Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference.
Journal
Genome biology
ISSN: 1474-760X
Titre abrégé: Genome Biol
Pays: England
ID NLM: 100960660
Informations de publication
Date de publication:
18 Jan 2024
18 Jan 2024
Historique:
received:
28
02
2023
accepted:
30
11
2023
medline:
19
1
2024
pubmed:
19
1
2024
entrez:
18
1
2024
Statut:
epublish
Résumé
Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features. We propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC). Here we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight.
Sections du résumé
BACKGROUND
BACKGROUND
Modeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features.
RESULTS
RESULTS
We propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC).
CONCLUSION
CONCLUSIONS
Here we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight.
Identifiants
pubmed: 38238840
doi: 10.1186/s13059-023-03134-1
pii: 10.1186/s13059-023-03134-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
24Subventions
Organisme : NIEHS NIH HHS
ID : R01HD096770
Pays : United States
Informations de copyright
© 2024. The Author(s).
Références
Casamassimi A, Ciccodicola A. Transcriptional Regulation: Molecules, Involved Mechanisms, and Misregulation. Int J Mol Sci. 2019;20(6). https://doi.org/10.3390/ijms20061281 .
Chasman D, Fotuhi Siahpirani A, Roy S. Network-based approaches for analysis of complex biological systems. Curr Opin Biotechnol. 2016;39:157–66. https://doi.org/10.1016/j.copbio.2016.04.007 .
pubmed: 27115495
doi: 10.1016/j.copbio.2016.04.007
Cho DY, Kim YA, Przytycka TM. Chapter 5: Network Biology Approach to Complex Diseases. PLOS Comput Biol. 2012;8(12):1–11. https://doi.org/10.1371/journal.pcbi.1002820 .
doi: 10.1371/journal.pcbi.1002820
Ritchie MD, Holzinger ER, Li R, Pendergrass SA, Kim D. Methods of integrating data to uncover genotype-phenotype interactions. Nat Rev Genet. 2015;16(2):85–97. https://doi.org/10.1038/nrg3868 .
pubmed: 25582081
doi: 10.1038/nrg3868
Goode D, Obier N, Vijayabaskar MS, Lie-A-Ling M, Lilly A, Hannah R, et al. Dynamic gene regulatory networks drive hematopoietic specification and differentiation. Dev Cell. 2016;36(5):572–87. https://doi.org/10.1016/j.devcel.2016.01.024 .
pubmed: 26923725
pmcid: 4780867
doi: 10.1016/j.devcel.2016.01.024
Bernadskaya Y, Christiaen L. Transcriptional control of developmental cell behaviors. Ann Rev Cell Dev Biol. 2016;32(1):77–101. https://doi.org/10.1146/annurev-cellbio-111315-125218 .
doi: 10.1146/annurev-cellbio-111315-125218
Latchman D. Transcription factors: an overview. Int J Exp Pathol. 1993;74(5):417–22.
pubmed: 8217775
pmcid: 2002184
Paraiso KD, Cho JS, Yong J, Cho KWY. Chapter Two - Early Xenopus gene regulatory programs, chromatin states, and the role of maternal transcription factors. In: Peter IS, editor. Gene Regulatory Networks. vol. 139 of Current Topics in Developmental Biology. Academic Press; 2020. p. 35–60. https://doi.org/10.1016/bs.ctdb.2020.02.009 .
Schacht T, Oswald M, Eils R, Eichmüller SB, König R. Estimating the activity of transcription factors by the effect on their target genes. Bioinformatics. 2014;30(17):i401–7. https://doi.org/10.1093/bioinformatics/btu446 .
pubmed: 25161226
pmcid: 4147899
doi: 10.1093/bioinformatics/btu446
Arrieta-Ortiz ML, Hafemeister C, Bate AR, Chu T, Greenfield A, Shuster B, et al. An experimentally supported model of the Bacillus subtilis global transcriptional regulatory network. Mol Syst Biol. 2015;11(11). https://doi.org/10.15252/msb.20156236 .
