The Gfeller lab develops a method to both simplify and accelerate the analysis and visualisation of very large single-cell RNA-Seq datasets.
Single-cell RNA sequencing (scRNA-seq) technologies offer unique opportunities for exploring heterogeneous cell populations like those found in a tumor. However, in-depth single-cell transcriptomic characterization of complex tissues often requires profiling tens to hundreds of thousands of cells. Such large numbers of cells constitute an important hurdle for downstream analyses, interpretation and visualization. Moreover, one expects that several cells are highly similar, and therefore carry redundant information.
In this work*, published in BMC Bioinformatics, Dr Mariia Bilous in Pr David Gfeller’s lab developed a computational framework to identify cells that display very high similarity at the gene expression level and group these into ‘metacells’. This study demonstrates that metacells can reduce by at least tenfold the size and complexity of scRNA-Seq data, while preserving, and sometime enhancing, the biologically relevant information.
The research was conducted in collaboration with the University of Geneva’s Pr Mikaël Pittet, ISREC Foundation chair in onco-immunology and member of the Ludwig Lausanne Branch.
Research for this project was supported by the Swiss National Science Foundation.
*Metacells untangle large and complex single-cell transcriptome networks.