Exploration Of Gene Expression Data Research Articles

Spatiotemporal patterns of wheat response to Pyrenophora tritici-repentis in asymptomatic regions revealed by transcriptomic and X-ray fluorescence microscopy analyses.

Pathogen attacks elicit dynamic and widespread molecular responses in plants. While our understanding of plant responses has advanced considerably, little is known of the molecular responses in the asymptomatic "green" regions (AGR) adjoining lesions. Here, we explore gene expression data and high-resolution elemental imaging to report the spatiotemporal changes in the AGR of susceptible and moderately resistant wheat cultivars infected with a necrotrophic fungal pathogen, Pyrenophora tritici-repentis (Ptr). We show, with improved spatiotemporal resolution, that calcium oscillations are modified in the susceptible cultivar, resulting in "frozen" host defence signals at the mature disease stage, and silencing of the host's recognition and defence mechanisms which would otherwise protect it from further attacks. In contrast, Ca accumulation and heightened defence response was observed in the moderately resistant cultivar in the later stage of disease development. Furthermore, in the susceptible interaction, the AGR was unable to recover post disease disruption. Our targeted sampling technique also enabled detection of eight previously predicted proteinaceous effectors in addition to the known ToxA effector. Collectively, our results highlight the benefits of spatially resolved molecular analysis and nutrient mapping to provide high-resolution spatiotemporal snapshots of host-pathogen interactions, paving the way for detangling complex disease interactions in plants.

One Step Nucleic Acid Amplification (OSNA) Lysate Samples Are Suitable to Establish a Transcriptional Metastatic Signature in Patients with Early Stage Hormone Receptors-Positive Breast Cancer.

The One Step Nucleic Acid Amplification (OSNA) is being adopted worldwide for sentinel lymph nodes (SLNs) staging in breast cancer (BC). As major disadvantage, OSNA precludes prognostic information based on structural evaluation of SLNs. Our aim is to identify biomarkers related to tumor-microenvironment interplay exploring gene expression data from the OSNA remaining lysate. This study included 32 patients with early stage hormone receptors-positive BC. Remaining OSNA lysates were prepared for targeted RNA-sequencing analysis. Identification of differentially expressed genes (DEGs) was performed by DESeq2 in R and data analysis in STATA. The results show that, in metastatic SLNs, several genes were upregulated: KRT7, VTCN1, CD44, GATA3, ALOX15B, RORC, NECTIN2, LRG1, CD276, FOXM1 and IGF1R. Hierarchical clustering analysis revealed three different clusters. The identified DEGs codify proteins mainly involved in cancer aggressiveness and with impact in immune response. The overexpression of the immune suppressive genes VTCN1 and CD276 may explain that no direct evidence of activation of immune response in metastatic SLNs was found. We show that OSNA results may be improved incorporating microenvironment-related biomarkers that may be useful in the future for prognosis stratification and immunotherapy selection. As OSNA assay is being implemented for SLNs staging in other cancers, this approach could also have a wider utility.

Endothelial Cell RNA-Seq Data: Differential Expression and Functional Enrichment Analyses to Study Phenotypic Switching.

RNA-seq is a common approach used to explore gene expression data between experimental conditions or cell types and ultimately leads to information that can shed light on the biological processes involved and inform further hypotheses. While the protocols required to generate samples for sequencing can be performed in most research facilities, the resulting computational analysis is often an area in which researchers have little experience. Here we present a user-friendly bioinformatics workflow which describes the methods required to take raw data produced by RNA sequencing to interpretable results. Widely used and well documented tools are applied. Data quality assessment and read trimming were performed by FastQC and Cutadapt, respectively. Following this, STAR was utilized to map the trimmed reads to a reference genome and the alignment was analyzed by Qualimap. The subsequent mapped reads were quantified by featureCounts. DESeq2 was used to normalize and perform differential expression analysis on the quantified reads, identifying differentially expressed genes and preparing the data for functional enrichment analysis. Gene set enrichment analysis identified enriched gene sets from the normalized count data and clusterProfiler was used to perform functional enrichment against the GO, KEGG, and Reactome databases. Example figures of the functional enrichment analysis results were also generated. The example data used in the workflow are derived from HUVECs, an in vitro model used in the study of endothelial cells, published and publicly available for download from the European Nucleotide Archive.

