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

By Lujia Chen, Chunhui Cai, Vicky Chen, Xinghua Lu. 2016

96 citations recorded by the source corpus.

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. 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. Contemporary deep hierarchical latent variable models, such as the autoencoder, can be used to partially recover the organization of transcriptomic machinery.

Subfields: Molecular Biology

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Citations in the corpus

Opportunities and obstacles for deep learning in biology and medicine (2017, methodology): “Denoising autoencoders have been used to cluster yeast expression microarrays into known modules representing cell cycle processes [142] and to stratify yeast strains based on chemical and mutational perturbations [143].”
SBSM-Pro: support bio-sequence machine for proteins (2023, methodology): “For instance, autoencoders have been applied to over one thousand yeast microarray datasets to study the yeast transcriptional regulatory code [21].”
Deep Learning based multi-omics integration robustly predicts survival in liver cancer (2017, methodology): “Autoencoders were successfully applied to analyze high-dimensional gene expression data (23, 24) and to integrate heterogeneous data (25, 26).”
Machine learning and computer vision approaches for phenotypic profiling (2017, methodology): “Deep learning has been applied to numerous types of biological data for modeling gene expression (Chen et al., 2016a,b) and predicting protein structure (Zhou and Troyanskaya, 2015), DNA methylation (Wang et al., 2016), and protein–nucleic acid interactions (Alipanahi et al., 2015).”
Evaluating deep variational autoencoders trained on pan-cancer gene expression (2017, background): “Previously, nonlinear dimensionality reduction approaches have revealed complex patterns and novel biology from publicly available gene expression data [8, 9], including drug response predictions [10].”