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SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles

Nan Papili Gao, S M Minhaz Ud-Dean, Olivier Gandrillon and 1 more

Bioinformatics · 2017 · CC BY

doi:10.1101/089110

Abstract

Motivation. Single cell transcriptional profiling opens up a new avenue in studying the functional role of cell-to-cell variability in physiological processes. The analysis of single cell expression profiles creates new challenges due to the distributive nature of the data and the stochastic dynamics of gene transcription process. The reconstruction of gene regulatory networks (GRNs) using single cell transcriptional profiles is particularly challenging, especially when directed gene-gene relationships are desired.

Results. We developed SINCERITIES (SINgle CEll Regularized Inference using TIme-stamped Expression profileS) for the inference of GRNs from single cell transcriptional profiles. We focused on time-stamped cross-sectional expression data, commonly generated from transcriptional profiling of single cells collected at multiple time points after cell stimulation. SINCERITIES recovers directed regulatory relationships among genes by employing regularized linear regression (ridge regression), using temporal changes in the distributions of gene expressions. Meanwhile, the modes of the gene regulations (activation and repression) come from partial correlation analyses between pairs of genes. We demonstrated the efficacy of SINCERITIES in inferring GRNs using in silico time-stamped single cell expression data and single cell transcriptional profiles of THP-1 monocytic human leukemia cells. The case studies showed that SINCERITIES could provide accurate GRN predictions, significantly better than other GRN inference algorithms such as TSNI, GENIE3 and JUMP3. Moreover, SINCERITIES has a low computational complexity and is amenable to problems of extremely large dimensionality. Finally, an application of SINCERITIES to single cell expression data of T2EC chicken erythrocytes pointed to BATF as a candidate novel regulator of erythroid development.

Availability and implementation. MATLAB and R version of SINCERITIES are freely available from the following websites: http://www.cabsel.ethz.ch/tools/sincerities.html and https://github.com/CABSEL/SINCERITIES. The single cell THP-1 and T2EC transcriptional profiles are available from the original publications (Kouno et al., 2013; Richard et al., 2016). The in silico single cell data are available on SINCERITIES websites.

Supplementary information. Supplementary data are available at Bioinformatics online.

References 75

  1. 1.

    Altschuler S.J., Wu L.F. (2010 ) Cellular Heterogeneity: Do Differences Make a Difference?Cell, 141, 559–563.

    Not in our library10.1016/j.cell.2010.04.033

  2. 2.

    Amir E.D. et al (2013 ) viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia. Nat. Biotechnol., 31, 545–552.

    Not in our library10.1038/nbt.2594

  3. 3.

    Anderson T.W. (1962 ) On the distribution of the Two-Sample Cramer-von Mises criterion. Ann. Math. Stat., 33, 1148–1159.

    Not in our library

  4. 4.

    Anderson T.W., Darling D.A. (1952 ) Asymptotic theory of certain ‘Goodness of Fit’ criteria based on stochastic processes. Ann. Math. Stat., 23, 193–212.

    Not in our library

  5. 5.
    Design and computational analysis of single-cell RNA-sequencing experiments · 2016
    No notice
  6. 6.

    Bansal M. et al (2006 ) Inference of gene regulatory networks and compound mode of action from time course gene expression profiles. Bioinformatics, 22, 815–822.

    Not in our library10.1093/bioinformatics/btl003

  7. 7.

    Bar-Joseph Z. et al (2012 ) Studying and modelling dynamic biological processes using time-series gene expression data. Nat. Rev. Genet., 13, 552–564.

    Not in our library10.1038/nrg3244

  8. 8.

    Bendall S.C. et al (2014 ) Single-cell trajectory detection uncovers progression and regulatory coordination in human B cell development. Cell, 157, 714–725.

    Not in our library10.1016/j.cell.2014.04.005

  9. 9.

    Buettner F. et al (2015 ) Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells. Nat. Biotechnol., 33, 155–160.

    Not in our library10.1038/nbt.3102

  10. 10.

    Buettner F. et al (2014 ) Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data. Bioinformatics, 30, 1867–1875.

    Not in our library10.1093/bioinformatics/btu134

  11. 11.

    Chang H.H. et al (2008 ) Transcriptome-wide noise controls lineage choice in mammalian progenitor cells. Nature, 453, 544–547.

    Not in our library10.1038/nature06965

  12. 12.
    Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool · 2013
    No notice
  13. 13.

    Chen H. et al (2015 ) Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development. Bioinformatics, 31, 1060–1066.

    Not in our library10.1093/bioinformatics/btu777

  14. 14.
    Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm · 2016
    No notice
  15. 15.

    Coifman R.R., Lafon S. (2006 ) Diffusion maps. Appl. Comput. Harmon. Anal., 21, 5–30.

