Utilizing gene co-expression networks for comparative transcriptomic analyses

The development of high-throughput technologies such as microarray and next-generation RNA sequencing (RNA-seq) has generated numerous transcriptomic data that can be used for comparative transcriptomics studies. Transcriptomes obtained from different species can reveal differentially expressed genes that underlie species-specific traits. It also has the potential to identify genes that have conserved gene expression patterns. However, differential expression alone does not provide information about how the genes relate to each other in terms of gene expression or if groups of genes are correlated in similar ways across species, tissues, etc. This makes gene expression networks, such as co-expression networks, valuable in terms of finding similarities or differences between genes based on their relationships with other genes. The desired outcome of this research was to develop methods for comparative transcriptomics, specifically for comparing gene co-expression networks (GCNs), either within or between any set of organisms. These networks represent genes as nodes in the network, and pairs of genes may be connected by an edge representing the strength of the relationship between the pairs. We begin with a review of currently utilized techniques available that can be used or adapted to compare gene co-expression networks. We also work to systematically determine the appropriate number of samples needed to construct reproducible gene co-expression networks for comparison purposes. In order to systematically compare these replicate networks, software to visualize the relationship between replicate networks was created to determine when the consistency of the networks begins to plateau and if this is affected by factors such as tissue type and sample size. Finally, we developed a tool called Juxtapose that utilizes gene embedding to functionally interpret the commonalities and differences between a given set of co-expression networks constructed using transcriptome datasets from various organisms. A set of transcriptome datasets were utilized from publicly available sources as well as from collaborators. GTEx and Gene Expression Omnibus (GEO) RNA-seq datasets were used for the evaluation of the techniques proposed in this research. Skeletal cell datasets of closely related species and more evolutionarily distant organisms were also analyzed to investigate the evolutionary relationships of several skeletal cell types. We found evidence that data characteristics such as tissue origin, as well as the method used to construct gene co-expression networks, can substantially impact the number of samples required to generate reproducible networks. In particular, if a threshold is used to construct a gene co-expression network for downstream analyses, the number of samples used to construct the networks is an important consideration as many samples may be required to generate networks that have a reproducible edge order when sorted by edge weight. We also demonstrated the capabilities of our proposed method for comparing GCNs, Juxtapose, showing that it is capable of consistently matching up genes in identical networks, and it also reflects the similarity between different networks using cosine distance as a measure of gene similarity. Finally, we applied our proposed method to skeletal cell networks and find evidence of conserved gene relationships within skeletal GCNs from the same species and identify modules of genes with similar embeddings across species that are enriched for biological processes involved in cartilage and osteoblast development. Furthermore, smaller sub-networks of genes reflect the phylogenetic relationships of the species analyzed using our gene embedding strategy to compare the GCNs. This research has produced methodologies and tools that can be used for evolutionary studies and generalizable to scenarios other than cross-species comparisons, including co-expression network comparisons across tissues or conditions within the same species

Integrated biclustering of heterogeneous genome-wide datasets for the inference of global regulatory networks

BACKGROUND: The learning of global genetic regulatory networks from expression data is a severely under-constrained problem that is aided by reducing the dimensionality of the search space by means of clustering genes into putatively co-regulated groups, as opposed to those that are simply co-expressed. Be cause genes may be co-regulated only across a subset of all observed experimental conditions, biclustering (clustering of genes and conditions) is more appropriate than standard clustering. Co-regulated genes are also often functionally (physically, spatially, genetically, and/or evolutionarily) associated, and such a priori known or pre-computed associations can provide support for appropriately grouping genes. One important association is the presence of one or more common cis-regulatory motifs. In organisms where these motifs are not known, their de novo detection, integrated into the clustering algorithm, can help to guide the process towards more biologically parsimonious solutions. RESULTS: We have developed an algorithm, cMonkey, that detects putative co-regulated gene groupings by integrating the biclustering of gene expression data and various functional associations with the de novo detection of sequence motifs. CONCLUSION: We have applied this procedure to the archaeon Halobacterium NRC-1, as part of our efforts to decipher its regulatory network. In addition, we used cMonkey on public data for three organisms in the other two domains of life: Helicobacter pylori, Saccharomyces cerevisiae, and Escherichia coli. The biclusters detected by cMonkey both recapitulated known biology and enabled novel predictions (some for Halobacterium were subsequently confirmed in the laboratory). For example, it identified the bacteriorhodopsin regulon, assigned additional genes to this regulon with apparently unrelated function, and detected its known promoter motif. We have performed a thorough comparison of cMonkey results against other clustering methods, and find that cMonkey biclusters are more parsimonious with all available evidence for co-regulation

Integrating biclustering techniques with de novo gene regulatory network discovery using RNA-seq from skeletal tissues

