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dc.contributor.authorTorroja, Carlos 
dc.contributor.authorSanchez-Cabo, Fatima 
dc.date.accessioned2019-11-13T13:17:50Z
dc.date.available2019-11-13T13:17:50Z
dc.date.issued2019-10
dc.identifier.citationFront Genet. 2019; 10:978es_ES
dc.identifier.issn1664-8021es_ES
dc.identifier.urihttp://hdl.handle.net/20.500.12105/8578
dc.description.abstractThe development of single cell transcriptome sequencing has allowed researchers the possibility to dig inside the role of the individual cell types in a plethora of disease scenarios. It also expands to the whole transcriptome what before was only possible for a few tenths of antibodies in cell population analysis. More importantly, it allows resolving the permanent question of whether the changes observed in a particular bulk experiment are a consequence of changes in cell type proportions or an aberrant behavior of a particular cell type. However, single cell experiments are still complex to perform and expensive to sequence making bulk RNA-Seq experiments yet more common. scRNA-Seq data is proving highly relevant information for the characterization of the immune cell repertoire in different diseases ranging from cancer to atherosclerosis. In particular, as scRNA-Seq becomes more widely used, new types of immune cell populations emerge and their role in the genesis and evolution of the disease opens new avenues for personalized immune therapies. Immunotherapy have already proven successful in a variety of tumors such as breast, colon and melanoma and its value in other types of disease is being currently explored. From a statistical perspective, single-cell data are particularly interesting due to its high dimensionality, overcoming the limitations of the "skinny matrix" that traditional bulk RNA-Seq experiments yield. With the technological advances that enable sequencing hundreds of thousands of cells, scRNA-Seq data have become especially suitable for the application of Machine Learning algorithms such as Deep Learning (DL). We present here a DL based method to enumerate and quantify the immune infiltration in colorectal and breast cancer bulk RNA-Seq samples starting from scRNA-Seq. Our method makes use of a Deep Neural Network (DNN) model that allows quantification not only of lymphocytes as a general population but also of specific CD8+, CD4Tmem, CD4Th and CD4Tregs subpopulations, as well as B-cells and Stromal content. Moreover, the signatures are built from scRNA-Seq data from the tumor, preserving the specific characteristics of the tumor microenvironment as opposite to other approaches in which cells were isolated from blood. Our method was applied to synthetic bulk RNA-Seq and to samples from the TCGA project yielding very accurate results in terms of quantification and survival prediction.es_ES
dc.description.sponsorshipThis work was supported by the European Union’s Horizon 2020 research and innovation program under grant agreement number 633592 (Project APERIM: Advanced bioinformatics platform for personalized cancer immunotherapy). We thank Francesca Finotello and Zlatko Trajanoski for fruitful discussions and to the CNIC Bioinformatics Unit members for continuous support and work. The CNIC is supported by MEIC and the ProCNIC Foundation, and is a Severo Ochoa Center of Excellence (MEIC award SEV-2015-0505).es_ES
dc.language.isoenges_ES
dc.relation.isversionofPublisher's versiones_ES
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectCanceres_ES
dc.subjectDeconvolution algorithmes_ES
dc.subjectMachine learninges_ES
dc.subjectimmunologyes_ES
dc.subjectsingle-celles_ES
dc.titleDigitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Dataes_ES
dc.typeArtículoes_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.identifier.pubmedID31708961es_ES
dc.format.volume10es_ES
dc.format.page978es_ES
dc.identifier.doi10.3389/fgene.2019.00978es_ES
dc.contributor.funderEuropean Commissiones_ES
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España)es_ES
dc.contributor.funderFundación ProCNICes_ES
dc.description.peerreviewedes_ES
dc.relation.publisherversionhttps://doi.org/10.3389/fgene.2019.00978es_ES
dc.identifier.journalFrontiers in geneticses_ES
dc.repisalud.orgCNICCNIC::Unidades técnicas::Bioinformáticaes_ES
dc.repisalud.institucionCNICes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/633592es_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/SEV-2015-0505es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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