Exploring Gene Expression Data, Using Plots

This paper describes how to explore gene expression data using a combination of graphical and numerical methods. We start from the general methodology for multivariate data visualization, describing heatmaps, par allel coordinate plots and scatterplots. We propose new methods for gene expression data analysis using direct manipulation graphics. With linked scatterplots and parallel coordinate plots we explore gene expression data differently than many common practices. To check replicates in relation to treatments we introduce a new type of plot called a “replicate line” plot. There is a worked example, that focuses on an experimental study containing two two-level factors, genotype and cofactor presence, with two replicates.

MuscleAtlasExplorer: a web service for studying gene expression in human skeletal muscle

MuscleAtlasExplorer is a freely available web application that allows for the exploration of gene expression data from human skeletal muscle. It draws from an extensive publicly available dataset of 1654 skeletal muscle expression microarray samples. Detailed, manually curated, patient phenotype data, with information such as age, sex, BMI and disease status, are combined with skeletal muscle gene expression to provide insights into gene function in skeletal muscle. It aims to facilitate easy exploration of the data using powerful data visualization functions, while allowing for sample selection, in-depth inspection and further analysis using external tools.AvailabilityMuscleAtlasExplorer is available at https://mae.crc.med.lu.se/mae2.

NutriGenomeDB: a nutrigenomics exploratory and analytical platform.

Habitual consumption of certain foods has shown beneficial and protective effects against multiple chronic diseases. However, it is not clear by which molecular mechanisms they may exert their beneficial effects. Multiple -omic experiments available in public databases have generated gene expression data following the treatment of human cells with different food nutrients and bioactive compounds. Exploration of such data in an integrative manner offers excellent possibilities for gaining insights into the molecular effects of food compounds and bioactive molecules at the cellular level. Here we present NutriGenomeDB, a web-based application that hosts manually curated gene sets defined from gene expression signatures, after differential expression analysis of nutrigenomics experiments performed on human cells available in the Gene Expression Omnibus (GEO) repository. Through its web interface, users can explore gene expression data with interactive visualizations. In addition, external gene signatures can be connected with nutrigenomics gene sets using a gene pattern-matching algorithm. We further demonstrate how the application can capture the primary molecular mechanisms of a drug used to treat hypertension and thus connect its mode of action with hosted food compounds.

Slfm: An R Package to Evaluate Coherent Patterns in Microarray Data via Factor Analysis

The development of simulation-based methods, such as Markov chain Monte Carlo (MCMC), has contributed to an increased interest in the Bayesian framework as an alternative to deal with factor models. Many studies have used Bayesian factor analysis to explore gene expression data. We are particularly interested in the application of a sparse latent factor model (SLFM) based on sparsity priors (mixtures) to assess the significance of factors. The SLFM measures how strong the observed coherent expression pattern is in the data, which is an important source of information to evaluate gene activity. In the literature, this type of model has shown better results than other approaches intended for identification of patterns and metagene groups related to the underlying biology. However, a full Bayesian factor model relying on MCMC algorithms has an expensive computational cost, which makes it unattractive for general users. In this paper, we present the package slfm which uses C++ implementation via Rcpp to improve the computational performance of the SLFM within the widely used statistical tool R. We investigate real and simulated microarray data related to breast cancer.

Robust clustering of noisy high-dimensional gene expression data for patients subtyping.

One of the most important research areas in personalized medicine is the discovery of disease sub-types with relevance in clinical applications. This is usually accomplished by exploring gene expression data with unsupervised clustering methodologies. Then, with the advent of multiple omics technologies, data integration methodologies have been further developed to obtain better performances in patient separability. However, these methods do not guarantee the survival separability of the patients in different clusters. We propose a new methodology that first computes a robust and sparse correlation matrix of the genes, then decomposes it and projects the patient data onto the first m spectral components of the correlation matrix. After that, a robust and adaptive to noise clustering algorithm is applied. The clustering is set up to optimize the separation between survival curves estimated cluster-wise. The method is able to identify clusters that have different omics signatures and also statistically significant differences in survival time. The proposed methodology is tested on five cancer datasets downloaded from The Cancer Genome Atlas repository. The proposed method is compared with the Similarity Network Fusion (SNF) approach, and model based clustering based on Student's t-distribution (TMIX). Our method obtains a better performance in terms of survival separability, even if it uses a single gene expression view compared to the multi-view approach of the SNF method. Finally, a pathway based analysis is accomplished to highlight the biological processes that differentiate the obtained patient groups. Our R source code is available online at https://github.com/angy89/RobustClusteringPatientSubtyping. Supplementary data are available at Bioinformatics online.

