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Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures

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  • Isabella Zwiener
  • Barbara Frisch
  • Harald Binder

Abstract

Gene expression measurements have successfully been used for building prognostic signatures, i.e for identifying a short list of important genes that can predict patient outcome. Mostly microarray measurements have been considered, and there is little advice available for building multivariable risk prediction models from RNA-Seq data. We specifically consider penalized regression techniques, such as the lasso and componentwise boosting, which can simultaneously consider all measurements and provide both, multivariable regression models for prediction and automated variable selection. However, they might be affected by the typical skewness, mean-variance-dependency or extreme values of RNA-Seq covariates and therefore could benefit from transformations of the latter. In an analytical part, we highlight preferential selection of covariates with large variances, which is problematic due to the mean-variance dependency of RNA-Seq data. In a simulation study, we compare different transformations of RNA-Seq data for potentially improving detection of important genes. Specifically, we consider standardization, the log transformation, a variance-stabilizing transformation, the Box-Cox transformation, and rank-based transformations. In addition, the prediction performance for real data from patients with kidney cancer and acute myeloid leukemia is considered. We show that signature size, identification performance, and prediction performance critically depend on the choice of a suitable transformation. Rank-based transformations perform well in all scenarios and can even outperform complex variance-stabilizing approaches. Generally, the results illustrate that the distribution and potential transformations of RNA-Seq data need to be considered as a critical step when building risk prediction models by penalized regression techniques.

Suggested Citation

  • Isabella Zwiener & Barbara Frisch & Harald Binder, 2014. "Transforming RNA-Seq Data to Improve the Performance of Prognostic Gene Signatures," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-13, January.
  • Handle: RePEc:plo:pone00:0085150
    DOI: 10.1371/journal.pone.0085150
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    References listed on IDEAS

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    1. van Wieringen, Wessel N. & Kun, David & Hampel, Regina & Boulesteix, Anne-Laure, 2009. "Survival prediction using gene expression data: A review and comparison," Computational Statistics & Data Analysis, Elsevier, vol. 53(5), pages 1590-1603, March.
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    2. Nicolas Jouvin & Pierre Latouche & Charles Bouveyron & Guillaume Bataillon & Alain Livartowski, 2021. "Greedy clustering of count data through a mixture of multinomial PCA," Computational Statistics, Springer, vol. 36(1), pages 1-33, March.
    3. Thanh Nguyen & Asim Bhatti & Samuel Yang & Saeid Nahavandi, 2016. "RNA-Seq Count Data Modelling by Grey Relational Analysis and Nonparametric Gaussian Process," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-18, October.
    4. Mohammadamin Edrisi & Xiru Huang & Huw A. Ogilvie & Luay Nakhleh, 2023. "Accurate integration of single-cell DNA and RNA for analyzing intratumor heterogeneity using MaCroDNA," Nature Communications, Nature, vol. 14(1), pages 1-15, December.

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