In order to improve upon stem cell therapy for osteoarthritis, it is necessary to understand the molecular and cellular processes behind bone development and the differences from cartilage formation. To further elucidate these processes would provide a means to analyze the relatedness of bone and cartilage tissue by determining genes that are expressed and regulated for stem cells to differentiate into skeletal tissues. It would also contribute to the classification of differences in normal skeletogenesis and degenerative conditions involving these tissues. The three predominant skeletal tissues of interest are bone, immature cartilage and mature cartilage. Analysis of the transcriptome of these skeletal tissues using RNA-seq technology was performed using differential expression, clustering and biclustering algorithms, to detect similarly expressed genes, which provides evidence for genes potentially interacting together to produce a particular phenotype. Identifying key regulators in the gene regulatory networks (GRNs) driving cartilage and bone development and the differences in the GRNs they drive will facilitate a means to make comparisons between the tissues at the transcriptomic level. Due to a small number of available samples for gene expression data in bone, immature and mature cartilage, it is necessary to determine how the number of samples influences the ability to make accurate GRN predictions. Machine learning techniques for GRN prediction that can incorporate multiple data types have not been well evaluated for complex organisms, nor has RNA-seq data been used often for evaluating these methods. Therefore, techniques identified to work well with microarray data were applied to RNA-seq data from mouse embryonic stem cells, where more samples are available for evaluation compared to the skeletal tissue RNA-seq samples. The RNA-seq data was combined with ChIP-seq data to determine if the machine learning methods outperform simple, correlation-based methods that have been evaluated using RNA-seq data alone. Two of the best performing GRN prediction algorithms from previous large-scale evaluations, which are incapable of incorporating data beyond expression data, were used as a baseline to determine if the addition of multiple data types could help reduce the number of gene expression samples. It was also necessary to identify a biclustering algorithm that could identify potentially biologically relevant modules. Publicly available ChIP-seq and RNA-seq samples from embryonic stem cells were used to measure the performance and consistency of each method, as there was a well-established network in mouse embryonic stem cells to compare results. The methods were then compared to cMonkey2, a biclustering method used in conjunction with ChIP-seq for two important transcription factors in the embryonic stem cell network. This was done to determine if any of these GRN prediction methods could potentially use the small number of skeletal tissue samples available to determine transcription factors orchestrating the expression of other genes driving cartilage and bone formation. Using the embryonic stem cell RNA-seq samples, it was found that sample size, if above 10, does not have a significant impact on the number of true positives in the top predicted interactions. Random forest methods outperform correlation-based methods when using RNA-seq, with area under ROC (AUROC) for evaluation, but the number of true positive interactions predicted when compared to a literature network were similar when using a strict cut-off. Using a limited set of ChIP-seq data was found to not improve the confidence in the transcription factor interactions and had no obvious affect on biclustering results. Correlation-based methods are likely the safest option when based on consistency of the results over multiple runs, but there is still the challenge of determining an appropriate cut-off to the predictions. To predict the skeletal tissue GRNs, cMonkey was used as an initial feature selection method to identify important genes in skeletal tissues and compared with other biclustering methods that do not use ChIP-seq. The predicted skeletal tissue GRNs will be utilized in future analyses of skeletal tissues, focussing on the evolutionary relationship between the GRNs driving skeletal tissue development

Unsupervised Algorithms for Microarray Sample Stratification

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.Peer reviewe

Automated Discovery of Functional Generality of Human Gene Expression Programs

An important research problem in computational biology is the identification of expression programs, sets of co-expressed genes orchestrating normal or pathological processes, and the characterization of the functional breadth of these programs. The use of human expression data compendia for discovery of such programs presents several challenges including cellular inhomogeneity within samples, genetic and environmental variation across samples, uncertainty in the numbers of programs and sample populations, and temporal behavior. We developed GeneProgram, a new unsupervised computational framework based on Hierarchical Dirichlet Processes that addresses each of the above challenges. GeneProgram uses expression data to simultaneously organize tissues into groups and genes into overlapping programs with consistent temporal behavior, to produce maps of expression programs, which are sorted by generality scores that exploit the automatically learned groupings. Using synthetic and real gene expression data, we showed that GeneProgram outperformed several popular expression analysis methods. We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules. GeneProgram produced a map of 104 expression programs, a substantial number of which were significantly enriched for genes involved in key signaling pathways and/or bound by NF-κB transcription factors in genome-wide experiments. Further, GeneProgram discovered expression programs that appear to implicate surprising signaling pathways or receptor types in the response to infection, including Wnt signaling and neurotransmitter receptors. We believe the discovered map of expression programs involved in the response to infection will be useful for guiding future biological experiments; genes from programs with low generality scores might serve as new drug targets that exhibit minimal “cross-talk,” and genes from high generality programs may maintain common physiological responses that go awry in disease states. Further, our method is multipurpose, and can be applied readily to novel compendia of biological data

