Dexter, A; Thomas, S A; Steven, R T; Robinson, K N; Taylor, A J; Elia, E A; Nikula, C; Campbell, A D; Panina, Y; Najumudeen, A K; Yan, B; Grabowski, P; Hamm, G; Swales, J; Tripp, A; Poulogiannis, G; Yuneva, M O; Barry, S; Goodwin, R J A; Sansom, O J; Takats, Z; Bunch, J (2025) A New Approach to Large Multiomics Data Integration. Analytical Chemistry, 97 (37). pp. 20058-20067. ISSN 0003-2700

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Abstract

Complete understanding of complex biological systems requires a deeper understanding of “omics” at different levels, as well as their interactions with one another. These multi-omics datasets are often large and very complex especially when obtained at the spatial and/or single cell level. High dimensionality omics and imaging datasets present difficult challenges for feature extraction and data mining due to large numbers of features that cannot be simultaneously examined. The sample numbers and variables of these methods are constantly growing as new technologies are developed, and computational analysis needs to evolve to keep up with growing demand.

Furthermore, there is a growing interest towards integration of data from multiple omics to get a more complete understanding of disease pathogenesis. Current state of the art algorithms can perform data mining, visualisation, and classification on routine datasets but struggle when datasets grow above a certain size. We present a new approach to large and multi-omic data integration ROSETTA (Reduction of Omics and Spatial omics by Neural networks for Translation and Integration) to extract, mine and integrate large multi-omics datasets to provide new insights into complex diseases including breast, brain, and colorectal cancer. Our method enables monitoring of metabolism using what was previously considered prohibitively large datasets, enabling the integration and translation between different omics data.

Item Type:	Article
Subjects:	Nanoscience > Surface and Nanoanalysis
Divisions:	Chemical & Biological Sciences
Identification number/DOI:	10.1021/acs.analchem.5c01812
Last Modified:	25 Mar 2026 15:22
URI:	https://eprintspublications.npl.co.uk/id/eprint/10361