Building online genomics applications using BioPyramid.

BioPyramid is a Python package, which serves as a scaffold for building an online application for the exploration of gene expression data. It is designed for bioinformaticians wishing to quickly share transformed data and interactive analyses with collaborators. Current R-based tools similarly address the need to quickly share 'omics'-data in an exploratory format, but these are generally small-scale, single-dataset solutions. Biopyramid is written in Python pyramid framework and scalable to address longer-term or more complex projects. It contains a number of components designed to reduce the time and effort in building such an application from scratch, including gene annotation, dataset models and visualization tools. Freely available at http://github.com/jarny/biopyramid. Implemented in python and javascript.

GO-PCA: An Unsupervised Method to Explore Gene Expression Data Using Prior Knowledge.

MethodGenome-wide expression profiling is a widely used approach for characterizing heterogeneous populations of cells, tissues, biopsies, or other biological specimen. The exploratory analysis of such data typically relies on generic unsupervised methods, e.g. principal component analysis (PCA) or hierarchical clustering. However, generic methods fail to exploit prior knowledge about the molecular functions of genes. Here, I introduce GO-PCA, an unsupervised method that combines PCA with nonparametric GO enrichment analysis, in order to systematically search for sets of genes that are both strongly correlated and closely functionally related. These gene sets are then used to automatically generate expression signatures with functional labels, which collectively aim to provide a readily interpretable representation of biologically relevant similarities and differences. The robustness of the results obtained can be assessed by bootstrapping.ResultsI first applied GO-PCA to datasets containing diverse hematopoietic cell types from human and mouse, respectively. In both cases, GO-PCA generated a small number of signatures that represented the majority of lineages present, and whose labels reflected their respective biological characteristics. I then applied GO-PCA to human glioblastoma (GBM) data, and recovered signatures associated with four out of five previously defined GBM subtypes. My results demonstrate that GO-PCA is a powerful and versatile exploratory method that reduces an expression matrix containing thousands of genes to a much smaller set of interpretable signatures. In this way, GO-PCA aims to facilitate hypothesis generation, design of further analyses, and functional comparisons across datasets.

Assessing Dissimilarity Measures for Sample-Based Hierarchical Clustering of RNA Sequencing Data Using Plasmode Datasets.

Sample- and gene- based hierarchical cluster analyses have been widely adopted as tools for exploring gene expression data in high-throughput experiments. Gene expression values (read counts) generated by RNA sequencing technology (RNA-seq) are discrete variables with special statistical properties, such as over-dispersion and right-skewness. Additionally, read counts are subject to technology artifacts as differences in sequencing depth. This possesses a challenge to finding distance measures suitable for hierarchical clustering. Normalization and transformation procedures have been proposed to favor the use of Euclidean and correlation based distances. Additionally, novel model-based dissimilarities that account for RNA-seq data characteristics have also been proposed. Adequacy of dissimilarity measures has been assessed using parametric simulations or exemplar datasets that may limit the scope of the conclusions. Here, we propose the simulation of realistic conditions through creation of plasmode datasets, to assess the adequacy of dissimilarity measures for sample-based hierarchical clustering of RNA-seq data. Consistent results were obtained using plasmode datasets based on RNA-seq experiments conducted under widely different conditions. Dissimilarity measures based on Euclidean distance that only considered data normalization or data standardization were not reliable to represent the expected hierarchical structure. Conversely, using either a Poisson-based dissimilarity or a rank correlation based dissimilarity or an appropriate data transformation, resulted in dendrograms that resemble the expected hierarchical structure. Plasmode datasets can be generated for a wide range of scenarios upon which dissimilarity measures can be evaluated for sample-based hierarchical clustering analysis. We showed different ways of generating such plasmodes and applied them to the problem of selecting a suitable dissimilarity measure. We report several measures that are satisfactory and the choice of a particular measure may rely on the availability on the software pipeline of preference.