Hierarchical Dirichlet Process-Based Models For Discovery of Cross-species Mammalian Gene Expression

An important research problem in computational biology is theidentification of expression programs, sets of co-activatedgenes orchestrating physiological processes, and thecharacterization of the functional breadth of these programs. Theuse of mammalian expression data compendia for discovery of suchprograms presents several challenges, including: 1) cellularinhomogeneity within samples, 2) genetic and environmental variationacross samples, and 3) uncertainty in the numbers of programs andsample populations. We developed GeneProgram, a new unsupervisedcomputational framework that uses expression data to simultaneouslyorganize genes into overlapping programs and tissues into groups toproduce maps of inter-species expression programs, which are sortedby generality scores that exploit the automatically learnedgroupings. Our method addresses each of the above challenges byusing a probabilistic model that: 1) allocates mRNA to differentexpression programs that may be shared across tissues, 2) ishierarchical, treating each tissue as a sample from a population ofrelated tissues, and 3) uses Dirichlet Processes, a non-parametricBayesian method that provides prior distributions over numbers ofsets while penalizing model complexity. Using real gene expressiondata, we show that GeneProgram outperforms several popularexpression analysis methods in recovering biologically interpretablegene sets. From a large compendium of mouse and human expressiondata, GeneProgram discovers 19 tissue groups and 100 expressionprograms active in mammalian tissues. Our method automaticallyconstructs a comprehensive, body-wide map of expression programs andcharacterizes their functional generality. This map can be used forguiding future biological experiments, such as discovery of genesfor new drug targets that exhibit minimal "cross-talk" withunintended organs, or genes that maintain general physiologicalresponses that go awry in disease states. Further, our method isgeneral, and can be applied readily to novel compendia of biologicaldata

A comprehensive evaluation of module detection methods for gene expression data

A critical step in the analysis of large genome-wide gene expression datasets is the use of module detection methods to group genes into co-expression modules. Because of limitations of classical clustering methods, numerous alternative module detection methods have been proposed, which improve upon clustering by handling co-expression in only a subset of samples, modelling the regulatory network, and/or allowing overlap between modules. In this study we use known regulatory networks to do a comprehensive and robust evaluation of these different methods. Overall, decomposition methods outperform all other strategies, while we do not find a clear advantage of biclustering and network inference-based approaches on large gene expression datasets. Using our evaluation workflow, we also investigate several practical aspects of module detection, such as parameter estimation and the use of alternative similarity measures, and conclude with recommendations for the further development of these methods

Inferring interaction networks from transcriptomic data: methods and applications

Transcriptomic data is a treasure-trove in modern molecular biology, as it offers a comprehensive viewpoint into the intricate nuances of gene expression dynamics underlying biological systems. This genetic information must be utilised to infer biomolecular interaction networks that can provide insights into the complex regulatory mechanisms underpinning the dynamic cellular processes. Gene regulatory networks and protein-protein interaction networks are two major classes of such networks. This chapter thoroughly investigates the wide range of methodologies used for distilling insightful revelations from transcriptomic data that include association based methods (based on correlation among expression vectors), probabilistic models (using Bayesian and Gaussian models), and interologous methods. We reviewed different approaches for evaluating the significance of interactions based on the network topology and biological functions of the interacting molecules, and discuss various strategies for the identification of functional modules. The chapter concludes with highlighting network based techniques of prioritising key genes, outlining the centrality based, diffusion based and subgraph based methods. The chapter provides a meticulous framework for investigating transcriptomic data to uncover assembly of complex molecular networks for their adaptable analyses across a broad spectrum of biological domains.Comment: 48 pages, 3 figure

Clustering Algorithms: Their Application to Gene Expression Data

Gene expression data hide vital information required to understand the biological process that takes place in a particular organism in relation to its environment. Deciphering the hidden patterns in gene expression data proffers a prodigious preference to strengthen the understanding of functional genomics. The complexity of biological networks and the volume of genes present increase the challenges of comprehending and interpretation of the resulting mass of data, which consists of millions of measurements; these data also inhibit vagueness, imprecision, and noise. Therefore, the use of clustering techniques is a first step toward addressing these challenges, which is essential in the data mining process to reveal natural structures and iden-tify interesting patterns in the underlying data. The clustering of gene expression data has been proven to be useful in making known the natural structure inherent in gene expression data, understanding gene functions, cellular processes, and subtypes of cells, mining useful information from noisy data, and understanding gene regulation. The other benefit of clustering gene expression data is the identification of homology, which is very important in vaccine design. This review examines the various clustering algorithms applicable to the gene expression data in order to discover and provide useful knowledge of the appropriate clustering technique that will guarantee stability and high degree of accuracy in its analysis procedure