Gene Regulatory Network Inference Using Time-Stamped Cross-Sectional Single Cell Expression Data

Abstract: In this paper we presented a novel method for inferring gene regulatory network (GRN) from time-stamped cross-sectional single cell data. Our strategy, called SNIFS (Sparse Network Inference For Single cell data) seeks to recover the causal relationships among genes by analyzing the evolution of the distribution of gene expression levels over time, more specifically using Kolmogorov-Smirnov (KS) distance. In the proposed method, we formulated the GRN inference as a linear regression problem, where we used Lasso regularization to obtain the optimal sparse solution. We tested SNIFS using in silico single cell data from 10 - and 20-gene GRNs, and compared the performance of our method with Time Series Network Inference (TSNI), GEne Network Inference with Ensemble of trees (GENIE3), and an extension of GENIE3 for time series data called JUMP3. The results showed that SNIFS outperformed existing algorithms based on the Area Under the Receiver Operating Characteristic (AUROC) and Area Under the Precision-Recall (AUPR) curves.

[1]  Carsten Peterson,et al.  Transcriptional Regulation of Lineage Commitment - A Stochastic Model of Cell Fate Decisions , 2013, PLoS Comput. Biol..

[2]  Diogo M. Camacho,et al.  Wisdom of crowds for robust gene network inference , 2012, Nature Methods.

[3]  Fabian J. Theis,et al.  Characterisation of transcriptional networks in blood stem and progenitor cells using high-throughput single cell gene expression analysis , 2013, Nature Cell Biology.

[4]  Berthold Göttgens,et al.  Preview: Published ahead of advance online publication Processing, visualising and reconstructing network models from single cell data , 2015 .

[5]  D. Floreano,et al.  Revealing strengths and weaknesses of methods for gene network inference , 2010, Proceedings of the National Academy of Sciences.

[6]  Steven Skiena,et al.  Identifying gene regulatory networks from experimental data , 2001, Parallel Comput..

[7]  Diego di Bernardo,et al.  Inference of gene regulatory networks and compound mode of action from time course gene expression profiles , 2006, Bioinform..

[8]  Trevor Hastie,et al.  Regularization Paths for Generalized Linear Models via Coordinate Descent. , 2010, Journal of statistical software.

[9]  Jay W. Shin,et al.  Temporal dynamics and transcriptional control using single-cell gene expression analysis , 2013, Genome Biology.

[10]  Sean C. Bendall,et al.  Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum , 2011, Science.

[11]  P. Geurts,et al.  Inferring Regulatory Networks from Expression Data Using Tree-Based Methods , 2010, PloS one.

[12]  A. G. de la Fuente,et al.  From Knockouts to Networks: Establishing Direct Cause-Effect Relationships through Graph Analysis , 2010, PloS one.

[13]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[14]  Dario Floreano,et al.  Generating Realistic In Silico Gene Networks for Performance Assessment of Reverse Engineering Methods , 2009, J. Comput. Biol..

[15]  Desmond J. Higham,et al.  An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations , 2001, SIAM Rev..

[16]  Martin Pieprzyk,et al.  Fluidigm Dynamic Arrays provide a platform for single-cell gene expression analysis , 2009 .

[17]  Fabian J Theis,et al.  Decoding the Regulatory Network for Blood Development from Single-Cell Gene Expression Measurements , 2015, Nature Biotechnology.

[18]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[19]  Julio R. Banga,et al.  Inference of complex biological networks: distinguishability issues and optimization-based solutions , 2011, BMC Systems Biology.

[20]  S. Teichmann,et al.  Computational and analytical challenges in single-cell transcriptomics , 2015, Nature Reviews Genetics.

[21]  Jing Guo,et al.  Single-cell transcriptional analysis to uncover regulatory circuits driving cell fate decisions in early mouse development , 2015, Bioinform..

[22]  Rudiyanto Gunawan,et al.  Ensemble Inference and Inferability of Gene Regulatory Networks , 2014, PloS one.

[23]  Mikael Huss,et al.  Resolution of cell fate decisions revealed by single-cell gene expression analysis from zygote to blastocyst. , 2010, Developmental cell.

[24]  Fabian J. Theis,et al.  Reconstructing gene regulatory dynamics from high-dimensional single-cell snapshot data , 2015, Bioinform..

[25]  Carsten Peterson,et al.  Single-Cell Network Analysis Identifies DDIT3 as a Nodal Lineage Regulator in Hematopoiesis , 2015, Cell reports.

[26]  D. Wilkinson Stochastic modelling for quantitative description of heterogeneous biological systems , 2009, Nature Reviews Genetics.

[27]  R. Sandberg Entering the era of single-cell transcriptomics in biology and medicine , 2013, Nature Methods.

[28]  D. Meldrum,et al.  RT-qPCR based quantitative analysis of gene expression in single bacterial cells. , 2011, Journal of microbiological methods.

[29]  Guido Sanguinetti,et al.  Combining tree-based and dynamical systems for the inference of gene regulatory networks , 2015, Bioinform..