Inference of gene regulatory networks using pseudo-time series data

Abstract

Motivation Inferring gene regulatory networks (GRNs) from high-throughput data is an important and challenging problem in systems biology. Although numerous GRN methods have been developed, most have focused on the verification of the specific data set. However, it is difficult to establish directed topological networks that are both suitable for time-series and non-time-series datasets due to the complexity and diversity of biological networks. Results Here, we proposed a novel method, GNIPLR (Gene networks inference based on projection and lagged regression) to infer GRNs from time-series or non-time-series gene expression data. GNIPLR projected gene data twice using the LASSO projection (LSP) algorithm and the linear projection (LP) approximation to produce a linear and monotonous pseudo-time series, and then determined the direction of regulation in combination with lagged regression analyses. The proposed algorithm was validated using simulated and real biological data. Moreover, we also applied the GNIPLR algorithm to the liver hepatocellular carcinoma (LIHC) and bladder urothelial carcinoma (BLCA) cancer expression datasets. These analyses revealed significantly higher accuracy and AUC values than other popular methods. Availability The GNIPLR tool is freely available at https://github.com/zyllluck/GNIPLR. Supplementary information Supplementary data are available at Bioinformatics online.

Depicting pseudotime-lagged causality across single-cell trajectories for accurate gene-regulatory inference

“…Building upon several deep learning-based methods 18, 22 , we have demonstrated that combining fully-supervised deep learning with joint-probability matrices of pseudotime-lagged single-cell trajectories can overcome certain limitations of current Granger causality-based methods of gene-regulatory inference 9, 12 , such as their inability to infer cyclic regulatory motifs. Moreover, although our convolutional neural network DELAY remains sensitive to the ordering of single cells in pseudotime, the network can accurately infer direct gene-regulatory interactions from both timecourse and snapshot datasets, unlike many supervised methods that rely strongly on the number of available time-course samples 15, 16 .…”

“…Several methods for gene-regulatory inference rely on pseudotime in Granger causality tests, which try to determine whether new time series can add predictive power to inferred models of gene regulation 8, 9 . However, Granger causality-based methods can be error-prone when genes display nonlinear or cyclic interactions, or when the sampling rate is uneven or too low 9–12 . Because pseudotime trajectories exhibit these problems, Granger causality-based methods often underperform model-free approaches that exploit pure statistical dependencies in gene-expression data 9, 13, 14 .…”

Development of a dynamic network biomarkers method and its application for detecting the tipping point of prior disease development

“…With the development of DNB methods, there have been many improvements in expanding the application of DNBs to other research. For example, Zhang proposed a new method, GNIPLR, to infer gene regulatory networks (GRNs) [21] . Alcudia developed a metaheuristic multiobjective optimization method for DNB identification using two steps [22] .…”

dynDeepDRIM: a dynamic deep learning model to infer direct regulatory interactions using single cell time-course gene expression data

“…In consideration of the cell dynamics over time, time-course scRNA-seq gene expression data is intrinsically much more informative and impressive than the static scRNA-seq data, particularly for the inference of putative regulatory signals. However, most existing methods for GRN reconstruction were designed for static scRNA-seq data [5], [7], [8] or required pseudotime ordered cells [9], [23]. The classical statistical Lu Zhang is the corresponding author.…”