Integrating Networks and Proteomics: Moving Forward

Wong

2017

BMC Genomics

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BackgroundIn proteomics, batch effects are technical sources of variation that confounds proper analysis, preventing effective deployment in clinical and translational research.ResultsUsing simulated and real data, we demonstrate existing batch effect-correction methods do not always eradicate all batch effects. Worse still, they may alter data integrity, and introduce false positives. Moreover, although Principal component analysis (PCA) is commonly used for detecting batch effects. The principal components (PCs) themselves may be used as differential features, from which relevant differential proteins may be effectively traced. Batch effect are removable by identifying PCs highly correlated with batch but not class effect.However, neither PC-based nor existing batch effect-correction methods address well subtle batch effects, which are difficult to eradicate, and involve data transformation and/or projection which is error-prone. To address this, we introduce the concept of batch-effect resistant methods and demonstrate how such methods incorporating protein complexes are particularly resistant to batch effect without compromising data integrity.ConclusionsProtein complex-based analyses are powerful, offering unparalleled differential protein-selection reproducibility and high prediction accuracy. We demonstrate for the first time their innate resistance against batch effects, even subtle ones. As complex-based analyses require no prior data transformation (e.g. batch-effect correction), data integrity is protected. Individual checks on top-ranked protein complexes confirm strong association with phenotype classes and not batch. Therefore, the constituent proteins of these complexes are more likely to be clinically relevant.Electronic supplementary materialThe online version of this article (doi:10.1186/s12864-017-3490-3) contains supplementary material, which is available to authorized users.

Section: Resultsmentioning

confidence: 99%

Protein complex-based analysis is resistant to the obfuscating consequences of batch effects --- a case study in clinical proteomics

Wong

2017

BMC Genomics

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“…Although subnets or clusters are predictable from large biological networks, real biological complexes are enriched for biological signal, far outperforming predicted complexes/subnets from reference networks [19, 31, 33, 34]. Here, known human protein complexes derived from the CORUM database are used [35].…”

Section: Methodsmentioning

confidence: 99%

Fuzzy-FishNET: a highly reproducible protein complex-based approach for feature selection in comparative proteomics

2016

BMC Med Genomics

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BackgroundThe hypergeometric enrichment analysis approach typically fares poorly in feature-selection stability due to its upstream reliance on the t-test to generate differential protein lists before testing for enrichment on a protein complex, subnetwork or gene group.MethodsSwapping the t-test in favour of a fuzzy rank-based weight system similar to that used in network-based methods like Quantitative Proteomics Signature Profiling (QPSP), Fuzzy SubNets (FSNET) and paired FSNET (PFSNET) produces dramatic improvements.ResultsThis approach, Fuzzy-FishNET, exhibits high precision-recall over three sets of simulated data (with simulated protein complexes) while excelling in feature-selection reproducibility on real data (based on evaluation with real protein complexes). Overlap comparisons with PFSNET shows Fuzzy-FishNET selects the most significant complexes, which are also strongly class-discriminative. Cross-validation further demonstrates Fuzzy-FishNET selects class-relevant protein complexes.ConclusionsBased on evaluation with simulated and real datasets, Fuzzy-FishNET is a significant upgrade of the traditional hypergeometric enrichment approach and a powerful new entrant amongst comparative proteomics analysis methods.Electronic supplementary materialThe online version of this article (doi:10.1186/s12920-016-0228-z) contains supplementary material, which is available to authorized users.

“…The advent of network-based analysis methods as featureselection methods can help resolve irreproducibility [4,[6][7][8][9]. Whilst design is nontrivial and requires proper integration of bio-statistics, networks and proteomics [4,[6][7][8][9], networkbased approaches are already contributing towards resolving idiosyncratic coverage and consistency problems in clinical proteomics [10][11][12]. Soh et al [13] and Lim et al [14,15] have further demonstrated that network-based feature-selection methods are highly reproducible, and select phenotypically relevant features.…”

Section: Significance Of the Studymentioning

confidence: 99%

“…For PFSNET, a similar function (as in FSNET) f s(g i , p k ) is defined but the difference being that it uses a score delta(C, p k , X, Y) for a complex C and tissue p k with regards to classes X and Y as the difference of the score of C and tissue p k weighted based on X from the score of C and tissue p k weighted based on Y. More precisely: delta (C, p k , X, Y) = s cor e (C, p k , X) − s cor e (C, p k , Y) (7) If a complex C is irrelevant to the difference between classes X and Y, the value of delta(C, p k , X, Y) ought to cen-ter around 0. So PFSNet defines the following one-sample t-statistic:…”

Section: Rbnas (Snet/fsnet/pfsnet/ppfsnet)mentioning

confidence: 99%

“…The advent of network‐based analysis methods as feature‐selection methods can help resolve irreproducibility . Whilst design is nontrivial and requires proper integration of bio‐statistics, networks and proteomics , network‐based approaches are already contributing towards resolving idiosyncratic coverage and consistency problems in clinical proteomics . Soh et al.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Class‐paired Fuzzy SubNETs: A paired variant of the rank‐based network analysis family for feature selection based on protein complexes

Wong

2017

Proteomics

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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 amongst 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 PFSNET, are particularly resistant against batch effects, and only select features strongly correlated with class but not batch. This article is protected by copyright. All rights reserved.