Time-Series Analysis of Gene Correlation Networks based on Single-Cell Transcriptome Data

Inflammation is an anatomical and physiological response underlying a variety of diseases, and the detection of tissue lesions without subjective symptoms is considered important for preventing of chronic inflammation. For this purpose, it is expected to be useful that recently developed technologies for obtaining single-cell transcriptome data and analytical techniques such as MAGIC and Monocle. In this study, we propose a gene correlation network based on single-cell transcriptome data as a novel analytical approach. Specifically, wep ropose a method for constructing a gene correlation network using MAGIC, and visualization and ranking methods for analyzing its time-series changes. In order to confirm the usefulness of the proposed methods, we conducted experiments using the time-sequence data of single-cell transcriptome obtained from mice induced pulmonary lung fibrosis bye xposure to silica particles. We observed significant changes in the gene correlation network consisting of “negative edges” with the progression of the disease state, as well as that characteristic gene groups with large fluctuations in ranking play important roles in the early stage of the disease.