A bi-ordering approach to linking gene expression with clinical annotations in gastric cancer

<p>Abstract</p> <p>Background</p> <p>In the study of cancer genomics, gene expression microarrays, which measure thousands of genes in a single assay, provide abundant information for the investigation of interesting genes or biological pathways. However, in order to analyze the large number of noisy measurements in microarrays, effective and efficient bioinformatics techniques are needed to identify the associations between genes and relevant phenotypes. Moreover, systematic tests are needed to validate the statistical and biological significance of those discoveries.</p> <p>Results</p> <p>In this paper, we develop a robust and efficient method for exploratory analysis of microarray data, which produces a number of different orderings (rankings) of both genes and samples (reflecting correlation among those genes and samples). The core algorithm is closely related to biclustering, and so we first compare its performance with several existing biclustering algorithms on two real datasets - gastric cancer and lymphoma datasets. We then show on the gastric cancer data that the sample orderings generated by our method are highly statistically significant with respect to the histological classification of samples by using the Jonckheere trend test, while the gene modules are biologically significant with respect to biological processes (from the Gene Ontology). In particular, some of the gene modules associated with biclusters are closely linked to gastric cancer tumorigenesis reported in previous literature, while others are potentially novel discoveries.</p> <p>Conclusion</p> <p>In conclusion, we have developed an effective and efficient method, Bi-Ordering Analysis, to detect informative patterns in gene expression microarrays by ranking genes and samples. In addition, a number of evaluation metrics were applied to assess both the statistical and biological significance of the resulting bi-orderings. The methodology was validated on gastric cancer and lymphoma datasets.</p

A systematic comparison and evaluation of biclustering methods for gene expression data

Motivation: In recent years, there have been various efforts to overcome the limitations of standard clustering approaches for the analysis of gene expression data by grouping genes and samples simultaneously. The underlying concept, which is often referred to as biclustering, allows to identify sets of genes sharing compatible expression patterns across subsets of samples, and its usefulness has been demonstrated for different organisms and datasets. Several biclustering methods have been proposed in the literature; however, it is not clear how the different techniques compare with each other with respect to the biological relevance of the clusters as well as with other characteristics such as robustness and sensitivity to noise. Accordingly, no guidelines concerning the choice of the biclustering method are currently available. Results: First, this paper provides a methodology for comparing and validating biclustering methods that includes a simple binary reference model. Although this model captures the essential features of most biclustering approaches, it is still simple enough to exactly determine all optimal groupings; to this end, we propose a fast divide-and-conquer algorithm (Bimax). Second, we evaluate the performance of five salient biclustering algorithms together with the reference model and a hierarchical clustering method on various synthetic and real datasets for Saccharomyces cerevisiae and Arabidopsis thaliana. The comparison reveals that (1) biclustering in general has advantages over a conventional hierarchical clustering approach, (2) there are considerable performance differences between the tested methods and (3) already the simple reference model delivers relevant patterns within all considered settings. Availability: The datasets used, the outcomes of the biclustering algorithms and the Bimax implementation for the reference model are available at Contact: [email protected] Supplementary information: Supplementary data are available a

G-Tric: enhancing triclustering evaluation using three-way synthetic datasets with ground truth

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Three-dimensional datasets, or three-way data, started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations _ features _ contexts). With an increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount.These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real three-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. G-Tric can replicate real-world datasets and create new ones that match researchers’ needs across several properties, including data type (numeric or symbolic), dimension, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled by defining the number of missing values, noise, and errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches. Besides reviewing the current state-of-the-art regarding triclustering approaches, comparison studies and evaluation metrics, this work also analyzes how the lack of frameworks to generate synthetic data influences existent evaluation methodologies, limiting the scope of performance insights that can be extracted from each algorithm. As well as exemplifying how the set of decisions made on these evaluations can impact the quality and validity of those results. Alternatively, a different methodology that takes advantage of synthetic data with ground truth is presented. This approach, combined with the proposal of an extension to an existing clustering extrinsic measure, enables to assess solutions’ quality under new perspectives

