No retraction, correction or concern found when last checked on Nov 3, 2026.

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

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.
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.
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.
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.
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.
Design and computational analysis of single-cell RNA-sequencing experiments · 2016
No notice
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.
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.
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.
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.
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.
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.
Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool · 2013
No notice
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.
Single-cell RNA-seq reveals novel regulators of human embryonic stem cell differentiation to definitive endoderm · 2016
No notice
15.
Coifman R.R., Lafon S. (2006 ) Diffusion maps. Appl. Comput. Harmon. Anal., 21, 5–30.
Not in our library
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.
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.
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.
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.
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.
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.
Granger C.W.J. (1969 ) Investigating Causal Relations by Econometric Models and Cross-spectral Methods. Econometrica, 37, 424–438.
Not in our library
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.
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.
Higham D.J. (2001 ) An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations. SIAM Rev., 43, 525–546.
Not in our library
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.
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.
Inferring Regulatory Networks from Expression Data Using Tree-Based Methods · 2010
No notice
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.
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis · 2016
No notice
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.
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.
Single-cell mRNA sequencing identifies subclonal heterogeneity in anti-cancer drug responses of lung adenocarcinoma cells · 2015
No notice
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.
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.
Kumar R.M. et al (2014 ) Deconstructing transcriptional heterogeneity in pluripotent stem cells. Nature, 516, 56–61.
Not in our library10.1038/nature13920
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.
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.
Single-cell transcriptome sequencing: recent advances and remaining challenges · 2016
No notice
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
41.
Marbach D. et al (2012 ) Wisdom of crowds for robust gene network inference. Nat. Methods, 9, 796–804.
Not in our library10.1038/nmeth.2016
42.
Marco E. et al (2014 ) Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape. Proc. Natl. Acad. Sci. USA, 111, 5643–5650.
Not in our library10.1073/pnas.1408993111
43.
Massey F.J. (1951 ) The Kolmogorov–Smirnov Test for Goodness of Fit. J. Am. Stat. Assoc., 46, 68–78.
Not in our library
44.
SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation · 2017
No notice
45.
Moignard V. et al (2015 ) Decoding the regulatory network of early blood development from single-cell gene expression measurements. Nat. Biotechnol., 33, 269–276.
Not in our library10.1038/nbt.3154
46.
Moris N. et al (2016 ) Transition states and cell fate decisions in epigenetic landscapes. Nat. Rev. Genet., 17, 693–703.
Not in our library10.1038/nrg.2016.98
47.
Murphy T.L. et al (2013 ) Specificity through cooperation: BATF–IRF interactions control immune-regulatory networks. Nat. Rev. Immunol., 13, 499–509.
Not in our library10.1038/nri3470
48.
Ocone A. et al (2015 ) Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data. Bioinformatics, 31, i89–i96.
Not in our library10.1093/bioinformatics/btv257
49.
Papili Gao N. et al (2016 ) Gene regulatory network inference using time-stamped cross-sectional single cell expression data. IFAC-PapersOnLine, 49, 147–152.
Not in our library
50.
Pieprzyk M., High H. (2009 ) Fluidigm Dynamic Arrays provide a platform for single-cell gene expression analysis. Nat. Methods, 6, iii–iv.
Not in our library
51.
ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis · 2015
No notice
52.
Pina C. et al (2015 ) Single-cell network analysis identifies DDIT3 as a nodal lineage regulator in hematopoiesis. Cell Rep., 11, 1503–1510.
Not in our library10.1016/j.celrep.2015.05.016
53.
From knockouts to networks: establishing direct cause-effect relationships through graph analysis. · 2010
Not checked
54.
Pollen A.A. et al (2014 ) Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex. Nat. Biotechnol., 32, 1053–1058.
Not in our library10.1038/nbt.2967
55.
Single-Cell-Based Analysis Highlights a Surge in Cell-to-Cell Molecular Variability Preceding Irreversible Commitment in a Differentiation Process · 2016
No notice
56.
Rosenberg A.B. et al (2017 ) Scaling single cell transcriptomics through split pool barcoding. bioRxiv.
Not in our library
57.
Sankaran V.G. et al (2009 ) Developmental and species-divergent globin switching are driven by BCL11A. Nature, 460, 1093–1097.
Not in our library10.1038/nature08243
58.
Schaffter T. et al (2011 ) GeneNetWeaver: in silico benchmark generation and performance profiling of network inference methods. Bioinformatics, 27, 2263–2270.
Not in our library10.1093/bioinformatics/btr373
59.
Shalek A.K. et al (2014 ) Single-cell RNA-seq reveals dynamic paracrine control of cellular variation. Nature, 510, 363–369.
Not in our library10.1038/nature13437
60.
Simpson E.H. (1951 ) The Interpretation of Interaction in Contingency Tables. J. R. Stat. Soc. Ser. B, 13, 238–241.
Not in our library
61.
Stegle O. et al (2015 ) Computational and analytical challenges in single-cell transcriptomics. Nat. Rev. Genet., 16, 133–145.
Not in our library10.1038/nrg3833
62.
Szederkényi G. et al (2011 ) Inference of complex biological networks: distinguishability issues and optimization-based solutions. BMC Syst. Biol., 5, 177.
Not in our library10.1186/1752-0509-5-177
63.
Telen M.J. (2000 ) Red blood cell surface adhesion molecules: their possible roles in normal human physiology and disease. Semin. Hematol., 37, 130–142.
Not in our library10.1016/s0037-1963(00)90038-6
64.
Teles J. et al (2013 ) Transcriptional regulation of lineage commitment–a stochastic model of cell fate decisions. PLoS Comput. Biol., 9, e1003197.
Not in our library10.1371/journal.pcbi.1003197
65.
Tibshirani R. (1996 ) Regression shrinkage and selection via the lasso. J R. Stat. Soc. B, 267–288.
Not in our library
66.
Tomaru Y. et al (2009 ) Regulatory interdependence of myeloid transcription factors revealed by Matrix RNAi analysis. Genome Biol., 10, R121.
Not in our library10.1186/gb-2009-10-11-r121
67.
Trapnell C. et al (2014 ) The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells. Nat. Biotechnol., 32, 381–386.
Not in our library10.1038/nbt.2859
68.
Ud-Dean S.M.M., Gunawan R. (2014 ) Ensemble inference and inferability of gene regulatory networks. PLoS One, 9, e103812.
Not in our library10.1371/journal.pone.0103812
69.
Vallejos C.A. et al (2016 ) Beyond comparisons of means: understanding changes in gene expression at the single-cell level. Genome Biol., 17, 70.
Not in our library10.1186/s13059-016-0930-3
70.
Vitezic M. et al (2010 ) Building promoter aware transcriptional regulatory networks using siRNA perturbation and deepCAGE. Nucleic Acids Res., 38, 8141–8148.
Not in our library10.1093/nar/gkq729
71.
Wilkinson D.J. (2009 ) Stochastic modelling for quantitative description of heterogeneous biological systems. Nat. Rev. Genet., 10, 122–133.
Not in our library10.1038/nrg2509
72.
Xiao Y. (2017 ) A fast algorithm for two-dimensional Kolmogorov–Smirnov two sample tests. Comput. Stat. Data Anal., 105, 53–58.
Not in our library
73.
Xu C., Su Z. (2015 ) Identification of cell types from single-cell transcriptomes using a novel clustering method. Bioinformatics, 31, 1974–1980.
Not in our library10.1093/bioinformatics/btv088
74.
Yanagihara H., Satoh K. (2010 ) An unbiased Cp criterion for multivariate ridge regression. J. Multivar. Anal., 101, 1226–1238.
Not in our library
75.
Zou H., Hastie T. (2005 ) Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B, 67, 301–320.
Not in our library

Cited by papers we hold 4

Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer’s disease: review, recommendation, implementation and application · 2022
Not checked
Single-cell network biology for resolving cellular heterogeneity in human diseases · 2020
No notice
Single-cell transcriptomics unveils gene regulatory network plasticity · 2019
No notice
Mapping gene regulatory networks from single-cell omics data · 2018
No notice

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.