his dataset is based on National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) DataSet accession GDS2778. girke.bioinformatics.ucr.edu +1

The dataset originates from a microarray experiment measuring global gene expression under specific experimental conditions. girke.bioinformatics.ucr.edu +1

Raw and processed expression data (for all probes/genes) are included, enabling downstream analysis such as normalization, differential expression, and clustering.

The dataset has been used to perform differential gene expression (DGE) analysis to identify genes that are up- or down-regulated under the experimental condition compared to control.

Data processing steps typically include normalization (e.g., log-transformation), quality control, probe-to-gene mapping, and statistical testing for significance (e.g., using packages such as limma or other DGE tools). mahsa-ehsanifard.github.io +1

Resulting differentially expressed genes (DEGs) include statistics such as log fold change (logFC), adjusted p‑values (adj.P.Val), and possibly other metrics (e.g., B-statistic), allowing assessment of both magnitude and significance of changes.

The dataset also includes a visualization file (heatmap image) that displays expression patterns of DEGs (or top variable genes) across samples — enabling clustering and pattern recognition across samples and genes.

The heatmap helps illustrate sample-wise and gene-wise expression variation: clustering groups together samples (e.g. control vs treatment) and genes with similar expression dynamics. NCBI +1

This dataset is suitable for further bioinformatics analysis: e.g. functional enrichment (GO/Pathway), co‑expression analysis, gene signature identification, or integration with other datasets.

Users who download this dataset can reproduce or extend analyses, such as re-normalization, alternative clustering, custom DEG thresholds, or downstream biological interpretation (pathway, network analysis).

Kinome microarrays are comprised of peptides that act as phosphorylation targets for protein kinases. This platform is growing in popularity due to its ability to measure phosphorylation-mediated cellular signaling in a high-throughput manner. While software for analyzing data from DNA microarrays has also been used for kinome arrays, differences between the two technologies and associated biologies previously led us to develop Platform for Intelligent, Integrated Kinome Analysis (PIIKA), a software tool customized for the analysis of data from kinome arrays. Here, we report the development of PIIKA 2, a significantly improved version with new features and improvements in the areas of clustering, statistical analysis, and data visualization. Among other additions to the original PIIKA, PIIKA 2 now allows the user to: evaluate statistically how well groups of samples cluster together; identify sets of peptides that have consistent phosphorylation patterns among groups of samples; perform hierarchical clustering analysis with bootstrapping; view false negative probabilities and positive and negative predictive values for t-tests between pairs of samples; easily assess experimental reproducibility; and visualize the data using volcano plots, scatterplots, and interactive three-dimensional principal component analyses. Also new in PIIKA 2 is a web-based interface, which allows users unfamiliar with command-line tools to easily provide input and download the results. Collectively, the additions and improvements described here enhance both the breadth and depth of analyses available, simplify the user interface, and make the software an even more valuable tool for the analysis of kinome microarray data. Both the web-based and stand-alone versions of PIIKA 2 can be accessed via http://saphire.usask.ca.

Background In microarray data analysis, the comparison of gene-expression profiles with respect to different conditions and the selection of biologically interesting genes are crucial tasks. Multivariate statistical methods have been applied to analyze these large datasets. Less work has been published concerning the assessment of the reliability of gene-selection procedures. Here we describe a method to assess reliability in multivariate microarray data analysis using permutation-validated principal components analysis (PCA). The approach is designed for microarray data with a group structure.

   Results
   We used PCA to detect the major sources of variance underlying the hybridization conditions followed by gene selection based on PCA-derived and permutation-based test statistics. We validated our method by applying it to well characterized yeast cell-cycle data and to two datasets from our laboratory. We could describe the major sources of variance, select informative genes and visualize the relationship of genes and arrays. We observed differences in the level of the explained variance and the interpretability of the selected genes.


   Conclusions
   Combining data visualization and permutation-based gene selection, permutation-validated PCA enables one to illustrate gene-expression variance between several conditions and to select genes by taking into account the relationship of between-group to within-group variance of genes. The method can be used to extract the leading sources of variance from microarray data, to visualize relationships between genes and hybridizations and to select informative genes in a statistically reliable manner. This selection accounts for the level of reproducibility of replicates or group structure as well as gene-specific scatter. Visualization of the data can support a straightforward biological interpretation.

