Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan.
BMC Bioinformatics. 2024 Sep 5;25(Suppl 2):292. doi: 10.1186/s12859-024-05895-3.
With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.
We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.
SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM .
随着单细胞 RNA 测序（scRNA-seq）技术的进步，从单细胞分辨率的表达谱中获取内在的生物系统信息成为可能。人们已经知道，通过估计基因之间的关联进行网络建模可以更好地揭示生物系统的动态变化。然而，准确构建单细胞网络（SCN）以捕获每个细胞的网络结构并进一步探索细胞间的异质性仍然具有挑战性。
我们引入了 SINUM，一种使用互信息构建单细胞网络的方法，该方法从 scRNA-seq 数据中估计任意两个基因之间的互信息，以确定它们在特定细胞中是相互依赖的还是独立的。基于八个性能指标（例如调整后的兰德指数和 F 度量指数）在具有不同细胞数量的各种 scRNA-seq 数据集上的实验验证了 SINUM 在细胞类型识别中的准确性和稳健性，优于最先进的 SCN 推断方法。此外，SINUM 的 SCN 与人类相互作用组高度重叠，并具有无标度特性。
SINUM 在网络层面上呈现了一种生物系统的视图，用于检测细胞类型标记基因/基因对，并研究胚胎发育过程中基因关联的时间依赖性变化。SINUM 的代码可在 https://github.com/SysMednet/SINUM 上免费获取。
