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Research Article
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Gene selection for optimal prediction of cell position in tissues from single-cell transcriptomics data

View ORCID ProfileJovan Tanevski, Thin Nguyen, Buu Truong, Nikos Karaiskos, Mehmet Eren Ahsen, Xinyu Zhang, View ORCID ProfileChang Shu, View ORCID ProfileKe Xu, Xiaoyu Liang, View ORCID ProfileYing Hu, View ORCID ProfileHoang VV Pham, Li Xiaomei, View ORCID ProfileThuc D Le, View ORCID ProfileAdi L Tarca, Gaurav Bhatti, Roberto Romero, View ORCID ProfileNestoras Karathanasis, Phillipe Loher, View ORCID ProfileYang Chen, View ORCID ProfileZhengqing Ouyang, Disheng Mao, View ORCID ProfileYuping Zhang, Maryam Zand, Jianhua Ruan, Christoph Hafemeister, Peng Qiu, View ORCID ProfileDuc Tran, View ORCID ProfileTin Nguyen, View ORCID ProfileAttila Gabor, Thomas Yu, Justin Guinney, View ORCID ProfileEnrico Glaab, View ORCID ProfileRoland Krause, View ORCID ProfilePeter Banda, DREAM SCTC Consortium, View ORCID ProfileGustavo Stolovitzky, Nikolaus Rajewsky, Julio Saez-Rodriguez, View ORCID ProfilePablo Meyer  Correspondence email
Jovan Tanevski
1Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
2Department of Knowledge Technologies, Jožef Stefan Institute, Ljubljana, Slovenia
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  • ORCID record for Jovan Tanevski
Thin Nguyen
3Deakin University, Geelong, Australia
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Buu Truong
4University of South Australia, Mawson Lakes, Australia
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Nikos Karaiskos
5Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
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Mehmet Eren Ahsen
6Icahn School of Medicine at Mount Sinai, New York City, NY, USA
7University of Illinois, Urbana-Champaign, Champaign, IL, USA
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Xinyu Zhang
8Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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Chang Shu
8Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
27Department of Pediatrics, Columbia University Irving Medical Center, New York, NY, USA
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Ke Xu
8Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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Xiaoyu Liang
8Department of Psychiatry, Yale School of Medicine, New Haven, CT, USA
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Ying Hu
9Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, MD, USA
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Hoang VV Pham
4University of South Australia, Mawson Lakes, Australia
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Li Xiaomei
4University of South Australia, Mawson Lakes, Australia
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Thuc D Le
4University of South Australia, Mawson Lakes, Australia
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Adi L Tarca
10Department of Obstetrics and Gynecology and Department of Computer Science, Wayne State University, Detroit, MI, USA
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Gaurav Bhatti
11Perinatology Research Branch, National Institute of Child Health and Human Development (NICHD)/National Insitutes of Health (NIH)/ Department of Health & Human Services (DHHS), Bethesda, MD, USA
12Perinatology Research Branch, NICHD/NIH/DHHS, Detroit, MI, USA
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Roberto Romero
11Perinatology Research Branch, National Institute of Child Health and Human Development (NICHD)/National Insitutes of Health (NIH)/ Department of Health & Human Services (DHHS), Bethesda, MD, USA
12Perinatology Research Branch, NICHD/NIH/DHHS, Detroit, MI, USA
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Nestoras Karathanasis
13Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA
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Phillipe Loher
13Computational Medicine Center, Thomas Jefferson University, Philadelphia, PA, USA
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Yang Chen
14The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Zhengqing Ouyang
15University of Massachusetts, Amherst, MA, USA
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Disheng Mao
16University of Connecticut, Storrs, CT, USA
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Yuping Zhang
16University of Connecticut, Storrs, CT, USA
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Maryam Zand
17University of Texas at San Antonio, San Antonio, TX, USA
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Jianhua Ruan
17University of Texas at San Antonio, San Antonio, TX, USA
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Christoph Hafemeister
18New York Genome Center, New York City, NY, USA
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Peng Qiu
19Georgia Institute of Technology, Atlanta, GA, USA
20Emory University, Atlanta, GA, USA
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Duc Tran
21University of Nevada, Reno, NV, USA
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Tin Nguyen
21University of Nevada, Reno, NV, USA
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Attila Gabor
1Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
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Thomas Yu
22Sage Bionetworks, Seattle, WA, USA
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Justin Guinney
22Sage Bionetworks, Seattle, WA, USA
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Enrico Glaab
23Biomedical Data Science Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur Alzette, Luxembourg
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Roland Krause
24Bioinformatics Core Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur Alzette, Luxembourg
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Peter Banda
24Bioinformatics Core Group, Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-sur Alzette, Luxembourg
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Gustavo Stolovitzky
25International Buisness Machines (IBM) T.J. Watson Research Center, Yorktown Heights, NY, USA
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Nikolaus Rajewsky
5Berlin Institute for Medical Systems Biology, Max Delbrück Center for Molecular Medicine in the Helmholtz Association, Berlin, Germany
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Julio Saez-Rodriguez
1Institute for Computational Biomedicine, Faculty of Medicine, Heidelberg University Hospital and Heidelberg University, Heidelberg, Germany
26Joint Research Centre for Computational Biomedicine, Faculty of Medicine, RWTH Aachen University, Aachen, Germany
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Pablo Meyer
25International Buisness Machines (IBM) T.J. Watson Research Center, Yorktown Heights, NY, USA
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  • For correspondence: pmeyerr@us.ibm.com
Published 24 September 2020. DOI: 10.26508/lsa.202000867
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Abstract

