Article
Zero-sum regression in action: A prognostic miRNA Signature in DLBCL
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Published: | September 6, 2019 |
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Outline
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OMICs data sets need preprocessing before analysis. This intrinsically includes the use of reference points like the mean expression of all features, a defined spike-in or the value of housekeeper genes.
Reference points have two major drawbacks: Measuring platforms become incomparable [1] and noise of the reference point is added to a predictive model [2].
The concept of zero-sum signatures, introduced by [3] with extensions from [1] and [2], enables that a predicted response is free of reference points.
If all possible, unique log-ratios of measurements are used in a LASSO-penalized regression, such signatures directly emerge. However, the feature space expands from p measurements to p choose 2 new features.
Here we use expression levels of 800 micro-RNAs (miRNAs), measured with the NanoString nCounter miRNA system. 228 DLBCL specimens were used to find a predictive signature on all high-count log-ratios for overall and progression free survival. We show that, besides the zero-sum property, the found log-ratio features are predictive but the corresponding single measurements are not.
The authors declare that an ethics committee vote is not required.
This contribution has already been published: [4]
References
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- Glehr G. Zero-sum regression in action: A prognostic miRNA Signature in DLBCL. In: Workshop on Computational Models in Biology and Medicine; 2019 March 7-8; BRICS, Braunschweig. 2019. p. 50. Available from: http://www.biometrische-gesellschaft.de/fileadmin/AG_Daten/MethodenBioinformatik/PDFs/program_workshop_2019.pdf