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Class‐paired Fuzzy SubNETs: A paired variant of the rank‐based network analysis family for feature selection based on protein complexes
Author(s) -
Goh Wilson Wen Bin,
Wong Limsoon
Publication year - 2017
Publication title -
proteomics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.26
H-Index - 167
eISSN - 1615-9861
pISSN - 1615-9853
DOI - 10.1002/pmic.201700093
Subject(s) - feature selection , rank (graph theory) , selection (genetic algorithm) , class (philosophy) , computer science , feature (linguistics) , sample size determination , fuzzy logic , data mining , artificial intelligence , sample (material) , pattern recognition (psychology) , machine learning , statistics , mathematics , linguistics , philosophy , chemistry , chromatography , combinatorics
Identifying reproducible yet relevant protein features in proteomics data is a major challenge. Analysis at the level of protein complexes can resolve this issue and we have developed a suite of feature‐selection methods collectively referred to as Rank‐Based Network Analysis (RBNA). RBNAs differ in their individual statistical test setup but are similar in the sense that they deploy rank‐defined weights among proteins per sample. This procedure is known as gene fuzzy scoring. Currently, no RBNA exists for paired‐sample scenarios where both control and test tissues originate from the same source (e.g. same patient). It is expected that paired tests, when used appropriately, are more powerful than approaches intended for unpaired samples. We report that the class‐paired RBNA, PPFSNET, dominates in both simulated and real data scenarios. Moreover, for the first time, we explicitly incorporate batch‐effect resistance as an additional evaluation criterion for feature‐selection approaches. Batch effects are class irrelevant variations arising from different handlers or processing times, and can obfuscate analysis. We demonstrate that PPFSNET and an earlier RBNA, PFSNET, are particularly resistant against batch effects, and only select features strongly correlated with class but not batch.