Lin Shiqi, Jia Peilin
National Genomics Data Center, China National Center for Bioinformation, Beijing 100101, China.
Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing 100101, China.
Gigascience. 2024 Jan 2;13. doi: 10.1093/gigascience/giae108.
Exploring the cellular processes of genes from the aspects of biological networks is of great interest to understanding the properties of complex diseases and biological systems. Biological networks, such as protein-protein interaction networks and gene regulatory networks, provide insights into the molecular basis of cellular processes and often form functional clusters in different tissue and disease contexts.
We present scGraph2Vec, a deep learning framework for generating informative gene embeddings. scGraph2Vec extends the variational graph autoencoder framework and integrates single-cell datasets and gene-gene interaction networks. We demonstrate that the gene embeddings are biologically interpretable and enable the identification of gene clusters representing functional or tissue-specific cellular processes. By comparing similar tools, we showed that scGraph2Vec clearly distinguished different gene clusters and aggregated more biologically functional genes. scGraph2Vec can be widely applied in diverse biological contexts. We illustrated that the embeddings generated by scGraph2Vec can infer disease-associated genes from genome-wide association study data (e.g., COVID-19 and Alzheimer's disease), identify additional driver genes in lung adenocarcinoma, and reveal regulatory genes responsible for maintaining or transitioning melanoma cell states.
scGraph2Vec not only reconstructs tissue-specific gene networks but also obtains a latent representation of genes implying their biological functions.
从生物网络的角度探索基因的细胞过程对于理解复杂疾病和生物系统的特性具有重要意义。生物网络，如蛋白质-蛋白质相互作用网络和基因调控网络，为细胞过程的分子基础提供了见解，并且在不同的组织和疾病背景下常常形成功能簇。
我们提出了scGraph2Vec，一种用于生成信息丰富的基因嵌入的深度学习框架。scGraph2Vec扩展了变分图自动编码器框架，并整合了单细胞数据集和基因-基因相互作用网络。我们证明基因嵌入具有生物学可解释性，能够识别代表功能或组织特异性细胞过程的基因簇。通过与类似工具的比较，我们表明scGraph2Vec能够清晰地区分不同的基因簇，并聚集更多具有生物学功能的基因。scGraph2Vec可广泛应用于各种生物学背景。我们举例说明了scGraph2Vec生成的嵌入可以从全基因组关联研究数据（如COVID-19和阿尔茨海默病）中推断疾病相关基因，识别肺腺癌中的其他驱动基因，并揭示负责维持或转变黑色素瘤细胞状态的调控基因。
scGraph2Vec不仅可以重建组织特异性基因网络，还能获得暗示基因生物学功能的潜在表示。
