Comparison of dimensionality reduction methods for multi-omics data obtained from single-cell analysis

Recently, it has become possible to obtain multi-omics data from a single cell.Thus, we can now observe the state of individual cells from a multi-omics perspective, which may provide useful knowledge in the fields of animal science and breeding.When multi-omics data are obtained at the cellular level, the data is generally compressed to a lower dimension by dimensionality reduction methods integrating high-dimensional and multi-modal multi-omics data.It is necessary to compare existing methods to select an appropriate dimensionality reduction method and to develop a new method; however, comparative studies are limited to date.Thus, in this study, we compared several multiple dimensionality reduction methods using two multi-omics data comprising gene expression and chromatin accessibility derived from single-cell analysis.The methods included in this study were multiple co-inertia analysis (MCIA), multi-omics factor analysis, single-cell aggregation and integration (scAI), Seurat, principal component analysis, and uniform manifold approximation and projection.The effectiveness of dimensionality reduction was evaluated by calculating the multiple co-inertia analysis(MCIA) multi-omics factor analysissingle-cell aggregation and integration(scAI) Seurat uniform manifold approximation and projection 6 MCIA scAI