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DNA Microarrays

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Jun 12, 2026

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

SEURAT: visual analytics for the integrated analysis of microarray data.

Alexander Gribov¹, Martin Sill, Sonja Lück

¹Department of Computer Oriented Statistics and Data Analysis, University of Augsburg, Universitätsstr, 14, 86159 Augsburg, Germany.

BMC Medical Genomics

|June 8, 2010

Summary

This summary is machine-generated.

Researchers need tools for analyzing complex cancer data. SEURAT is an open-source software enabling integrated analysis of gene expression, genomic, and clinical data with interactive visualizations.

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Area of Science:

Bioinformatics
Genomics
Translational Cancer Research

Background:

Translational cancer research generates high-dimensional gene expression, clinical, and genomic data.
Existing tools often lack integrated analysis capabilities for these diverse datasets.

Purpose of the Study:

To develop an open-source software tool for the integrated analysis of high-dimensional cancer-related data.
To provide interactive visualization for exploring combined gene expression, genomic, and clinical information.

Main Methods:

Developed SEURAT, an open-source software with a comprehensive data manager.
Integrated interactive visualization tools including heatmaps, dendrograms, and a chromosome browser.
Incorporated unsupervised data analytics such as clustering and biclustering algorithms.

Main Results:

SEURAT offers interactive and linked visualizations for multiple data types (gene expression, CGH, SNP arrays).
The software facilitates exploration of genetic variations across the genome.
Unsupervised analytics support exploratory data analysis for high-dimensional datasets.

Conclusions:

SEURAT addresses the need for joint analysis of gene expression, genomic, and clinical data in cancer research.
The software enhances researchers' ability to integrate and interpret complex biological datasets.