Kvik: three-tier data exploration tools for flexible analysis of genomic data in epidemiological studies.

Kvik is an open-source framework that we developed for explorative analysis of functional genomics data from large epidemiological studies. Creating such studies requires a significant amount of time and resources. It is therefore usual to reuse the data from one study for several research projects. Often each project requires implementing new analysis code, integration with specific knowledge bases, and specific visualizations. Although existing data exploration tools are available for single study data exploration, no tool provides all the required functionality for multistudy data exploration. We have therefore used the Kvik framework to develop Kvik Pathways, an application for exploring gene expression data in the context of biological pathways. We have used Kvik Pathways to explore data from both a cross-sectional study design and a case-control study within the Norwegian Women and Cancer (NOWAC) cohort. Kvik Pathways follows the three-tier architecture in web applications using a powerful back-end for statistical analyses and retrieval of metadata.In this note, we describe how we used the Kvik framework to develop the Kvik Pathways application. Kvik Pathways was used by our team of epidemiologists toexplore gene expression data from healthy women with high and low plasma ratios of essential fatty acids.

Kvik: three-tier data exploration tools for flexible analysis of genomic data in epidemiological studies

Kvik is an open-source framework that we developed for explorative analysis of functional genomics data from large epidemiological studies. Creating such studies requires a significant amount of time and resources. It is therefore usual to reuse the data from one study for several research projects. Often each project requires implementing new analysis code, integration with specific knowledge bases, and specific visualizations. Although existing data exploration tools are available for single study data exploration, no tool provides all the required functionality for multistudy data exploration. We have therefore used the Kvik framework to develop Kvik Pathways, an application for exploring gene expression data in the context of biological pathways. We have used Kvik Pathways to explore data from both a cross-sectional study design and a case-control study within the Norwegian Women and Cancer (NOWAC) cohort. Kvik Pathways follows the three-tier architecture in web applications using a powerful back-end for statistical analyses and retrieval of metadata.In this note, we describe how we used the Kvik framework to develop the Kvik Pathways application. Kvik Pathways was used by our team of epidemiologists toexplore gene expression data from healthy women with high and low plasma ratios of essential fatty acids.

Kvik: three-tier data exploration tools for flexible analysis of genomic data in epidemiological studies

Kvik is an open-source system that we developed for explorative analysis of functional genomics data from large epidemiological studies. Creating such studies requires a significant amount of time and resources. It is therefore usual to reuse the data from one study for several research projects. Often each project requires implementing new analysis code, integration with specific knowledge bases, and specific visualizations. Existing data exploration tools do not provide all the required functionality for such multi-study data exploration. We have therefore developed the Kvik framework which makes it easy to implement specialized data exploration tools for specific projects. Applications in Kvik follow the three-tier architecture commonly used in web applications, with REST interfaces between the tiers. This makes it easy to adapt the applications to new statistical analyses, metadata, and visualizations. Kvik uses R to perform on-demand data analyses when researchers explore the data. In this note, we describe how we used Kvik to develop the Kvik Pathways application to explore gene expression data from healthy women with high and low plasma ratios of essential fatty acids using biological pathway visualizations. Researchers interact with Kvik Pathways through a web application that uses the JavaScript libraries Cytoscape.js and D3. We use Docker containers to make deployment of Kvik Pathways simple.

Highlights from Recent Cancer Literature

Highlights from Recent Cancer Literature

Gene expression modular analysis: an overview from the data mining perspective

AbstractIn this review, we discuss the main problems and state‐of‐the‐art solutions applied to the field of gene expression. Specific data analysis workflows have been developed in parallel with the technology and currently cover a very wide spectrum of methods and applications needed to give answers to a lot of scientific questions that this type of data are producing. Computer science and, more specifically, the data mining area is still benefiting from a large set of real‐case scenarios to apply and develop new ideas and tools for discovering biological knowledge and new information from this experimental data. In this article, we make the reader aware of the main problems that still persist and provide a description of the methodologies that are applied for classification, clustering, and functional exploration of gene expression data. © 2011 John Wiley & Sons, Inc. WIREs Data Mining Knowl Discov 2011 1 381–396 DOI: 10.1002/widm.29This article is categorized under: Algorithmic Development > Biological Data Mining Application Areas > Science and Technology Technologies > Classification

