multi-omics-integration

Choosing a multi-omics integration strategy requires evaluating sample size, data overlap, and biological objectives to select between early, intermediate, or late integration. This prevents common pitfalls like improper normalization and batch effect confounding during data fusion.

MOFA+ is recommended for integrating transcriptomics and proteomics data with partial sample overlap. It handles heterogeneous datasets and missing views effectively, enabling latent factor discovery without requiring complete sample overlap across all omics views.

Early, intermediate, and late multi-omics integration differ by the stage at which data fusion occurs relative to individual omics processing. Strategy selection depends on sample size and data overlap, determining whether datasets are combined before, during, or after initial processing.

Performing batch correction during multi-omics integration requires a principled approach to avoid confounding batch effects with biological signals. Proper strategy selection based on data overlap and normalization ensures batch effects do not compromise downstream latent factor discovery.

After discovering latent factors in multi-omics data, downstream enrichment analysis is the standard next step. This interprets the biological significance of integrated factors identified through methods like MOFA+ or iCluster+, validating the biological objectives of your data fusion.

iCluster+ and kernel-based fusion methods are both guided for multi-omics data fusion, offering expert-level parameter configuration. The optimal choice between them depends on your specific biological objectives, sample size, and the extent of data overlap across views.