Complementary Domain Prioritization: A Method to Improve Biologically Relevant Detection in Multi-Omic Data Sets
Benjamin A. Neely, Paul E. Anderson
As the speed and quality of different analytical platforms increase, it is more common to collect data across multiple biological domains in parallel (i.e., genomics, transcriptomics, proteomics, and metabolomics). There is a growing interest in algorithms and tools that leverage heterogeneous data streams in a meaningful way. Since these domains are typically non- linearly related, we evaluated whether results from one domain could be used to prioritize another domain to increase the power of detection, maintain type 1 error, and highlight biologically relevant changes in the secondary domain. To perform this feature prioritization, we developed a methodology called Complementary Domain Prioritization that utilizes the underpinning biology to relate complementary domains. Herein, we evaluate how proteomic data can guide transcriptomic differential expression analysis by analyzing two published colorectal cancer proteotranscriptomic data sets. The proposed strategy improved detection of cancer- related genes compared to standard permutation invariant filtering approaches and did not increase type I error. Moreover, this approach detected differentially expressed genes that would not have been detected using filtering alone, and also highlighted pathways that might have otherwise been overlooked. These results demonstrate how this strategy can effectively prioritize transcriptomic data and drive new hypotheses, though subsequent validation studies are still required.
and Anderson, P.
Complementary Domain Prioritization: A Method to Improve Biologically Relevant Detection in Multi-Omic Data Sets, Nature - Scientific Reports, [online], https://doi.org/10.5220/0006151500680080
(Accessed December 1, 2023)