Shi Q, Zhang C, Guo W, Zeng T, Lu L, Jiang Z, et al. Local network component analysis for quantifying transcription factor activities. Methods. 2017;124:25–35. https://doi.org/10.1016/j.ymeth.2017.06.018 . Integrative Analysis of Omics Data.
Kao KC, Yang YL, Boscolo R, Sabatti C, Roychowdhury V, Liao JC. Transcriptome-based determination of multiple transcription regulator activities in Escherichia coli by using network component analysis. Proc Natl Acad Sci USA. 2004;101(2):641–6. https://doi.org/10.1073/pnas.0305287101 .
pubmed: 14694202
doi: 10.1073/pnas.0305287101
Jackson CA, Castro DM, Saldi GA, Bonneau R, Gresham D. Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments. eLife. 2020;9:e51254. https://doi.org/10.7554/eLife.51254 .
pubmed: 31985403
pmcid: 7004572
doi: 10.7554/eLife.51254
Castro DM, de Veaux NR, Miraldi ER, Bonneau R. Multi-study inference of regulatory networks for more accurate models of gene regulation. PLOS Comput Biol. 2019;15(1):1–22. https://doi.org/10.1371/journal.pcbi.1006591 .
doi: 10.1371/journal.pcbi.1006591
Kamimoto K, Hoffmann CM, Morris SA. CellOracle: dissecting cell identity via network inference and in silico gene perturbation. bioRxiv. 2020. https://doi.org/10.1101/2020.02.17.947416 .
Chen Y, Li Y, Narayan R, Subramanian A, Xie X. Gene expression inference with deep learning. Bioinformatics. 2016;32(12):1832–9. https://doi.org/10.1093/bioinformatics/btw074 .
pubmed: 26873929
pmcid: 4908320
doi: 10.1093/bioinformatics/btw074
Chen L, Cai C, Chen V, Lu X. Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model. BMC Bioinformatics. 2016;17(1):S9. https://doi.org/10.1186/s12859-015-0852-1 .
doi: 10.1186/s12859-015-0852-1
Chicco D, Sadowski P, Baldi P. Deep autoencoder neural networks for gene ontology annotation predictions. In: Proceedings of the 5th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics. BCB ’14. New York: Association for Computing Machinery; 2014. p. 533–40. https://doi.org/10.1145/2649387.2649442 .
doi: 10.1145/2649387.2649442
Dwivedi SK, Tjärnberg A, Tegnér J, Gustafsson M. Deriving disease modules from the compressed transcriptional space embedded in a deep autoencoder. Nat Commun. 2020;11(1):856. https://doi.org/10.1038/s41467-020-14666-6 .
pubmed: 32051402
pmcid: 7016183
doi: 10.1038/s41467-020-14666-6
Chen HIH, Chiu YC, Zhang T, Zhang S, Huang Y, Chen Y. GSAE: an autoencoder with embedded gene-set nodes for genomics functional characterization. BMC Syst Biol. 2018;12(8):142. https://doi.org/10.1186/s12918-018-0642-2 .
pubmed: 30577835
pmcid: 6302374
doi: 10.1186/s12918-018-0642-2
Yuan Y, Bar-Joseph Z. Deep learning for inferring gene relationships from single-cell expression data. Proc Natl Acad Sci. 2019;116(52):27151–8. https://doi.org/10.1073/pnas.1911536116 .
pubmed: 31822622
pmcid: 6936704
doi: 10.1073/pnas.1911536116
Lopez R, Regier J, Cole MB, Jordan MI, Yosef N. Deep generative modeling for single-cell transcriptomics. Nat Methods. 2018;15(12):1053–8. https://doi.org/10.1038/s41592-018-0229-2 .
pubmed: 30504886
pmcid: 6289068
doi: 10.1038/s41592-018-0229-2
Novakovsky G, Dexter N, Libbrecht MW, Wasserman WW, Mostafavi S. Obtaining genetics insights from deep learning via explainable artificial intelligence. Nat Rev Genet. 2022. https://doi.org/10.1038/s41576-022-00532-2 .
