 (2014).Article 

Google Scholar 
Zeng, H. What is a cell type and how to define it? Cell 185, 2739–2755 (2022).Article 

Google Scholar 
Fulcher, B. D., Murray, J. D., Zerbi, V. & Wang, X.-J. Multimodal gradients across mouse cortex. Proc. Natl Acad. Sci. USA 116, 4689–4695 (2019).Article 

Google Scholar 
Stanley, G., Gokce, O., Malenka, R. C., Südhof, T. C. & Quake, S. R. Continuous and discrete neuron types of the adult murine striatum. Neuron 105, 688–699 (2020).Article 

Google Scholar 
Harris, K. D. et al. Classes and continua of hippocampal ca1 inhibitory neurons revealed by single-cell transcriptomics. PLoS Biol 16, e2006387 (2018).Article 

Google Scholar 
DeFelipe, J. et al. New insights into the classification and nomenclature of cortical gabaergic interneurons. Nat. Rev. Neurosci. 14, 202–216 (2013).Article 

Google Scholar 
Mukamel, E. A. & Ngai, J. Perspectives on defining cell types in the brain. Curr. Opin. Neurobiol. 56, 61–68 (2019).Article 

Google Scholar 
Yao, Z. et al. A taxonomy of transcriptomic cell types across the isocortex and hippocampal formation. Cell 184, 3222–3241 (2021).Article 

Google Scholar 
Bugeon, S. et al. A transcriptomic axis predicts state modulation of cortical interneurons. Nature 607, 330–338 (2022).Article 

Google Scholar 
Kingma, D. P. et al. An introduction to variational autoencoders. Found. Trends Mach. Learn. 12, 307–392 (2019).Article 

Google Scholar 
Cao, J. et al. The single-cell transcriptional landscape of mammalian organogenesis. Nature 566, 496–502 (2019).Article 

Google Scholar 
Mohammadi, S., Davila-Velderrain, J. & Kellis, M. A multiresolution framework to characterize single-cell state landscapes. Nat.Commun. 11, 5399 (2020).Article 

Google Scholar 
Hao, Y. et al. Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587 (2021).Article 

Google Scholar 
Pierson, E. & Yau, C. Zifa: dimensionality reduction for zero-inflated single-cell gene expression analysis. Genome Biol. 16, 1–10 (2015).Article 

Google Scholar 
Ding, J., Condon, A. & Shah, S. P. Interpretable dimensionality reduction of single cell transcriptome data with deep generative models. Nat. Commun. 9, 2002 (2018).Article 

Google Scholar 
Lopez, R., Regier, J., Cole, M. B., Jordan, M. I. & Yosef, N. Deep generative modeling for single-cell transcriptomics. Nat. Methods 15, 1053–1058 (2018).Article 

Google Scholar 
Ding, J. & Regev, A. Deep generative model embedding of single-cell rna-seq profiles on hyperspheres and hyperbolic spaces. Nat. Commun. 12, 2554 (2021).Article 

Google Scholar 
Tran, D. et al. Fast and precise single-cell data analysis using a hierarchical autoencoder. Nat. Commun. 12, 1029 (2021).Article 

Google Scholar 
Gayoso, A. et al. Joint probabilistic modeling of single-cell multi-omic data with totalvi. Nat. Methods 18, 272–282 (2021).Article 

Google Scholar 
Lopez, R. et al. Destvi identifies continuums of cell types in spatial transcriptomics data. Nat. Biotechnol. 40, 1360–1369 (2022).Article 

Google Scholar 
Gala, R. et al. Consistent cross-modal identification of cortical neurons with coupled autoencoders. Nat. Comput. Sci. 1, 120–127 (2021).Article 

Google Scholar 
Xu, C. et al. Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models. Mol. Syst. Biol. 17, e9620 (2021).Article 

Google Scholar 
Tasic, B. et al. Shared and distinct transcriptomic cell types across neocortical areas. Nature 563, 72–78 (2018).Article 

Google Scholar 
Gouwens, N. W. et al. Integrated morphoelectric and transcriptomic classification of cortical gabaergic cells. Cell 183, 935–953 (2020).Article 

Google Scholar 
Sorensen, S. A. et al. Connecting single-cell transcriptomes to the projectome in mouse visual cortex. Preprint at bioRxiv https://doi.org/10.1101/2023.11.25.568393 (2023).Gabitto, M. et al. Integrated multimodal cell atlas of Alzheimer’s disease. Preprint at bioRxiv https://doi.org/10.1101/2023.05.08.539485 (2023).Dupont, E. Learning disentangled joint continuous and discrete representations. In Proc. Advanced Neural Information Processing Systems 710–720 (Neural Information Processing Systems Foundation, 2018).Jeong, Y. & Song, H. O. Learning discrete and continuous factors of data via alternating disentanglement. In International Conference on Machine Learning 3091–3099 (PMLR, 2019).Jang, E., Gu, S. & Poole, B. Categorical reparameterization with Gumbel-Softmax. In 5th International Conference on Learning Representations (2017).Aitchison, J. The statistical analysis of compositional data. J. R. Stat Soc. B 44, 139–160 (1982).Article 
MathSciNet 

