Xu Q, Zhu J, Luo Y, Li W. Cell Features Reconstruction from Gene Association Network of Single Cell.
Interdiscip Sci 2023;
15:202-216. [PMID:
36977959 DOI:
10.1007/s12539-023-00553-3]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 01/19/2023] [Accepted: 01/27/2023] [Indexed: 03/30/2023]
Abstract
Gene expression as an unstable form of cell characterization has been widely used for single-cell analyses. Although there are cell-specific networks (CSN) to explore stable gene associations within a single cell, the amount of information in CSN is huge and there is no method to measure the interaction level between genes. Therefore, this paper presents a two-level approach to reconstructing single-cell features, which transforms the original gene expression feature into the gene ontology feature and gene interaction feature. Specifically, we first squeeze all CSNs into a cell network feature matrix (CNFM) by fusing the global position and neighborhood influence of genes. Next, we propose a computational method of gene gravitation based on CNFM to quantify the extent of gene-gene interaction, and we can construct a gene gravitation network for single cells. Finally, we further design a novel index of gene gravitation entropy to quantitatively evaluate the level of single-cell differentiation. The experiments on eight different scRNA-seq datasets show the effectiveness and broad application prospects of our method.
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