Differential correlation across ranked samples for single cell RNA-sequencing data

Ghazanfar S, Strbenac D, Ormerod JT, Yang JYH and Patrick E

The University of Sydney.

Genes act as a system and not in isolation. Thus, it is important to consider coordinated changes of gene expression rather than single genes when investigating biological phenomena. We have developed an approach for quantifying how changes in the association between pairs of genes may inform the outcome of interest called Differential Correlation across Ranked Samples (DCARS). Modelling gene correlation across a continuous sample ranking does not require the dichotomisation of samples into two distinct classes and can identify differences in gene correlation across early, mid or late stages of the outcome of interest. We have recently demonstrated the utility of DCARS in the context of assessing differential correlation across survival ranking in TCGA, and further explore the use of DCARS in the context of single cell RNA-Sequencing data. Furthermore, we demonstrate that DCARS can be used in conjunction with network analysis techniques to extract biological meaning from multi-layered and complex data.