Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.

/pdf/cytoscape-a-software-environment-for-integrated-models-of-2fp1mf0u7b.pdf

Cytoscape: A Software Environment for Integrated Models of Biomolecular Interaction Networks

Although cancer classification has improved over the past 30 years, there has been no general approach for identifying new cancer classes (class discovery) or for assigning tumors to known classes (class prediction). Here, a generic approach to cancer classification based on gene expression monitoring by DNA microarrays is described and applied to human acute leukemias as a test case. A class discovery procedure automatically discovered the distinction between acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) without previous knowledge of these classes. An automatically derived class predictor was able to determine the class of new leukemia cases. The results demonstrate the feasibility of cancer classification based solely on gene expression monitoring and suggest a general strategy for discovering and predicting cancer classes for other types of cancer, independent of previous biological knowledge.

/pdf/molecular-classification-of-cancer-class-discovery-and-class-22p4huwudl.pdf

Molecular classification of cancer: class discovery and class prediction by gene expression monitoring.

DNA microarrays can be used to identify gene expression changes characteristic of human disease. This is challenging, however, when relevant differences are subtle at the level of individual genes. We introduce an analytical strategy, Gene Set Enrichment Analysis, designed to detect modest but coordinate changes in the expression of groups of functionally related genes. Using this approach, we identify a set of genes involved in oxidative phosphorylation whose expression is coordinately decreased in human diabetic muscle. Expression of these genes is high at sites of insulin-mediated glucose disposal, activated by PGC-1α and correlated with total-body aerobic capacity. Our results associate this gene set with clinically important variation in human metabolism and illustrate the value of pathway relationships in the analysis of genomic profiling experiments.

/pdf/pgc-1alpha-responsive-genes-involved-in-oxidative-1t3qxw5cbf.pdf

PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes

Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.

/pdf/survey-of-clustering-algorithms-6zezqhbjyo.pdf

Survey of clustering algorithms

Cluster analysis is the automated search for groups of related observations in a dataset. Most clustering done in practice is based largely on heuristic but intuitively reasonable procedures, and most clustering methods available in commercial software are also of this type. However, there is little systematic guidance associated with these methods for solving important practical questions that arise in cluster analysis, such as how many clusters are there, which clustering method should be used, and how should outliers be handled. We review a general methodology for model-based clustering that provides a principled statistical approach to these issues. We also show that this can be useful for other problems in multivariate analysis, such as discriminant analysis and multivariate density estimation. We give examples from medical diagnosis, minefield detection, cluster recovery from noisy data, and spatial density estimation. Finally, we mention limitations of the methodology and discuss recent development...

https://calvino.polito.it/~gasparini/teaching/fraley2002.pdf

Model-Based Clustering, Discriminant Analysis, and Density Estimation

Array technologies have made it straightfor- ward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidi- mensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that per- forms the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentia- tion—for example, highlighting certain genes and pathways involved in ''differentiation therapy'' used in the treatment of acute promyelocytic leukemia.

