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Deep learning decodes the principles of differential gene expression

Nature Machine Intelligence volume 2, pages 376–386 (2020)Cite this article

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A preprint version of the article is available at bioRxiv.

Abstract

Identifying the molecular mechanisms that control differential gene expression (DE) is a major goal of basic and disease biology. Here, we develop a systems biology model to predict DE and mine the biological basis of the factors that influence predicted gene expression to understand how it may be generated. This model, called DEcode, utilizes deep learning to predict DE based on genome-wide binding sites on RNAs and promoters. Ranking predictive factors from DEcode indicates that clinically relevant expression changes between thousands of individuals can be predicted mainly through the joint action of post-transcriptional RNA-binding factors. We also show the broad potential applications of DEcode to generate biological insights, by predicting DE between tissues, differential transcript usage, and drivers of ageing throughout the human lifespan, of gene co-expression relationships on a genome-wide scale, and of frequently differentially expressed genes across diverse conditions. DEcode is freely available to researchers to identify influential molecular mechanisms for any human expression data.

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Fig. 1: Overview of building and evaluating the DEcode transcriptome prediction model.
Fig. 2: Performance of DEcode for predicting DE across 53 tissues.
Fig. 3: Identification and characterization of key predictors.
Fig. 4: Performance of the person-specific models.
Fig. 5: Application of the person-specific models to analyse phenotype-related gene signatures.
Fig. 6: Regulatory basis of gene co-expression relationships.

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Data availability

The data sources for GTEx data, TF and RBP binding peaks, miRNA binding locations, disease-related genes, protein–protein interaction data, pathways, gene ontology and the DE prior rank that were used for model training and interpretation are available in Supplementary Table 8. Processed data are available through our Code Ocean capsule (https://doi.org/10.24433/CO.0084803.v1).

Code availability

DEcode software and pre-trained models for tissue- and person-specific transcriptomes are available at www.differentialexpression.org, https://github.com/stasaki/DEcode and from our Code Ocean capsule (https://doi.org/10.24433/CO.0084803.v1). DEcode is licensed under a BSD 3-Clause license.

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Acknowledgements

We thank L. Yu for managing access to Genotype-Tissue Expression (GTEx) data. The study was supported by NIH grants P30AG010161, R01AG061798 and R01AG057911. The GTEx Project is supported by the Common Fund of the Office of the Director of the National Institutes of Health, and by NCI, NHGRI, NHLBI, NIDA, NIMH and NINDS. The data used for the analyses described in this Article were obtained from the GTEx Portal on 1 October 2018.

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Authors and Affiliations

  1. Rush Alzheimer’s Disease Center, Rush University Medical Center, Chicago, IL, USA

    Shinya Tasaki, Chris Gaiteri & Yanling Wang

  2. University of British Columbia, Vancouver, BC, Canada

    Sara Mostafavi

Authors
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Contributions

S.T. contributed to the conception and design of the study. S.T. performed the computational analysis. S.T., C.G., S.M. and Y.W. interpreted the results. S.T. wrote the first draft of the manuscript. All authors contributed to manuscript revision, then read and approved the submitted version.

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Correspondence to Shinya Tasaki.

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Extended data

Extended Data Fig. 1 Characterizations of the person-specific models.

a, The consistency between the predictive contributions of regulators and their expression. Spearman’s correlation was used to evaluate the relation between DeepLIFT scores vs log2-TPMs in each tissue. A relation with the absolute correlation > 0.3 and FDR < 5% was defined as significant. b, The consistency of predictive contributions across tissues. We selected the regulators that showed the significant relationships between their DeepLIFT scores with log2-TPMs in multiple tissues. Then, the consistency of directions of the correlations was assessed and visualized as a histogram. c, The actual correlation profile between DeepLIFT scores and log2-TPMs. We selected 99 regulators that showed consistent relationships in more than four tissues. d, The pairwise similarity of per-gene prediction accuracy between tissues. Spearman’s correlation was used for this comparison. e, The association of per-gene prediction accuracy with the gene status. The gene status indicates whether genes are registered in multiple databases (Known) or only in the GENCODE database (Novel or Putative). f, Decomposition of variance in the per-gene prediction accuracy. To compare the variances in the per-gene prediction accuracy explained by the gene status, the number of promoter features, and RNA features, we used a variance decomposition method (lmg) implemented in relaimpo R package.

Extended Data Fig. 2 The major feature types contributed to the gene co-expression.

We defined the gene pairs with the absolute Spearman’s correlation greater than 0.3 and the sign of the correlation matched with one with the ground truth as the successfully predicted gene pairs. The successfully predicted gene pairs of the model trained with the full set of features were split into three groups based on the performance of the models trained with only RNA features or promoter features.

Extended Data Fig. 3 DEcode predicts the regulatory principles behind frequently DE genes across diverse conditions.

a, The scatter plots showing the relations between predicted and actual DE prior rank. The predicted logit of DE prior rank was converted to probability and compared with actual DE prior rank with Spearman’s correlation. b, The performances of the models trained with a distinct feature set. ROCs represent the performances of models in predicting genes with whose DE prior rank greater than 0.9.

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Supplementary Figs. 1–12.

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Supplementary Tables 1–8.

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Tasaki, S., Gaiteri, C., Mostafavi, S. et al. Deep learning decodes the principles of differential gene expression. Nat Mach Intell 2, 376–386 (2020). https://doi.org/10.1038/s42256-020-0201-6

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