Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study

TL;DR

This paper reviews existing methods and empirically compares prediction approaches for multi-omics data with block-wise missingness, highlighting their performance differences and practical implications.

Contribution

It provides the first comprehensive literature review and empirical comparison of prediction methods tailored for partly missing multi-omics data.

Findings

Certain methods outperform others depending on missingness patterns

No single approach is best for all data sets or missingness scenarios

Empirical results guide practical method selection for multi-omics prediction

Abstract

As the availability of omics data has increased in the last few years, more multi-omics data have been generated, that is, high-dimensional molecular data consisting of several types such as genomic, transcriptomic, or proteomic data, all obtained from the same patients. Such data lend themselves to being used as covariates in automatic outcome prediction because each omics type may contribute unique information, possibly improving predictions compared to using only one omics data type. Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients. We refer to this type of data as block-wise missing multi-omics data. First, we provide a literature review on existing prediction methods applicable to such data. Subsequently, using a collection of 13 publicly available multi-omics data sets, we compare the predictive performances of several of these approaches for different block-wise missingness patterns. Finally, we discuss the results of this empirical comparison study and draw some tentative conclusions.