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Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model

Chen, L and Cai, C and Chen, V and Lu, X (2016) Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model. BMC Bioinformatics, 17 (1).

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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.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Chen, Lluc17@pitt.eduLUC17
Cai, Cchunhuic@pitt.eduCHUNHUIC
Chen, Vvic14@pitt.eduVIC14
Lu, Xxinghua@pitt.eduXINGHUA
Date: 11 January 2016
Date Type: Publication
Journal or Publication Title: BMC Bioinformatics
Volume: 17
Number: 1
DOI or Unique Handle: 10.1186/s12859-015-0852-1
Schools and Programs: School of Medicine > Biomedical Informatics
Refereed: Yes
Date Deposited: 25 Jul 2016 17:42
Last Modified: 27 Mar 2021 10:55


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