Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model. Issue 1 (January 2016)

• Record Type: Journal Article
• Authors: Chen, Lujia; Cai, Chunhui; Chen, Vicky; Lu, Xinghua
• Is Part Of: BMC bioinformatics. Volume 17:Issue 1(2016)
• Page Start: 97
• Page End: 107
• Publication Date: 2016-01
• Subjects: Yeast -- Transcription -- Gene expression -- Transcriptomic machinery -- Signal transduction -- Deep learning -- Deep hierarchical neural network -- Unsupervised learning -- Data mining; Bioinformatics -- Periodicals; Computational biology -- Periodicals; 570.285
• DOI: 10.1186/s12859-015-0852-1
• Languages: English
• ISSNs: 1471-2105

Abstract

Abstract Background A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery. Results We have designed a sparse autoencoder model consisting of a layer of observed variables and four layers of hidden variables. We applied the model to over a thousand of yeast microarrays to learn the encoding system of yeast transcriptomic machinery. After model selection, we evaluated whether the trained models captured biologically sensible information. We show that the latent variables in the first hidden layer correctly captured the signals of yeast transcription factors (TFs), obtaining a close to one-to-one mapping between latent variables and TFs. We further show that genes regulated by latent variables at higher hidden layers are often involved in a common biological process, and the hierarchical relationships between latent variables conform to existing knowledge. Finally, we show that information captured by the latent variables provide more abstract and concise representations of each microarray, enabling the identification of better separated clusters in comparison to gene-based representation. Conclusions Contemporary deep hierarchical latent variable models, such as the autoencoder, can be used to partially recover the organization of transcriptomic machinery.