Visualization Of Gene Expression Data Research Articles

GENAVi: a shiny web application for gene expression normalization, analysis and visualization

BackgroundThe development of next generation sequencing (NGS) methods led to a rapid rise in the generation of large genomic datasets, but the development of user-friendly tools to analyze and visualize these datasets has not developed at the same pace. This presents a two-fold challenge to biologists; the expertise to select an appropriate data analysis pipeline, and the need for bioinformatics or programming skills to apply this pipeline. The development of graphical user interface (GUI) applications hosted on web-based servers such as Shiny can make complex workflows accessible across operating systems and internet browsers to those without programming knowledge.ResultsWe have developed GENAVi (Gene Expression Normalization Analysis and Visualization) to provide a user-friendly interface for normalization and differential expression analysis (DEA) of human or mouse feature count level RNA-Seq data. GENAVi is a GUI based tool that combines Bioconductor packages in a format for scientists without bioinformatics expertise. We provide a panel of 20 cell lines commonly used for the study of breast and ovarian cancer within GENAVi as a foundation for users to bring their own data to the application. Users can visualize expression across samples, cluster samples based on gene expression or correlation, calculate and plot the results of principal components analysis, perform DEA and gene set enrichment and produce plots for each of these analyses. To allow scalability for large datasets we have provided local install via three methods. We improve on available tools by offering a range of normalization methods and a simple to use interface that provides clear and complete session reporting and for reproducible analysis.ConclusionThe development of tools using a GUI makes them practical and accessible to scientists without bioinformatics expertise, or access to a data analyst with relevant skills. While several GUI based tools are currently available for RNA-Seq analysis we improve on these existing tools. This user-friendly application provides a convenient platform for the normalization, analysis and visualization of gene expression data for scientists without bioinformatics expertise.

GEDS: A Gene Expression Display Server for mRNAs, miRNAs and Proteins.

High-throughput technologies generate a tremendous amount of expression data on mRNA, miRNA and protein levels. Mining and visualizing the large amount of expression data requires sophisticated computational skills. An easy to use and user-friendly web-server for the visualization of gene expression profiles could greatly facilitate data exploration and hypothesis generation for biologists. Here, we curated and normalized the gene expression data on mRNA, miRNA and protein levels in 23,315, 9009 and 9244 samples, respectively, from 40 tissues (The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GETx)) and 1594 cell lines (Cancer Cell Line Encyclopedia (CCLE) and MD Anderson Cell Lines Project (MCLP)). Then, we constructed the Gene Expression Display Server (GEDS), a web-based tool for quantification, comparison and visualization of gene expression data. GEDS integrates multiscale expression data and provides multiple types of figures and tables to satisfy several kinds of user requirements. The comprehensive expression profiles plotted in the one-stop GEDS platform greatly facilitate experimental biologists utilizing big data for better experimental design and analysis. GEDS is freely available online.

Computationally Modeling Axonal Guidance Cue Expression in The Developing Olfactory Bulb

Olfactory sensory neurons (OSNs) in most mammals have cell bodies in the olfactory epithelium (OE). OSNs project axons to glomeruli in the olfactory bulb (OB) of the brain. Developmentally, signal guidance cues direct axons to their target glomeruli. We created a program that displays a model of the OB using a series of sagittal section images based on in situ hybridization (ISH) images from the Allen Brain Atlas (ABA). The ABA's program, Brain Explorer 2, allows for the limited 3D visualization of gene expression data. Our program adds functionality by allowing users to clearly visualize and quantify the expression of several genes simultaneously via superimposition.To optimize visualization of multiple ISH data sets simultaneously, we aimed to improve the signal to noise ration of those ISH data. We use color thresholding and density thresholding algorithms to computationally detect and filter guidance cue expression data. Color thresholds that establish gene expression from the background of the image were determined using ImageJ. The expression data were filtered using a density threshold in a 2D sliding window algorithm. The 2D sliding window algorithm is implemented for every pixel in the image. It distinguishes a window of the image around each pixel. The density of gene expression within that window is calculated. The expression for the current pixel is then decided using a density threshold. Methods for visualization of the superimposed genes were derived using the matplotlib and PIL libraries. Our program provides a tool for investigating olfactory axon targeting, glomeruli formation and glomeruli related pathologies in mammalian models. For example, users can examine the expression of different genes and investigate if guidance cue expression results in unique locations for mapping specific glomeruli. This program can also be adapted to examine guidance cue expression for other regions of nervous tissue.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

