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

Nan Papili Gao,Rudiyanto Gunawan,S M Minhaz Ud-Dean,Olivier Gandrillon,Alfonso Valencia

doi:10.1093/bioinformatics/btx575

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.

Highlights

Cell profiling technologies have enabled scientists to measure intracellular molecules (DNA, RNA, proteins, metabolites) at wholegenome level and down to single cell resolution
In the gold standard gene regulatory networks (GRNs), we assumed that there exist no self-regulatory edges, since some of the existing algorithms used in the comparison, namely GENIE3 and JUMP3, could not identify such edges
We computed the numbers of true positive (TP), true negative (TN), false positive (FP) and false negative (FN) edges by comparing the regulatory edges in the gold standard network with the top q edges from the ranked list output of SINCERITIES

Summary

Introduction

Cell profiling technologies have enabled scientists to measure intracellular molecules (DNA, RNA, proteins, metabolites) at wholegenome level and down to single cell resolution. High-throughput single cell assays have experienced tremendous progress, thanks to advanced microfluidics techniques and increased sensitivity in cell profiling assays. The ability to assay individual cells and to examine intra-population cellular heterogeneity brings great benefits to fields such as stem cell and cancer biology. In the last few years, single cell analyses have demonstrated the ubiquity of cellular heterogeneity, even within cell populations or cell types that have been traditionally perceived as homogeneous (Buettner et al, 2015; Gupta et al, 2011; Kumar et al, 2014; Pollen et al, 2014; Shalek et al, 2014). Many single cell studies have provided evidence for the physiological roles of cell-to-cell variability in normal and diseased cells (Chang et al, 2008; Fang et al, 2013; Lee et al, 2014; Kim et al, 2015; Richard et al, 2016)

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