Google Scholar 
Smith, S. J. et al. Single-cell transcriptomic evidence for dense intracortical neuropeptide networks. eLife 8, e47889 (2019).Article 

Google Scholar 
Joshi, C. J., Ke, W., Drangowska-Way, A., O’Rourke, E. J. & Lewis, N. E. What are housekeeping genes? PLoS Comput. Biol. 18, e1010295 (2022).Article 

Google Scholar 
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: new perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361 (2017).Article 

Google Scholar 
Wang, X., He, Y., Zhang, Q., Ren, X. & Zhang, Z. Direct comparative analyses of 10× genomics chromium and smart-seq2. Genom. Proteom. Bioinf. 19, 253–266 (2021).Article 

Google Scholar 
Cadwell, C. R. et al. Electrophysiological, transcriptomic and morphologic profiling of single neurons using patch-seq. Nat. Biotechnol. 34, 199–203 (2016).Article 

Google Scholar 
Gala, R. et al. In Advances in Neural Information Processing Systems 9263–9272 (2019).Kobak, D. et al. Sparse reduced-rank regression for exploratory visualisation of paired multivariate data. J. R. Stat. Soc. C 70, 980–1000 (2021).Article 
MathSciNet 

Google Scholar 
Scala, F. et al. Phenotypic variation of transcriptomic cell types in mouse motor cortex. Nature 598, 144–150 (2021).Article 

Google Scholar 
Cadwell, C. R. et al. Multimodal profiling of single-cell morphology, electrophysiology, and gene expression using patch-seq. Nat. Protoc. 12, 2531–2553 (2017).Article 

Google Scholar 
Inestrosa, N. C. & Arenas, E. Emerging roles of Wnts in the adult nervous system. Nat. Rev. Neurosci. 11, 77–86 (2010).Article 

Google Scholar 
Thomas, G. M. & Huganir, R. L. MAPK cascade signalling and synaptic plasticity. Nat. Rev. Neurosci. 5, 173–183 (2004).Article 

Google Scholar 
Mathys, H. et al. Single-cell atlas reveals correlates of high cognitive function, dementia, and resilience to Alzheimer’s disease pathology. Cell 186, 4365–4385.e27 (2023).Article 

Google Scholar 
Jorstad, N. L. et al. Transcriptomic cytoarchitecture reveals principles of human neocortex organization. Science 382, 6667 (2023).Article 

Google Scholar 
Yao, Z. et al. A high-resolution transcriptomic and spatial atlas of cell types in the whole mouse brain. Nature 624, 317–332 (2023).Article 

Google Scholar 
Kingma, D. P. & Welling, M. Auto-encoding variational Bayes. Preprint at https://arxiv.org/abs/1312.6114 (2013).Blei, D. M., Kucukelbir, A. & McAuliffe, J. D. Variational inference: a review for statisticians. J. Am. Stat. Assoc. 112, 859–877 (2017).Article 
MathSciNet 

Google Scholar 
Minka, T. et al. Divergence Measures and Message Passing (Citeseer, 2005).Bouchacourt, D., Tomioka, R. & Nowozin, S. Multi-level variational autoencoder: learning disentangled representations from grouped observations. In Proc. AAAI Conference on Artificial Intelligence Vol. 32 (2018).Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S. & Theis, F. J. Single-cell rna-seq denoising using a deep count autoencoder. Nat. Commun. 10, 390 (2019).Article 

Google Scholar 
Hauberg, S., Freifeld, O., Larsen, A. B. L., Fisher, J. & Hansen, L. In Proc. Artificial Intelligence and Statistics 342–350 (PMLR, 2016).Jaiswal, A., Wu, R. Y., Abd-Almageed, W. & Natarajan, P. Unsupervised adversarial invariance. Adv. Neural Inf. Process. Syst. 5092–5102 (2018).Antoniou, A., Storkey, A. & Edwards, H. Data augmentation generative adversarial networks. Preprint at https://arxiv.org/abs/1711.04340 (2017).Larsen, A. B. L., Sønderby, S. K., Larochelle, H. & Winther, O. Autoencoding beyond pixels using a learned similarity metric. In International Conference On Machine Learning 1558–1566 (PMLR, 2016).Pereyra, G., Tucker, G., Chorowski, J. Kaiser, Ł. and Hinton, G. Regularizing neural networks by penalizing confident output distributions. Preprint at https://arxiv.org/abs/1701.06548 (2017).Higgins, I. et al. beta-VAE: learning basic visual concepts with a constrained variational framework. In Proc. International Conference on Learning Representations 2, 6 (2017).
Google Scholar 
Egozcue, J. J., Pawlowsky-Glahn, V., Mateu-Figueras, G. & Barcelo-Vidal, C. Isometric logratio transformations for compositional data analysis. Math. Geol. 35, 279–300 (2003).Article 
MathSciNet 

Google Scholar 
Lucas, J., Tucker, G., Grosse, R. B. & Norouzi, M. Don’t blame the ELBO! a linear VAE perspective on posterior collapse. Adv. Neural Inf. Process. Syst. 9403–9413 (2019).

Hot Topics

Related Articles