CANCERTOOL: A Visualization and Representation Interface to Exploit Cancer Datasets

With the advent of OMICs technologies, both individual research groups and consortia have spear-headed the characterization of human samples of multiple pathophysiologic origins, resulting in thousands of archived genomes and transcriptomes. Although a variety of web tools are now available to extract information from OMICs data, their utility has been limited by the capacity of nonbioinformatician researchers to exploit the information. To address this problem, we have developed CANCERTOOL, a web-based interface that aims to overcome the major limitations of public transcriptomics dataset analysis for highly prevalent types of cancer (breast, prostate, lung, and colorectal). CANCERTOOL provides rapid and comprehensive visualization of gene expression data for the gene(s) of interest in well-annotated cancer datasets. This visualization is accompanied by generation of reports customized to the interest of the researcher (e.g., editable figures, detailed statistical analyses, and access to raw data for reanalysis). It also carries out gene-to-gene correlations in multiple datasets at the same time or using preset patient groups. Finally, this new tool solves the time-consuming task of performing functional enrichment analysis with gene sets of interest using up to 11 different databases at the same time. Collectively, CANCERTOOL represents a simple and freely accessible interface to interrogate well-annotated datasets and obtain publishable representations that can contribute to refinement and guidance of cancer-related investigations at all levels of hypotheses and design.Significance: In order to facilitate access of research groups without bioinformatics support to public transcriptomics data, we have developed a free online tool with an easy-to-use interface that allows researchers to obtain quality information in a readily publishable format. Cancer Res; 78(21); 6320-8. ©2018 AACR.

A Human "eFP" Browser for Generating Gene Expression Anatograms.

Transcriptomic studies help to further our understanding of gene function. Human transcriptomic studies tend to focus on a particular subset of tissue types or a particular disease state; however, it is possible to collate into a compendium multiple studies that have been profiled using the same expression analysis platform to provide an overview of gene expression levels in many different tissues or under different conditions. In order to increase the knowledge and understanding we gain from such studies, intuitive visualization of gene expression data in such a compendium can be useful. The Human eFP (“electronic Fluorescent Pictograph”) Browser presented here is a tool for intuitive visualization of large human gene expression data sets on pictographic representations of the human body as gene expression “anatograms”. Pictographic representations for new data sets may be generated easily. The Human eFP Browser can also serve as a portal to other gene-specific information through link-outs to various online resources.

ExAtlas: An interactive online tool for meta-analysis of gene expression data.

We have developed ExAtlas, an on-line software tool for meta-analysis and visualization of gene expression data. In contrast to existing software tools, ExAtlas compares multi-component data sets and generates results for all combinations (e.g. all gene expression profiles versus all Gene Ontology annotations). ExAtlas handles both users' own data and data extracted semi-automatically from the public repository (GEO/NCBI database). ExAtlas provides a variety of tools for meta-analyses: (1) standard meta-analysis (fixed effects, random effects, z-score, and Fisher's methods); (2) analyses of global correlations between gene expression data sets; (3) gene set enrichment; (4) gene set overlap; (5) gene association by expression profile; (6) gene specificity; and (7) statistical analysis (ANOVA, pairwise comparison, and PCA). ExAtlas produces graphical outputs, including heatmaps, scatter-plots, bar-charts, and three-dimensional images. Some of the most widely used public data sets (e.g. GNF/BioGPS, Gene Ontology, KEGG, GAD phenotypes, BrainScan, ENCODE ChIP-seq, and protein-protein interaction) are pre-loaded and can be used for functional annotations.

HeatmapGenerator: high performance RNAseq and microarray visualization software suite to examine differential gene expression levels using an R and C++ hybrid computational pipeline.

