Spectral analysis of gene expression profiles using gene networks

Microarrays have become extremely useful for analysing genetic phenomena, but establishing a relation between microarray analysis results (typically a list of genes) and their biological significance is often difficult. Currently, the standard approach is to map a posteriori the results onto gene networks to elucidate the functions perturbed at the level of pathways. However, integrating a priori knowledge of the gene networks could help in the statistical analysis of gene expression data and in their biological interpretation. Here we propose a method to integrate a priori the knowledge of a gene network in the analysis of gene expression data. The approach is based on the spectral decomposition of gene expression profiles with respect to the eigenfunctions of the graph, resulting in an attenuation of the high-frequency components of the expression profiles with respect to the topology of the graph. We show how to derive unsupervised and supervised classification algorithms of expression profiles, resulting in classifiers with biological relevance. We applied the method to the analysis of a set of expression profiles from irradiated and non-irradiated yeast strains. It performed at least as well as the usual classification but provides much more biologically relevant results and allows a direct biological interpretation

DNA Microarray Data Analysis: A New Survey on Biclustering

There are subsets of genes that have similar behavior under subsets of conditions, so we say that they coexpress, but behave independently under other subsets of conditions. Discovering such coexpressions can be helpful to uncover genomic knowledge such as gene networks or gene interactions. That is why, it is of utmost importance to make a simultaneous clustering of genes and conditions to identify clusters of genes that are coexpressed under clusters of conditions. This type of clustering is called biclustering.Biclustering is an NP-hard problem. Consequently, heuristic algorithms are typically used to approximate this problem by finding suboptimal solutions. In this paper, we make a new survey on biclustering of gene expression data, also called microarray data

EDISA: extracting biclusters from multiple time-series of gene expression profiles

<p>Abstract</p> <p>Background</p> <p>Cells dynamically adapt their gene expression patterns in response to various stimuli. This response is orchestrated into a number of gene expression modules consisting of co-regulated genes. A growing pool of publicly available microarray datasets allows the identification of modules by monitoring expression changes over time. These time-series datasets can be searched for gene expression modules by one of the many clustering methods published to date. For an integrative analysis, several time-series datasets can be joined into a three-dimensional <it>gene-condition-time </it>dataset, to which standard clustering or biclustering methods are, however, not applicable. We thus devise a probabilistic clustering algorithm for <it>gene-condition-time </it>datasets.</p> <p>Results</p> <p>In this work, we present the EDISA (Extended Dimension Iterative Signature Algorithm), a novel probabilistic clustering approach for 3D <it>gene-condition-time </it>datasets. Based on mathematical definitions of gene expression modules, the EDISA samples initial modules from the dataset which are then refined by removing genes and conditions until they comply with the module definition. A subsequent extension step ensures gene and condition maximality. We applied the algorithm to a synthetic dataset and were able to successfully recover the implanted modules over a range of background noise intensities. Analysis of microarray datasets has lead us to define three biologically relevant module types: 1) We found modules with independent response profiles to be the most prevalent ones. These modules comprise genes which are co-regulated under several conditions, yet with a different response pattern under each condition. 2) Coherent modules with similar responses under all conditions occurred frequently, too, and were often contained within these modules. 3) A third module type, which covers a response specific to a single condition was also detected, but rarely. All of these modules are essentially different types of biclusters.</p> <p>Conclusion</p> <p>We successfully applied the EDISA to different 3D datasets. While previous studies were mostly aimed at detecting coherent modules only, our results show that coherent responses are often part of a more general module type with independent response profiles under different conditions. Our approach thus allows for a more comprehensive view of the gene expression response. After subsequent analysis of the resulting modules, the EDISA helped to shed light on the global organization of transcriptional control. An implementation of the algorithm is available at http://www-ra.informatik.uni-tuebingen.de/software/IAGEN/.</p

Learning predictive models from temporal three-way data using triclustering: applications in clinical data analysis

