In kevinwang09/top: Cross Platform Omics Prediction

CPOP - Cross-Platform Omics Prediction

Due to data scale differences between multiple omics data, a model constructed from a training data tends to have poor prediction power on a validation data. While the usual bioinformatics approach is to re-normalise both the training and the validation data, this step may not be possible due to ethics constrains. CPOP avoids re-normalisation of additional data through the use of log-ratio features and thus also enable prediction for single omics samples.

The novelty of the CPOP procedure lies in its ability to construct a transferable model across gene expression platforms and for prospective experiments. Such a transferable model can be trained to make predictions on independent validation data with an accuracy that is similar to a re-substituted model. The CPOP procedure also has the flexibility to be adapted to suit the most common clinical response variables, including linear response, binomial and Cox PH models.

Vignette

See https://sydneybiox.github.io/CPOP/articles/CPOP.html.

Installation

devtools::install_github("sydneybiox/CPOP")

References

Cross-Platform Omics Prediction procedure: a statistical machine learning framework for wider implementation of precision medicine Kevin Y.X. Wang, Gulietta M. Pupo, Varsha Tembe, Ellis Patrick, Dario Strbenac, Sarah-Jane Schramm, John F. Thompson, Richard A. Scolyer, Samuel Mueller, Garth Tarr, Graham J. Mann, Jean Y.H. Yang bioRxiv 2020.12.09.415927; doi: https://doi.org/10.1101/2020.12.09.415927

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