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Feature selection using differential correlation across ranked samples. Patrick, Ellis


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 change across an 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 distinct classes and can hence identify differences in gene correlation across the full range of an outcome as opposed to just its extremities. We have recently demonstrated the utility of DCARS when assessing differential correlation across survival ranking in various cancers and have extended its use to single-cell RNA-sequencing data. As examples, when examining prognosis in melanoma and other cancers, DCARS consistently finds communities of genes that are enriched for known cancer related genes, as well as further associations with somatic mutations in the genes belonging to the communities. When applied to single-cell RNA-sequencing of hepatoblasts, we observe clear evidence of a high level of network coordination to force cells down alternate differentiation paths. In these contexts, when DCARS is used in conjunction with network analysis and visualisation techniques it becomes a powerful tool for extracting biological meaning from multi-layered and complex data.

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