用深度模型从单细胞时间数据中重建动态基因网络
Recovering Time-Varying Networks From Single-Cell Data
- 基于自注意力与循环单元构建随时间演化的基因调控图
- 在稀有细胞类型下仍能准确恢复动态网络结构
- 可识别新冠、纤维化和衰老相关的基因互作
基因调控是人类发育、疾病反应等关键生物过程背后的动态机制。传统方法依赖回归分析或相关网络重建时间基因调控网络。随着单细胞时间序列数据的激增，亟需新方法应对其规模与特性。本文提出深度神经网络 Marlene，从单细胞基因表达的时间序列数据中推断动态图结构。Marlene 利用自注意力机制构建有向基因网络，通过循环单元使权重随时间演化。结合元学习，模型可在稀有细胞类型中仍实现高精度的时序网络恢复。此外，Marlene 能识别与特定生物学响应相关的基因互作，包括新冠免疫反应、纤维化及衰老。
原文摘要 · Abstract (English)
Gene regulation is a dynamic process that underlies all aspects of human development, disease response, and other key biological processes. The reconstruction of temporal gene regulatory networks has conventionally relied on regression analysis, graphical models, or other types of relevance networks. With the large increase in time series single-cell data, new approaches are needed to address the unique scale and nature of this data for reconstructing such networks. Here, we develop a deep neural network, Marlene, to infer dynamic graphs from time series single-cell gene expression data. Marlene constructs directed gene networks using a self-attention mechanism where the weights evolve over time using recurrent units. By employing meta learning, the model is able to recover accurate temporal networks even for rare cell types. In addition, Marlene can identify gene interactions relevant to specific biological responses, including COVID-19 immune response, fibrosis, and aging.
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