Gene expression dissection by non-negative well-grounded source separation

Yitan Zhu, Tsung-Han Chan, E. Hoffman, Y. Wang
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引用次数: 4

Abstract

A linear mixture model of non-negative sources is used to dissect the gene expression data into components that are putative underlying active biological processes. Each biological process/component is characterized by its specific genes that are exclusively highly expressed in it and expected to be functional enriched; while a majority of all the genes maintain basic cellular structure and functions to support these specific genes and thus are roughly commonly expressed across all components. Such components form non-negative well-grounded, but dependent and non-sparse sources in the model. The unique identifiability of the model is proved. A blind source separation method utilizing convex analysis and sector-based clustering is developed with stability analysis based model order selection scheme to identify the components and their activity curves. When applied on muscle regeneration data, our method revealed four underlying active biological processes associated with four successive phases in muscle regeneration.
非负性良好接地源分离的基因表达分离
非负源的线性混合模型被用来剖析基因表达数据的组成部分,被认为是潜在的活性生物过程。每个生物过程/组成部分都有其特定的基因特征,这些基因在其中高度表达,并期望功能丰富;虽然大多数基因维持基本的细胞结构和功能来支持这些特定的基因,因此在所有成分中大致普遍表达。这些分量在模型中形成非负的良好接地,但依赖且非稀疏的源。证明了模型的唯一可辨识性。提出了一种基于凸分析和扇区聚类的盲源分离方法,并结合基于稳定性分析的模型阶次选择方案来识别组件及其活度曲线。当应用于肌肉再生数据时,我们的方法揭示了与肌肉再生的四个连续阶段相关的四个潜在的活跃生物过程。
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