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

Abstract

MotivationSingle 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.ResultsWe 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 implementationMATLAB 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.

Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data

“…Here, to quantitatively reconstruct the epigenetic landscape, an underlying GRN structure must be assumed a priori. In reality, the GRN driving cell fate determination is complex and proved to be challenging to infer from data 6,[24][25][26][27][28] . Even when the complete regulatory system is known, the high-dimensionality of the parameter space makes the landscape generation computationally prohibitive 21 , especially in the absence of an analytical solution, which are slowly emerging 29 .…”

Integration of single-cell multi-omics for gene regulatory network inference

“…Single-cell regularized inference using timestamped expression profiles (SINCERITIES) applies regularized linear regression and partial correlation analysis to reconstruct GRNs based on temporal changes in the distributions of gene expression [80] . This method assumes the expression change of a target gene linearly depends on the expression changes of TFs at a time delay.…”

“…SINCERITIES reconstructs the GRNs with low computational complexity and suits for high-dimensional data (e.g., a network with 5000 genes) [80] , [83] . As the regressions for all genes are independent of each other, the running time could be depleted by employing parallel computation technique.…”

“… Regression-based GENIE3 [97] E. coli :907*4297 Not require temporal information; explain more complicated underlying GRNs; fast running when using parallel computation. SINCERITIES [80] THP-1:960*45 Low computational complexity; parallel computation is available; the relationship between distributional distance of the regulators and target gene is assumed to be linear; require expression data with temporal information. Correlation\information-based LEAP [90] Dendritic:564*557 Fast and efficient algorithm; identify more interactions; relationships between all genes are assumed to be linear; require expression data with temporal information.…”

Predicting gene regulatory networks from cell atlases

“…The GRNs define and govern individual cell type definition, transcriptional states, spatial patterning and responses to signalling, and cell fate cues ( 11 ). Recent computational approaches have enabled inference of the gene regulatory circuitry from scRNA-seq datasets ( 9 , 12 , 13 , 14 , 15 , 16 ).…”