Abstract
BACKGROUND: As high-throughput studies advance, more and more high-dimensional multi-omics data are available and collected from the same patient cohort. Using multi-omics data as predictors to predict survival outcomes is challenging due to the complex structure of such data. RESULTS: In this article, we introduce an adaptive sparse multi-block partial least square (asmbPLS) regression method by assigning different penalty factors to different blocks in different PLS components for feature selection and prediction. We compared the proposed method with several competitive algorithms in many aspects including prediction performance, feature selection and computation efficiency. The performance and the efficiency of our method were demonstrated using both the simulated and the real data. CONCLUSIONS: In summary, asmbPLS achieved a competitive performance in prediction, feature selection, and computation efficiency. We anticipate asmbPLS to be a valuable tool for multi-omics research. An R package called asmbPLS implementing this method is made publicly available on GitHub.