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Research Article
Transparent Process
Open Access

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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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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  • ORCID record for Ying Hu
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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  • ORCID record for Thuc D Le
Adi L Tarca
10Department of Obstetrics and Gynecology and Department of Computer Science, Wayne State University, Detroit, MI, USA
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  • ORCID record for Adi L Tarca
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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  • ORCID record for Pablo Meyer
  • For correspondence: pmeyerr@us.ibm.com
Published 24 September 2020. DOI: 10.26508/lsa.202000867
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Figures

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  • Figure 1.
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    Figure 1. Overview of the challenge and results.

    (A) In the DREAM Single-Cell Transcriptomics challenge, participants were asked to map the location of 1,297 cells to 3,039 location bins of an embryo of Drosophila melanogaster, by combining the single-cell RNA-sequencing measurements of 8,924 genes for each cell and the spatial expression patterns from in situ hybridization of 60, 40, or 20 genes, for subchallenge 1, 2, and 3, respectively, for each embryonic location bin, selected from a total of 84 mapped genes. (B) Ranking of the top 10 best performing teams and a wisdom of the crowds (WOC) solution, based on results from a post-challenge cross-validated selection and prediction performance measured with three complementary scoring metrics. The boxplots show the distribution of ranks for each team on the 10 test folds. The rank for each fold is calculated as the average of the ranking on each scoring metric.

  • Figure S1.
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    Figure S1. Results from the challenge showing boxplots of the average ranking across the three scoring schemes for the participating teams for 1,000 bootstraps of the silver standard.

    The horizontal line signifies the Bayesian factor of three or more between the ranks of two teams, which was considered as a significantly better performance, separating the winners for the subchallenge from the other participants.

  • Figure S2.
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    Figure S2. Results from the challenge showing boxplots of the average ranking across the three scoring schemes for the participating teams for 1,000 bootstraps of the silver standard.

    The horizontal line signifies the Bayesian factor of three or more between the ranks of two teams, which was considered as a significantly better performance, separating the winners for the subchallenge from the other participants.

  • Figure S3.
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    Figure S3. Results from the challenge showing boxplots of the average ranking across the three scoring schemes for the participating teams for 1,000 bootstraps of the silver standard.

    The horizontal line signifies the Bayesian factor of three or more between the ranks of two teams, which was considered as a significantly better performance, separating the winners for the subchallenge from the other participants.

  • Figure S4.
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    Figure S4. Boxplots of the Jaccard similarity between the genes selected for each of the 10 cross-validation scheme in all 3 Drosophila subchallenges.

    The teams that used the statistical properties of the genes as selection criteria, for example, maximum variance, selected the same set of genes for all folds. This is expected because the distribution of a random subsample was selected to have the same properties as the original sample. Dotted line represents the limit for significance, that is, the expected Jaccard similarity between two sets of randomly selected 60, 40, or 20 genes.

  • Figure S5.
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    Figure S5. Distributions of the Jaccard coefficient and the size of the intersection between the 10 most probable locations predicted by DistMap and Seurat for all cells in the zebra fish dataset.
  • Figure S6.
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    Figure S6. Results from the analysis of the zebra fish embryo dataset showing boxplots of the average ranking across the three scoring schemes for the top 10 teams for 1,000 bootstraps of the silver standard when using 40 genes.
  • Figure S7.
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    Figure S7. Results from the analysis of the zebra fish embryo dataset showing boxplots of the average ranking across the three scoring schemes for the top 10 teams for 1,000 bootstraps of the silver standard when using 20 genes.
  • Figure S8.
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    Figure S8. Properties of selected genes for the zebra fish dataset.

    (A) Double violin plots of the distribution of entropy and spatial autocorrelation statistic of (left, green) all in situs calculated on all embryonic location bins and (right, red) the most frequently selected 40 and 20 genes in the respective subchallenges. Bottom table: P-values of a one sided Mann–Whitney U test of location shift comparing the selected (red part of the violin plot) genes versus the non-selected genes. Shapiro–Wilk test of normality was rejected as the null hypothesis for both entropy and join count metrics (P < 2.3 · 10 − 6 and P < 1.8 · 10 − 15). (B) Top: visualization of the transcriptomics data containing all 48 genes from the zebra fish data (embedding to 2D by t-SNE). Each point (cell) is filled with the color of the cluster that it belongs to (density-based clustering with DBSCAN). Middle: visualization and clustering of the zebra fish embryo transcriptomics data containing the 40 most frequently selected genes by the top performing teams. Bottom: visualization and clustering of the zebra fish embryo transcriptomics data containing the 20 most frequently selected genes by the top performing teams.