BackgroundThe graphical visualization of gene expression data using heatmaps has become an integral component of modern-day medical research. Heatmaps are used extensively to plot quantitative differences in gene expression levels, such as those measured with RNAseq and microarray experiments, to provide qualitative large-scale views of the transcriptonomic landscape. Creating high-quality heatmaps is a computationally intensive task, often requiring considerable programming experience, particularly for customizing features to a specific dataset at hand.MethodsSoftware to create publication-quality heatmaps is developed with the R programming language, C++ programming language, and OpenGL application programming interface (API) to create industry-grade high performance graphics.ResultsWe create a graphical user interface (GUI) software package called HeatmapGenerator for Windows OS and Mac OS X as an intuitive, user-friendly alternative to researchers with minimal prior coding experience to allow them to create publication-quality heatmaps using R graphics without sacrificing their desired level of customization. The simplicity of HeatmapGenerator is that it only requires the user to upload a preformatted input file and download the publicly available R software language, among a few other operating system-specific requirements. Advanced features such as color, text labels, scaling, legend construction, and even database storage can be easily customized with no prior programming knowledge.ConclusionWe provide an intuitive and user-friendly software package, HeatmapGenerator, to create high-quality, customizable heatmaps generated using the high-resolution color graphics capabilities of R. The software is available for Microsoft Windows and Apple Mac OS X. HeatmapGenerator is released under the GNU General Public License and publicly available at: http://sourceforge.net/projects/heatmapgenerator/. The Mac OS X direct download is available at: http://sourceforge.net/projects/heatmapgenerator/files/HeatmapGenerator_MAC_OSX.tar.gz/download. The Windows OS direct download is available at: http://sourceforge.net/projects/heatmapgenerator/files/HeatmapGenerator_WINDOWS.zip/download.

Functional Based Analysis and Visualization of Gene Expression Data from Hepatocytes Grown on Different Substrates

The ability to grow a fully functional Hepatocyte in vitro would be a prodigious breakthrough for modern day drug testing and regenerative medicine. However there are many factors affecting Hepatocyte functionality in vitro that need to be studied before this is possible, like the effect of the underlying substrate that the Hepatocyte is grown on. To better understand this interaction, gene expression profiles of Hepatocytes were measured on four different substrates: Corning(r) CellBIND(r), Collagen, Corning Matrigel(r) and Locust Bean Gum (S906). Here, we present data analysis methods that enable data reduction, identification of differentially expressed genes, and grouping of genes with similar intensities by implementing data clustering. The optimum number of clusters in a given data set is estimated using the gap statistic method. We further employ Cytoscape to help visualize the data and incorporate GeneMania to help identify potentially interesting patterns across substrates. One of the interesting findings from our analysis suggests up-regulation of genes involved in vasculature development on all four substrates. Such findings can be used to design new experiments that can provide further insight on the interactions between hepatocytes and their substrates and help understand the mechanism behind responses of hepatocytes on these substrates.

BAR expressolog identification: expression profile similarity ranking of homologous genes in plant species

Large numbers of sequences are now readily available for many plant species, allowing easy identification of homologous genes. However, orthologous gene identification across multiple species is made difficult by evolutionary events such as whole-genome or segmental duplications. Several developmental atlases of gene expression have been produced in the past couple of years, and it may be possible to use these transcript abundance data to refine ortholog predictions. In this study, clusters of homologous genes between seven plant species - Arabidopsis, soybean, Medicago truncatula, poplar, barley, maize and rice - were identified. Following this, a pipeline to rank homologs within gene clusters by both sequence and expression profile similarity was devised by determining equivalent tissues between species, with the best expression profile match being termed the 'expressolog'. Five electronic fluorescent pictograph (eFP) browsers were produced as part of this effort, to aid in visualization of gene expression data and to complement existing eFP browsers at the Bio-Array Resource (BAR). Within the eFP browser framework, these expression profile similarity rankings were incorporated into an Expressolog Tree Viewer to allow cross-species homolog browsing by both sequence and expression pattern similarity. Global analyses showed that orthologs with the highest sequence similarity do not necessarily exhibit the highest expression pattern similarity. Other orthologs may show different expression patterns, indicating that such genes may require re-annotation or more specific annotation. Ultimately, it is envisaged that this pipeline will aid in improvement of the functional annotation of genes and translational plant research.

EpiChIP: gene-by-gene quantification of epigenetic modification levels

The combination of chromatin immunoprecipitation with next-generation sequencing technology (ChIP-seq) is a powerful and increasingly popular method for mapping protein–DNA interactions in a genome-wide fashion. The conventional way of analyzing this data is to identify sequencing peaks along the chromosomes that are significantly higher than the read background. For histone modifications and other epigenetic marks, it is often preferable to find a characteristic region of enrichment in sequencing reads relative to gene annotations. For instance, many histone modifications are typically enriched around transcription start sites. Calculating the optimal window that describes this enrichment allows one to quantify modification levels for each individual gene. Using data sets for the H3K9/14ac histone modification in Th cells and an accompanying IgG control, we present an analysis strategy that alternates between single gene and global data distribution levels and allows a clear distinction between experimental background and signal. Curve fitting permits false discovery rate-based classification of genes as modified versus unmodified. We have developed a software package called EpiChIP that carries out this type of analysis, including integration with and visualization of gene expression data.

