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Tissue-specific enhancer–gene maps from multimodal single-cell data identify causal disease alleles

Nature Genetics volume 56, pages 615–626 (2024)Cite this article

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Abstract

Translating genome-wide association study (GWAS) loci into causal variants and genes requires accurate cell-type-specific enhancer–gene maps from disease-relevant tissues. Building enhancer–gene maps is essential but challenging with current experimental methods in primary human tissues. Here we developed a nonparametric statistical method, SCENT (single-cell enhancer target gene mapping), that models association between enhancer chromatin accessibility and gene expression in single-cell or nucleus multimodal RNA sequencing and ATAC sequencing data. We applied SCENT to 9 multimodal datasets including >120,000 single cells or nuclei and created 23 cell-type-specific enhancer–gene maps. These maps were highly enriched for causal variants in expression quantitative loci and GWAS for 1,143 diseases and traits. We identified likely causal genes for both common and rare diseases and linked somatic mutation hotspots to target genes. We demonstrate that application of SCENT to multimodal data from disease-relevant human tissue enables the scalable construction of accurate cell-type-specific enhancer–gene maps, essential for defining noncoding variant function.

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Fig. 1: Schematic overview of SCENT and SCENT enhancer–gene pairs across nine single-cell multimodal datasets.
Fig. 2: SCENT identified functionally active and evolutionarily conserved cis-regulatory regions from single-cell multimodal data.
Fig. 3: SCENT enhancers are enriched in putative causal variants of eQTL and GWAS.
Fig. 4: SCENT defined causal variants and genes in complex trait GWAS.

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Data availability

The publicly available datasets were downloaded via Gene Expression Omnibus (accession codes GSE140203, GSE156478, GSE178707, GSE194122, GSE193240 and GSE178453) or web repository (https://www.10xgenomics.com/resources/datasets?query=&page=1&configure%5Bfacets%5D%5B0%5D=chemistryVersionAndThroughput&configure%5Bfacets%5D%5B1%5D=pipeline.version&configure%5BhitsPerPage%5D=500&menu%5Bproducts.name%5D=Single%20Cell%20Multiome%20ATAC%20%2B%20Gene%20Expression). The raw data for arthritis-tissue dataset (single-cell multimodal RNA/ATAC–seq and single-cell ATAC–seq) are deposited at the NIH Database of Genotypes and Phenotypes (dbGaP accession number phs003417.v1.p1) and the Gene Expression Omnibus (GEO accession number GSE243917).

Code availability

The computational scripts related to this manuscript are available at https://github.com/immunogenomics/SCENT (https://doi.org/10.5281/zenodo.10452116)124.

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Acknowledgements

We sincerely thank participants of this study who provided tissue samples. We thank A. Gupta, J. Kang and K. Lagattuta for their comments and helpful discussion on the manuscript. This work is supported in part by funding from the National Institutes of Health (R01AR063759, U01HG012009 and UC2AR081023 to S.R.). S.S. was in part supported by the Uehara Memorial Foundation and The Osamu Hayaishi Memorial Scholarship. K. Weinand was supported by NIH NIAMS T32AR007530. K. Wei was supported by a Burroughs Wellcome Fund Career Awards for Medical Scientists, a Doris Duke Charitable Foundation Clinical Scientist Development Award, a Rheumatology Research Foundation Innovative Research Award, and NIH NIAMS K08AR077037. We thank the Brigham and Women’s Hospital Center for Cellular Profiling Single Cell Multiomics Core for experimental design and protocol optimization. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Authors and Affiliations

  1. Center for Data Sciences, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Saori Sakaue, Kathryn Weinand, Shakson Isaac, Michelle Curtis, Maria Gutierrez-Arcelus, Siddarth Gurajala, Kazuyoshi Ishigaki, Joyce B. Kang, Ilya Korsunsky, Joseph Mears, Nghia Millard, Aparna Nathan, Yakir Reshef, Laurie Rumker, Qian Xiao, Fan Zhang & Soumya Raychaudhuri

  2. Divisions of Genetics and Rheumatology, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Saori Sakaue, Kathryn Weinand, Shakson Isaac, Michelle Curtis, Maria Gutierrez-Arcelus, Siddarth Gurajala, Kazuyoshi Ishigaki, Joyce B. Kang, Ilya Korsunsky, Joseph Mears, Nghia Millard, Aparna Nathan, Yakir Reshef, Laurie Rumker, Qian Xiao, Fan Zhang & Soumya Raychaudhuri

