NIA Array Analysis Tool 2.0 – Statistical Significance Analysis of Microarray Data

NIA Array Analysis Tool 2.0


NIA Array Analysis Tool is designed to test statistical significance of gene microarray data, visualize the results, and provide links to clone information and gene index.

  • Evaluate the statistical significance of differential gene expression based on microarray data
  • Make log-ratio plots and scatter-plots
  • Find clusters of tissues with similar expression patterns and identify specific genes for each cluster
  • Find major patterns of variability in gene expression using Principal Component Analysis (PCA) and biplot
  • Cluster genes according to their contribution to principal components
  • Find genes whose expression matches a given pattern (pattern matching)
  • Plot the dendrogram for replications to check abnormal arrays
  • Plot the error function (SD vs. expression level)
  • Make a correlation matrix
  • Normalize input data
  • Import principal components from an earlier analysis to overlay 2 sets of results
  • Save results of analysis for personal or public access

Arrayjoin Tool is designed for compiling an input file from multiple scanner files.

ANOVA Tool – Statistical analysis is based on the single-factor ANalysis Of VAriance

Hierarchical clustering of tissues is done using the average distance method.

Principal component analysis (PCA) is done using the Singular Value Decomposition (SVD) method that generates eigenvectors both for rows and columns of the log-transformed data matrix (Gabriel 1971.

Pattern matching can be used to find genes with expression pattern similar to some other gene (or group of genes).

Gene list analysis – Getting information on selected genes is one of most important components of microarray analysis.


Laboratory of Genetics, National Institute on Aging,  NIH




NIA Array Analysis Tool


Sharov, A.A., Dudekula, D.B., Ko, M.S.H. 2005. A web-based tool for principal component and significance analysis of microarray data. Bioinformatics, 21(10): 2548-9. Epub 2005 Feb 25. PMID: 15734774

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