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

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Date
2017Type
- Journal Article
Citations
Cited 58 times in
Web of Science
Cited 66 times in
Scopus
ETH Bibliography
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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. Show more
Permanent link
https://doi.org/10.3929/ethz-b-000230685Publication status
publishedExternal links
Journal / series
BioinformaticsVolume
Pages / Article No.
Publisher
Oxford University PressOrganisational unit
03898 - Gunawan, Rudiyanto (ehemalig)
Funding
157154 - Ensemble Inference of Gene Regulatory Networks (SNF)
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Show all metadata
Citations
Cited 58 times in
Web of Science
Cited 66 times in
Scopus
ETH Bibliography
yes
Altmetrics