# Adventures in Multi-Omics I: Combining heterogeneous data sets via relationships matrices

arXiv: https://arxiv.org/abs/1912.03358 · HTML: https://mlsanity.com/paper/1912.03358/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [Multimodal Learning for Multi-Omics: A Survey](https://mlsanity.com/paper/2211.16509/) (2022)

## Abstract (arXiv)

In this article, we propose a covariance based method for combining partial data sets in the genotype to phenotype spectrum. In particular, an expectation-maximization algorithm that can be used to combine partially overlapping relationship/covariance matrices is introduced. Combining data this way, based on relationship matrices, can be contrasted with a feature imputation based approach. We used several public genomic data sets to explore the accuracy of combining genomic relationship matrices. We have also used the heterogeneous genotype/phenotype data sets in the https://triticeaetoolbox.org/ to illustrate how this new method can be used in genomic prediction, phenomics, and graphical modeling.