/pdf/interpreting-patterns-of-gene-expression-with-self-1e8ex2i7l3.pdf

Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation

Array technologies have made it straightforward to monitor simultaneously the expression pattern of thousands of genes. The challenge now is to interpret such massive data sets. The first step is to extract the fundamental patterns of gene expression inherent in the data. This paper describes the application of self-organizing maps, a type of mathematical cluster analysis that is particularly well suited for recognizing and classifying features in complex, multidimensional data. The method has been implemented in a publicly available computer package, GENECLUSTER, that performs the analytical calculations and provides easy data visualization. To illustrate the value of such analysis, the approach is applied to hematopoietic differentiation in four well studied models (HL-60, U937, Jurkat, and NB4 cells). Expression patterns of some 6,000 human genes were assayed, and an online database was created. GENECLUSTER was used to organize the genes into biologically relevant clusters that suggest novel hypotheses about hematopoietic differentiation—for example, highlighting certain genes and pathways involved in ‘‘differentiation therapy’’ used in the treatment of acute promyelocytic leukemia. Array technologies have made it straightforward to monitor simultaneously the expression patterns of thousands of genes during cellular differentiation and response (1–5). The challenge now is to make sense of such massive data sets. For simple experiments comparing just two samples, it is enough to rank the genes by their relative induction. Richer experimental designs, however, could involve hundreds of samples— for example, complete developmental time courses in many cell lines. No two genes are likely to exhibit precisely the same response, and many distinct types of behavior may be present. A key goal is to extract the fundamental patterns of gene expression inherent in the data. Many mathematical techniques have been developed for identifying underlying patterns in complex data for such diverse applications as object recognition by machine vision systems, phoneme detection in speech processing, bandwidth compression in telecommunications, and signal classification in electrocardiography and sleep research (6–10). The techniques are essentially different ways to cluster points in multidimensional space. They can be directly applied to gene expression by regarding the quantitative expression levels of n genes in k samples as defining n points in k-dimensional space. Clustering Techniques. The question is, which clustering techniques are likely to be most useful for interpreting gene expression? One simple approach is to use direct visual inspection to group together genes with similar expression patterns. This approach was recently used by Cho et al. (4) to cluster genes whose expression correlated with particular phases of the cell cycle. The method is best suited for instances in which the patterns of interest are clear in advance (such as a periodic fluctuation in phase with the cell cycle), but it does not scale well to larger data sets and is less appropriate for discovering unexpected patterns. A common computational approach is hierarchical clustering (6–8). Data points are forced into a strict hierarchy of nested subsets: the closest pair of points is grouped and replaced by a single point representing their set average, the next closest pair of points is treated similarly, and so on. The data points are thus fashioned into a phylogenetic tree whose branch lengths represent the degree of similarity between the sets. Hierarchical clustering has recently been described for gene expression and has clearly proven valuable (11–13). Hierarchical clustering, however, has a number of shortcomings for the study of gene expression. Strict phylogenetic trees are best suited to situations of true hierarchical descent (such as in the evolution of species) and are not designed to reflect the multiple distinct ways in which expression patterns can be similar; this problem is exacerbated as the size and complexity of the data set grows. Hierarchical clustering has been noted by statisticians to suffer from lack of robustness, nonuniqueness, and inversion problems that complicate interpretation of the hierarchy (see ref. 14 for a detailed study). Finally, the deterministic nature of hierarchical clustering can cause points to be grouped based on local decisions, with no opportunity to reevaluate the clustering. It is known that the resulting trees can lock in accidental features, reflecting idiosyncrasies of the agglomeration rule. Various other clustering techniques are used in biological applications but have not yet been applied to the analysis of gene expression. These techniques include Bayesian clustering, k-means clustering, and self-organizing maps (SOMs). Bayesian clustering is a highly structured approach appropriate when a strong prior distribution on the data is available. k-means clustering is a completely unstructured approach, which proceeds in an entirely local fashion and produces an unorganized collection of clusters that is not conducive to interpretation. SOMs (9, 10) have a number of features that make them particularly well suited to clustering and analysis of gene expression patterns. They are ideally suited to exploratory data analysis, allowing one to impose partial structure on the clusters (in contrast to the rigid structure of hierarchical clustering, the strong prior hypotheses used in Bayesian clustering, and the nonstructure of k-means clustering) and facilitating easy visualization and interpretation. SOMs have good The publication costs of this article were defrayed in part by page charge payment. This article must therefore be hereby marked ‘‘advertisement’’ in accordance with 18 U.S.C. §1734 solely to indicate this fact. PNAS is available online at www.pnas.org. Abbreviations: SOM, self-organizing maps; ATRA, all trans-retinoic acid; PMA, phorbol 12-myristate 13-acetate. ¶To whom reprint requests should be addressed at: Whitehead/ Massachusetts Institute of Technology Center for Genome Research, Building 300, 1 Kendall Square, Cambridge, MA 02139. e-mail: lander@genome.wi.mit.edu or golub@genome.wi.mit.edu.

application to hematopoietic differentiation Interpreting patterns of gene expression with self-organizing maps: Methods and

Polyascus gregaria is a specialized crustacean parasite of Chinese mitten crab; however, only limited information on interactions between parasite and its host is available. This study examined interactions between P. gregaria and Chinese mitten crab with regard to immune-related enzyme activities and expression of respective genes in the hepatopancreas. To further elucidate the pathogen–host interactions, untargeted gas chromatography–mass spectrometry (GC–MS) metabolomics was used to analyse changes in metabolites in response to P. gregaria infection. Superoxide dismutase, acid phosphatase and alkaline phosphatase activities were significantly increased in parasitized crabs, whereas phenoloxidase activity was significantly decreased. Moreover, the expression of three immune-related genes, that is proPO, Toll1 and Toll2, was markedly downregulated in parasitized crabs. In the metabolomic assay, 73 differential metabolites were identified, and they were significantly enriched in 71 pathways. Among these metabolites, 58 were significantly upregulated and 15 were downregulated. Lysolipids lysoPE, lysoPC, lysoPG, LysoPI and lysoPS were the most strongly upregulated metabolites, and 9-cis-retinol, glycerophosphocholine and 5-thymidylic acid were the top three downregulated metabolites. Most of these metabolites were enriched in the glycerophospholipid metabolism pathway, which was one of the most significantly activated pathway responses to P. gregaria infection. This increase in lysoglycerophospholipids caused major structural and functional abnormalities in the membranes of host tissues, especially in the hepatopancreas. We propose that long-term infection with P. gregaria induces silence of the innate immune system, disturbs intracellular homeostasis and causes cell membrane immune-related dysfunction in Chinese mitten crab. These results may help us better understand P. gregaria infection in Chinese mitten crab. 

Detection of immune‐related enzyme activity, gene expression and metabolomic profiling in Chinese mitten crab
 Eriocheir sinensis
 parasitized by
 Polyascus gregaria

Qing Zhu

Papers

Interpreting patterns of gene expression with self-organizing maps: Methods and application to hematopoietic differentiation

application to hematopoietic differentiation Interpreting patterns of gene expression with self-organizing maps: Methods and

Detection of immune‐related enzyme activity, gene expression and metabolomic profiling in Chinese mitten crab Eriocheir sinensis parasitized by Polyascus gregaria