Covert I, Lundberg S, Lee SI. Explaining by removing: a unified framework for model explanation. arXiv. 2020 . https://doi.org/10.48550/ARXIV.2011.14878 .
Sung AH. Ranking importance of input parameters of neural networks. Exp Syst Appl. 1998;15(3):405–11. https://doi.org/10.1016/S0957-4174(98)00041-4 .
doi: 10.1016/S0957-4174(98)00041-4
Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int J Comput Vis. 2019;128(2):336–59. https://doi.org/10.1007/s11263-019-01228-7 .
doi: 10.1007/s11263-019-01228-7
Di Bernardo D, Gardner TS, Collins JJ. Robust identification of large genetic networks. Pac Symp Biocomput Pacific Symp Biocomput. 2004;497:486–97.
Bonneau R, Reiss DJ, Shannon P, Facciotti M, Hood L, Baliga NS, et al. The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 2006;7(5):R36. https://doi.org/10.1186/gb-2006-7-5-r36 .
pubmed: 16686963
pmcid: 1779511
doi: 10.1186/gb-2006-7-5-r36
Bonneau R. Learning biological networks: from modules to dynamics. Nat Chem Biol. 2008;4(11):658–64. https://doi.org/10.1038/nchembio.122 .
pubmed: 18936750
doi: 10.1038/nchembio.122
Hastie T, Tibshirani R, Friedman J. The elements of statistical learning: data mining, inference and prediction. 2nd ed. Springer; 2009. https://hastie.su.domains/ElemStatLearn/ .
Tjärnberg A, Nordling TEM, Studham M, Sonnhammer ELL. Optimal Sparsity Criteria for Network Inference. J Comput Biol. 2013;20(5):398–408. https://doi.org/10.1089/cmb.2012.0268 .
pubmed: 23641867
doi: 10.1089/cmb.2012.0268
Wonkap SK, Butler G. BENIN: Biologically enhanced network inference. J Bioinform Comput Biol. 2020;18(03):2040007. https://doi.org/10.1142/S0219720020400077 .
pubmed: 32698722
doi: 10.1142/S0219720020400077
Madar A, Greenfield A, Vanden-Eijnden E, Bonneau R. DREAM3: network inference using dynamic context likelihood of relatedness and the inferelator. PLoS ONE. 2010;5(3):e9803–e9803. https://doi.org/10.1371/journal.pone.0009803 .
pubmed: 20339551
pmcid: 2842436
doi: 10.1371/journal.pone.0009803
Huynh-Thu VA, Irrthum A, Wehenkel L, Geurts P. Inferring regulatory networks from expression data using tree-based methods. PLoS ONE. 2010;5(9):1–10. https://doi.org/10.1371/journal.pone.0012776 .
doi: 10.1371/journal.pone.0012776
Magnusson R, Gustafsson M. LiPLike: towards gene regulatory network predictions of high certainty. Bioinformatics. 2020;36(8):2522–9. https://doi.org/10.1093/bioinformatics/btz950 .
pubmed: 31904818
pmcid: 7178405
doi: 10.1093/bioinformatics/btz950
Fortelny N, Bock C. Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data. Genome Biol. 2020;21(1):190. https://doi.org/10.1186/s13059-020-02100-5 .
pubmed: 32746932
pmcid: 7397672
doi: 10.1186/s13059-020-02100-5
Aibar S, González-Blas CB, Moerman T, Huynh-Thu VA, Imrichova H, Hulselmans G, et al. SCENIC: single-cell regulatory network inference and clustering. Nat Methods. 2017;14(11):1083–6. https://doi.org/10.1038/nmeth.4463 .
pubmed: 28991892
pmcid: 5937676
doi: 10.1038/nmeth.4463
Bauckhage C, Ojeda C, Schücker J, Sifa R, Wrobel S. Informed machine learning through functional composition. In: Lernen, Wissen, Daten, Analysen. 2018. https://api.semanticscholar.org/CorpusID:52162764 .
