Microarray Web Sites

Full featured commercial products
Product	Company	Novel features
BioMine	Gene Network Sciences	Unique clustering methods; statistical methods for validating clusters; experiment design tool
BioMiner	MicroDiscovery	Novel normalization methods; support vector machines
Expressionist	GeneData	Enterprise product
GeneLinker	Molecular Mining Corporation	Novel mutual nearest neighbors clustering
GeneMaths	Applied Maths	Statistical methods for validating clusters; discriminant analysis
GenePlus	Enodar	Regression techniques to assess significance of expression changes
GeneSight	BioDiscovery	Novel normalization methods to correct dye bias; discriminant analysis
GeneSpring	Silicon Genetics	A market leader that represents the gold standard for this class of product. Discriminant analysis; scripting tools
J-Express	MolMine	Sammon maps; multidimensional scaling
Partek	Partek	More than twenty distance measures for clustering; neural nets, discriminant analysis; multidimensional scaling; scripting tools
Pathways 4	ResGen	Modular architecture for plug-in extensions
Spotfire	Spotfire	A market leader that integrates microarray tools with Spotfire’s visualization engine. Formerly called Array Explorer. Can cluster on text, ontology classifications, etc. in addition to expression values
Xpression NTI	InforMax	Can filter data by variability, e.g., to eliminate data deemed to be unreliable; novel QT-clustering; Sammon maps
Resolver	Rosetta	Enterprise product

Commercial banquets

Enterprise products provide an integrated multi-course banquet – consisting of tools and a central database -- to feed an entire research organization. Several of the vendors go further and offer a complete meal plan of software tools for other areas of bioinformatics. These products are great if you like the cuisine!

The market leaders in this category are GeneData’s Expressionist (http://www.genedata.com/) and Rosetta’s Resolver (http://www.rosettabio.com/). Resolver was one of the first commercial products to cook with statistics. The product calculates error estimates and propagates these through the analysis. The two largest bioinformatics software vendors, LION Bioscience and Informax, also have products in this category: ArraySCOUT (http://www.lionbioscience.com/) and GenoMax Gene Expression Module (http://www.informaxinc.com/), respectively. A fascinating new product is GeneTraffic from Iobion Informatics (http://www.iobion.com/). GeneTraffic is a network appliance that runs on dedicated, inexpensive Linux computers.

Full course academic dinners for one

Now for the academic meals.

BRB ArrayTools, Simon Rich, NCI, http://linus.nci.nih.gov/BRB-ArrayTools.html/

Excel plug-in; statistical methods for validating clusters; novel classification method; multidimensional scaling

Cluster/Treeview, Michael Eisen, LBNL, http://rana.lbl.gov/

A market leader that pioneered clustering and other aspects of microarray analysis. Its data format is a de facto standard. It has no unique features, because everyone has copied it!

MAExplorer, the Laboratory of Experimental and Computational Biology, National Cancer Institute, http://www.lecb.ncifcrf.gov/mae/

Java program that can run as standalone application or applet.

TIGR MultipleExperimentViewer (TMEV), The Institute for Genomic Research (TIGR), http://www.tigr.org/softlab/

Java application.

XCluster, Gavin Sherlock at Stanford, http://genome-www.stanford.edu/~sherlock/cluster.html

Another pioneering academic program, similar to Cluster, but runs on Unix and Linux.

Commercial nibbles

The next group of snacks offer unique features for specific problems. Except as noted, are all desktop products.

ArrayStat, Imaging Research, http://imaging.brocku.ca/products/

Robust statistical methods to estimate measurement error

BioinformatiXEngine, Xpogen, http://www.xpogen.com/

Web-based product intended for use on intranet. Novel clustering method based on relevance networks; modular architecture for plug-in extensions

OmniViz Pro, OmniViz, http://www.omniviz.com/

Impressive collection of novel visualization and dimensional reduction methods.