  • Figure S9.
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    Figure S9. Distribution of the correlation between the measured single-cell RNA-sequencing expression of all pairs of 84 mapped genes across cells in Drosophila.

    Only 59 (1.7%) and 332 (9.5%) pairs of all possible 3,486 have an absolute value of the correlation coefficient larger than 0.5 or 0.3, respectively.

  • Figure 2.
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    Figure 2. Analysis of gene selection.

    The results in all figures were generated from the genes that were selected by the top performing teams in the post-challenge cross-validation scenario. (A) Frequency of selected genes in subchallenge 1 (blue), subchallenge 2 (green), and subchallenge 3 (red). The genes are ordered according to their cumulative frequency. (B) Venn diagrams of the most frequently selected genes in the subchallenges with cutoff at 20, 40, and 60 most frequently selected genes, corresponding to the number of genes required for each subchallenge. (C) Left: the similarity of most frequently selected genes for pairs of subchallenges. The Jaccard similarity measures |A ∩ B| the ratio of the size of the intersection and the union of two sets J(A, B) = |A ∪ B|. Right: table of correlations between gene rankings (by frequency) for pairs of subchallenges. (D) Validation of the performances of the most frequently selected 60, 40, and 20 genes in the respective subchallenges, also used as the wisdom of the crowds (WOC) selection of genes. The violin plots represent null distribution of scores obtained by 100 randomly selected sets of 60, 40, and 20 genes using DistMap. The red dots represent the performance obtained by using DistMap with the most frequently selected genes equivalent to the WOC selection of genes.

  • Figure S10.
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    Figure S10. Boxplots of the Jaccard similarity between the genes selected for each fold in the 10 cross-validation scheme for the selection of 40 and 20 genes from the zebra fish embryo dataset.

    Dotted line represents the limit for significance, that is, the expected Jaccard similarity between two sets of randomly selected 40 or 20 genes.

  • Figure 3.
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    Figure 3. Properties of selected genes.

    (A) Double violin plots of the distribution of entropy and spatial autocorrelation statistic of (left, green) all in situs calculated on all embryonic location bins and (right, red) the most frequently selected 60, 40, and 20 genes in the respective subchallenges. Bottom table: P-values of a one-sided Mann–Whitney U test of location shift comparing the selected (red part of the violin plot) genes versus the non-selected genes (green part of the violin plot). (B) Top left: visualization of the transcriptomics data containing only the most frequently selected 60 genes from subchallenge 1 by the top-performing teams (embedding to 2D by t-SNE). Each point (cell) is filled with the color of the cluster that it belongs to (density-based clustering with DBSCAN). Top right: spatial mapping of the cells in the Drosophila embryo as assigned by DistMap using only the 60 most frequently selected genes from subchallenge 1. The color of each point corresponds to the color of the cluster from the t-SNE visualization. Bottom: highlighted (red) location mapping of cells in the Drosophila embryo for each cluster separately.

  • Figure S11.
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    Figure S11. Visualization of the transcriptomics data containing only the most frequently selected.

    (A, B) 40 genes from subchallenge 2 and (B) 20 genes from subchallenge 3 by the top performing teams (embedding to 2D by t-SNE).Left: each point (cell) is filled with the color of the cluster that it belongs to (density-based clustering with DBSCAN). Middle: spatial mapping of the cells in the Drosophila embryo as assigned by DistMap using only the 60 most frequently selected genes from subchallenge 1. The color of each point corresponds to the color of the cluster from the t-SNE visualization. Right: highlighted (red) location mapping of cells in the Drosophila embryo for each cluster separately.

  • Figure S12.
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    Figure S12. One-versus-all differential expression analysis (Wilcoxon test, Bonferroni correction) for the different clusters for subchallenge 1 in Drosophila using the single-cell RNA-sequencing measurements of the most frequently selected 60 genes.

    Hierarchical biclustering using Euclidean distance and Ward’s linkage criterion.

  • Figure S13.
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    Figure S13. One-versus-all differential expression analysis (Wilcoxon test, Bonferroni correction) for the different clusters for subchallenge 2 in Drosophila using the single-cell RNA-sequencing measurements of the most frequently selected 40 genes.

    Hierarchical biclustering using Euclidean distance and Ward’s linkage criterion.