VisHiC--hierarchical functional enrichment analysis of microarray data

Measuring gene expression levels with microarrays is one of the key technologies of modern genomics. Clustering of microarray data is an important application, as genes with similar expression profiles may be regulated by common pathways and involved in related functions. Gene Ontology (GO) analysis and visualization allows researchers to study the biological context of discovered clusters and characterize genes with previously unknown functions. We present VisHiC (Visualization of Hierarchical Clustering), a web server for clustering and compact visualization of gene expression data combined with automated function enrichment analysis. The main output of the analysis is a dendrogram and visual heatmap of the expression matrix that highlights biologically relevant clusters based on enriched GO terms, pathways and regulatory motifs. Clusters with most significant enrichments are contracted in the final visualization, while less relevant parts are hidden altogether. Such a dense representation of microarray data gives a quick global overview of thousands of transcripts in many conditions and provides a good starting point for further analysis. VisHiC is freely available at http://biit.cs.ut.ee/vishic.

Expansion and Diversification of thePopulusR2R3-MYB Family of Transcription Factors

The R2R3-MYB proteins comprise one of the largest families of transcription factors in plants. R2R3-MYB family members regulate plant-specific processes, such as the elaboration of specialized cell types, including xylem, guard cells, trichomes, and root hairs, and the biosynthesis of specialized branches of metabolism, including phenylpropanoid biosynthesis. As such, R2R3-MYB family members are hypothesized to contribute to the emergence of evolutionary innovations that have arisen in specific plant lineages. As a first step in determining the role played by R2R3-MYB family members in the emergence of lineage-specific innovations in the genus Populus, the entire Populus trichocarpa R2R3-MYB family was characterized. The Populus R2R3-MYB complement is much larger than that found in other angiosperms with fully sequenced genomes. Phylogenetic analyses, together with chromosome placement, showed that the expansion of the Populus R2R3-MYB family was not only attributable to whole genome duplication but also involved selective expansion of specific R2R3-MYB clades. Expansion of the Populus R2R3-MYB family prominently involved members with expression patterns that suggested a role in specific components of Populus life history, including wood formation and reproductive development. An expandable compendium of microarray-based expression data (PopGenExpress) and associated Web-based tools were developed to better enable within- and between-species comparisons of Populus R2R3-MYB gene expression. This resource, which includes intuitive graphic visualization of gene expression data across multiple tissues, organs, and treatments, is freely available to, and expandable by, scientists wishing to better understand the genome biology of Populus, an ecologically dominant and economically important forest tree genus.

Co-clustering and visualization of gene expression data and gene ontology terms for Saccharomyces cerevisiae using self-organizing maps

We propose a novel co-clustering algorithm that is based on self-organizing maps (SOMs). The method is applied to group yeast ( Saccharomyces cerevisiae) genes according to both expression profiles and Gene Ontology (GO) annotations. The combination of multiple databases is supposed to provide a better biological definition and separation of gene clusters. We compare different levels of genome-wide co-clustering by weighting the involved sources of information differently. Clustering quality is determined by both general and SOM-specific validation measures. Co-clustering relies on a sufficient correlation between the different datasets. We investigate in various experiments how much GO information is contained in the applied gene expression dataset and vice versa. The second major contribution is a visualization technique that applies the cluster structure of SOMs for a better biological interpretation of gene (expression) clusterings. Our GO term maps reveal functional neighborhoods between clusters forming biologically meaningful functional SOM regions. To cope with the high variety and specificity of GO terms, gene and cluster annotations are mapped to a reduced vocabulary of more general GO terms. In particular, this advances the ability of SOMs to act as gene function predictors.

