Characterizing gene sets using discriminative random walks with restart on heterogeneous biological networks

We review several commonly used methods for the design and analysis of microarray data. To begin with, some experimental design issues are addressed. Several approaches for pre‐processing the data (filtering and normalization) before the statistical analysis stage are then discussed. A common first step in this type of analysis is gene selection based on statistical testing. Two approaches, permutation and model‐based methods are explained and we emphasize the need to correct for multiple testing. Moreover, powerful approaches based on gene sets are mentioned. Clustering of either genes or samples is frequently performed when analyzing microarray data. We summarize the basics of both supervised and unsupervised clustering (classification). The latter may be of use for creating diagnostic arrays, for example. Construction of biological networks, such as pathways, is a statistically challenging but complex task that is a relatively new development and hence mentioned only briefly. We finish with some remarks on literature and software. The emphasis in this paper is on the philosophy behind several statistical issues and on a critical interpretation of microarray related analysis methods.

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Predicting genetic interactions with random walks on biological networks

BMC Bioinformatics ◽

10.1186/1471-2105-10-17 ◽

2009 ◽

Vol 10 (1) ◽

Cited By ~ 39

Author(s):

Kyle C Chipman ◽

Ambuj K Singh

Keyword(s):

Random Walks ◽

Biological Networks ◽

Genetic Interactions

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eXamine: Visualizing annotated networks in Cytoscape

F1000Research ◽

10.12688/f1000research.14612.1 ◽

2018 ◽

Vol 7 ◽

pp. 519

Author(s):

Philipp Spohr ◽

Kasper Dinkla ◽

Gunnar W. Klau ◽

Mohammed El-Kebir

Keyword(s):

Social Networks ◽

Biological Networks ◽

App Store ◽

Gene Sets ◽

Set Membership ◽

Small Set ◽

Reproducible Analysis

eXamine is a Cytoscape app that displays set membership as contours on top of a node-link layout of a small graph. In addition to facilitating interpretation of enriched gene sets of small biological networks, eXamine can be used in other domains such as the visualization of communities in small social networks. eXamine was made available on the Cytoscape App Store in March 2014, has since registered more than 7,200 downloads, and has been highly rated by more than 25 users. In this paper, we present eXamine's new automation features that enable researchers to compose reproducible analysis workflows to generate visualizations of small, set-annotated graphs.

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Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data

Statistical Applications in Genetics and Molecular Biology ◽

10.2202/1544-6115.1055 ◽

2004 ◽

Vol 3 (1) ◽

pp. 1-29 ◽

Cited By ~ 58

Author(s):

Jörg Rahnenführer ◽

Francisco S Domingues ◽

Jochen Maydt ◽

Thomas Lengauer

Keyword(s):

Gene Expression ◽

Gene Expression Data ◽

Biological Networks ◽

Permutation Test ◽

Statistical Significance ◽

Data Sets ◽

Expression Data ◽

Biologically Relevant ◽

Gene Sets ◽

Best Fitting

We present a statistical approach to scoring changes in activity of metabolic pathways from gene expression data. The method identifies the biologically relevant pathways with corresponding statistical significance. Based on gene expression data alone, only local structures of genetic networks can be recovered. Instead of inferring such a network, we propose a hypothesis-based approach. We use given knowledge about biological networks to improve sensitivity and interpretability of findings from microarray experiments.Recently introduced methods test if members of predefined gene sets are enriched in a list of top-ranked genes in a microarray study. We improve this approach by defining scores that depend on all members of the gene set and that also take pairwise co-regulation of these genes into account. We calculate the significance of co-regulation of gene sets with a nonparametric permutation test. On two data sets the method is validated and its biological relevance is discussed. It turns out that useful measures for co-regulation of genes in a pathway can be identified adaptively.We refine our method in two aspects specific to pathways. First, to overcome the ambiguity of enzyme-to-gene mappings for a fixed pathway, we introduce algorithms for selecting the best fitting gene for a specific enzyme in a specific condition. In selected cases, functional assignment of genes to pathways is feasible. Second, the sensitivity of detecting relevant pathways is improved by integrating information about pathway topology. The distance of two enzymes is measured by the number of reactions needed to connect them, and enzyme pairs with a smaller distance receive a higher weight in the score calculation.

