A Subset Selection Method for Accurate Gene Regulatory Network Inference of Uninformative Datasets

• Motivation: The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where approximately 1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. First we identified key properties of the datasets, i.e., signal-to-noise ratio (SNR) and condition number which we have shown to affect the performance of various inference methods.Results: We found that all L1000 datasets have a very low SNR level causing them to be highly uninformative not suitable to infer accurate GRNs. Therefore, we have developed a gene reduction pipeline in which we eliminate the uninformative genes from the system using a selection criteria based on SNR until reaching an informative subset. The results show that our pipeline can identify an informative subset in an uninformative dataset, improving the accuracy of the GRN inference significantly.

Subject headings

NATURVETENSKAP  -- Biologi -- Bioinformatik och systembiologi (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Bioinformatics and Systems Biology (hsv//eng)

Keyword

Biochemistry towards Bioinformatics

biokemi med inriktning mot bioinformatik

Publication and Content Type

vet (subject category)

ovr (subject category)