Finding consistent disease subnetworks using PFSNet

Lim, Kevin; Wong, Limsoon

doi:10.1093/bioinformatics/btt625

Cited by 51 publications

(72 citation statements)

References 27 publications

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“…The idea of fuzzification has also been used earlier in a few gene expression profile analysis methods [7, 8] and also proteomic profile analysis methods [6, 9]. However, these works merely use it as a component of their respective methods, and do not study its role and effectiveness as a normalization procedure.…”

Section: Introductionmentioning

confidence: 99%

GFS: fuzzy preprocessing for effective gene expression analysis

Belorkar

Wong

2016

BMC Bioinformatics

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BackgroundGene expression data produced on high-throughput platforms such as microarrays is susceptible to much variation that obscures useful biological information. Therefore, preprocessing data with a suitable normalization method is necessary, and has a direct and massive impact on the quality of downstream data analysis. However, it is known that standard normalization methods perform poorly, specially in the presence of substantial batch effects and heterogeneity in gene expression data.ResultsWe present Gene Fuzzy Score (GFS), a simple preprocessing technique, that is able to largely reduce obscuring variation while retaining useful biological information. Using four sets of publicly available datasets containing batch effects and heterogeneity, we compare GFS with three standard normalization techniques as well as raw gene expression. Each method is evaluated with respect to the quality, consistency, and biological coherence of its processed output. It is found that GFS outperforms other transformation techniques in all three aspects.ConclusionOur approach to preprocessing is a stronger alternative to popular normalization techniques. We demonstrate that it achieves the essential goal of preprocessing – it is effective at making expression values from multiple samples comparable, even when they are from separate platforms, in independent batches, or belong to a heterogeneous phenotype.

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Section: Introductionmentioning

confidence: 99%

GFS: fuzzy preprocessing for effective gene expression analysis

Belorkar

Wong

2016

BMC Bioinformatics

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“…The most abundant proteins above a certain percentile (the value is denoted as alpha1) are first selected. A second percentile value (defined as alpha2) is then used to extend the protein list [30]. To penalize lower-ranked proteins below alpha1 and above alpha2, proteins are assigned interpolated weights based on their ranks.…”

Section: Methodsmentioning

confidence: 99%

“…QPSP, and the rank-based network approaches (RBNAs), SNET (SubNET) [29], FSNET (Fuzzy SNET) and PFSNET (Paired FSNET) [30] have been shown to be highly stable and robust, these techniques are similar in that they use a fuzzy weighting system on proteins ranked by expression [31] (see Methods). …”

Section: Introductionmentioning

confidence: 99%

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

Goh

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.

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“…PFSNet is available for implementation in R programming. 22 Here, PFSNet is used as a baseline method, which can be incorporated in the next release of GAT. For the parameters setting, as there may be some limitation of the tool that we have not found yet, by using the default parameters set, it cannot identify the disease sub-network in most datasets except for Colorectal1 datasets.…”

Section: Comparison With Pfsnetmentioning

confidence: 99%

“…Paired fuzzy SNet or PFSNet is another existing network-based analysis. 22 PFSNet uses majority vote to select a subset of genes based on calculated \fuzzy scores". Then, the whole gene network is separated into sub-networks by mapping those genes onto it.…”

Section: Introductionmentioning

confidence: 99%

Gene-set activity toolbox (GAT): A platform for microarray-based cancer diagnosis using an integrative gene-set analysis approach

Engchuan

Meechai

Tongsima

et al. 2016

J. Bioinform. Comput. Biol.

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Cancer is a complex disease that cannot be diagnosed reliably using only single gene expression analysis. Using gene-set analysis on high throughput gene expression profiling controlled by various environmental factors is a commonly adopted technique used by the cancer research community. This work develops a comprehensive gene expression analysis tool (gene-set activity toolbox: (GAT)) that is implemented with data retriever, traditional data pre-processing, several gene-set analysis methods, network visualization and data mining tools. The gene-set analysis methods are used to identify subsets of phenotype-relevant genes that will be used to build a classification model. To evaluate GAT performance, we performed a cross-dataset validation study on three common cancers namely colorectal, breast and lung cancers. The results show that GAT can be used to build a reasonable disease diagnostic model and the predicted markers have biological relevance. GAT can be accessed from http://gat.sit.kmutt.ac.th where GAT’s java library for gene-set analysis, simple classification and a database with three cancer benchmark datasets can be downloaded.

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Finding consistent disease subnetworks using PFSNet

Abstract: http://compbio.ddns.comp.nus.edu.sg:8080/pfsnet/

Cited by 51 publications

References 27 publications

GFS: fuzzy preprocessing for effective gene expression analysis

GFS: fuzzy preprocessing for effective gene expression analysis

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

Gene-set activity toolbox (GAT): A platform for microarray-based cancer diagnosis using an integrative gene-set analysis approach

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