Single-cell RNA sequencing (scRNAseq) is revolutionizing biology and medicine. The possibility to assess cellular heterogeneity at a previously inaccessible resolution, has profoundly impacted our understanding of development, of the immune system functioning and of many diseases. While scRNAseq is now mature, the single-cell technological development has shifted to other large-scale quantitative measurements, a.k.a. ‘omics’, and even spatial positioning. In addition, combined omics measurements profiled from the same single cell are becoming available.
Each single-cell omics presents intrinsic limitations and provides a different and complementary information on the same cell. Single-cell multi-omics integration, i.e. the simultaneous analysis of multiple single-cell omics, is thus expected to compensate for missing or unreliable information in any single omics and to provide tremendous power to untangle the complexity of human cells.
However, single-cell multi-omics integration is challenging. Different single-cell omics vary widely in signal range, in coverage depth and in the number and nature of the measured features. The challenge is thereby to extract biological signals shared across the multiple omics and masked by the wide across-omics variations. In addition, the huge number of profiled cells, billions in the near future, introduces all the computational and statistical challenges typical of “Big Data”. There is thus the imperative need for powerful and robust methodologies able to overcome such challenges and produce new biological knowledge through single-cell omics data integration.
scMOmix will contribute to this methodological breakthrough. Our aim is indeed to develop rigorous methods for multi-omics integration able to overcome the numerous intrinsic challenges of single-cell data and exploit their richness. In particular, we propose to develop dimensionality reduction (WP1) and network-based (WP2) approaches enabling the integration of multi-omics single-cell data and we will convert such methods to Open Source algorithms (WP3). By applying the developed approaches to real patient-derived data, scMOmix ultimately aims at improving our understanding of cancer heterogeneity and its underlying molecular mechanisms.