Construction of gene regulatory networks using biclustering and bayesian networks

<p>Abstract</p> <p>Background</p> <p>Understanding gene interactions in complex living systems can be seen as the ultimate goal of the systems biology revolution. Hence, to elucidate disease ontology fully and to reduce the cost of drug development, gene regulatory networks (GRNs) have to be constructed. During the last decade, many GRN inference algorithms based on genome-wide data have been developed to unravel the complexity of gene regulation. Time series transcriptomic data measured by genome-wide DNA microarrays are traditionally used for GRN modelling. One of the major problems with microarrays is that a dataset consists of relatively few time points with respect to the large number of genes. Dimensionality is one of the interesting problems in GRN modelling.</p> <p>Results</p> <p>In this paper, we develop a biclustering function enrichment analysis toolbox (BicAT-plus) to study the effect of biclustering in reducing data dimensions. The network generated from our system was validated via available interaction databases and was compared with previous methods. The results revealed the performance of our proposed method.</p> <p>Conclusions</p> <p>Because of the sparse nature of GRNs, the results of biclustering techniques differ significantly from those of previous methods.</p

An effective measure for assessing the quality of biclusters

Biclustering is becoming a popular technique for the study of gene expression data. This is mainly due to the capability of biclustering to address the data using various dimensions simultaneously, as opposed to clustering, which can use only one dimension at the time. Different heuristics have been proposed in order to discover interesting biclusters in data. Such heuristics have one common characteristic: they are guided by a measure that determines the quality of biclusters. It follows that defining such a measure is probably the most important aspect. One of the popular quality measure is the mean squared residue (MSR). However, it has been proven that MSR fails at identifying some kind of patterns. This motivates us to introduce a novel measure, called virtual error (VE), that overcomes this limitation. Results obtained by using VE confirm that it can identify interesting patterns that could not be found by MSR

Discovery of error-tolerant biclusters from noisy gene expression data

An important analysis performed on microarray gene-expression data is to discover biclusters, which denote groups of genes that are coherently expressed for a subset of conditions. Various biclustering algorithms have been proposed to find different types of biclusters from these real-valued gene-expression data sets. However, these algorithms suffer from several limitations such as inability to explicitly handle errors/noise in the data; difficulty in discovering small bicliusters due to their top-down approach; inability of some of the approaches to find overlapping biclusters, which is crucial as many genes participate in multiple biological processes. Association pattern mining also produce biclusters as their result and can naturally address some of these limitations. However, traditional association mining only finds exact biclusters, whic

