Aaron M Smith1, Jonathan R Walsh2, John Long3, Craig B Davis4, Peter Henstock5, Martin R Hodge6, Mateusz Maciejewski6, Xinmeng Jasmine Mu7, Stephen Ra3, Shanrong Zhao3, Daniel Ziemek8, Charles K Fisher2. 1. Unlearn.AI, Inc., San Francisco, CA, USA. drams@unlearn.ai. 2. Unlearn.AI, Inc., San Francisco, CA, USA. 3. Computational Sciences, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA. 4. Oncology Global Product Development, Pfizer Inc., San Diego, CA, USA. 5. Business Technology, Pfizer Inc., Cambridge, MA, USA. 6. Inflammation and Immunology, Worldwide Research & Development, Pfizer Inc., Cambridge, MA, USA. 7. Oncology Research & Development, Worldwide Research & Development, Pfizer Inc., San Diego, CA, USA. 8. Inflammation and Immunology, Worldwide Research & Development, Pfizer Pharma GmbH., Berlin, Germany.
Abstract
BACKGROUND: The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research. RESULTS: Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall. CONCLUSIONS: Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.
BACKGROUND: The ability to confidently predict health outcomes from gene expression would catalyze a revolution in molecular diagnostics. Yet, the goal of developing actionable, robust, and reproducible predictive signatures of phenotypes such as clinical outcome has not been attained in almost any disease area. Here, we report a comprehensive analysis spanning prediction tasks from ulcerative colitis, atopic dermatitis, diabetes, to many cancer subtypes for a total of 24 binary and multiclass prediction problems and 26 survival analysis tasks. We systematically investigate the influence of gene subsets, normalization methods and prediction algorithms. Crucially, we also explore the novel use of deep representation learning methods on large transcriptomics compendia, such as GTEx and TCGA, to boost the performance of state-of-the-art methods. The resources and findings in this work should serve as both an up-to-date reference on attainable performance, and as a benchmarking resource for further research. RESULTS: Approaches that combine large numbers of genes outperformed single gene methods consistently and with a significant margin, but neither unsupervised nor semi-supervised representation learning techniques yielded consistent improvements in out-of-sample performance across datasets. Our findings suggest that using l2-regularized regression methods applied to centered log-ratio transformed transcript abundances provide the best predictive analyses overall. CONCLUSIONS: Transcriptomics-based phenotype prediction benefits from proper normalization techniques and state-of-the-art regularized regression approaches. In our view, breakthrough performance is likely contingent on factors which are independent of normalization and general modeling techniques; these factors might include reduction of systematic errors in sequencing data, incorporation of other data types such as single-cell sequencing and proteomics, and improved use of prior knowledge.
Authors: Soufiane M C Mourragui; Marco Loog; Daniel J Vis; Kat Moore; Anna G Manjon; Mark A van de Wiel; Marcel J T Reinders; Lodewyk F A Wessels Journal: Proc Natl Acad Sci U S A Date: 2021-12-07 Impact factor: 12.779
Authors: Benjamin D Lee; Anthony Gitter; Casey S Greene; Sebastian Raschka; Finlay Maguire; Alexander J Titus; Michael D Kessler; Alexandra J Lee; Marc G Chevrette; Paul Allen Stewart; Thiago Britto-Borges; Evan M Cofer; Kun-Hsing Yu; Juan Jose Carmona; Elana J Fertig; Alexandr A Kalinin; Brandon Signal; Benjamin J Lengerich; Timothy J Triche; Simina M Boca Journal: PLoS Comput Biol Date: 2022-03-24 Impact factor: 4.475