{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-deep-generative-model-for-gene-expression","title":"A deep generative model for gene expression profiles from single-cell RNA sequencing","arxiv_id":"1709.02082","date":"2017-09-07","proceeding":null,"authors":["Romain Lopez","Jeffrey Regier","Michael Cole","Michael Jordan","Nir Yosef"],"abstract":"We propose a probabilistic model for interpreting gene expression levels that\nare observed through single-cell RNA sequencing. In the model, each cell has a\nlow-dimensional latent representation. Additional latent variables account for\ntechnical effects that may erroneously set some observations of gene expression\nlevels to zero. Conditional distributions are specified by neural networks,\ngiving the proposed model enough flexibility to fit the data well. We use\nvariational inference and stochastic optimization to approximate the posterior\ndistribution. The inference procedure scales to over one million cells, whereas\ncompeting algorithms do not. Even for smaller datasets, for several tasks, the\nproposed procedure outperforms state-of-the-art methods like ZIFA and\nZINB-WaVE. We also extend our framework to account for batch effects and other\nconfounding factors, and propose a Bayesian hypothesis test for differential\nexpression that outperforms DESeq2.","url_abs":"http://arxiv.org/abs/1709.02082v4","url_pdf":"http://arxiv.org/pdf/1709.02082v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-deep-generative-model-for-gene-expression","repo_url":"https://github.com/YosefLab/scVI","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":null},{"paper_slug":"a-deep-generative-model-for-gene-expression","repo_url":"https://github.com/romain-lopez/scVI-reproducibility","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"stochastic-optimization","task_name":"Stochastic Optimization"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}