  3. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Saori Sakaue, Kathryn Weinand, Shakson Isaac, Kushal K. Dey, Karthik Jagadeesh, Masahiro Kanai, Michelle Curtis, Maria Gutierrez-Arcelus, Siddarth Gurajala, Kazuyoshi Ishigaki, Joyce B. Kang, Ilya Korsunsky, Joseph Mears, Nghia Millard, Aparna Nathan, Yakir Reshef, Laurie Rumker, Qian Xiao, Fan Zhang, Alkes L. Price & Soumya Raychaudhuri

  4. Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA

    Kathryn Weinand, Shakson Isaac, Joyce B. Kang, Katherine P. Liao, Nghia Millard, Aparna Nathan, Laurie Rumker, Kamil Slowikowski & Soumya Raychaudhuri

  5. Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Kushal K. Dey, Karthik Jagadeesh & Alkes L. Price

  6. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA

    Masahiro Kanai

  7. Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Masahiro Kanai

  8. Center for Computational and Integrative Biology, Massachusetts General Hospital, Boston, MA, USA

    Masahiro Kanai

  9. Division of Rheumatology, Inflammation, and Immunity, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    Gerald F. M. Watts, Zhu Zhu, Adam Chicoine, Ellen M. Gravallese, Anna Helena Jonsson, Gregory Keras, Zhihan J. Li, Yuhong Li, Katherine P. Liao, Deepak A. Rao, Dana Weisenfeld, Michael B. Brenner & Kevin Wei

  10. Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA

    Andrew McDavid

  11. Hospital for Special Surgery, New York, NY, USA

    S. Louis Bridges Jr, Vivian P. Bykerk, Susan M. Goodman, Lionel B. Ivashkiv, Amit Lakhanpal, Ian Mantel, Dana E. Orange, Melanie H. Smith & Laura T. Donlin

  12. Weill Cornell Medicine, New York, NY, USA

    S. Louis Bridges Jr, Vivian P. Bykerk, Susan M. Goodman, Lionel B. Ivashkiv, Amit Lakhanpal, Ian Mantel & Laura T. Donlin

  13. Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA

    Alkes L. Price

  14. Division of Allergy, Immunology and Rheumatology, Department of Medicine, University of Rochester Medical Center, Rochester, NY, USA

    Jennifer Albrecht, Jennifer H. Anolik, Jennifer L. Barnas, Debbie Campbell, Nida Meednu, Javier Rangel-Moreno, Christopher Ritchlin & Darren Tabechian

  15. Accelerating Medicines Partnership® Program: Rheumatoid Arthritis and Systemic Lupus Erythematosus (AMP® RA/SLE) Network, Boston, MA, USA

    William Apruzzese

  16. Division of Rheumatology., University of Colorado School of Medicine, Aurora, CO, USA

    Nirmal Banda

  17. Division of Rheumatology, Columbia University College of Physicians and Surgeons, New York, NY, USA

    Joan M. Bathon & Laura Geraldino-Pardilla

  18. Division of Rheumatology, Cedars-Sinai Medical Center, Los Angeles, CA, USA

    Ami Ben-Artzi & Michael H. Weisman

  19. Department of Pathology and Laboratory Medicine, University of Rochester Medical Center, Rochester, NY, USA

    Brendan F. Boyce

  20. Division of Rheumatology, Allergy and Immunology, University of California, San Diego, La Jolla, CA, USA

    David L. Boyle, Arnold Ceponis & Gary S. Firestein

  21. Rheumatology Research Group, Institute for Inflammation and Ageing, University of Birmingham, NIHR Birmingham Biomedical Research Center and Clinical Research Facility, University of Birmingham, Queen Elizabeth Hospital, Birmingham, UK

    Hayley L. Carr, Andrew Filer, Lindsy Forbess, Mark Maybury, Karim Raza, Ilfita Sahbudin & Dagmar Scheel-Toellner

  22. Department of Radiology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA

    Andrew Cordle

  23. Division of Rheumatology, University of Colorado School of Medicine, Aurora, CO, USA

    Kevin D. Deane, V. Michael Holers, Larry W. Moreland & Jennifer A. Seifert

  24. Department of Pathology and Laboratory Medicine, Hospital for Special Surgery, New York, NY, USA

    Edward DiCarlo

  25. Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, National Institutes of Health, Bethesda, MD, USA

