Current technological developments allow for the sequencing of transcriptomes and epigenomes of individual single cells. In particular, gene expression can be assessed at the single cell level with the use of single-cell RNA-seq (scRNA-seq), DNA methylation status can be assessed with the use of single-cell bisulfite sequencing (scBS-seq), and open chromatin patterns are investigated at individual cells using single-cell ATAC-seq (scATAC-seq). Several paired single-cell measurements are also available, such as scM&T-seq (paired transcriptome and methylome), scNOME-seq (paired transcriptome, methylome and nucleosome positioning), CITE-seq (paired transcriptomics and proteomics) or sci-CAR (paired transcriptomics and open chromatin).

Thanks to dropping sequencing costs and well described protocols, current experimental designs afford to interrogate the epigenome of thousands of cells at the time. Despite these advances, the interpretation of the data produced by these technologies remains challenging as computational methods for single-cell epigenomics data analysis are mostly lacking. A key challenge in the analysis of this big data is to device efficient computational tools that can integrate multiple functional measurements of the same single cell (i.e. single-cell multi-omics), and overcome the extensive amount of missing data inherent in single cell sequencing experiments.

The goal of this project is to develop state-of-the-art computational methods to integrate and characterize multiple single cell functional measurements in a biologically meaningful manner, and overcome the extensive amount of missing data inherent in single-cell sequencing experiments. These includes methods for the clustering and visualization of single-cell paired data using large populations of single cells, as well as denoising and imputation methods to take into account the large amount of missing data present in the different –omics levels. Finally, methods to reconstruct cell lineages along a dynamical trajectory, such as cellular differentiation, will also be developed taking into account similarity measurements developed for the paired –omics measurements.

Background requirements:
- Appropriate studies (Masters/Diploma) in physics, mathematics, computer science
- Good knowledge of statistics
- Strong programming skills (C++, phython, R)
- Strong interest in molecular biology