07-31, 13:10–14:00 (Asia/Taipei), TR411
Language: 漢語
Gene regulatory network governs the complex gene expression programs in various biological phenomena,
including cell development, cell fate decision and oncogenesis. Single-cell techniques provide higher
resolution than traditional bulk RNA sequencing in gene expression, but also bring larger noises and
sparse expression measurements. It is difficult to infer gene regulatory network with noisy and sparse
gene expression profiles. Furthermore, inference of a complete gene regulatory network across different
cell types is also challenging. Here, we propose to address the problem by constructing context-dependent
gene regulatory networks (CDGRN) with single-cell RNA sequencing data. A gene regulatory network is
decomposed into subgraphs, which correspond to distinct transcriptomic contexts. Each subgraph of gene
regulatory network is composed of the consensus active regulation pairs of transcription factors and
their target genes shared by a group of cells. The activities of each regulation pair in different cell
groups were inferred by a Gaussian mixture model using both the spliced and unspliced transcript
expression levels. In addition, we found that the union of gene regulation pairs in all contexts poses
sufficient information for reconstruction of differentiation trajectory. The connection between gene
regulation in molecular level and cell differentiation in macroscopic view can be established by CDGRN.
The cell cycle, cell differentiation or tissue-specific functions are enriched along developmental
progression in each context. Surprisingly, we observed that network entropy for CDGRN decreases along
differentiation progression, which implies the differentiation direction. In conclusion, we leverage the
advantage of single-cell RNA sequencing and establish the connection in both molecular regulation and
differentiation trajectory. The context-dependent network entropy may indicate maturity of cells in
certain context.
對基因體科學或是生物資訊有興趣的人
Difficulty –中階
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Julia Taiwan 社群主持人，中央研究院/台灣大學生物資訊學國際學程博士候選人，擁有生物醫學及電腦科學背景，曾任工研院機器學習講師，活躍於台北及台中的深度學習及人工智慧相關社群。熱愛數學、電腦科學及自然科學，為開源軟體貢獻者，Julia 的 GNN 套件 GeometricFlux.jl 維護者。擁有著作《Julia 程式設計：新世代資料科學與數值運算語言》及《Julia 資料科學與科學計算》二書。