Journal
CELL CYCLE
Volume 20, Issue 21, Pages 2248-2263Publisher
TAYLOR & FRANCIS INC
DOI: 10.1080/15384101.2021.1978265
Keywords
Single-cell RNA; perturbed network; significance analysis; gene association; hub gene; embryonic stem cell
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Funding
- National Natural Science Foundation of China [61672011, 61474267, 60973153]
- Natural Science Foundation of Hunan Province [2018JJ2461]
- National Key Research and Development Program [2017YFC1311003]
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The recent development of high throughput single-cell RNA sequencing allows detailed study of entire transcriptomes and characterization of cell subtypes. Cancer epigenetics can be induced by individual molecules, system dysfunction, and gene coupling effects. Integration of network perturbation theory and significant analysis can be utilized to develop cell-specific networks and identify gene associations based on molecular expressions at single-cell resolution, potentially benefiting personalized drug design and therapeutics.
The recent development of a high throughput single-cell RNA sequence devises the opportunity to study entire transcriptomes in the smallest detail. It also leads to the characterization of molecules and subtypes of a cell. Cancer epigenetics induced not only from individual molecules but also from the dysfunction of the system and the coupling effect of genes. While rapid advances are being made in the development of tools for single-cell RNA-seq data analysis, few slants are noticed in the potential advantages of single-cell network construction. Here, we used network perturbation theory with significant analysis to develop a cell-specific network that provides an insight into gene-gene association based on molecular expressions in a single-cell resolution. Besides, using this method, we can characterize each cell by inspecting how genes are connected and can identify the hub genes using network degree theory. Pathway & Gene enrichment analysis of the identified cell-specific high network degree genes supported the effectiveness of this method. This method could be beneficial for personalized drug design and even therapeutics.
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