Summary: Open source software encourages innovation by allowing users to extend the functionality of existing applications. Treeview is a popular application for the visualization of microarray data, but is closed-source and platform-specific, which limits both its current utility and suitability as a platform for further development. Java Treeview is an open-source, cross-platform rewrite that handles very large datasets well, and supports extensions to the file format that allow the results of additional analysis to be visualized and compared. The combination of a general file format and open source makes Java Treeview an attractive choice for solving a class of visualization problems. An applet version is also available that can be used on any website with no special server-side setup.

Availability:http://jtreeview.sourceforge.net under GPL.

/pdf/java-treeview-extensible-visualization-of-microarray-data-4shebeaux7.pdf

Java Treeview---extensible visualization of microarray data

‡We describe the use of singular value decomposition in transforming genome-wide expression data from genes 3 arrays space to reduced diagonalized ‘‘eigengenes’’ 3 ‘‘eigenarrays’’ space, where the eigengenes (or eigenarrays) are unique orthonormal superpositions of the genes (or arrays). Normalizing the data by filtering out the eigengenes (and eigenarrays) that are inferred to represent noise or experimental artifacts enables meaningful comparison of the expression of different genes across different arrays in different experiments. Sorting the data according to the eigengenes and eigenarrays gives a global picture of the dynamics of gene expression, in which individual genes and arrays appear to be classified into groups of similar regulation and function, or similar cellular state and biological phenotype, respectively. After normalization and sorting, the significant eigengenes and eigenarrays can be associated with observed genome-wide effects of regulators, or with measured samples, in which these regulators are overactive or underactive, respectively.

Singular Value Decomposition for Genome-Wide Expression Data Processing and Modeling

Summary: A versatile, platform independent and easy to use Java suite for large-scale gene expression analysis was developed. Genesis integrates various tools for microarray data analysis such as filters, normalization and visualization tools, distance measures as well as common clustering algorithms including hierarchical clustering, selforganizing maps, k-means, principal component analysis, and support vector machines. The results of the clustering are transparent across all implemented methods and enable the analysis of the outcome of different algorithms and parameters. Additionally, mapping of gene expression data onto chromosomal sequences was implemented to enhance promoter analysis and investigation of transcriptional control mechanisms. Availability: http://genome.tugraz.at Contact: zlatko.trajanoski@tugraz.at

https://bioinformatics.cs.vt.edu/~easychair/SturnEtAl_Bioinformatics_2002.pdf

Genesis: cluster analysis of microarray data

▪ Abstract Systems biology studies biological systems by systematically perturbing them (biologically, genetically, or chemically); monitoring the gene, protein, and informational pathway responses; integrating these data; and ultimately, formulating mathematical models that describe the structure of the system and its response to individual perturbations. The emergence of systems biology is described, as are several examples of specific systems approaches.

/pdf/a-new-approach-to-decoding-life-systems-biology-n44vmzq97g.pdf

A NEW APPROACH TO DECODING LIFE: Systems Biology

Molecular and Functional Profiling of Memory CD8 T Cell Differentiation

Microarray experiments generate data sets with information on the expression levels of thousands of genes in a set of biological samples. Unfortunately, such experiments often produce multiple missing expression values, normally due to various experimental problems. As many algorithms for gene expression analysis require a complete data matrix as input, the missing values have to be estimated in order to analyze the available data. Alternatively, genes and arrays can be removed until no missing values remain. However, for genes or arrays with only a small number of missing values, it is desirable to impute those values. For the subsequent analysis to be as informative as possible, it is essential that the estimates for the missing gene expression values are accurate. A small amount of badly estimated missing values in the data might be enough for clustering methods, such as hierachical clustering or K-means clustering, to produce misleading results. Thus, accurate methods for missing value estimation are needed. We present novel methods for estimation of missing values in microarray data sets that are based on the least squares principle, and that utilize correlations between both genes and arrays. For this set of methods, we use the common reference name LSimpute. We compare the estimation accuracy of our methods with the widely used KNNimpute on three complete data matrices from public data sets by randomly knocking out data (labeling as missing). From these tests, we conclude that our LSimpute methods produce estimates that consistently are more accurate than those obtained using KNNimpute. Additionally, we examine a more classic approach to missing value estimation based on expectation maximization (EM). We refer to our EM implementations as EMimpute, and the estimate errors using the EMimpute methods are compared with those our novel methods produce. The results indicate that on average, the estimates from our best performing LSimpute method are at least as accurate as those from the best EMimpute algorithm.

