bigSCale: an analytical framework for big-scale single-cell data

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• dc.contributor.author Iacono, Giovanni
• dc.contributor.author Mereu, Elisabetta
• dc.contributor.author Guillaumet-Adkins, Amy
• dc.contributor.author Corominas, Roser
• dc.contributor.author Cuscó Martí, Ivon, 1973-
• dc.contributor.author Rodríguez Esteban, Gustavo
• dc.contributor.author Gut, Marta
• dc.contributor.author Pérez Jurado, Luis Alberto
• dc.contributor.author Gut, Ivo Glynne
• dc.contributor.author Heyn, Holger
• dc.date.accessioned 2019-05-08T07:31:44Z
• dc.date.available 2019-05-08T07:31:44Z
• dc.date.issued 2018
• dc.description.abstract Single-cell RNA sequencing (scRNA-seq) has significantly deepened our insights into complex tissues, with the latest techniques capable of processing tens of thousands of cells simultaneously. Analyzing increasing numbers of cells, however, generates extremely large data sets, extending processing time and challenging computing resources. Current scRNA-seq analysis tools are not designed to interrogate large data sets and often lack sensitivity to identify marker genes. With bigSCale, we provide a scalable analytical framework to analyze millions of cells, which addresses the challenges associated with large data sets. To handle the noise and sparsity of scRNA-seq data, bigSCale uses large sample sizes to estimate an accurate numerical model of noise. The framework further includes modules for differential expression analysis, cell clustering, and marker identification. A directed convolution strategy allows processing of extremely large data sets, while preserving transcript information from individual cells. We evaluated the performance of bigSCale using both a biological model of aberrant gene expression in patient-derived neuronal progenitor cells and simulated data sets, which underlines the speed and accuracy in differential expression analysis. To test its applicability for large data sets, we applied bigSCale to assess 1.3 million cells from the mouse developing forebrain. Its directed down-sampling strategy accumulates information from single cells into index cell transcriptomes, thereby defining cellular clusters with improved resolution. Accordingly, index cell clusters identified rare populations, such as reelin (Reln)-positive Cajal-Retzius neurons, for which we report previously unrecognized heterogeneity associated with distinct differentiation stages, spatial organization, and cellular function. Together, bigSCale presents a solution to address future challenges of large single-cell data sets.
• dc.description.sponsorship H.H. is a Miguel Servet (CP14/00229) researcher funded by the Spanish Institute of Health Carlos III (ISCIII). This work was further supported by the grants from Fundación Ramón Areces, the marathon “Todos Somos Raros, Todos Somos Únicos” (Proyecto 52). R.C. is recipient of a Marie Skłodowska-Curie Actions fellowship (656359, H2020). Core funding is from the ISCIII and the Generalitat de Catalunya.
• dc.format.mimetype application/pdf
• dc.identifier.citation Iacono G, Mereu E, Guillaumet-Adkins A, Corominas R, Cuscó I, Rodríguez-Esteban G et al. BigSCale: an analytical framework for big-scale single-cell data. Genome Res. 2018 Jun;28(6):878-90. DOI: 10.1101/gr.230771.117
• dc.identifier.doi http://dx.doi.org/10.1101/gr.230771.117
• dc.identifier.issn 1088-9051
• dc.identifier.uri http://hdl.handle.net/10230/37188
• dc.language.iso eng
• dc.publisher Cold Spring Harbor Laboratory Press (CSHL Press)
• dc.relation.ispartof Genome Research. 2018 Jun;28(6):878-90
• dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/656359
• dc.rights © 2018 Iacono et al. This article, published in Genome Research, is available under a Creative Commons License (Attribution-Non Commercial 4.0 International), as described at http://creativecommons.org/licenses/by-nc/4.0/.
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc/4.0/
• dc.subject.other Cèl·lules
• dc.subject.other RNA -- Investigació
• dc.title bigSCale: an analytical framework for big-scale single-cell data
• dc.type info:eu-repo/semantics/article
• dc.type.version info:eu-repo/semantics/publishedVersion