    Patrick Dunn

  26. Northrop Grumman Health Solutions, Rockville, MD, USA

    Patrick Dunn

  27. Feinstein Institute for Medical Research, Northwell Health, Manhasset, New York, NY, USA

    Peter K. Gregersen & Diane Horowitz

  28. Department of Arthritis & Clinical Immunology, Oklahoma Medical Research Foundation, Oklahoma City, OK, USA

    Joel M. Guthridge & Judith A. James

  29. Division of Immunology, Department of Pediatrics, Boston Children’s Hospital and Harvard Medical School, Boston, MA, USA

    Maria Gutierrez-Arcelus

  30. Division of Clinical Immunology and Rheumatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA

    Laura B. Hughes

  31. Laboratory for Human Immunogenetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan

    Kazuyoshi Ishigaki

  32. Department of Surgery, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA

    James A. Lederer

  33. Division of Rheumatology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA

    Arthur M. Mandelin II & Harris Perlman

  34. Division of Rheumatology and Clinical Immunology, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA

    Larry W. Moreland

  35. Centre for Experimental Medicine and Rheumatology, William Harvey Research Institute, Queen Mary University of London, London, UK

    Alessandra Nerviani, Costantino Pitzalis & Felice Rivellese

  36. Laboratory of Molecular Neuro-Oncology, The Rockefeller University, New York, NY, USA

    Dana E. Orange

  37. Division of Immunology and Rheumatology, Institute for Immunity, Transplantation and Infection, Stanford University School of Medicine, Stanford, CA, USA

    William H. Robinson, Paul J. Utz & Michael H. Weisman

  38. Center for Immunology and Inflammatory Diseases, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

    Kamil Slowikowski

  39. MGH Cancer Center, Boston, MA, USA

    Kamil Slowikowski

  40. Division of Rheumatology and the Center for Health Artificial Intelligence, University of Colorado School of Medicine, Aurora, CO, USA

    Fan Zhang

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  1. Saori Sakaue
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  14. Soumya Raychaudhuri
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Consortia

Accelerating Medicines Partnership® RA/SLE Program and Network

  • Jennifer Albrecht
  • , Jennifer H. Anolik
  • , William Apruzzese
  • , Nirmal Banda
  • , Jennifer L. Barnas
  • , Joan M. Bathon
  • , Ami Ben-Artzi
  • , Brendan F. Boyce
  • , David L. Boyle
  • , S. Louis Bridges Jr
  • , Vivian P. Bykerk
  • , Debbie Campbell
  • , Hayley L. Carr
  • , Arnold Ceponis
  • , Adam Chicoine
  • , Andrew Cordle
  • , Michelle Curtis
  • , Kevin D. Deane
  • , Edward DiCarlo
  • , Patrick Dunn
  • , Andrew Filer
  • , Gary S. Firestein
  • , Lindsy Forbess
  • , Laura Geraldino-Pardilla
  • , Susan M. Goodman
  • , Ellen M. Gravallese
  • , Peter K. Gregersen
  • , Joel M. Guthridge
  • , Maria Gutierrez-Arcelus
  • , Siddarth Gurajala
  • , V. Michael Holers
  • , Diane Horowitz
  • , Laura B. Hughes
  • , Kazuyoshi Ishigaki
  • , Lionel B. Ivashkiv
  • , Judith A. James
  • , Anna Helena Jonsson
  • , Joyce B. Kang
  • , Gregory Keras
  • , Ilya Korsunsky
  • , Amit Lakhanpal
  • , James A. Lederer
  • , Zhihan J. Li
  • , Yuhong Li
  • , Katherine P. Liao
  • , Arthur M. Mandelin II
  • , Ian Mantel
  • , Mark Maybury
  • , Joseph Mears
  • , Nida Meednu
  • , Nghia Millard
  • , Larry W. Moreland
  • , Aparna Nathan
  • , Alessandra Nerviani
  • , Dana E. Orange
  • , Harris Perlman
  • , Costantino Pitzalis
  • , Javier Rangel-Moreno
  • , Deepak A. Rao
  • , Karim Raza
  • , Yakir Reshef
  • , Christopher Ritchlin
  • , Felice Rivellese
  • , William H. Robinson
  • , Laurie Rumker
  • , Ilfita Sahbudin
  • , Jennifer A. Seifert
  • , Kamil Slowikowski
  • , Melanie H. Smith
  • , Darren Tabechian
  • , Dagmar Scheel-Toellner
  • , Paul J. Utz
  • , Dana Weisenfeld
  • , Michael H. Weisman
  • , Qian Xiao
  •  & Fan Zhang