    Not in our library

  16. 16.

    Coulon A. et al (2010 ) On the spontaneous stochastic dynamics of a single gene: complexity of the molecular interplay at the promoter. BMC Syst. Biol., 4, 2.

    Not in our library10.1186/1752-0509-4-2

  17. 17.

    Dazy S. et al (2003 ) The MEK-1/ERKs signalling pathway is differentially involved in the self-renewal of early and late avian erythroid progenitor cells. Oncogene, 22, 9205–9216.

    Not in our library10.1038/sj.onc.1207049

  18. 18.

    Fang M. et al (2013 ) Stochastic cytokine expression induces mixed T helper cell States. PLoS Biol., 11, e1001618.

    Not in our library10.1371/journal.pbio.1001618

  19. 19.

    Fasano G., Franceschini A. (1987 ) A multidimensional version of the Kolmogorov–Smirnov test. Mon. Not. R. Astron. Soc., 225, 155–170.

    Not in our library

  20. 20.

    Friedman J. et al (2010 ) Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw., 33, 1–22.

    Not in our library

  21. 21.

    Gandrillon O. et al (1999 ) TGF-beta cooperates with TGF-alpha to induce the self-renewal of normal erythrocytic progenitors: evidence for an autocrine mechanism. EMBO J., 18, 2764–2781.

    Not in our library10.1093/emboj/18.10.2764

  22. 22.

    Granger C.W.J. (1969 ) Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37, 424–438.

    Not in our library

  23. 23.

    Gupta P.B. et al (2011 ) Stochastic state transitions give rise to phenotypic equilibrium in populations of cancer cells. Cell, 146, 633–644.

    Not in our library10.1016/j.cell.2011.07.026

  24. 24.

    Haghverdi L. et al (2015 ) Diffusion maps for high-dimensional single-cell analysis of differentiation data. Bioinformatics, 31, 2989–2998.

    Not in our library10.1093/bioinformatics/btv325

  25. 25.

    Higham D.J. (2001 ) An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations. SIAM Rev., 43, 525–546.

    Not in our library

  26. 26.

    Hoppe P.S. et al (2016 ) Early myeloid lineage choice is not initiated by random PU.1 to GATA1 protein ratios. Nature, 535, 299–302.

    Not in our library10.1038/nature18320

  27. 27.

    Huang W. et al (2014 ) Time-variant clustering model for understanding cell fate decisions. Proc. Natl. Acad. Sci. USA, 111, E4797–E4806.

    Not in our library10.1073/pnas.1407388111

  28. 28.
    Inferring Regulatory Networks from Expression Data Using Tree-Based Methods · 2010
    No notice
  29. 29.

    Huynh-Thu V.A., Sanguinetti G. (2015 ) Combining tree-based and dynamical systems for the inference of gene regulatory networks. Bioinformatics, 31, 1614–1622.

    Not in our library10.1093/bioinformatics/btu863

  30. 30.
    TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis · 2016
    No notice
  31. 31.

    Justel A. et al (1997 ) A multivariate Kolmogorov–Smirnov test of goodness of fit. Stat. Probab. Lett., 35, 251–259.

    Not in our library

  32. 32.

    Kaimal V. et al (2010 ) ToppCluster: a multiple gene list feature analyzer for comparative enrichment clustering and network-based dissection of biological systems. Nucleic Acids Res., 38, W96–W102.

    Not in our library10.1093/nar/gkq418

  33. 33.
    Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells · 2015
    No notice
  34. 34.

    Kohavi R. (1995 ) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proc. 14th Int. Jt. Conf. Artif. Intell., pp. 1137–1143.

    Not in our library

  35. 35.

    Kouno T. et al (2013 ) Temporal dynamics and transcriptional control using single-cell gene expression analysis. Genome Biol., 14, R118.

    Not in our library10.1186/gb-2013-14-10-r118

  36. 36.

    Kumar R.M. et al (2014 ) Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature, 516, 56–61.

    Not in our library10.1038/nature13920

  37. 37.

    Lee J. et al (2014 ) Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions. Proc. Natl. Acad. Sci. USA, 111, 364–373.

    Not in our library10.1073/pnas.1304840111

  38. 38.

    Lim C.Y. et al (2016 ) BTR: training asynchronous Boolean models using single-cell expression data. BMC Bioinformatics, 17, 355.

    Not in our library10.1186/s12859-016-1235-y

  39. 39.
    Single-cell transcriptome sequencing: recent advances and remaining challenges · 2016
    No notice
  40. 40.

    Marbach D. et al (2009 ) Generating realistic in silico gene networks for performance assessment of reverse engineering methods. J. Comput. Biol., 16, 229–239.

    Not in our library10.1089/cmb.2008.09tt

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