/pdf/lsimpute-accurate-estimation-of-missing-values-in-microarray-187rkrvv6v.pdf

LSimpute: accurate estimation of missing values in microarray data with least squares methods.

J-Express is a Java application that allows the user to analyze gene expression (microarray) data in a flexible way giving access to multidimensional scaling, clustering, and visualization methods in an integrated manner. Specifically, J-Express includes implementations of hierarchical clustering, k-means, principal component analysis, and self-organizing maps. At present, it does not include methods for comparing two or more experiments for differentially expressed genes. The application is completely portable and requires only that a Java runtime environment 1.2 is installed on the system. Its efficiency allows interactive clustering of thousands of expression profiles on standard personal computers.

https://bioinformatics.cs.vt.edu/~easychair/DysvikJonassen_Bioinformatics_2001.pdf

J-Express: exploring gene expression data using Java.

Visualizing region growing in 3D voxel volumes relates to generating a 3D scene having a plurality of voxels for representing a volume data set of seismic data collected from the oilfield, defining a segmentation algorithm for segmenting the volume data within the 3D scene, the segmentation algorithm comparing a pre-determined threshold to an attribute of a voxel of the plurality of voxels, defining a control parameter associated with the attribute for controlling the segmentation algorithm, adjusting the control parameter to guide the segmentation algorithm in segmenting the volume data set to generate a visualized geobody, and displaying the visualized geobody.

Visualizing region growing in three dimensional voxel volumes

The J-Express package has been designed to facilitate the analysis of microarray data with an emphasis on efficiency, usability, and comprehensibility. The J-Express system provides a powerful and integrated platform for the analysis of microarray gene expression data. It is platform-independent in that it requires only the availability of a Java virtual machine on the system. The system includes a range of analysis tools and a project management system supporting the organization and documentation of an analysis project. This unit describes the J-Express tool, emphasizing central concepts and principles, and gives examples of how it can be used to explore gene expression data sets.

Analysis of Gene‐Expression Data Using J‐Express

Lepeophtheirus salmonis is an ectoparasitic copepod feeding on skin, mucus and blood from salmonid hosts. Initial analysis of EST sequences from pre adult and adult stages of L. salmonis revealed a large proportion of novel transcripts. In order to link unknown transcripts to biological functions we have combined EST sequencing and microarray analysis to characterize female salmon louse transcriptomes during post molting maturation and egg production. EST sequence analysis shows that 43% of the ESTs have no significant hits in GenBank. Sequenced ESTs assembled into 556 contigs and 1614 singletons and whenever homologous genes were identified no clear correlation with homologous genes from any specific animal group was evident. Sequence comparison of 27 L. salmonis proteins with homologous proteins in humans, zebrafish, insects and crustaceans revealed an almost identical sequence identity with all species. Microarray analysis of maturing female adult salmon lice revealed two major transcription patterns; up-regulation during the final molting followed by down regulation and female specific up regulation during post molting growth and egg production. For a third minor group of ESTs transcription decreased during molting from pre-adult II to immature adults. Genes regulated during molting typically gave hits with cuticula proteins whilst transcripts up regulated during post molting growth were female specific, including two vitellogenins. The copepod L.salmonis contains high a level of novel genes. Among analyzed L.salmonis proteins, sequence identities with homologous proteins in crustaceans are no higher than to homologous proteins in humans. Three distinct processes, molting, post molting growth and egg production correlate with transcriptional regulation of three groups of transcripts; two including genes related to growth, one including genes related to egg production. The function of the regulated transcripts is discussed in relation to post molting morphological changes in adult female salmon louse. There is clear evidence that transcription of the major yolk proteins is not induced before some of the post molting growth of abdomen and the genital segment has occurred. A hallmark for the observed growth is transcription of many putative cuticula proteins prior to the size increase.

https://link.springer.com/content/pdf/10.1186%2F1471-2164-9-126.pdf

Salmon louse (Lepeophtheirus salmonis) transcriptomes during post molting maturation and egg production, revealed using EST-sequencing and microarray analysis

Bjarte Dysvik

Papers

LSimpute: accurate estimation of missing values in microarray data with least squares methods.

J-Express: exploring gene expression data using Java.

Visualizing region growing in three dimensional voxel volumes

Analysis of Gene‐Expression Data Using J‐Express

Salmon louse (Lepeophtheirus salmonis) transcriptomes during post molting maturation and egg production, revealed using EST-sequencing and microarray analysis