Buenrostro JD, Giresi PG, Zaba LC, Chang HY, Greenleaf WJ. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat Methods. 2013;10:1213–8. https://doi.org/10.1038/nmeth.2688 .
pubmed: 24097267
pmcid: 3959825
doi: 10.1038/nmeth.2688
Lee TI, Rinaldi NJ, Robert F, Odom DT, Bar-Joseph Z, Gerber GK, et al. Transcriptional regulatory networks in Saccharomyces cerevisiae. Science. 2002;298(5594):799–804. https://doi.org/10.1126/science.1075090 .
pubmed: 12399584
doi: 10.1126/science.1075090
Skok Gibbs C, Jackson CA, Saldi GA, Tjärnberg A, Shah A, Watters A, et al. High-performance single-cell gene regulatory network inference at scale: the Inferelator 3.0. Bioinformatics. 2022;38(9):2519–28. https://doi.org/10.1093/bioinformatics/btac117 .
pubmed: 35188184
pmcid: 9048651
doi: 10.1093/bioinformatics/btac117
Teixeira MC, Viana R, Palma M, Oliveira J, Galocha M, Mota MN, et al. YEASTRACT+: a portal for the exploitation of global transcription regulation and metabolic model data in yeast biotechnology and pathogenesis. Nucleic Acids Res. 2023;51(D1):D785–91. https://doi.org/10.1093/nar/gkac1041
Anderson-Sprecher R. Model comparisons and R
Goodfellow I, Bengio Y, Courville A. Deep Learning. MIT Press; 2016. http://www.deeplearningbook.org .
Bähler J. Cell-cycle control of gene expression in budding and fission yeast. Annu Rev Genet. 2005;39:69–94.
pubmed: 16285853
doi: 10.1146/annurev.genet.39.110304.095808
Zaman S, Lippman SI, Zhao X, Broach JR. How Saccharomyces responds to nutrients. Annu Rev Genet. 2008;42:27–81.
pubmed: 18303986
doi: 10.1146/annurev.genet.41.110306.130206
Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30.
pubmed: 10592173
pmcid: 102409
doi: 10.1093/nar/28.1.27
Hao Y, Hao S, Andersen-Nissen E, Mauck WM, Zheng S, Butler A, et al. Integrated analysis of multimodal single-cell data. Cell. 2021;184(13):3573-3587.e29. https://doi.org/10.1016/j.cell.2021.04.048 .
pubmed: 34062119
pmcid: 8238499
doi: 10.1016/j.cell.2021.04.048
Traag VA, Waltman L, van Eck NJ. From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep. 2019;9(1):5233. https://doi.org/10.1038/s41598-019-41695-z .
Sangaletti S, Tripodo C, Chiodoni C, Guarnotta C, Cappetti B, Casalini P, et al. Neutrophil extracellular traps mediate transfer of cytoplasmic neutrophil antigens to myeloid dendritic cells toward ANCA induction and associated autoimmunity. Blood. 2012;120(15):3007–18. https://doi.org/10.1182/blood-2012-03-416156 .
pubmed: 22932797
doi: 10.1182/blood-2012-03-416156
Luo Q, Ma X, Wahl SM, Bieker JJ, Crossley M, Montaner LJ. Activation and repression of interleukin-12 p40 transcription by erythroid Kruppel-like factor in macrophages *. J Biol Chem. 2004;279(18):18451–6. https://doi.org/10.1074/jbc.M400320200 .
pubmed: 14976188
doi: 10.1074/jbc.M400320200
Cobaleda C, Schebesta A, Delogu A, Busslinger M. Pax5: the guardian of B cell identity and function. Nat Immunol. 2007;8(5):463–70. https://doi.org/10.1038/ni1454 .
pubmed: 17440452
doi: 10.1038/ni1454
Malhotra N, Kang J. SMAD regulatory networks construct a balanced immune system. Immunology. 2013;139(1):1–10. https://doi.org/10.1111/imm.12076 .
