A single-cell Systems Biology approach for disease-specific subpathway extraction

Abstract

Single-cell sequencing technologies offer a platform to explore deeper the complex diseases and biological processes. On parallel, Systems Biology approaches can tackle part of this complexity, since they are based on biological networks elucidating genotype to phenotype relationships in a more comprehensive framework. Hence, research community is shifting towards singlecell systems biology approaches to identify and validate disease-specific biomarkers. Under this perspective, we propose a methodological framework aimed to infer disease-specific subpathway activities, using cell signaling pathway topology and transcriptomics data derived from single-cell RNA-seq technologies. More specifically, we concatenated and integrated pathway network refining with single-cell expression profiles. Linear subpathways were extracted and a subpathway profile score was calculated taking into account the pathway information flow. The core of our method is XGBoost supervised classification based on the aforementioned score in order to identify important subpathways that control the rest of the network and cause major changes from a control to a disease state under study. The proposed methodology was applied to a real experimental study with single-cell RNA-seq expression profiles revealing changes in type 2 diabetes. Exported subpathways showed promising results, suggesting that the proposed approach can reliably identify subpathways, acting as potential disease-specific biomarkers.