A tool for mapping transcriptome data and for creating a database with an overview of the entire pathway, a web-based resource consisting of a web-application for the visualization of complex omics data onto KEGG pathways to overview all entities in the context of cellular pathways, and databases created with the software to visualize a series of microarray data. The web-application accepts transcriptome, proteome, metabolome, or the combination of these data as input, and because of this scalability it is advantageous for the visualization of cell simulation results. Several databases of transcriptome data obtained at Mori Laboratory, Nara Institute of Science and Technology, Japan, are also presented.

This dataset is based on GEO series GSE5583. OmicsDI

The experiment compares gene expression profiles between wild‑type mouse embryonic stem cells (ES cells) and ES cells in which Histone deacetylase 1 (HDAC1) has been knocked out. OmicsDI

The organism used is mouse (Mus musculus). OmicsDI

Microarray technology was employed to measure transcript abundance across the genome, aiming to identify putative HDAC1 target genes. OmicsDI +1

The dataset includes processed expression data (after normalization and log2 transformation), allowing for downstream exploratory data analysis (EDA) and differential gene expression (DGE) analysis.

As part of EDA, sample‑wise distribution plots (e.g. boxplots) are provided to assess normalization across all arrays.

The dataset also includes downstream visualizations and analysis results, such as boxplots, which help in evaluating the consistency and quality of the processed data.

Researchers can use this dataset to perform differential expression analysis between HDAC1 knockout vs wild‑type ES cells, investigate epigenetic regulation, or explore downstream effects of histone deacetylation loss.

Additionally, the dataset can serve as a reference example for microarray data preprocessing, normalization, transformation (e.g. log2), and exploratory visualization workflows.

The dataset is publicly available and sourced from a trusted repository (GEO), ensuring transparency and reproducibility of the experiment.

Database for microarray data storage, retrieval, analysis, and visualization.

BackgroundSerial Analysis of Gene Expression (SAGE) is a DNA sequencing-based method for large-scale gene expression profiling that provides an alternative to microarray analysis. Most analyses of SAGE data aimed at identifying co-expressed genes have been accomplished using various versions of clustering approaches that often result in a number of false positives.Principal FindingsHere we explore the use of seriation, a statistical approach for ordering sets of objects based on their similarity, for large-scale expression pattern discovery in SAGE data. For this specific task we implement a seriation heuristic we term ‘progressive construction of contigs’ that constructs local chains of related elements by sequentially rearranging margins of the correlation matrix. We apply the heuristic to the analysis of simulated and experimental SAGE data and compare our results to those obtained with a clustering algorithm developed specifically for SAGE data. We show using simulations that the performance of seriation compares favorably to that of the clustering algorithm on noisy SAGE data.ConclusionsWe explore the use of a seriation approach for visualization-based pattern discovery in SAGE data. Using both simulations and experimental data, we demonstrate that seriation is able to identify groups of co-expressed genes more accurately than a clustering algorithm developed specifically for SAGE data. Our results suggest that seriation is a useful method for the analysis of gene expression data whose applicability should be further pursued.

Supplementary Materials. (DOCX)

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on September 6,2023. Many Laboratories chose to design and print their own microarrays. At present, the choice of the genes to include on a certain microarray is a very laborious process requiring a high level of expertise. Onto-Design database is able to assist the designers of custom microarrays by providing the means to select genes based on their experiment. Design custom microarrays based on GO terms of interest. User account required. Platform: Online tool

This dataset provides the inputs used in the Galaxy Training Network (GTN) training 'End-to-End Tissue Microarray Image Analysis with Galaxy-ME'. The tutorial demonstrates how to use the Galaxy-ME tool suite for primary image processing, data analysis, and interactive visualization of multiple tissue imaging datasets. Original data was published by Schapiro et al.

NA: not available. –: not computable because no posterior probabilities were provided.Performance metrics of classifiers on the lung cancer test set (AC and SCC subtype classification).

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on May 2nd,2023. TMAD stores raw and processed data from Tissue Microarray experiments along with their corresponding stained tissue images. In addition, TMAD provides methods for data retrieval, grouping of data, analysis and visualization as well as export to standard formats. Researchers at the Stanford University School of Medicine and their collaborators worldwide have constructed many tissue microarrays for use in basic research.

