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The analysis of the resulting data poses significant challenges, and proper statistical methods are required to analyze and extract information from scRNA-Seq datasets. Sample classification based on gene expression data has proven effective and valuable for precision medicine applications. However, standard classification schemas are often not suitable for scRNA-Seq due to their unique characteristics, and new algorithms are required to effectively analyze and classify samples at the single-cell level. Furthermore, existing methods for this purpose have limitations in their usability. Those reasons motivated us to develop singleDeep, an end-to-end pipeline that streamlines the analysis of scRNA-Seq data training deep neural networks, enabling robust prediction and characterization of sample phenotypes. We used singleDeep to make predictions on scRNA-Seq datasets from different conditions, including systemic lupus erythematosus, Alzheimer\u2019s disease and coronavirus disease 2019. Our results demonstrate strong diagnostic performance, validated both internally and externally. Moreover, singleDeep outperformed traditional machine learning methods and alternative single-cell approaches. In addition to prediction accuracy, singleDeep provides valuable insights into cell types and gene importance estimation for phenotypic characterization. This functionality provided additional and valuable information in our use cases. For instance, we corroborated that some interferon signature genes are consistently relevant for autoimmunity across all immune cell types in lupus. On the other hand, we discovered that genes linked to dementia have relevant roles in specific brain cell populations, such as APOE in astrocytes.<\/jats:p>","DOI":"10.1093\/bib\/bbae673","type":"journal-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T15:20:01Z","timestamp":1736781601000},"source":"Crossref","is-referenced-by-count":6,"title":["Explainable deep neural networks for predicting sample phenotypes from single-cell transcriptomics"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5186-0735","authenticated-orcid":false,"given":"Jordi","family":"Martorell-Marug\u00e1n","sequence":"first","affiliation":[{"name":"GENYO, Centre for Genomics and Oncological Research: Pfizer \/ University of Granada \/ Andalusian Regional Government, PTS Granada , Avenida de la Ilustraci\u00f3n 114, Granada 18016 ,","place":["Spain"]},{"name":"Fundaci\u00f3n para la Investigaci\u00f3n Biosanitaria de Andaluc\u00eda Oriental-Alejandro Otero (FIBAO) , Avenida de Madrid 15, Granada 18012 ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8634-117X","authenticated-orcid":false,"given":"Ra\u00fal","family":"L\u00f3pez-Dom\u00ednguez","sequence":"additional","affiliation":[{"name":"GENYO, Centre for Genomics and Oncological Research: Pfizer \/ University of Granada \/ Andalusian Regional Government, PTS Granada , Avenida de la Ilustraci\u00f3n 114, Granada 18016 ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5752-7784","authenticated-orcid":false,"given":"Juan Antonio","family":"Villatoro-Garc\u00eda","sequence":"additional","affiliation":[{"name":"GENYO, Centre for Genomics and Oncological Research: Pfizer \/ University of Granada \/ Andalusian Regional Government, PTS Granada , Avenida de la Ilustraci\u00f3n 114, Granada 18016 ,","place":["Spain"]},{"name":"Department of Statistics and Operational Research, University of Granada , Avenida de la Fuente Nueva S\/N, Granada 18071 ,","place":["Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8440-312X","authenticated-orcid":false,"given":"Daniel","family":"Toro-Dom\u00ednguez","sequence":"additional","affiliation":[{"name":"Unit of Inflammatory Diseases, Department of Environmental Medicine, Karolinska Institutet , Nobels v\u00e4g 13, Solna 171 77 ,","place":["Sweden"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9791-9301","authenticated-orcid":false,"given":"Marco","family":"Chierici","sequence":"additional","affiliation":[{"name":"Data Science for Health Research Unit, Fondazione Bruno Kessler , Via Sommarive 18, Trento 38123 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