Biclustering fMRI time series

Tese de mestrado, Ciência de Dados, Universidade de Lisboa, Faculdade de Ciências, 2020Biclustering é um método de análise que procura gerar clusters tendo em conta simultaneamente as linhas e as colunas de uma matriz de dados. Este método tem sido vastamente explorado em análise de dados genéticos. Apesar de diversos estudos reconhecerem as capacidades deste método de análise em outras áreas de investigação, as últimas duas décadas tem sido marcadas por um número elevado de estudos aplicados em dados genéticos e pela ausência de uma linha de investigação que explore as capacidades de biclustering fora desta área tradicional Esta tese segue pistas que sugerem potencial no uso de biclustering em dados de natureza espaço-temporal. Considerando o contexto particular das neurociências, esta tese explora as capacidades dos algoritmos de biclustering em extrair conhecimento das séries temporais geradas por técnicas de imagem por ressonância magnética funcional (fMRI). Eta tese propõe uma metodologia para avaliar a capacidade de algoritmos de biclustering em estudar dados fMRI, considerando tanto dados sintéticos como dados reais. Para avaliar estes algoritmos, usamos métricas de avaliação interna. Os nossos resultados discutem o uso de diversas estratégias de busca, revelando a superioridade de estratégias exaustivos para obter os biclusters mais homogéneos. No entanto, o elevado custo computacional de estratégias exaustivas ainda são um desafio e é necessário pesquisa adicional para a busca eficiente de biclusters no contexto de análise de dados fMRI. Propomos adicionalmente uma nova metodologia de análise de biclusters baseada em algoritmos de descoberta de padrões para determinar os padrões mais frequentes presentes nas soluções de biclustering geradas. Um bicluster não é mais que um hipervértice num hipergrafo . Extrair padrões frequentes numa solução de biclustering implica extrair os hipervértices mais significativos. Numa primeira abordagem, isto permite entender relações entre regiões do cérebro e traçar perfis temporais que métodos tradicionais de estudos de correlação não são capazes de detetar. Adicionalmente, o processo de gerar os biclusters permite filtrar ligações pouco interessantes, permitindo potencialmente gerar hipergrafos de forma eficiente. A questão final é o que podemos fazer com este conhecimento. Conhecer a relação entre regiões do cérebro é o objetivo central das neurociências. Entender as ligações entre regiões do cérebro para vários sujeitos permitem traçar perfis. Nesse caso, propomos uma metodologia para extrapolar biclusters para dados tridimensionais e efetuar triclustering. Adicionalmente, entender a ligação entre zonas cerebrais permite identificar doenças como a esquizofrenia, demência ou o Alzheimer. Este trabalho aponta caminhos para o uso de biclustering na análise de dados espaço-temporais, em particular em neurociências. A metodologia de avaliação proposta mostra evidências da eficácia do biclustering para encontrar padrões locais em dados de fMRI, embora mais trabalhos sejam necessários em relação à escalabilidade para promover a aplicação em cenários reais.The effectiveness of biclustering, simultaneous clustering of both rows and columns in a data matrix, has been primarily shown in gene expression data analysis. Furthermore, several researchers recognize its potentialities in other research areas. Nevertheless, the last two decades witnessed many biclustering algorithms targeting gene expression data analysis and a lack of consistent studies exploring the capacities of biclustering outside this traditional application domain. Following hints that suggest potentialities for biclustering on Spatiotemporal data, particularly in neurosciences, this thesis explores biclustering’s capacity to extract knowledge from fMRI time series. This thesis proposes a methodology to evaluate biclustering algorithms’ feasibility to study the fMRI signal, considering both synthetic and realworld fMRI datasets. In the absence of ground truth to compare bicluster solutions with a reference one, we used internal valuation metrics. Results discussing the use of different search strategies showed the superiority of exhaustive approaches, obtaining the most homogeneous biclusters. However, their high computational cost is still a challenge, and further work is needed for the efficient use of biclustering in fMRI data analysis. We propose a new methodology for analyzing biclusters based on performing pattern mining algorithms to determine the most frequent patterns present in the generated biclustering solutions. A bicluster is nothing more than a hyperlink in a hypergraph. Extracting frequent patterns in a biclustering solution implies extracting the most significant hyperlinks. In a first approach, this allows to understand relationships between regions of the brain and draw temporal profiles that traditional methods of correlation studies cannot detect. Additionally, the process of generating biclusters allows filtering uninteresting links, potentially allowing to generate hypergraphs efficiently. The final question is, what can we do with this knowledge. Knowing the relationship between brain regions is the central objective of neurosciences. Understanding the connections between regions of the brain for various subjects allows one to draw profiles. In this case, we propose a methodology to extrapolate biclusters to threedimensional data and perform triclustering. Additionally, understanding the link between brain zones allows identifying diseases like schizophrenia, dementia, or Alzheimer’s. This work pinpoints avenues for the use of biclustering in Spatiotemporal data analysis, in particular neurosciences applications. The proposed evaluation methodology showed evidence of biclustering’s effectiveness in finding local fMRI data patterns, although further work is needed regarding scalability to promote the application in real scenarios