snapshot-tab-pane Microarray Biochips Market Size 2024-2028The microarray biochips market size is forecast to increase by USD 17.28 billion, at a CAGR of 22.2% between 2023 and 2028.The market is characterized by a growing number of collaborations among key players, which is expanding market presence and driving innovation. This strategic approach is essential in the capital-intensive market, where significant investments are required for research and development. A notable trend in the market is the emergence of Label-One-Component (LOC) technology, offering advantages such as improved sensitivity and specificity. However, the high cost of microarray biochips remains a significant challenge for market growth. Companies seeking to capitalize on opportunities must navigate this obstacle by focusing on cost reduction through economies of scale and process optimization.Additionally, collaborations and partnerships can help share research and development costs and accelerate time-to-market for innovative products. The strategic landscape of the market is dynamic, with ongoing advancements in technology and a growing demand for personalized medicine, creating opportunities for companies to differentiate themselves and gain a competitive edge.What will be the Size of the Microarray Biochips Market during the forecast period?Explore in-depth regional segment analysis with market size data - historical 2018-2022 and forecasts 2024-2028 - in the full report. Request Free SampleThe market continues to evolve, driven by advancements in technology and expanding applications across various sectors. Protein microarray technology, a crucial component, enables high-throughput analysis of protein-protein interactions and antibody discovery. Reproducibility metrics and spot morphology analysis ensure consistency and accuracy in data generation. Label incorporation methods, such as biotinylated target cDNA and reverse transcription PCR, facilitate efficient probe attachment. Gene ontology enrichment and pathway analysis tools provide insights into biological functions and molecular interactions. Data mining algorithms, including clustering algorithms and fold change calculations, facilitate pattern recognition and discovery. Microarray data normalization techniques, such as CDNA microarray platforms and genomic DNA extraction, ensure data consistency.Microarray experimental design, hybridization kinetics, and high-throughput screening are essential for optimizing data generation and analysis. Single nucleotide polymorphism (SNP) detection and comparative genomic hybridization offer valuable insights into genetic variations. Data quality assessment, signal-to-noise ratios, and background correction methods ensure data accuracy and reliability. In situ hybridization and fluorescence detection methods facilitate visualization and analysis of gene expression at the cellular level. Differential gene expression analysis provides insights into disease mechanisms and therapeutic targets. Microarray scanner systems and image analysis software facilitate efficient and accurate data analysis. DNA microarray technology continues to evolve, offering exciting possibilities for research and diagnostic applications.How is this Microarray Biochips Industry segmented?The microarray biochips industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD million" for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ApplicationDrug discovery and developmentDiagnostics and treatmentsResearch and consumablesForensic medicinesOthersGeographyNorth AmericaUSEuropeGermanyUKAPACChinaJapanRest of World (ROW)By Application InsightsThe drug discovery and development segment is estimated to witness significant growth during the forecast period.The market is witnessing significant growth due to its increasing application in drug discovery, driven by the rising preference for personalized medicines. With the global population aging, the demand for better healthcare solutions is escalating, leading manufacturers to continually innovate and improve microarray technology. In genomics and proteomics, microarray biochips are increasingly utilized, further fueling market growth. Advancements in protein microarray technology ensure greater reproducibility and accuracy, while spot morphology analysis and label incorporation enhance data reliability. Gene ontology enrichment and pathway analysis tools enable deeper insights into biological processes, and clustering algorithms facilitate the identification of complex relationships between genes.Genomic DNA extraction and microarray data normalization are crucial steps in ensuring data quality, while high-throughput screening and single nuc

The datasets presented here are the description of the dataset Brain cancer gene expression - CuMiD. They include the total count, mean, standard deviation, minimum, maximum, percentiles(25%,50%,70%), and maximum, for Normal samples and cancer types: Ependymoma, Glioblastoma, Medulloblastoma, and Pilocytic Astrocytoma. These datasets could be helpful with the visualization of the large dataset by CuMiD

BackgroundIntrauterine growth restriction (IUGR) is highly associated with fetal as well as neonatal morbidity, mortality, and an increased risk metabolic disease development later in life. The mechanism involved in the increased risk has not been established. We compared differentially expressed genes between the liver of appropriate for gestational age (AGA) and IUGR rat models and identified their effects on molecular pathways involved in the metabolic syndrome.MethodsWe extracted RNA from the liver of IUGR and AGA rats and profiled gene expression by microarray analysis. GO function and KEGG pathway enrichment analyses were conducted using the Search Tool for the Retrieval of Interacting Genes database. Then, the Cytoscape software was used to visualize regulatory interaction networks of IUGR-related genes. The results were further verified via quantitative reverse transcriptase PCR analysis.ResultsIn this study, 815 genes were found to be markedly differentially expressed (fold-change >1.5, p < 0.05) between IUGR and AGA, with 347 genes elevated and 468 suppressed in IUGR, relative to AGA. Enrichment and protein–protein interaction network analyses of target genes revealed that core genes including Ppargc1a, Prkaa2, Slc2a1, Rxrg, and Gcgr, and pathways, including the PPAR signaling pathway and FoxO signaling pathway, had a potential association with metabolic syndrome development in IUGR. We also confirmed that at the mRNA level, five genes involved in glycometabolism were differentially expressed between IUGR and AGA.ConclusionOur findings elucidate on differential gene expression profiles in IUGR and AGA. Moreover, they elucidate on the pathogenesis of IUGR-associated metabolic syndromes. The suggested candidates are potential biomarkers and eventually intended to treat them appropriately.