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020O conceito de triclustering estende o conceito de biclustering para um espaço tridimensional, cujo o objetivo é encontrar subespaços coerentes em dados tridimensionais. Considerando dados com dimensão temporal, a necessidade de aprender padrões temporais interessantes e usá-los para aprender modelos preditivos efetivos e interpretáveis, despoleta necessidade em investigar novas metodologias para análise de dados tridimensionais. Neste trabalho, propomos duas metodologias para esse efeito. Na primeira metodologia, encontramos os melhores parâmetros a serem usados em triclustering para descobrir os melhores triclusters (conjuntos de objetos com um padrão coerente ao longo de um dado conjunto de pontos temporais) para que depois estes padrões sejam usados como features por um dos mais apropriados classificadores encontrados na literatura. Neste caso, propomos juntar o classificador com uma abordagem de triclustering temporal. Para isso, idealizámos um algoritmo de triclustering com uma restrição temporal, denominado TCtriCluster para desvendar triclusters temporalmente contínuos (constituídos por pontos temporais contínuos). Na segunda metodologia, adicionámos uma fase de biclustering para descobrir padrões nos dados estáticos (dados que não mudam ao longo do tempo) e juntá-los aos triclusters para melhorar o desempenho e a interpretabilidade dos modelos. Estas metodologias foram usadas para prever a necessidade de administração de ventilação não invasiva (VNI) em pacientes com Esclerose Lateral Amiotrófica (ELA). Neste caso de estudo, aprendemos modelos de prognóstico geral, para os dados de todos os pacientes, e modelos especializados, depois de feita uma estratificação dos pacientes em 3 grupos de progressão: Lentos, Neutros e Rápidos. Os resultados demonstram que, além de serem bastante equiparáveis e por vezes superiores quando comparados com os resultados obtidos por um classificador de alto desempenho (Random Forests), os nossos classificadores são capazes de refinar as previsões através das potencialidades da interpretabilidade do modelo. De facto, quando usados os triclusters (e biclusters) como previsores, estamos a promover o uso de padrões de progressão da doença altamente interpretáveis. Para além disso, quando usados para previsão de prognóstico em doentes com ELA, os nossos modelos preditivos interpretáveis desvendaram padrões clinicamente relevantes para um grupo específico de padrões de progressão da doença, ajudando os médicos a entender a elevada heterogeneidade da progressão da ELA. Os resultados mostram ainda que a restrição temporal tem impacto na melhoria da efetividade e preditividade dos modelos.Triclustering extends biclustering to the three-dimensional space, aiming to find coherent subspaces in three-way data (sets of objects described by subsets of features in a subset of contexts). When the context is time, the need to learn interesting temporal patterns and use them to learn effective and interpretable predictive models triggers the need for new research methodologies to be used in three-way data analysis. In this work, we propose two approaches to learn predictive models from three-way data: 1) a triclustering-based classifier (considering just temporal data) and 2) a mixture of biclustering (with static data) and triclustering (with temporal data). In the first approach, we find the best triclustering parameters to uncover the best triclusters (sets of objects with a coherent pattern along a set of time-points) and then use these patterns as features in a state-of-the-art classifier. In the case of temporal data, we propose to couple the classifier with a temporal triclustering approach. With this aim, we devised a temporally constrained triclustering algorithm, termed TCtriCluster algorithm to mine time-contiguous triclusters. In the second approach, we extended the triclustering-based classifier with a biclustering task, where biclusters are discovered in static data (not changed over the time) and integrated with triclusters to improve performance and model explainability. The proposed methodologies were used to predict the need for non-invasive ventilation (NIV) in patients with Amyotrophic Lateral Sclerosis (ALS). In this case study, we learnt a general prognostic model from all patients data and specialized models after patient stratification into Slow, Neutral and Fast progressors. Our results show that besides comparable and sometimes outperforming results, when compared to a high performing random forest classifier, our predictive models enhance prediction with the potentialities of model interpretability. Indeed, when using triclusters (and biclusters) as predictors, we promoting the use of highly interpretable disease progression patterns. Furthermore, when used for prognostic prediction in ALS, our interpretable predictive models unravelled clinically relevant and group-specific disease progression patterns, helping clinicians to understand the high heterogeneity of ALS disease progression. Results further show that the temporal restriction is effective in improving the effectiveness of the predictive models

Mining SOM expression portraits: Feature selection and integrating concepts of molecular function

Background: &#xd;&#xa;Self organizing maps (SOM) enable the straightforward portraying of high-dimensional data of large sample collections in terms of sample-specific images. The analysis of their texture provides so-called spot-clusters of co-expressed genes which require subsequent significance filtering and functional interpretation. We address feature selection in terms of the gene ranking problem and the interpretation of the obtained spot-related lists using concepts of molecular function.&#xd;&#xa;&#xd;&#xa;Results: &#xd;&#xa;Different expression scores based either on simple fold change-measures or on regularized Students t-statistics are applied to spot-related gene lists and compared with special emphasis on the error characteristics of microarray expression data. The spot-clusters are analyzed using different methods of gene set enrichment analysis with the focus on overexpression and/or overrepresentation of predefined sets of genes. Metagene-related overrepresentation of selected gene sets was mapped into the SOM images to assign gene function to different regions. Alternatively we estimated set-related overexpression profiles over all samples studied using a gene set enrichment score. It was also applied to the spot-clusters to generate lists of enriched gene sets. We used the tissue body index data set, a collection of expression data of human tissues, as an illustrative example. We found that tissue related spots typically contain enriched populations of gene sets well corresponding to molecular processes in the respective tissues. In addition, we display special sets of housekeeping and of consistently weak and highly expressed genes using SOM data filtering. &#xd;&#xa;&#xd;&#xa;Conclusions:&#xd;&#xa;The presented methods allow the comprehensive downstream analysis of SOM-transformed expression data in terms of cluster-related gene lists and enriched gene sets for functional interpretation. SOM clustering implies the ability to define either new gene sets using selected SOM spots or to verify and/or to amend existing ones