Contributions

S.S. and S.R. conceived the work and wrote the manuscript with critical input from co-authors. S.S. and K. Weinand analyzed the arthritis-tissue dataset and S.S. analyzed publicly available datasets with help and guidance from K.K.D., K.J., M.K., A.M., A.L.P. and S.R. G.F.M.W., Z.Z., M.B.B., L.T.D. and K. Wei provided samples and generated the arthritis-tissue dataset. S.I. refactored the SCENT software implementation as an R package.

Corresponding author

Correspondence to Soumya Raychaudhuri.

Ethics declarations

Competing interests

S.R. is a founder for Mestag, Inc., a scientific advisor for Rheos, Jannsen and Pfizer, and serves as a consultant for Sanofi and Abbvie. The other authors declare no competing interests.

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Extended data

Extended Data Fig. 1 Distribution of gene expression counts in single-cell RNA-seq and statistics from association between gene expression and chromatin accessibility under null simulation.

a. In an example dataset of arthritis-dataset, mean gene count was strongly correlated with standard deviation of the gene count. b. The correlation between max expression count per gene (x-axis) and the mean naïve association chi-square values (χ2) from Poisson regression between gene expression and chromatin accessibility under null simulation (y-axis). c. The quantile-quantile (QQ) plot of two-sided P values from the Poisson regression between gene expression count and chromatin accessibility under null simulation. d. The QQ plot of two-sided P values from the negative binomial regression between gene expression count and chromatin accessibility under null simulation. e. The QQ plot of two-sided P values from the linear regression between log-normalized and inverse-normal-transformed gene expression and chromatin accessibility under null simulation. f. The QQ plot of two-sided P values estimated from bootstrapping based on the statistics distributions from the Poisson regression between gene expression count and chromatin accessibility under null simulation. g. The QQ plot of two-sided P values estimated from bootstrapping based on the statistics distributions from the negative binomial regression between gene expression count and chromatin accessibility under null simulation. h. Computational runtime benchmarking for Poisson regression with binarized ATAC-seq peak (red), negative binomial regression with binarized ATAC-seq peak (teal), and Poisson regression with non-binarized ATAC-seq peak (blue). The values are relative to the computational time for Poisson regression, and bars are the mean across n=100 randomly selected peak-gene pairs. Horizontal lines (error bars) indicate one standard deviation from the mean.

Extended Data Fig. 2 Schematic overview of SCENT model using Poisson regression and non-parametric bootstrapping.

We first run Poisson regression associating the raw gene expression count (RNA-seq) with the peak accessibility (ATAC-seq) accounting for technical covariates across the entire cells in the multimodal data to estimate βpeak. Then, we resampled cells with replacement from the full data in each of the bootstrapping round and re-estimated \({\beta {\prime} }_{{peak}}\) for N times. We compared this empirical distribution of \({\beta {\prime} }_{{peak}}\) against the null hypothesis (\({\beta {\prime} }_{{peak}}\) = 0) to derive the significance of βpeak (that is, two-sided bootstrapping-based P value = Pbootstrap).

Extended Data Fig. 3 The QQ plot of SCENT P values by bootstrapping.

We applied SCENT to each of 23 broad cell types from 9 single-cell multimodal datasets. Each QQ plot represents two-sided Pbootstrap values in each cell type in each dataset (a. arthritis-tissue, b. public PBMC, c. NeurIPS, d. SHARE-seq, e. Dogma-seq (control), f. Dogma-seq (stimulated) g. NEAT-seq, h. Brain, i. Pituitary.

