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bioRxiv · 10.1101/020867

Overcoming analytical reliability issues in clinical proteomics using rank-based network approaches

Wilson Wen Bin Goh·Limsoon Wong
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Abstract

Proteomics is poised to play critical roles in clinical research. However, due to limited coverage and high noise, integration with powerful analysis algorithms is necessary. In particular, network-based algorithms can improve selection of reproducible features in spite of incomplete proteome coverage, technical inconsistency or high inter-sample variability. We define analytical reliability on three benchmarks --- precision/recall rates, feature-selection stability and cross-validation accuracy. Using these, we demonstrate the insufficiencies of commonly used Students t-test and Hypergeometric enrichment. Given advances in sample sizes, quantitation accuracy and coverage, we are now able to introduce and evaluate Ranked-Based Network Approaches (RBNAs) for the first time in proteomics. These include SNET (SubNETwork), FSNET (FuzzySNET), PFSNET (PairedFSNET). We also introduce for the first time, PPFSNET(samplePairedPFSNET), which is a paired-sample variant of PFSNET. RBNAs (particularly PFSNET and PPFSNET) excelled on all three benchmarks and can make consistent and reproducible predictions even in the small-sample size scenario (n=4). Given these qualities, RBNAs represent an important advancement in network biology, and is expected to see practical usage, particularly in clinical biomarker and drug target prediction.

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Wilson Wen Bin Goh, Limsoon Wong. 2015-06-15. Overcoming analytical reliability issues in clinical proteomics using rank-based network approaches. https://doi.org/10.1101/020867

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