An experiment in web-database access to large multi-dimensional data sets using a standardized experimental platform to determine if the larger scientific community can be given simple, intuitive, and user-friendly web-based access to large microarray data sets. All data in PEPR is also available via NCBI GEO. The structure and goals of PEPR differ from other mRNA expression profiling databases in a number of important ways. * The experimental platform in PEPR is standardized, and is an Affymetrix - only database. All microarrays available in the PEPR web database should ascribe to quality control and standard operating procedures. A recent publication has described the QC/SOP criteria utilized in PEPR profiles ( The Tumor Analysis Best Practices Working Group 2004 ). * PEPR permits gene-based queries of large Affymetrix array data sets without any specialized software. For example, a number of large time series projects are available within PEPR, containing 40-60 microarrays, yet these can be simply queried via a dynamic web interface with no prior knowledge of microarray data analysis. * Projects in PEPR originate from scientists world-wide, but all data has been generated by the Research Center for Genetic Medicine, Children''''s National Medical Center, Washington DC. Future developments of PEPR will allow remote entry of Affymetrix data ascribing to the same QC/SOP protocols. They have previously described an initial implementation of PEPR, and a dynamic web-queried time series graphical interface ( Chen et al. 2004 ). A publication showing the utility of PEPR for pharmacodynamic data has recently been published ( Almon et al. 2003 ).

Might-be wrongly labeled samples identified by Ben-Hamo's study.

Ameloblastoma is a highly aggressive odontogenic tumor, and its pathogenesis is associated with multiple participating genes. Objective: Our aim was to identify and validate new critical genes of conventional ameloblastoma using microarray and bioinformatics analysis. Methods: Gene expression microarray and bioinformatic analysis were performed to use CHIP H10KA and DAVID software for enrichment. Protein-protein interactions (PPI) were visualized using STRING-Cytoscape with MCODE plugin, followed by Kaplan-Meier and GEPIA analysis that were employed for the candidate's postulation. RT-qPCR and IHC assays were performed to validate the bioinformatic approach. Results: 376 upregulated genes were identified. PPI analysis revealed 14 genes that were validated by Kaplan-Meier and GEPIA resulting in PDGFA and IL2RA as candidate genes. The RT-qPCR analysis confirmed their intense expression. Immunohistochemistry analysis showed that PDGFA expression is parenchyma located. Conclusion: With bioinformatics methods, we can identify upregulated genes in conventional ameloblastoma, and with RT-qPCR and immunoexpression analysis validate that PDGFA could be a more specific and localized therapeutic target.

Microarray-based expression profiling of living systems is a quick and inexpensive method to obtain insights into the nature of various diseases and phenotypes. A typical microarray profile can yield hundreds or even thousands of differentially expressed genes and finding biologically plausible themes or regulatory mechanisms underlying these changes is a non-trivial and daunting task. We describe a novel approach for systems-level interpretation of microarray expression data using a manually constructed “overview” pathway depicting the main cellular signaling channels (Atlas of Signaling). Currently, the developed pathway focuses on signal transduction from surface receptors to transcription factors and further transcriptional regulation of cellular “workhorse” proteins. We show how the constructed Atlas of Signaling in combination with an enrichment analysis algorithm allows quick identification and visualization of the main signaling cascades and cellular processes affected in a gene expression profiling experiment. We validate our approach using several publicly available gene expression datasets.

• This dataset contains expression matrix handling and normalization results derived from GEO dataset GSE32138. • It includes raw gene expression values processed using standardized bioinformatics workflows. • The dataset demonstrates quantile normalization applied to microarray-based expression data. • It provides visualization outputs used to assess data distribution before and after normalization. • The goal of this dataset is to support reproducible analysis of GSE32138 preprocessing and quality control. • Researchers can use the files for practice in normalization, exploratory data analysis, and visualization. • This dataset is useful for learning microarray preprocessing techniques in R or Python.