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CORRECTION article

Front. Genet., 06 February 2020
Sec. Statistical Genetics and Methodology
This article is part of the Research Topic Single-Cell Data Analysis: Resources, Challenges and Perspectives View all 8 articles

Corrigendum: Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data

  • Bioinformatics Unit, Fundación Centro Nacional de Investigaciones Cardiovasculares (CNIC), Madrid, Spain

A Corrigendum on
Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data

by Torroja C and Sanchez-Cabo F (2019). Front. Genet. 10:978. doi: 10.3389/fgene.2019.00978

In the original article, the funder “Ministerio de Ciencia, Innovación, y Universidades (MCIU), RTI2018-102084-B-I00” to “Fatima Sanchez-Cabo” was missing. The corrected Funding Statement follows below:

“The results shown here are in part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga. This 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) and by the Ministerio de Ciencia, Innovación, y Universidades (MCIU) [grant no. RTI2018-102084-B-I00]. The CNIC is supported by MCIU and the Pro-CNIC Foundation and is a Severo Ochoa Center of Excellence [MCIU award SEV-2015-0505].”

Additionally, the Ethics Statement, Author Contributions and Acknowledgements statement were not included in the original Article. The Statements follows below:

Ethics Statement:

“This study was carried with human open access data from with their corresponding ethics committee approval.”

Author Contributions:

“FS-C conceived the study, CT implemented all analysis and produced the figures. CT and FS-C wrote the manuscript.”

Acknowledgements:

“We would like to thank Francesca Finotello and Zlatko Trajanoski for fruitful discussions and to the CNIC Bioinformatics Unit members for continuous support and work.”

The authors apologize for these errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.

Keywords: machine learning, deconvolution algorithm, cancer, immunology, single-cell

Citation: Torroja C and Sanchez-Cabo F (2020) Corrigendum: Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data. Front. Genet. 10:1373. doi: 10.3389/fgene.2019.01373

Received: 12 December 2019; Accepted: 16 December 2019;
Published: 06 February 2020.

Approved by: Frontiers Editorial Office, Frontiers Media SA, Switzerland

Copyright © 2020 Torroja and Sanchez-Cabo. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Carlos Torroja, ctorroja@cnic.es; Fatima Sanchez-Cabo, fscabo@cnic.es

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