Category Genomics>Gene Expression Analysis/Profiling/Tools

Abstract BAMarray™ is a graphically oriented, user friendly, Java software package for the analysis of microarray data.

BAMarray implements the Bayesian ANOVA for microarray (BAM) methodology for detecting differentially expressing genes in multi-group microarray experiments.

BAM is a new statistical technique for detecting differentially expressing genes from microarray data. At the heart of the method is a special type of 'signal detection' technique that squashes down ‘false signals’ while leaving ‘real signals’ alone.

Standard methods look for genes by relying on elementary test statistics using data from one gene at a time (such as t-tests, or contrasts from ANOVA models). Gene lists are obtained by filtering statistics by attempting to control overall type-I error, or false discovery rates, or by filtering using a user specified cutoff level.

Patterns of Interest -- BAM takes a fundamentally different approach and focuses instead on estimating the differential effect of a gene by synthesizing information across all genes simultaneously.

Rather than filtering genes, estimated effects are mapped into 'pattern types'.

BAM's special 'signal detection' mechanism shrinks to zero estimated effects for genes unlikely to be differentially expressing, and consequently 'patterns of interest' become highly interpretable using graphical visualization tools.

‘Patterns of interest’ might be "hit-and-run" genes which affect a biologic system for only a certain amount of time, or genes involved throughout the process.

Types of problems BAM can be used for -- BAM applies to multi-group experimental designs, such as data collected over different stages of a disease.

Other applications include 'gene expression time profiling' for time- course data, outlier detection, invariant set normalization, gene atlas transcriptome mappings, as well an many other problems.

Key Methodological features/capabilities include:

1) The underlying methodology for BAM has been rigorously studied theoretically (Ishwaran and Rao 2005a, 2005b - see below...).

2) BAM has been shown to be robust to non-normality of gene expression measurements and to correlations between expression measurements on a given chip (Ishwaran and Rao 2003 - see below...).

3) BAM discovers more genes by optimally balancing the number of falsely detected and falsely non-detected genes. This is a property guaranteeing lower total gene misclassification (Ishwaran and Rao 2005a - see below...).

This differs from methods which control type-I error rates or false detection rates. Controlling such rates tends to identify obvious changing genes but misses many subtle changes.

4) BAM adaptively reduces correlations between estimated differential effects for a gene. This allows true gene expression patterns to be readily identified and reduces the number of implausible patterns (Ishwaran and Rao 2005a - see below...).

BAMarray Software features/capabilities include:

1) BAMarray is a user friendly Java application that runs on different operating systems.

2) BAMarray can also be run unattended in 'Batch Mode' initiated by a script file. Batch Mode can process several data files sequentially and save the resulting analysis to disk.

Writing custom designed scripts will allow users to interface with different types of software, such as BioConductor, and R.

3) BAMarray has a 'Save Run' feature allowing users to save the results of a run for later retrieval. A run that can take minutes to execute can be restored in only seconds using the 'Restore Run' feature. Save Run can also be triggered in Batch Mode.

4) Experimental designs with unlimited groups are accommodated. Visualization plots are available for identifying specific types of gene patterns. BAMarray automatically classifies genes into 'gene patterns'.

5) BAMarray is nearly automatic, and free of complicated tuning parameters.

6) Most statistical methods require a user specified filtering value for identifying significant genes. BAMarray provides an automated value.

7) Unequal variances across genes and experimental groups are systematically handled by an automated pre-processing step that does Not artificially dampen or amplify group differences across genes (as seen with other transformations, such as logarithms).

8) Graphical tools using zoom-in and lassoing allow users to interactively generate lists of genes.

9) Gene labels can be toggled on or off allowing genes of interest to be readily identified. Specific genes can be highlighted using a drop down list or by populating a tracking list from gene labels found in an existing file.

10) Gene lists can be exported. Exporting lists can be automated using the Batch Mode.

11) Figures can be saved as publication quality color graphics.

12) Plots for testing the underlying assumptions of the model are included as part of the graphics suite.

Papers Referenced:

1) Ishwaran H. and Rao J.S. (2003). Detecting differentially expressed genes in microarrays using Bayesian model selection. Journal of the American Statistical Association, Vol 98, 438-455.

2) Ishwaran, H. and Rao J.S. (2005a). Spike and slab gene selection for multigroup microarray data. Journal of the American Statistical Association, 100, 764-780.

3) Ishwaran H. and Rao J.S. (2005b). Spike and slab variable selection: frequentist and Bayesian strategies. Annals of Statistics, 33, 730-773.

4) Ishwaran, H., Rao, J.S. and Kogalur U.B. (2006). BAMarray™: Java software for Bayesian analysis of variance for microarray data. BMC Bioinformatics, 7:59.

5) Papana, A. and Ishwaran, H. (2006). CART variance stabilization and regularization for high-throughput genomic data. Bioinformatics, 22 (18), 2254-2261.

6) Ishwaran H. and Rao J.S. (2008). Clustering gene expression profile data by selective shrinkage. Stat. Prob. Letters, 78, 1490-1497.

System Requirements

The minimum hardware requirements are primarily dependent on the size of the data sets that the user plans to analyze. In general, the manufacturer recommends the following:


Windows XP -

Mac OS X -

Note: that the necessary Java Runtime Environment will already be installed on these operating systems.


Manufacturer Web Site BAMarray

Price Contact manufacturer.

G6G Abstract Number 20398

G6G Manufacturer Number 104030