Extended Data Fig. 4 Properties of SCENT peaks.

a. The number of significant SCENT peaks per gene across genes we investigated in at least one dataset-cell type pair. b. The number of significant gene-peak pairs discovered by SCENT with FDR < 10% in each dataset (y-axis) as a function of the total number of ATAC-seq fragments in each dataset (x-axis), colored by the dataset. c. The number of significant gene-peak pairs discovered by SCENT with FDR < 10% in each dataset (y-axis) as a function of the total number of unique RNA molecules in each dataset (x-axis), colored by the dataset. d. The effect size correlation r by Pearson’s correlation between arthritis-tissue dataset and the other dataset for the same cell type (left) and the directional (sign) concordance between arthritis-tissue dataset and the other dataset for the same cell type (right). e. Fraction of overlap with ENCODE cCREs in SCENT (teal) or non-SCENT peaks (orange) in each dataset and random set of cis-non-coding regions (pink). f. The mean Δ phastCons score for SCENT with excluding promoter peaks (teal) and all cis-ATAC peaks with excluding promoter peaks (yellow) in each of the three example multimodal datasets. The bars indicate the 95% CI by bootstrapping genes (nbootstrap=1000). g. The mean Δ phastCons score between SCENT peaks and TSS-distance-matched non-SCENT peaks across all the genes. The bars indicate the 95% CI by bootstrapping genes (nbootstrap=1000).

Extended Data Fig. 5 Mutational constraint on genes with a high number of SCENT peaks.

For each gene, the number of SCENT peaks were counted and binned as shown in the x-asis, and mutational constraint metric (pLI (the probability of being loss of function intolerant): a, LOEUF (the loss-of-function observed/expected upper bound fraction): b) for genes within each bin are shown as a violin plot on the y-axis. The dots indicate the mean score in each bin, and the error bars indicate one standard deviation from the mean. Each bin consists of 555-4071 genes in a and 568-4265 genes in b.

Extended Data Fig. 6 Causal variant enrichment for eQTLs.

a. The mean causal variant enrichment for eQTL within SCENT peaks with excluding all promoters (teal) or cis-regulatory ATAC-seq peaks with excluding all promoters (yellow) in each dataset. b. The mean causal variant enrichment for eQTL within SCENT peaks (teal) or non-SCENT peaks with matching distance to TSS (pink). c. Comparison of the mean causal variant enrichment for eQTL (y-axis) among SCENT (teal), ArchR (pink), and Signac (purple) as a function of the number of significant peak-gene pairs at each threshold of significance by FDR in SCENT and correlation r in ArchR and Signac. d. Comparison of the mean causal variant enrichment for eQTL among SCENT, ArchR, and Signac as a function of the number of significant peak-gene pairs at each threshold of FDR in SCENT, ArchR and Signac. The ArchR results with > 180,000 peak-gene linkages are omitted. e. Comparison of the mean causal variant enrichment for eQTL among SCENT, ArchR, and ArchR filtered on RNA expression as a function of the number of significant peak-gene pairs. f. Comparison of the mean causal variant enrichment for eQTL among SCENT, Signac, and Signac filtered on RNA expression as a function of the number of significant peak-gene pairs. g. Comparison of the mean causal variant enrichment for eQTL among SCENT, the default Pearson’s correlation version of Signac, and the optional Spearman’s correlation version of Signac as a function of the number of significant peak-gene pairs. h. Comparison of the mean causal variant enrichment for eQTL among original SCENT (Poisson regression + non-parametric bootstrapping), Poisson-only strategy without bootstrapping, and Cicero (correlation method using sc-ATAC-seq alone) as a function of the number of significant peak-gene pairs up to 100,000 peak-gene linkages. i. Comparison of the mean causal variant enrichment for eQTL between SCENT and Cicero peaks with adding all accessible promoter regions (1 kb regions from TSS) to account for potential promoter bias. j. Tissue-specific causal variant enrichment within SCENT peaks. The dots and lines are colored by the eQTL source tissue in GTEx that we assessed. In all panels, the bars indicate 95% confidence intervals by bootstrapping genes (nbootstrap=1000).

Extended Data Fig. 7 Causal variant enrichment for GWAS.

a and b. The mean causal variant enrichment for GWAS within cell-type-specific and aggregated SCENT enhancers (teal), ENCODE cCREs (pink), group-specific and aggregated EpiMap enhancers (red) and sample-specific and aggregated ABC enhancers (blue). GWAS results were based on FinnGen (a) and UK Biobank (b). The bars indicate 95% confidence intervals by bootstrapping traits (nbootstrap=1000). c. The mean causal variant enrichment for FinnGen GWAS (see Methods) within SCENT peaks with excluding all promoters (teal) or cis-regulatory ATAC-seq peaks with excluding all promoters (yellow) in each of the 9 single-cell datasets. The bars indicate 95% confidence intervals by bootstrapping traits (nbootstrap=1000). d. The mean causal variant enrichment for FinnGen GWAS (see Methods) within SCENT peaks (teal) or non-SCENT peaks with matching distance to TSS (pink) in each of the 9 single-cell datasets. The bars indicate 95% confidence intervals by bootstrapping traits (nbootstrap=1000). e. The fraction of known genes from Mendelian autoimmune diseases among all the genes identified by SCENT, EpiMap, and ABC model. The color of the bars indicates the cell types in each linking method.