Fibronectin 1 is a potential biomarker for radioresistance in head and neck squamous cell carcinoma

Radiotherapy remains the backbone of head and neck cancer therapy but response is sometimes impeded by tumor radioresistance. Identifying predictive biomarkers of radiotherapy response is a crucial step towards personalized therapy. The aim of this study was to explore gene expression data in search of biomarkers predictive of the response to radiotherapy in head and neck squamous cell carcinoma (HNSCC).Microarray analysis was performed on five cell lines with various intrinsic radiosensitivity, selected from a panel of 29 HNSCC cell lines. The bioinformatics approach included Gene Ontology (GO) enrichment profiling and Ingenuity Pathway Analysis (IPA). The GO-analysis detected 16 deregulated categories from which development, receptor activity, and extracellular region represented the largest groups. Fourteen hub genes (CEBPA, CEBPB, CTNNB1, FN1, MYC, MYCN, PLAU, SDC4, SERPINE1, SP1, TAF4B, THBS1, TP53 and VLDLR) were identified from the IPA network analysis. The hub genes in the highest ranked network, (FN1, SERPINE1, THBS1 and VLDLR) were further subjected to qPCR analysis in the complete panel of 29 cell lines. Of these genes, high FN1 expression associated to high intrinsic radiosensitivity (p=0.047). In conclusion, gene ontologies and hub genes of importance for intrinsic radiosensitivity were defined. The overall results suggest that FN1 should be explored as a potential novel biomarker for radioresistance.

ITRAQ-based Proteomics Profiling Reveals Increased Metabolic Activity and Cellular Cross-talk in Angiogenic Compared with Invasive Glioblastoma Phenotype

Malignant gliomas (glioblastoma multiforme) have a poor prognosis with an average patient survival under current treatment regimens ranging between 12 and 14 months. The tumors are characterized by rapid cell growth, extensive neovascularization, and diffuse cellular infiltration of normal brain structures. We have developed a human glioblastoma xenograft model in nude rats that is characterized by a highly infiltrative non-angiogenic phenotype. Upon serial transplantation this phenotype will develop into a highly angiogenic tumor. Thus, we have developed an animal model where we are able to establish two characteristic tumor phenotypes that define human glioblastoma (i.e. diffuse infiltration and high neovascularization). Here we aimed at identifying potential biomarkers expressed by the non-angiogenic and the angiogenic phenotypes and elucidating the molecular pathways involved in the switch from invasive to angiogenic growth. Focusing on membrane-associated proteins, we profiled protein expression during the progression from an invasive to an angiogenic phenotype by analyzing serially transplanted glioma xenografts in rats. Applying isobaric peptide tagging chemistry (iTRAQ) combined with two-dimensional LC and MALDI-TOF/TOF mass spectrometry, we were able to identify several thousand proteins in membrane-enriched fractions of which 1460 were extracted as quantifiable proteins (isoform- and species-specific and present in more than one sample). Known and novel candidate proteins were identified that characterize the switch from a non-angiogenic to a highly angiogenic phenotype. The robustness of the data was corroborated by extensive bioinformatics analysis and by validation of selected proteins on tissue microarrays from xenograft and clinical gliomas. The data point to enhanced intercellular cross-talk and metabolic activity adopted by tumor cells in the angiogenic compared with the non-angiogenic phenotype. In conclusion, we describe molecular profiles that reflect the change from an invasive to an angiogenic brain tumor phenotype. The identified proteins could be further exploited as biomarkers or therapeutic targets for malignant gliomas.

Analysis of Gene‐Expression Data Using J‐Express

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.

An integrative approach for biological data mining and visualisation

The emergence of systems biology necessitates development of platforms to organise and interpret plentitude of biological data. We present a system to integrate data across multiple bioinformatics databases and enable mining across various conceptual levels of biological information. The results are represented as complex networks. Context dependent mining of these networks is achieved by use of distances. Our approach is demonstrated with three applications: full metabolic network retrieval with network topology study, exploration of properties and relationships of a set of selected proteins, and combined visualisation and exploration of gene expression data with related pathways and ontologies.