Testing for Pathway (in)Activation by Using Gaussian Graphical Models

Wieringen, Wessel N. van; Peeters, Carel F.W.; Menezes, Renée X. de; Wiel, Mark A. van de

doi:10.1111/rssc.12282

Cited by 6 publications

(7 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Estimation of three probability density functions (PDFs), i.e., ( ), ( ) and ( , ) are required for the calculation of MI using (3). In general, the analytical formulae of the three PDFs are unknown, but kernel density estimation can be used to provide an efficient and robust estimation of PDFs for datasets with small sample size.…”

Section: A Mutual Information and Data Processing Inequalitymentioning

confidence: 99%

“…Pathway analysis has become an useful tool in the field of metabolomics, as it provides functional insights into the roles of differential metabolites in the development and treatment of numerous diseases. Over the past two decades, more than a dozen of pathway analysis methods have been developed [1][2][3][4][5][6][7][8][9][10]. Ma and colleagues [9] divide these methods into three generations: over-representation analysis (ORA) [11][12][13], functional class scoring (FCS) [14,15], and network topology analysis [16,17].…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, another set of network-based methods is used that take advantage of the relationships among detected metabolite abundances, correlations, partial correlations, mutual information, etc. to build a metabolite level association network [1][2][3]. Then, the topology analysis is carried out on the association network.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Network Modelling for Metabolite Set Analysis: A Case Study on CRC Metabolomics

Liu

Deng

et al. 2020

IEEE Access

View full text Add to dashboard Cite

In metabolomics, pathway analysis normally refers to analysis of a pre-defined sets of metabolites (metabolite set) associated to the metabolic pathways. The metabolite set analysis is useful to facilitate biological interpretation of metabolomics data. The currently available methods may be divided into three generations: over-representation analysis, functional class scoring, and network topology analysis. Among the three generations of tools, the network topology methods have been shown to have lower false discovery rates and better biological interpretability than the other two earlier generations of tools. However, most of the current network topology methods focus the analysis only at the metabolite-level network. The interaction between pathways are not taken into consideration. To address this issue, we propose a new metabolite sets association network (MSAN) modelling scheme. In the developed method, the metabolite sets are defined based on the KEGG databases. By using the metabolite sets as vertexes, the MSAN network evaluated the relationships between pairs of metabolite sets based on their mutual information. The impact of a single metabolite set on the overall network was evaluated by the MSAN network, which may help to uncover differential metabolite sets relevant to the underlying biology mechanism of the study. A metabolomic dataset from a published colorectal cancer (CRC) study is used to evaluate the performance of MSAN network to identify perturbed metabolite sets in colorectal cancer patients. The current results are compared to that of two commonly used methods, NetGSA and MetaboAnalyst, which are based on the metabolite-level network approach. The current method highlights a number of metabolites sets consistent with recent published CRC reports. Taken together, the proposed method may provide an alternative tool for the identification of dysregulated pathways and facilitate biological interpretation of metabolomics data. INDEX TERMS Metabolite sets association network (MSAN), colorectal cancer (CRC) metabolomics, mutual information, pathway analysis

show abstract

Section: A Mutual Information and Data Processing Inequalitymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Network Modelling for Metabolite Set Analysis: A Case Study on CRC Metabolomics

Liu

Deng

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Pathway enrichment has become a standard tool in the analytic pipeline for Omics data, since it reduces the complexity and provides a systems view of the biological question under investigation [1][2][3][4][5]. Dozens of methods have been proposed in the literature, ranging in modeling sophistication and effectiveness [6][7][8][9][10][11][12][13][14][15][16][17][18][19]. A number of papers have provided comprehensive reviews of available methods, which utilize information about the interconnections of genes (or other biomolecules) within the pathways, and offer improved performance over conventional second generation methods [6,7].…”

Section: Introductionmentioning

confidence: 99%

Faculty Opinions recommendation of A comparative study of topology-based pathway enrichment analysis methods.

Auffray

Meulder

2020

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature

View full text Add to dashboard Cite

Background: Pathway enrichment analysis is extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples. Results: The findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment. Conclusion: The analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

show abstract

“…Pathway enrichment has become a standard tool in the analytic pipeline for Omics data, since it reduces the complexity and provides a systems view of the biological question under investigation [1,2,3,4,5]. Dozens of methods have been proposed in the literature, ranging in modeling sophistication and effectiveness [6,7,8,9,10,11,12,13,14,15,16,17,18,19]. A number of papers have provided comprehensive reviews of available methods [20,21,22] capturing the evolving technical landscape, as well as the range of data types and applications (genes, proteins, etc.).…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Topology-based Pathway Enrichment Analysis Methods

Shojaie

Michailidis

2019

Preprint

View full text Add to dashboard Cite

Background: Pathway enrichment analysis is extensively used in the analysis of Omics data for gaining biological insights into the functional roles of pre-defined subsets of genes, proteins and metabolites. A large number of methods have been proposed in the literature for this task. The vast majority of these methods use as input expression levels of the biomolecules under study together with their membership in pathways of interest. The latest generation of pathway enrichment methods also leverages information on the topology of the underlying pathways, which as evidence from their evaluation reveals, lead to improved sensitivity and specificity. Nevertheless, a systematic empirical comparison of such methods is still lacking, making selection of the most suitable method for a specific experimental setting challenging. This comparative study of nine network-based methods for pathway enrichment analysis aims to provide a systematic evaluation of their performance based on three real data sets with different number of features (genes/metabolites) and number of samples. Results:The findings highlight both methodological and empirical differences across the nine methods. In particular, certain methods assess pathway enrichment due to differences both across expression levels and in the strength of the interconnectedness of the members of the pathway, while others only leverage differential expression levels. In the more challenging setting involving a metabolomics data set, the results show that methods that utilize both pieces of information (with NetGSA being a prototypical one) exhibit superior statistical power in detecting pathway enrichment. Conclusion:The analysis reveals that a number of methods perform equally well when testing large size pathways, which is the case with genomic data. On the other hand, NetGSA that takes into consideration both differential expression of the biomolecules in the pathway, as well as changes in the topology exhibits a superior performance when testing small size pathways, which is usually the case for metabolomics data.

show abstract

Testing for Pathway (in)Activation by Using Gaussian Graphical Models

Cited by 6 publications

References 38 publications

A Novel Network Modelling for Metabolite Set Analysis: A Case Study on CRC Metabolomics

A Novel Network Modelling for Metabolite Set Analysis: A Case Study on CRC Metabolomics

Faculty Opinions recommendation of A comparative study of topology-based pathway enrichment analysis methods.

A Comparative Study of Topology-based Pathway Enrichment Analysis Methods

Contact Info

Product

Resources

About