  • Figure S14.
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    Figure S14. One-versus-all differential expression analysis (Wilcoxon test, Bonferroni correction) for the different clusters for subchallenge 3 in Drosophila using the single-cell RNA-sequencing measurements of the most frequently selected 20 genes.

    Hierarchical biclustering using Euclidean distance and Ward’s linkage criterion.

  • Figure 4.
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    Figure 4. Identifying cluster-specific differentially expressed genes.

    Top left: For each subchallenge, the top three differentially expressed from the set of 60, 40, and 20 most frequently selected genes for each cluster were used to identify common and subchallenge-specific representative genes. Bottom left: the intersection of the sets of representative genes for each subchallenge contains 11 common genes. Right: examples of expression of remaining genes for subchallenge 1 are shown as an illustration of how they can be used to identify specific clusters.

  • Figure S15.
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    Figure S15. Spatial distribution of the genes in the intersection of the representative top-3 differentially expressed genes per cluster for all subchallenges in Drosophila.

    See Fig 4 for reference to the procedure used.

  • Figure S16.
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    Figure S16. Spatial distribution of subchallenge specific representative differentially expressed genes in Drosophila.

    See Fig 4 for reference to the procedure used.

  • Figure 5.
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    Figure 5. Wisdom of crowds location prediction.

    The location predictions for each cell by the top performing teams in the post-challenge cross-validation phase were aggregated in the wisdom of the crowds solution based on a k-means clustering approach.

  • Figure S17.
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    Figure S17. Gene regulatory network of early Drosophila development.

    Not all regulations are represented, nor pair-rule genes odd & prd. Frequently selected genes are represented in bold.

Tables

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    Table 1.

    Best mean score for metrics s1, s2, and s3 achieved by the teams (Thin Nguyen, WhatATeam, and OmicsEngineering) and the WOC solution.

    s1s2s3
    TeamsWOCTeamsWOCTeamsWOC
    Subchallenge 10.76 (±0.04)0.73 (±0.04)2.52 (±0.28)2.16 (±0.20)0.59 (±0.01)0.62 (±0.01)
    Subchallenge 20.69 (±0.03)0.70 (±0.05)1.16 (±0.12)1.84 (±0.26)0.67 (±0.02)0.65 (±0.01)
    Subchallenge 30.65 (±0.05)0.68 (±0.03)0.88 (±0.13)1.42 (±0.16)0.79 (±0.02)0.71 (±0.01)
    • The SD of scores across folds is in parenthesis. s1 measures how well the expression of the cell at the predicted location correlates to the expression from the reference atlas and includes the variance of the predicted locations for each cell, s2 measures the accuracy of the predicted location, and s3 measures how well the gene-wise spatial patterns were reconstructed. For more details on the scoring metrics, see the Materials and Methods section.

    • View popup
    Table 2.

    Correlations of transcriptomics to in situ properties of the genes where both measurements are available.

    In situ
    CorrelationHZ
    scRNASeqσ20.50.18
    CV−0.690.26
    0−0.640.29
    Hb0.72−0.3
    • σ2, variance of a gene across cells; CV, coefficient of variation; 0, number of cells with zero expression; Hb, entropy of binarized expression; H, entropy; Z, join count test statistic.

Supplementary Materials

  • Figures
  • Tables
  • Table S1 Best mean score for metrics s1, s2, and s3 achieved by the top-performing teams per Drosophila subchallenge.

  • Table S2 Methods used by the top 10 teams (ordered alphabetically) for gene selection and location prediction.

  • Table S3 Summary of methods used by the top 10 teams for gene selection and location prediction.

  • Table S4 Links to the write-up and code for the approaches used by the top 10 teams.

  • Table S5 Best mean score for metrics s1, s2, and s3 achieved by the top-performing teams per number of selected genes for the zebra fish dataset.

  • Table S6 Correlations of transcriptomics to in situ properties of the genes where both measurements are available for the zebra fish dataset.

  • Table S7 Most frequently selected 60, 40, and 20 genes in Drosophila subchallenges 1, 2, and 3, respectively, in alphabetical order, colored according to Figure S17. That is yellow are gap genes and green are pair-rule genes.

  • Supplemental Data 1.

    Most frequently selected 60, 40, and 20 genes in Drosophila subchallenges 1, 2, and 3, respectively, in alphabetical order, colored according to Fig S17.[LSA-2020-00867_Supplemental_Data_1.pdf]

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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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