Visual Gene, Visipoint, http://www.visipoint.fi/

Uses self organization maps for analysis and visualization, in contrast to most products that use SOMs only for clustering

Bring on the spice

The real hot stuff are academic dishes that push the frontiers of microarray analysis. This software is not for the faint of heart. Some programs are command line utilities, and many others are code libraries or subroutines. A few have Web versions, but usually these are just a demos that offer a quick taste. Much of this software is open source, some of which is available from the GeneX project (http://genex.ncgr.org/) at the National Center for Genome Research (NCGR); GeneX also operates a Web site where these tools can be tried out.

Several of the programs implement versions of a technique called borrowing power described in the Sidebar.

BCLUST, Hongyu Zhao at Yale University School of Medicine, http://bioinformatics.med.yale.edu/

Statistical method for validating clusters using bootstrapping.

CLEAVER (Classification of Expression Arrays), Russ Altman at Stanford University, http://classify.stanford.edu/

Web server that provides k-means clustering, discriminant analysis, and PCA

CLICK, Ron Shamir and Roded Sharan, at Tel Aviv University, http://www.math.tau.ac.il/~roded/click.html

Novel clustering algorithm that uses graph-theoretic and statistical techniques.

CLUSFAVOR, Leif Peterson at Baylor, http://mbcr.bcm.tmc.edu/genepi/

Bayesian methods for normalization; factor analysis (similar to PCA).

CyberT, Tony Long, at University of California, Irvine, http://genebox.ncgr.org/genex/cybert/

Part of GeneX. Borrows power and then uses a Bayesian model to assess the significance of expression changes.

GEDA: Gene Expression Data Analysis, Christina Kendziorski, Wisconsin, http://www.biostat.wisc.edu/geda/eba.html

A highly referenced program that also has a Web version and can be accessed via email. Borrows power and then uses a Bayesian model to assess the significance of expression changes.

Kimono: K-means Integrated Models for Oligonucleotide Arrays, Ian Holmes, now at the Berkeley Drosophila Genome Project, http://whitefly.lbl.gov/~ihh/kimono/

Jointly clusters promoter sequences and expression profiles to find promoters that regulate various genes.

MA-ANOVA programs for microarray data, Gary Churchill at the Jackson Laboratory.

Implements pioneering ANOVA error model that handles many kinds of measurement errors.

microarray.zip, Brian S. Yandell at the University of Wisconsin, http://www.stat.wisc.edu/~yandell/statgen/tr1031.html

Borrows power and uses results to improve measurements of low-abundance transcripts.

PaGE, Christian J. Stoeckert at the Penn Center for Bioinformatics, University of Pennsylvania, http://www.cbil.upenn.edu/PaGE/

Borrows power and then computes confidence levels for direction, but not magnitude, of expression change.

Plaid, Laura Lazzeroni and Art Owen at Stanford University, http://www-stat.stanford.edu/~owen/plaid/

Implements new “fuzzy” clustering method that clusters genes and samples simultaneously. Not open source.

RCluster, Karen Schlauch of the National Center for Genome Research, http://genex.ncgr.org/genex/rcluster/help.html

Part of GeneX. Implements several standard clustering methods, and statistical method for validating clusters using bootstrapping..

SAM: Significance Analysis of Microarrays, Rob Tibshirani of Stanford University, http://www-stat.stanford.edu/~tibs/

Excel plug-in that correlates gene expression data with clinical parameters.

SMA: Statistics for Microarray Analysis, Terry Speed at the University of California, Berkeley, http://www.stat.berkeley.edu/users/terry/zarray/Software/smacode.html

A very influential suite of programs, providing basic microarray statistical routines. Also provides normalization functions that correct dye bias and print tip effects.

SVDMAN: Singular Value Decomposition Microarray Analysis, Michael Wall, Los Alamos National Laboratory, http://public.lanl.gov/mewall/svdman/

Uses singular value decomposition (similar to PCA) to partially cluster genes; also calculates confidence measures for clusters.

VERA: Variability and Error Assessment & SAM: Significance of Array Measurement, from Trey Ideker at the Institute for Systems Biology, http://www.systemsbiology.org/VERAandSAM/?id=yvfw4

A pair of programs for assessing significance of expression changes using statistical error models.

Last updated January 29, 2003 by Nat Goodman