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eXamine: Visualizing annotated networks in Cytoscape

F1000Research ◽

10.12688/f1000research.14612.2 ◽

2018 ◽

Vol 7 ◽

pp. 519

Author(s):

Philipp Spohr ◽

Kasper Dinkla ◽

Gunnar W. Klau ◽

Mohammed El-Kebir

Keyword(s):

Social Networks ◽

Biological Networks ◽

App Store ◽

Gene Sets ◽

Set Membership ◽

Small Set ◽

Reproducible Analysis

eXamine is a Cytoscape app that displays set membership as contours on top of a node-link layout of a small graph. In addition to facilitating interpretation of enriched gene sets of small biological networks, eXamine can be used in other domains such as the visualization of communities in small social networks. eXamine was made available on the Cytoscape App Store in March 2014, has since registered more than 7,700 downloads, and has been highly rated by more than 25 users. In this paper, we present eXamine's new automation features that enable researchers to compose reproducible analysis workflows to generate visualizations of small, set-annotated graphs.

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Gene prioritization based on random walks with restarts and absorbing states, to define gene sets regulating drug pharmacodynamics from single-cell analyses

10.1101/2021.02.19.431974 ◽

2021 ◽

Author(s):

Augusto Sales de Queiroz ◽

Guilherme Sales Santa Cruz ◽

Alain Jean-Marie ◽

Dorian Mazauric ◽

Jérémie Roux ◽

...

Keyword(s):

Random Walks ◽

Drug Sensitivity ◽

Single Cells ◽

Interaction Network ◽

Gene Signature ◽

Large Sets ◽

Pairwise Interactions ◽

Gene Sets ◽

Underlying Network ◽

Absorbing States

AbstractMotivationPrioritizing genes for their role in drug sensitivity, is an important step in understanding drugs mechanisms of action and discovering new molecular targets for co-treatment. To formalize this problem, we consider two sets of genes X and P respectively composing the predictive gene signature of sensitivity to a drug and the genes involved in its mechanism of action, as well as a protein interaction network (PPIN) containing the products of X and P as nodes. We introduce GENetRank, a method to prioritize the genes in X for their likelihood to regulate the genes in P.ResultsGENetRank uses asymmetric random walks with restarts and absorbing states to focus on certain nodes of the PPIN, as well as novel saturation indices providing insights on the visited regions of the PPIN. Using MINT as underlying network, we apply GENetRank to a predicitive gene signature of cancer cells sensitivity to tumor-necrosis-factor-related apoptosis-inducing ligand (TRAIL), performed in single-cells. Our ranking provides biological insights on drug sensitivity and a gene set considerably enriched in genes regulating TRAIL pharmacodynamics when compared to the most significant differentially expressed genes obtained from a statistical analysis framework alone. We also introduce gene expression radars, a visualization tool to assess all pairwise interactions at a glance.Availability and ImplementationGENetRank is made available in the Structural Bioinformatics Library (https://sbl.inria.fr/doc/Genetrank-user-manual.html). It should prove useful for mining gene sets in conjunction with a signaling pathway, whenever other approaches yield relatively large sets of genes.

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de novo identification of maximally deregulated subnetworks based on multi-omics data with DeRegNet

10.1101/2021.05.11.443638 ◽

2021 ◽

Author(s):

Sebastian S Winkler ◽

Ivana Winkler ◽

Mirjam Figaschewski ◽

Thorsten Tiede ◽

Alfred Nordheim ◽

...

Keyword(s):

Biological Networks ◽

De Novo ◽

Directed Graphs ◽

Simulated Data ◽

Likelihood Estimation ◽

Difficult Problem ◽

Omics Data ◽

Cancer Dataset ◽

Gene Sets ◽

Or Gene

With a growing amount of (multi-)omics data being available, the extraction of knowledge from these datasets is still a difficult problem. Classical enrichment-style analyses require predefined pathways or gene sets that are tested for significant deregulation to assess whether the pathway is functionally involved in the biological process under study. De novo identification of these pathways can reduce the bias inherent in predefined pathways or gene sets. At the same time, the definition and efficient identification of these pathways de novo from large biological networks is a challenging problem. We present a novel algorithm, DeRegNet, for the identification of maximally deregulated subnetworks on directed graphs based on deregulation scores derived from (multi-)omics data. DeRegNet can be interpreted as maximum likelihood estimation given a certain probabilistic model for de-novo subgraph identification. We use fractional integer programming to solve the resulting combinatorial optimization problem. We can show that the approach outperforms related algorithms on simulated data with known ground truths. On a publicly available liver cancer dataset we can show that DeRegNet can identify biologically meaningful subgraphs suitable for patient stratification. DeRegNet is freely available as open-source software.

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