eADAGE是一种新的无监督方法，通过集成神经网络和启发式策略来挖掘公共基因表达数据。该方法能更清晰地捕捉生物途径，揭示细菌如Pseudomonas aeruginosa的未知调节机制，例如在中等磷酸盐浓度下KinB传感器激酶在磷酸盐饥饿响应中的关键作用。这种方法为从公共数据中发现微妙且特定条件的调控交互提供了机会。
Abstract
Abundant public expression data capture gene expression across diverse conditions. These steady state mRNA measurements could reveal the transcriptional consequences of cells’ genetic backgrounds or their responses to the environment. However, public data remain relatively untapped, in part because extracting biological signal as opposed to technical noise remains challenging. Here we introduce a procedure, termed eADAGE, that performs unsupervised integration of public expression data using an ensemble of neural networks as well as heuristics that, given a dataset, help users identify an appropriate level of model complexity. This ensemble modeling approach captures biological pathways more clearly than existing methods, enabling analyses that span entire public gene expression compendia such as that for the bacterium Pseudomonas aeruginosa. These analyses reveal a previously undiscovered feature of the phosphate starvation response apparent in public data: a sensor kinase, KinB, that is required for full activation of the response to phosphate at intermediate concentrations. Our molecular validation experiments confirm this role of KinB and our screen of a histidine kinase knock out collection confirmed the prediction’s specificity. Public data are captured from a broad range of conditions in diverse organism backgrounds and may provide a unique opportunity to identify these subtle and context-specific regulatory interactions. Algorithms that extract biological signal from these data, such as eADAGE, can highlight opportunities to discover mechanisms that are apparent from but unrealized in public data.
扫一扫
被折叠的 条评论
为什么被折叠?
到【灌水乐园】发言
