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  • team
  • department
  • center
  • program_project
  • nrc
  • whocc
  • project
  • software
  • tool
  • patent
  • Administrative Staff
  • Assistant Professor
  • Associate Professor
  • Clinical Research Assistant
  • Clinical Research Nurse
  • Clinician Researcher
  • Department Manager
  • Dual-education Student
  • Full Professor
  • Honorary Professor
  • Lab assistant
  • Master Student
  • Non-permanent Researcher
  • Nursing Staff
  • Permanent Researcher
  • Pharmacist
  • PhD Student
  • Physician
  • Post-doc
  • Prize
  • Project Manager
  • Research Associate
  • Research Engineer
  • Retired scientist
  • Technician
  • Undergraduate Student
  • Veterinary
  • Visiting Scientist
  • Deputy Director of Center
  • Deputy Director of Department
  • Deputy Director of National Reference Center
  • Deputy Head of Facility
  • Director of Center
  • Director of Department
  • Director of Institute
  • Director of National Reference Center
  • Group Leader
  • Head of Facility
  • Head of Operations
  • Head of Structure
  • Honorary President of the Departement
  • Labex Coordinator
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Scientific Fields
Diseases
Organisms
Applications
Technique
Starting Date
07
Feb 2023
Ending Date
31
Dec 2024
Status
Ongoing
Members
3
Structures
2
Publications
1

About

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.

Fundings