pubmed: 23347175
pmcid: 3634534
doi: 10.1111/imm.12076
Trikha P, Moseman JE, Thakkar A, Campbell AR, Elmas E, Foltz JA, et al. Defining the AHR-regulated transcriptome in NK cells reveals gene expression programs relevant to development and function. Blood Adv. 2021;5(22):4605–18. https://doi.org/10.1182/bloodadvances.2021004533 .
pubmed: 34559190
pmcid: 8759121
doi: 10.1182/bloodadvances.2021004533
Kano Si, Sato K, Morishita Y, Vollstedt S, Kim S, Bishop K, et al. The contribution of transcription factor IRF1 to the interferon-[Formula: see text]–interleukin 12 signaling axis and TH1 versus TH-17 differentiation of CD4+ T cells. Nat Immunol. 2008;9(1):34–41. https://doi.org/10.1038/ni1538 .
Korinfskaya S, Parameswaran S, Weirauch MT, Barski A. Runx transcription factors in T cells-what is beyond thymic development? Front Immunol. 2021;12. https://doi.org/10.3389/fimmu.2021.701924 .
Liu X, Wang Y, Lu H, Li J, Yan X, Xiao M, et al. Genome-wide analysis identifies NR4A1 as a key mediator of T cell dysfunction. Nature. 2019;567(7749):525–9. https://doi.org/10.1038/s41586-019-0979-8 .
pubmed: 30814730
pmcid: 6507425
doi: 10.1038/s41586-019-0979-8
Arunachalam PS, Wimmers F, Mok CKP, Perera RAPM, Scott M, Hagan T, et al. Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans. Science. 2020;369(6508):1210–20. https://doi.org/10.1126/science.abc6261 .
pubmed: 32788292
pmcid: 7665312
doi: 10.1126/science.abc6261
Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM III, et al. Comprehensive integration of single-cell data. Cell. 2019;177(7):1888-1902.e21. https://doi.org/10.1016/j.cell.2019.05.031 .
pubmed: 31178118
pmcid: 6687398
doi: 10.1016/j.cell.2019.05.031
Kingma DP, Ba J. Adam: A Method for Stochastic Optimization. 2014.
Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et al. PyTorch: an imperative style, high-performance deep learning library. In: Wallach H, Larochelle H, Beygelzimer A, d Alché-Buc F, Fox E, Garnett R, editors. Advances in Neural Information Processing Systems. vol 32. Curran Associates, Inc.; 2019. p. 8024–35. https://openreview.net/forum?id=BJJsrmfCZ .
Hilt DE, Seegrist DW, United States. Forest Services. Northeastern Forest Experiment Station (Radnor. Pa.). Ridge, a computer program for calculating ridge regression estimates. vol. no.236. Upper Darby, Pa, Dept. of Agriculture, Forest Service, Northeastern Forest Experiment Station, 1977; 1977. https://www.biodiversitylibrary.org/bibliography/68934 .
Beck JV, Arnold KJ. Parameter estimation in engineering and science. Probability and Statistics Series. Wiley; 1977. https://books.google.com/books?id=_qAYgYN87UQC .
Golub GH, Heath M, Wahba G. Generalized cross-validation as a method for choosing a good ridge parameter. Technometrics. 1979;21(2):215–23. https://doi.org/10.2307/1268518 .
Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R. Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res. 2014;15(56):1929–58.
Vogel C, Marcotte EM. Insights into the regulation of protein abundance from proteomic and transcriptomic analyses. Nat Rev Genet. 2012;13:227–32. https://doi.org/10.1038/nrg3185 .
pubmed: 22411467
pmcid: 3654667
doi: 10.1038/nrg3185
Nicolas P, Mäder U, Dervyn E, Rochat T, Leduc A, Pigeonneau N, et al. Condition-dependent transcriptome reveals high-level regulatory architecture in Bacillus subtilis. Science. 2012;335(6072):1103–6.
pubmed: 22383849
doi: 10.1126/science.1206848
Tchourine K, Vogel C, Bonneau R. Condition-specific modeling of biophysical parameters advances inference of regulatory networks. Cell Rep. 2018;23(2):376–88. https://doi.org/10.1016/j.celrep.2018.03.048 .