Extended Data Fig. 8 Causal variant enrichment for GWAS and comparison with published bulk methods and single-cell methods.

a. Comparison of the mean causal variant enrichment for FinnGen GWAS (y-axis) among SCENT (teal), EpiMap (red), and ABC model (blue) as a function of the number of significant peak-gene pairs (x-axis) at each threshold of significance. The bars indicate 95% confidence intervals by bootstrapping traits (nbootstrap=1000). b. We calculated the causal variant enrichment for FinnGen GWAS among SCENT (teal), EpiMap (reds), and ABC model (blues) by changing the PIP thresholds in defining putative causal variants from fine-mapping. The bars indicate 95% confidence intervals by bootstrapping traits (nbootstrap=1000). c and d. The mean causal variant enrichment for GWAS within SCENT enhancers (teal), ArchR (pink) and Signac enhancers (purple). GWAS results were based on FinnGen (c) and UK Biobank (d) using the FDR < 10% threshold in each software and eight benchmarking datasets (see Methods). The bars indicate 95% confidence intervals by bootstrapping traits (nbootstrap=1000).

Extended Data Fig. 9 SMAD3 locus in asthma GWAS.

Rs17293632 in asthma GWAS (a) was prioritized and connected to SMAD3 gene by SCENT in myeloid cells (b). The panel a is a GWAS regional plot, with x-axis representing the position of each genetic variant and y-axis representing -log10(P) from GWAS (a two-sided P value). The rs17293632 has a significant caQTL effect, as shown in c and d. In panel c, the read coverage from single-cell ATAC-seq in each of donors with heterozygous genotype at this accessible region is presented, and at rs17293632, we observed allele-specific increased accessibility with C allele when compared T allele across donors. In panel d, normalized chromatin accessibility based on the read coverage for an individual after regressing out covariates is presented by the genotype of rs17293632 (CC, CT and TT). The horizontal bars within boxes indicate the median, and the lower and upper hinges represent 25% and 75% quantile. The upper whisker extends from the hinge to the largest value no further than 1.5 * inter-quartile range (IQR) from the hinge. The lower whisker extends from the hinge to the smallest value at most 1.5 * IQR of the hinge. All individual points are plotted as dots.

Extended Data Fig. 10 Cells to be included in the regression framework.

a. An example situation of correlated gene expression without biological regulatory function. b. Benchmarking models for statistical power to define biologically plausible peak-gene linkage over false-associations due to correlated genes. c. Benchmarking results regarding cells and covariates included in the SCENT regression model. The x-axis represents the number of statistically significant peak-gene linkages among 5,000 randomly selected peak-gene linkages in cis, and the y-axis represents the number of statistically significant peak-gene linkages in cis divided by the number of statistically significant peak-gene linkages in trans among 5,000 randomly selected peak-gene linkages on different chromosomes, as a proxy metric for capability of identifying regulatory elements over ‘correlated’ elements. Red dots indicate the analyses conducted in all cells including different cell types (n = 8,881), whereas blue dots indicate the analyses conducted in only T cells (n = 8,881). d and e. False positive rate and precision for peak-gene linkages from analyses conducted in all cells (teal) or in only T cells (orange) by using experimentally validated enhancer-gene linkages (that is, CRISPR-Flow FISH data in d and H3K27ac data in e). False negative rate and precision were defined as follows: \(false\,negative\,rate=\#\,false\,negative/(\#\,true\,positive+\#\,false\,negative)=1-recall\).

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Sakaue, S., Weinand, K., Isaac, S. et al. Tissue-specific enhancer–gene maps from multimodal single-cell data identify causal disease alleles. Nat Genet 56, 615–626 (2024). https://doi.org/10.1038/s41588-024-01682-1

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