Data Integration of Hybrid Microarray and Single Cell Expression Data to Enhance Gene Network Inference

Author(s): Wei Zhang, Wenchao Li, Jianming Zhang*, Ning Wang.

Journal Name: Current Bioinformatics

Volume 14 , Issue 3 , 2019

Become EABM
Become Reviewer

Graphical Abstract:


Abstract:

Background: Gene Regulatory Network (GRN) inference algorithms aim to explore casual interactions between genes and transcriptional factors. High-throughput transcriptomics data including DNA microarray and single cell expression data contain complementary information in network inference.

Objective: To enhance GRN inference, data integration across various types of expression data becomes an economic and efficient solution.

Method: In this paper, a novel E-alpha integration rule-based ensemble inference algorithm is proposed to merge complementary information from microarray and single cell expression data. This paper implements a Gradient Boosting Tree (GBT) inference algorithm to compute importance scores for candidate gene-gene pairs. The proposed E-alpha rule quantitatively evaluates the credibility levels of each information source and determines the final ranked list.

Results: Two groups of in silico gene networks are applied to illustrate the effectiveness of the proposed E-alpha integration. Experimental outcomes with size50 and size100 in silico gene networks suggest that the proposed E-alpha rule significantly improves performance metrics compared with single information source.

Conclusion: In GRN inference, the integration of hybrid expression data using E-alpha rule provides a feasible and efficient way to enhance performance metrics than solely increasing sample sizes.

Keywords: Gene regulatory network, ensemble inference, gradient boosting tree, data integration.

Rights & PermissionsPrintExport Cite as


Article Details

VOLUME: 14
ISSUE: 3
Year: 2019
Page: [255 - 268]
Pages: 14
DOI: 10.2174/1574893614666190104142228

Article Metrics

PDF: 27