Understand gene-to-phenotype relations in the context of rare cell subpopulations

Introduction

Gene expression data is an important resource for the understanding of physiological phenotypes. A typical bulk gene expression profile is composed of an uncharacterized mixture of various cell types, thus limiting our ability to investigate gene-to-phenotype relationships. Single cell data opens the way to a high-resolution analysis of such biological relations.

Material and Methods

Here we devised a novel computational methodology in which single-cell data is utilized to find gene-to-phenotype interactions that have a role in a certain subset of cells.

Results and discussion

We use synthetic data to show the utility of our approach compared to existing methods. In addition, the algorithm was applied on data of phenotypic diversity during in vivo influenza infection, as well as in the context of available cancer datasets.

Conclusion

Overall, our computational method is general and can be applied on a variety of biological systems, revealing new gene-to-disease connections in the context of specific cell subpopulations.