pubmed: 29641998
pmcid: 5987223
doi: 10.1016/j.celrep.2018.03.048
Hackett SR, Baltz EA, Coram M, Wranik BJ, Kim G, Baker A, et al. Learning causal networks using inducible transcription factors and transcriptome-wide time series. Mol Syst Biol. 2020;16(3):e9174. https://doi.org/10.15252/msb.20199174 .
pubmed: 32181581
pmcid: 7076914
doi: 10.15252/msb.20199174
Monteiro PT, Oliveira J, Pais P, Antunes M, Palma M, Cavalheiro M, et al. YEASTRACT+: a portal for cross-species comparative genomics of transcription regulation in yeasts. Nucleic Acids Res. 2020;48(D1):D642–9.
pubmed: 31586406
doi: 10.1093/nar/gkz859
Jariani A, Vermeersch L, Cerulus B, Perez-Samper G, Voordeckers K, Van Brussel T, et al. A new protocol for single-cell RNA-seq reveals stochastic gene expression during lag phase in budding yeast. eLife. 2020;9:e55320.
Spellman PT, Sherlock G, Zhang MQ, Iyer VR, Anders K, Eisen MB, et al. Comprehensive identification of cell cycle-regulated genes of the yeast Saccharomyces cerevisiae by microarray hybridization. Mol Biol Cell. 1998;9(12):3273–97.
pubmed: 9843569
pmcid: 25624
doi: 10.1091/mbc.9.12.3273
Gasch AP, Yu FB, Hose J, Escalante LE, Place M, Bacher R, et al. Single-cell RNA sequencing reveals intrinsic and extrinsic regulatory heterogeneity in yeast responding to stress. PLoS Biol. 2017;15(12): e2004050.
pubmed: 29240790
pmcid: 5746276
doi: 10.1371/journal.pbio.2004050
genomics X. Paired PBMC scRNA-seq and scATAC-seq. 2022. https://support.10xgenomics.com/single-cell-multiome-atac-gex/datasets/1.0.0/pbmc_granulocyte_sorted_10k . Accessed 15 Dec 2022.
Danese A, Richter ML, Chaichoompu K, Fischer DS, Theis FJ, Colomé-Tatché M. EpiScanpy: integrated single-cell epigenomic analysis. Nat Commun. 2021;12(1):5228. https://doi.org/10.1038/s41467-021-25131-3 .
pubmed: 34471111
pmcid: 8410937
doi: 10.1038/s41467-021-25131-3
Wolf FA, Angerer P, Theis FJ. SCANPY: large-scale single-cell gene expression data analysis. Genome Biol. 2018;19(1):15. https://doi.org/10.1186/s13059-017-1382-0 .
pubmed: 29409532
pmcid: 5802054
doi: 10.1186/s13059-017-1382-0
Arunachalam PS, Wimmers F, Mok CKP, Perera RAPM, Scott M, Hagan T, et al. Healty and Covid patient single cell data. 2023. https://ndownloader.figshare.com/files/27458837 . Accessed 11 Jan 2023.
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O, et al. Scikit-learn: machine learning in Python. J Mach Learn Res. 2011;12:2825–30.
Hunter JD. Matplotlib: a 2D graphics environment. Comput Sci Eng. 2007;9(3):90–5. https://doi.org/10.1109/MCSE.2007.55 .
doi: 10.1109/MCSE.2007.55
Waskom ML. seaborn: statistical data visualization. J Open Source Softw. 2021;6(60):3021. https://doi.org/10.21105/joss.03021 .
Tjärnberg A. StrUcture Primed Inference of Regulation using latent Factor ACTivity. 2023. https://gitlab.com/Xparx/supirfactor . Accessed 31 Jan 2023.
Tjärnberg A, Beheler-Amass M, Jackson C, Christiaen L, Gresham D, Bonneau R. SupirFactor published models and generated data. Zenodo. 2023. https://doi.org/10.5281/zenodo.10161546 .