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

Overview of attention for article published in BMC Genomics, March 2017
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Title
Protein complex-based analysis is resistant to the obfuscating consequences of batch effects --- a case study in clinical proteomics
Published in
BMC Genomics, March 2017
DOI 10.1186/s12864-017-3490-3
Pubmed ID
Authors

Wilson Wen Bin Goh, Limsoon Wong

Abstract

In proteomics, batch effects are technical sources of variation that confounds proper analysis, preventing effective deployment in clinical and translational research. Using 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. Protein 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.

Twitter Demographics

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Mendeley readers

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 26%
Researcher 5 26%
Student > Bachelor 3 16%
Other 1 5%
Student > Master 1 5%
Other 2 11%
Unknown 2 11%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 7 37%
Computer Science 2 11%
Agricultural and Biological Sciences 1 5%
Business, Management and Accounting 1 5%
Environmental Science 1 5%
Other 3 16%
Unknown 4 21%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 12 September 2017.
All research outputs
#10,420,593
of 11,753,826 outputs
Outputs from BMC Genomics
#6,144
of 6,985 outputs
Outputs of similar age
#224,386
of 265,066 outputs
Outputs of similar age from BMC Genomics
#85
of 93 outputs
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