A fisheye viewer for microarray-based gene expression data

Min Wu,Cheng Thao,Ethan V Munson,Xiangming Mu

doi:10.1186/1471-2105-7-452

Abstract

BackgroundMicroarray has been widely used to measure the relative amounts of every mRNA transcript from the genome in a single scan. Biologists have been accustomed to reading their experimental data directly from tables. However, microarray data are quite large and are stored in a series of files in a machine-readable format, so direct reading of the full data set is not feasible. The challenge is to design a user interface that allows biologists to usefully view large tables of raw microarray-based gene expression data. This paper presents one such interface – an electronic table (E-table) that uses fisheye distortion technology.ResultsThe Fisheye Viewer for microarray-based gene expression data has been successfully developed to view MIAME data stored in the MAGE-ML format. The viewer can be downloaded from the project web site . The fisheye viewer was implemented in Java so that it could run on multiple platforms. We implemented the E-table by adapting JTable, a default table implementation in the Java Swing user interface library. Fisheye views use variable magnification to balance magnification for easy viewing and compression for maximizing the amount of data on the screen.ConclusionThis Fisheye Viewer is a lightweight but useful tool for biologists to quickly overview the raw microarray-based gene expression data in an E-table.

Highlights

Microarray has been widely used to measure the relative amounts of every mRNA transcript from the genome in a single scan
Microarray enables researchers to measure the relative amounts of every mRNA transcript from the genome in a single scan, increasing the number of data points from an experiment by several thousand folds
This paper presents one such interface – an electronic table (E-table) that uses fisheye distortion technology

Summary

Introduction

Microarray has been widely used to measure the relative amounts of every mRNA transcript from the genome in a single scan. Microarray data are quite large and are stored in a series of files in a machine-readable format, so direct reading of the full data set is not feasible. The challenge is to design a user interface that allows biologists to usefully view large tables of raw microarray-based gene expression data. Microarray enables researchers to measure the relative amounts of every mRNA transcript from the genome in a single scan, increasing the number of data points from an experiment by several thousand folds. Biological researchers, who have been used to studying a small number of genes thoroughly over a period of years, must use new concepts and methods to store and analyze their experimental results.

Results

Discussion

Conclusion