25.1 Concatenation-based integration

Concatenation-based integration combines multiple omic datasets, raw or pre-processed, into a single large matrix. One of the advantages of these approaches is their simplicity, since once the concatenation of multi-omic datasets is achieved, unsupervised and supervised analysis methods can be applied to the joint matrix, as in the case of the independent analysis of omic layers. Concatenation-based techniques offer a straightforward approach to utilising machine learning for the examination of both continuous and categorical data. Once the individual omics are concatenated, these methods can analyse all the combined features in an even-handed manner and pinpoint the most distinguishing features associated with a given phenotype. One of the main challenges of concatenation-based approaches is to ensure that the features of the different omic layers are comparable.

Several examples of unsupervised concatenation-based methods for multi-omic integration have been developed in recent years, most of them based on matrix-factorisation [58]. Joint non-negative matrix factorisation (Joint NMF) allowed integrating non-negative multi-omic data by decomposing the joint matrix into factors and loadings [28]. Joint and Individual Variation Explained (JIVE) is an adaptation of NMF framework [59] which was later improved by Joint Bayes Factor (JBF) to handle the problems derived from the high sparsity of multi-omic datasets [60]. iCluster framework is based in similar principles to NMF but allows integration of datasets having negative values [61]. MoCluster [62], RLAcluster [63] and iClusterBayes [64] have further developed the framework and improved it in terms of diversity of handled data types, computation speed and clustering accuracy. Multi-Omics Factor Analysis (MOFA) is another recent development that allows discovering the principal sources of variability across different omic datasets [65]. Regarding supervised analyses, any of the algorithms for supervised analysis of single omic layers can be used to analyse concatenated multi omic data. RF [66], SVM [67], LASSO regression [68] or DL [69] algorithms have been used, among others, for concatenation-based supervised analysis in multi-omic literature.

Contents of this section were created by Iñaki Odriozola and Antton Alberdi.


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