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

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

2017

BMC Genomics

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“…31 ) Over-Representation Analysis (ORA) is a two-stage procedure: univariate feature selection on proteins followed by enrichment test. ORA can be highly unstable as it is very sensitive to the test type and stringency conditions in the univariate feature-selection step 30,41 as well as the appropriateness of the null hypothesis of the enrichment test. 42 As mitigation, Direct Group (DG) analysis does away with the univariate feature-selection step and directly determine whether a complex is differential by comparing the distribution of constituent protein expression between phenotype classes against that of proteins outside the complex.…”

Section: Complex-based Feature-selection Methodsmentioning

confidence: 99%

NetProt: Complex-based Feature Selection

2017

J. Proteome Res.

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Protein complex-based feature selection (PCBFS) provides unparalleled reproducibility with high phenotypic relevance on proteomics data. Currently, there are five PCBFS paradigms, but not all representative methods have been implemented or made readily available. To allow general users to take advantage of these methods, we developed the R-package NetProt, which provides implementations of representative feature-selection methods. NetProt also provides methods for generating simulated differential data and generating pseudocomplexes for complex-based performance benchmarking. The NetProt open source R package is available for download from https://github.com/gohwils/NetProt/releases/ , and online documentation is available at http://rpubs.com/gohwils/204259 .

“…To incorporate this element, a standardized Euclidean distance (between 0 and 1) is determined for all significant protein pairs across all samples. The significant proteins are reordered based on similarity (i.e., 1 – Euclidean distance) . This reordered protein list is then split at regular intervals to generate 100 complexes of size 7, and one complex of size 10 (710 significant proteins; see Section above).…”

Section: Methodsmentioning

confidence: 99%

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

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.