Pathway-Based Microarray Analysis for Defining Statistical Significant Phenotype-Related Pathways: A Review of Common Approaches

Misman, Muhammad Faiz; Deris, Safaai; Hashim, Siti Zaiton Mohd; Jumali, Rizuan; Mohamad, Mohd Saberi

doi:10.1109/icime.2009.103

Cited by 10 publications

(7 citation statements)

References 41 publications

(68 reference statements)

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“…Comprehensive lists of gene set analysis approaches, as well as comparisons between them, can be found in well-developed surveys (Emmert-Streib & Glazko, 2011;Khatri, Sirota, & Butte, 2012;Misman et al, 2009). Comprehensive lists of gene set analysis approaches, as well as comparisons between them, can be found in well-developed surveys (Emmert-Streib & Glazko, 2011;Khatri, Sirota, & Butte, 2012;Misman et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

“…ORA and FCS approaches are often referred as gene set enrichment methods. Comprehensive lists of gene set analysis approaches, as well as comparisons between them, can be found in well‐developed surveys (Emmert‐Streib & Glazko, ; Kelder, Conklin, Evelo, & Pico, ; Khatri, Sirota, & Butte, ; Misman et al., ). While useful for the purpose for which they have been developed—to analyze sets of genes—these methods completely ignore the topology and interaction between genes.…”

Section: Introductionmentioning

confidence: 99%

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Network‐Based Approaches for Pathway Level Analysis

Nguyen

Mitrea

Drăghici

2018

CP in Bioinformatics

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Identification of impacted pathways is an important problem because it allows us to gain insights into the underlying biology beyond the detection of differentially expressed genes. In the past decade, a plethora of methods have been developed for this purpose. The last generation of pathway analysis methods are designed to take into account various aspects of pathway topology in order to increase the accuracy of the findings. Here, we cover 34 such topology-based pathway analysis methods published in the past 13 years. We compare these methods on categories related to implementation, availability, input format, graph models, and statistical approaches used to compute pathway level statistics and statistical significance. We also discuss a number of critical challenges that need to be addressed, arising both in methodology and pathway representation, including inconsistent terminology, data format, lack of meaningful benchmarks, and, more importantly, a systematic bias that is present in most existing methods. © 2018 by John Wiley & Sons, Inc.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Network‐Based Approaches for Pathway Level Analysis

Nguyen

Mitrea

Drăghici

2018

CP in Bioinformatics

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“…Here, we will refer to these as gene set analysis methods, rather than pathway analysis methods. A comprehensive list of such techniques, as well as some comparisons between them can be found in several well-developed surveys (Misman et al, 2009; Chuang et al, 2010; Kelder et al, 2010; Emmert-Streib and Glazko, 2011; Khatri et al, 2012). While useful for the purpose for which they have been developed - to analyze sets of genes - these methods do not take into consideration the topology of the pathways, and hence completely ignore the interactions described by the pathways, the different types of genes, the position of the genes on their respective pathways, etc.…”

Section: Introductionmentioning

confidence: 99%

Methods and approaches in the topology-based analysis of biological pathways

et al. 2013

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The goal of pathway analysis is to identify the pathways significantly impacted in a given phenotype. Many current methods are based on algorithms that consider pathways as simple gene lists, dramatically under-utilizing the knowledge that such pathways are meant to capture. During the past few years, a plethora of methods claiming to incorporate various aspects of the pathway topology have been proposed. These topology-based methods, sometimes referred to as “third generation,” have the potential to better model the phenomena described by pathways. Although there is now a large variety of approaches used for this purpose, no review is currently available to offer guidance for potential users and developers. This review covers 22 such topology-based pathway analysis methods published in the last decade. We compare these methods based on: type of pathways analyzed (e.g., signaling or metabolic), input (subset of genes, all genes, fold changes, gene p-values, etc.), mathematical models, pathway scoring approaches, output (one or more pathway scores, p-values, etc.) and implementation (web-based, standalone, etc.). We identify and discuss challenges, arising both in methodology and in pathway representation, including inconsistent terminology, different data formats, lack of meaningful benchmarks, and the lack of tissue and condition specificity.

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“…Many researchers discussed the advantages of pathway-based analysis. Subramanian, for instance, considered an enrichment-based approach using various Kolmogorove-Smirnov statistics [ 3 ]; Curtis gave a good review of computational approaches proposed for pathway-based analysis [ 4 ]; Goeman et al proposed the global test based on a generalized linear model [ 12 ]; Pang et al described the random forest-based pathway analysis [ 13 ]; Harris et al considered gene grouping based on gene ontology [ 14 ]; Misman et al provide good reviews on those in [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Applications of Different Weighting Schemes to Improve Pathway-Based Analysis

Kim

Wang

et al. 2011

Comparative and Functional Genomics

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Conventionally, pathway-based analysis assumes that genes in a pathway equally contribute to a biological function, thus assigning uniform weight to genes. However, this assumption has been proved incorrect, and applying uniform weight in the pathway analysis may not be an appropriate approach for the tasks like molecular classification of diseases, as genes in a functional group may have different predicting power. Hence, we propose to use different weights to genes in pathway-based analysis and devise four weighting schemes. We applied them in two existing pathway analysis methods using both real and simulated gene expression data for pathways. Among all schemes, random weighting scheme, which generates random weights and selects optimal weights minimizing an objective function, performs best in terms of P value or error rate reduction. Weighting changes pathway scoring and brings up some new significant pathways, leading to the detection of disease-related genes that are missed under uniform weight.

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Pathway-Based Microarray Analysis for Defining Statistical Significant Phenotype-Related Pathways: A Review of Common Approaches

Cited by 10 publications

References 41 publications

Network‐Based Approaches for Pathway Level Analysis

Network‐Based Approaches for Pathway Level Analysis

Methods and approaches in the topology-based analysis of biological pathways

Applications of Different Weighting Schemes to Improve Pathway-Based Analysis

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