(398c) Inferring Gene Regulatory Networks from Single Cell Expression Data
 Conference: AIChE Annual Meeting
 Year: 2016
 Proceeding: 2016 AIChE Annual Meeting
 Group: Computing and Systems Technology Division
 Session:
 Time: Tuesday, November 15, 2016  3:51pm4:09pm
In this work, we focused on the inference of gene regulatory network (GRN) from single cell expression data. More specifically, we considered timestamped crosssectional expression datasets, consistent with time series measurements taken using Fluidigm Biomark^{©}platform. Recently, several algorithms have been published for such GRN inference based on Boolean networks (Chen et al. 2014; Moignard et al. 2015), stochastic modelling (Teles et al. 2013), gene coexpression/correlation (Kouno et al. 2013; Moignard et al. 2013; Pina et al. 2015), and nonlinear ordinary differential equation models (Ocone et al. 2015). But, the direct application of these algorithms to timestamped crosssectional datasets face a few challenges due to, for example, the requirement of dense time course data and high computational complexity that scales exponentially with the size of the network.
Here, we developed a novel method for inferring the GRN structure, called Sparse Network Inference For Single cell data (SNIFS). SNIFS produces a directed graph model of the GRN by analyzing the time evolution of the distribution of single cell gene expression levels. Briefly, the algorithm begins with the computation of the changes in single cell transcriptional expression distribution over time for each gene. By employing the KolmogorovSmirnov (KS) distribution distances (Massey 1951) between two subsequent time points, the GRN inference involves solving a linear regression problem of the type y=XÎ±. More specifically, the KS distance of a gene at each time step y is modelled as a linear function of the KS distances of all other genes at a previous time step X. SNIFS then uses the elasticnet regularization (Zou and Hastie 2005) to find the optimal (sparse) solution Î±Â by solving the following penalized least square optimization problem:
min yXÎ±_{2}^{2} +Â Î»(mÎ±_{1} + (1m)Î±_{2}) subject toÂ Î±_{j}â?¥0.
Note that by setting mÂ to 1 or to 0 turns the elastic net regularization into Lasso or Tikhonov (ridge regression) regularization, respectively. In the implementation of SNIFS, we used GLMNET (r (Friedman et al. 2010) to solve for the optimalÂ Î±.
We evaluated the performance of SNIFS by inferring 10 and 20gene random subnetworks of E. coli and yeast GRNs using in silicotimestamped crosssectional single cell expression datasets. Given the structure of the GRN, we generated single cell expression data by simulating a stochastic differential equation (SDE) model: (Pinna et al. 2010)
dx_{j} = V(Î²Â Î (1+Î±_{ij}x_{i}/(x_{i} + 1))  Î¸x_{j}) +Â Ï?x_{j}dW(t)
where x_{j} represents the mRNA level of gene j, Î±_{i,j} describes the regulation of the expression of gene j by gene i, Î² denotes the basal transcriptional rate, q is the mRNA degradation rate constant, and Ï? and V are scaling parameters. The variable dW(t) describes the random Wiener process, which accounted for intrinsic stochastic dynamics of the gene expression (Wilkinson 2009). We set Î±_{ij}to 1 for activation, to â??1 for repression, and to 0 otherwise. For the main datasets in the case study, we further set the parameters to the following: V=30, Î² =1, q=0.2, and Ï?=0.1. In total, we generated single cell data for 8 equallyspaced time points between t = 0.1 and t = 2.
We assessed the accuracy of the GRN predictions by computing the area under the receiver operating characteristics (AUROC) and the precision recall (AUPR) curves. We compared the GRNs predicted by SNIFS with those predicted using the populationaveraged expression data by TSNI (Time Series Network Inference) (Bansal et al. 2006), and using a treebased ensemble regression method called GENIE3 (GEne Network Inference with Ensemble of trees) (HuynhThu et al. 2010). The averaged AUROC and AUPR values in Table 1 indicated that for any mvalues, SNIFS could significantly outperform the predictions of TSNI and GENIE3. This result demonstrated the advantage of considering information contained in the single cell distributional data for the purpose of GRN inference, as done in SNIFS.

Table 1. Evaluation of GRN Inference using TSNI, GENIE3, and SNIFS


10GENE NETWORK 
20GENE NETWORK


AUROC 
AUPR 
AUROC 
AUPR 

m 
TSNI 
GENIE3 
SNIFS 
TSNI 
GENIE3 
SNIFS 
TSNI 
GENIE3 
SNIFS 
TSNI 
GENIE3 
SNIFS 
0 (Ridge) 
0.41 
0.48 
0.75 
0.10 
0.14 
0.31 
0.41 
0.50 
0.63 
0.06 
0.07 
0.15 
0.1 
0.41 
0.48 
0.76 
0.10 
0.14 
0.31 
0.41 
0.50 
0.68 
0.06 
0.07 
0.19 
0.2 
0.41 
0.48 
0.73 
0.10 
0.14 
0.29 
0.41 
0.50 
0.66 
0.06 
0.07 
0.20 
0.3 
0.41 
0.48 
0.70 
0.10 
0.14 
0.28 
0.41 
0.50 
0.66 
0.06 
0.07 
0.21 
0.4 
0.41 
0.48 
0.67 
0.10 
0.14 
0.27 
0.41 
0.50 
0.65 
0.06 
0.07 
0.22 
0.5 
0.41 
0.48 
0.65 
0.10 
0.14 
0.25 
0.41 
0.50 
0.64 
0.06 
0.07 
0.22 
0.6 
0.41 
0.48 
0.63 
0.10 
0.14 
0.25 
0.41 
0.50 
0.64 
0.06 
0.07 
0.23 
0.7 
0.41 
0.48 
0.61 
0.10 
0.14 
0.25 
0.41 
0.50 
0.63 
0.06 
0.07 
0.23 
0.8 
0.41 
0.48 
0.61 
0.10 
0.14 
0.26 
0.41 
0.50 
0.62 
0.06 
0.07 
0.23 
0.9 
0.41 
0.48 
0.60 
0.10 
0.14 
0.25 
0.41 
0.50 
0.61 
0.06 
0.07 
0.23 
1 (Lasso) 
0.41 
0.48 
0.58 
0.10 
0.14 
0.25 
0.41 
0.50 
0.60 
0.06 
0.07 
0.24 
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