SINCERITIES: inferring gene regulatory networks from time-stamped single cell transcriptional expression profiles
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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.
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Single-cell RNA sequencing data enables cell type-specific trans-regulation inference through co-expression analysis.
“Single-cell RNA sequencing (scRNA-seq) data enables cell type-specific trans-regulation inference through co-expression analysis such as PIDC and SCENIC21–30.”
From Inferring gene regulatory networks from single-cell multiome data using atlas-scale external data (2025) · cites this paper as [24]
The Introduction and Discussion say that single-cell expression data can be used to infer gene regulatory networks while revealing cellular heterogeneity. But the Discussion describes SINCERITIES as inferring networks from time-stamped changes in expression distributions, not co-expression analysis; the paper does not establish the claim specifically for cell type-specific trans-regulation or identify PIDC and SCENIC as examples. (LLM, conf=0.84)
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- Authors
- Papili Gao, N., Ud-Dean, S. M. M., Gandrillon, O., & Gunawan, R.
- Affiliations
- Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland
- Columbia University (US)
- Lyon 1 Université (FR)
- SIB Swiss Institute of Bioinformatics (CH)
- Corresponding author
- Gunawan, Rudiyanto
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-09-14
- Volume
- 34
- Issue
- 2
- Pages
- 258-266
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC5860204
- OpenAlex
- W2951850020
- Cited by
- 235
- References
- 14
- Citations by year
- 2026: 18 · 2025: 48 · 2024: 37 · 2023: 38 · 2022: 30 · 2021: 23 · 2020: 20 · 2019: 13 · 2018: 6 · 2017: 2
- FWCI
- 4.58
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RERO DOC (Universität Bern) (submittedVersion)
- HAL (submittedVersion)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by)
- ETH Zürich Research Collection (cc-by)
- Funders
- Swiss National Science Foundation (157154)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- gene regulatory network inference, single-cell transcriptomics, ridge regression, partial correlation, cell-to-cell variability, chicken erythrocytes, erythroid development, BATF, transcriptional activation and repression
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