Single-cell RNA-sequencing (scRNAseq) technologies are rapidly evolving. Although very informative, in standard scRNAseq experiments, the spatial organization of the cells in the tissue of origin is lost. Conversely, spatial RNA-seq technologies designed to maintain cell localization have limited throughput and gene coverage. Mapping scRNAseq to genes with spatial information increases coverage while providing spatial location. However, methods to perform such mapping have not yet been benchmarked. To fill this gap, we organized the DREAM Single-Cell Transcriptomics challenge focused on the spatial reconstruction of cells from the Drosophila embryo from scRNAseq data, leveraging as silver standard, genes with in situ hybridization data from the Berkeley Drosophila Transcription Network Project reference atlas. The 34 participating teams used diverse algorithms for gene selection and location prediction, while being able to correctly localize clusters of cells. Selection of predictor genes was essential for this task. Predictor genes showed a relatively high expression entropy, high spatial clustering and included prominent developmental genes such as gap and pair-rule genes and tissue markers. Application of the top 10 methods to a zebra fish embryo dataset yielded similar performance and statistical properties of the selected genes than in the Drosophila data. This suggests that methods developed in this challenge are able to extract generalizable properties of genes that are useful to accurately reconstruct the spatial arrangement of cells in tissues.

Footnotes

  • ↵† Nikolaus Rajewsky and Julio Saez-Rodriguez are the senior authors

  • ↵‡ DREAM SCTC Consortium authors and affiliations are listed in the supplementary material

  • Received July 31, 2020.
  • Revision received August 26, 2020.
  • Accepted August 31, 2020.
  • © 2020 Tanevski et al.
Creative Commons logoCreative Commons logohttps://creativecommons.org/licenses/by/4.0/

This article is available under a Creative Commons License (Attribution 4.0 International, as described at https://creativecommons.org/licenses/by/4.0/).

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Predicting cells position from single-cell transcriptomics
Jovan Tanevski, Thin Nguyen, Buu Truong, Nikos Karaiskos, Mehmet Eren Ahsen, Xinyu Zhang, Chang Shu, Ke Xu, Xiaoyu Liang, Ying Hu, Hoang VV Pham, Li Xiaomei, Thuc D Le, Adi L Tarca, Gaurav Bhatti, Roberto Romero, Nestoras Karathanasis, Phillipe Loher, Yang Chen, Zhengqing Ouyang, Disheng Mao, Yuping Zhang, Maryam Zand, Jianhua Ruan, Christoph Hafemeister, Peng Qiu, Duc Tran, Tin Nguyen, Attila Gabor, Thomas Yu, Justin Guinney, Enrico Glaab, Roland Krause, Peter Banda, DREAM SCTC Consortium, Gustavo Stolovitzky, Nikolaus Rajewsky, Julio Saez-Rodriguez, Pablo Meyer
Life Science Alliance Sep 2020, 3 (11) e202000867; DOI: 10.26508/lsa.202000867

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Predicting cells position from single-cell transcriptomics
Jovan Tanevski, Thin Nguyen, Buu Truong, Nikos Karaiskos, Mehmet Eren Ahsen, Xinyu Zhang, Chang Shu, Ke Xu, Xiaoyu Liang, Ying Hu, Hoang VV Pham, Li Xiaomei, Thuc D Le, Adi L Tarca, Gaurav Bhatti, Roberto Romero, Nestoras Karathanasis, Phillipe Loher, Yang Chen, Zhengqing Ouyang, Disheng Mao, Yuping Zhang, Maryam Zand, Jianhua Ruan, Christoph Hafemeister, Peng Qiu, Duc Tran, Tin Nguyen, Attila Gabor, Thomas Yu, Justin Guinney, Enrico Glaab, Roland Krause, Peter Banda, DREAM SCTC Consortium, Gustavo Stolovitzky, Nikolaus Rajewsky, Julio Saez-Rodriguez, Pablo Meyer
Life Science Alliance Sep 2020, 3 (11) e202000867; DOI: 10.26508/lsa.202000867
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Volume 3, No. 11
November 2020
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