Category Genomics>Gene Expression Analysis/Profiling/Tools

Abstract ArrayNorm is an platform-independent Java tool for the normalization and statistical analysis of microarray-experiment data.

It provides modules for visualization (scatter-plot, histogram, box-plot, etc.), normalization and analysis.

The user can upload any number of microarray-datasets, resulting from one experiment. According to experimental design and relationships between the microarrays, the data is organized for later analysis and replicate handling.

ArrayNorm features a variety of normalization methods, such as 'global median', 'dye-swap-pairs normalization', 'lowess fitting' and 'normalization using control-spots'.

Differentially expressed (DE) genes can be found using fold-change- detection or statistical tests (t-test).

All DE genes can be sent to a text-file, allowing further analysis with other software-tools [such as, Genesis, GeneSpring (see G6G Abstract Number 20003R), etc.].

ArrayNorm guides the user through the normalization process, accounting for the known sources of non-biological variability introduced in the microarray data.

Starting from the result files of the image analysis software (e.g. GenePix); ArrayNorm organizes the loaded arrays in classes, i.e. the biological conditions to be compared.

ArrayNorm enables the user to characterize the experiment according to several features:

(1) replicated slides within a class;

(2) slides for which the dyes were swapped;

(3) spotted controls and

(4) replicated measurements within a slide.

ArrayNorm graphically summarizes this experimental design information. ArrayNorm allows the visualization of the data before and after normalization.

To test the quality of the data, the Array Viewer (Arrayview) reconstructs the original images representing every spot with a single pixel.

The bad spots marked by the image analysis software are colored to show potentially contaminated areas.

A Slide Report shows the percentage of spots excluded from the analysis and background subtraction can be performed if desired.

Spatial effects due to different print tips can be detected with the Box- plots of the different print-tip groups.

MA-plots (Yang et al., 2002) were implemented to detect intensity- dependent effects in the log ratios distribution.

The same information can be visualized with the Scatter-plots. They are essential to decide which normalization method to choose.

Histograms were implemented to analyze the distribution of the intensity level of both channels and to decide if a t -test is suitable for further detection of differentially expressed genes.

ArrayNorm features/capabilities include:

1) Upload of any number of source-files.

2) Variety of possible file-formats [GenePix, ImaGene (see G6G Abstract Number 20128R), Agilent, etc.].

3) Organization of loaded ’slides’ in experiment-classes.

4) Possibility to define the experiment’s design.

5) Data tree to illustrate the experiment’s organization.

6) Tools for visualization (Arrayview, Scatter-plot, Histogram, etc.).

7) Variety of normalization methods (within and between slides).

8) Possibilities of Background Subtraction and Data Reset.

9) Possibility of averaging replicated data and merging of Slides.

10) Simple tools for analysis (fold-change detection, etc.).

11) Statistical tests for finding differentially expressed genes (T-test, Mann-Whitney test).

12) One-way-ANOVA for finding differentially expressed genes.

13) Exporting of results to text-files (compatible with Genesis software).

14) Connection to the MARS-database for loading predefined microarray-experiments and uploading results.

System Requirements

PC or Mac --


Manufacturer Web Site ArrayNorm

Price Contact manufacturer.

G6G Abstract Number 20282

G6G Manufacturer Number 101146