Multidimensional support vector machines for visualization of gene expression data

Since DNA microarray experiments provide us with huge amount of gene expression data, they should be analyzed with statistical methods to extract the meanings of experimental results. Some dimensionality reduction methods such as Principal Component Analysis (PCA) are used to roughly visualize the distribution of high dimensional gene expression data. However, in the case of binary classification of gene expression data, PCA does not utilize class information when choosing axes. Thus clearly separable data in the original space may not be so in the reduced space used in PCA. For visualization and class prediction of gene expression data, we have developed a new SVM-based method called multidimensional SVMs, that generate multiple orthogonal axes. This method projects high dimensional data into lower dimensional space to exhibit properties of the data clearly and to visualize a distribution of the data roughly. Furthermore, the multiple axes can be used for class prediction. The basic properties of conventional SVMs are retained in our method: solutions of mathematical programming are sparse, and nonlinear classification is implemented implicitly through the use of kernel functions. The application of our method to the experimentally obtained gene expression datasets for patients' samples indicates that our algorithm is efficient and useful for visualization and class prediction. komura@hal.rcast.u-tokyo.ac.jp.

A Computational Approach to Gene Expression Data Extraction and Analysis

The rapid advancement of DNA microarray technology has revolutionalized genetic research in bioscience. Due to the enormous amount of gene expression data generated by such technology, computer processing and analysis of such data has become indispensable. In this paper, we present a computational framework for the extraction, analysis and visualization of gene expression data from microarray experiments. A novel, fully automated, spot segmentation algorithm for DNA microarray images, which makes use of adaptive thresholding, morphological processing and statistical intensity modeling, is proposed to: (i) segment the blocks of spots, (ii) generate the grid structure, and (iii) to segment the spot within each subregion. For data analysis, we propose a binary hierarchical clustering (BHC) framework for the clustering of gene expression data. The BHC algorithm involves two major steps. Firstly, the fuzzy C-means algorithm and the average linkage hierarchical clustering algorithm are used to split the data into two classes. Secondly, the Fisher linear discriminant analysis is applied to the two classes to assess whether the split is acceptable. The BHC algorithm is applied to the sub-classes recursively and ends when all clusters cannot be split any further. BHC does not require the number of clusters to be known in advance. It does not place any assumption about the number of samples in each cluster or the class distribution. The hierarchical framework naturally leads to a tree structure representation for effective visualization of gene expressions.

Gene-Ontology-based clustering of gene expression data.

The expected correlation between genetic co-regulation and affiliation to a common biological process is not necessarily the case when numerical cluster algorithms are applied to gene expression data. GO-Cluster uses the tree structure of the Gene Ontology database as a framework for numerical clustering, and thus allowing a simple visualization of gene expression data at various levels of the ontology tree. The 32-bit Windows application is freely available at http://www.mpibpc.mpg.de/go-cluster/

GAAS: gene array analyzer software for management, analysis and visualization of gene expression data.

GAAS, Gene Array Analyzer Software supports multi-user efficient management and suitable analyses of large amounts of gene expression data across replicated experiments. Its management framework handles input data generated by different technologies. A multi-user environment allows each user to store his/her own data visualization scheme, analysis parameters used, values and formats of the output data. The analysis engine performs: background and spot quality evaluation, data normalization, differential gene expression analyses in single and multiple replica experiments. Results of expression profiles can be interactively navigated through graphical interfaces and stored into output databases.

Analysis and visualization of gene expression data using Self-Organizing Maps

Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships within the data and cluster structures can be visualized and interpreted. The methods are demonstrated by computing a SOM of yeast genes. The relationships of known functional classes of genes are investigated by analyzing their distribution on the SOM, the cluster structure is visualized by the U-matrix method, and the clusters are characterized in terms of the properties of the expression profiles of the genes. Finally, it is shown that the SOM visualizes the similarity of genes in a more trustworthy way than two alternative methods, multidimensional scaling and hierarchical clustering.

VIZARD: analysis of Affymetrix Arabidopsis GeneChip data.

The Affymetrix GeneChip Arabidopsis genome array has proved to be a very powerful tool for the analysis of gene expression in Arabidopsis thaliana, the most commonly studied plant model organism. VIZARD is a Java program created at the University of California, Berkeley, to facilitate analysis of Arabidopsis GeneChip data. It includes several integrated tools for filtering, sorting, clustering and visualization of gene expression data as well as tools for the discovery of regulatory motifs in upstream sequences. VIZARD also includes annotation and upstream sequence databases for the majority of genes represented on the Affymetrix Arabidopsis GeneChip array. VIZARD is available free of charge for educational, research, and not-for-profit purposes, and can be downloaded at http://www.anm.f2s.com/research/vizard/ moseyko@uclink4.berkeley.edu