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This bugfix release addresses several issues reported by our users. The MeV team recommends that all MeV users upgrade to this release.

Fixes in v4.5.1

• Data matrices received as a gaggle broadcast with data type “intensity” are now loaded into the correct internal data structure.

• When data is cleared using File->Clear Data, the Sample and Gene cluster managers are now reset properly so they will reappear on loading of new data.

• GSEA Experiment viewer for individual gene sets now displays the correct genes.

• Updated SupportFileDefinition to check for appropriate suffix in filename when attempting to match files in the repository.

• Missing header for SOTA and SOM modules is fixed.

• Mislabeling in NMF’s consensus viewer is resolved.

• Mislabeled group numbers in LIMMA is fixed.

• Bug where EASE and BN dialogs would clear the list of arrays supported for the default-selected organism has been fixed.

As of v4.5, MeV for Mac OS requires R v2.9, and will not function properly if another version is installed. This means that any MeV for Mac users that have installed R v2.10 will find that MEV is unable to run the LIMMA module, as it depends on R. When version 2.10.1 of R is released (currently scheduled for Decepber 14th by the R project) we will be assembling a version of MeV that will work with R v2.10. We will also be providing an upgrade script that will allow previous installations of MeV to work with R v2.10.x.

The MeV development team is proud to announce that MeV v4.5 is now available for download. This release includes many new features and improvements to several existing systems, including state-saving and the annotation model.

Full Release Notes

R project: an under-the-hood improvement that allows MeV to use pre-built R libraries

New module: Linear Models for Microarray Data (LIMMA)

New module: Non-Negative Matrix Factorization (NMF)

A re-write of the GSEA module

A new feature in the EASE module, Nested EASE

Venn diagram displays of gene cluster membership

Many bugfixes and minor enhancements

Hierarchical Clustering display of a K-means generated cluster. Samples are color-coded by disease state and cancer subtype.MeV's strength lies in its easy user-interface coupled with a powerful suite of statistical tools.

• Load a variety of data types, such as expression, SNP, exon, PPI and copy number data
• Test for differential expression, template matching, and functional enrichment of groups of features.
• Group and label features with color-coded tags and track those features through different analyses.
• Automatically download annotation data for arrays made by many manufacturers, such as Affymetrix, Illumina and Agilent.

MeV is capable of sending and receiving expression and annotation data within the Gaggle framework of bioinformatics applications. As of v4.5, MeV will broadcast certain pre-defined metadata tags in addition to the matrix, namelist and network data. It will also look for these tags when receiving broadcasts.

MeV is capable of loading genomic data from many different types of files. Affymetrix, Agilent, Illumina, GenePix and others are availble.  MeV also supports several platform-independent file formats such as TDMS, MAGE-TAB and GEO.

The Tab-Delimited Multiple Sample (TDMS) file loader.Tab-delimited Multiple Sample files

Download an example file

Loaded with the TDMS file loader. This file format is a flexible, vendor-independent format where each row contains a record for one gene, with samples arranged by column. Any number of gene or sample annotation rows are allowed. MeV will attempt to "guess" which rows and columns contain annotation and will color-code them accordingly. Please verify that MeV has guessed well. If it has not, click to select the upper-leftmost cell that contains expression data. MeV will re-color the cells to reflect your selections. Gene annotations are colored blue/purple, sample annotations in blue-green, and sample annotation headers are in yellow. Expression values are striped in blue and white.

What is GSEA?

Gene Set Enrichment Analysis (GSEA) is a computational method that determines whether an a priori defined set of genes shows statistically significant, concordant differences between two biological states (e.g. phenotypes).

MeV has supported the Gaggle framework since September 2008 (MeV v4.2). Gaggle is a powerful communications system that allows connects supported programs (geese) to seamlessly transmit data to one another, without the need for intermediate flat files. MeV can use the Gaggle to send and receive data with other systems biology platforms, such as R, Cytoscape, and various web databases.

MultiExperiment ViewerMeV is a desktop application for the analysis, visualization and data-mining of large-scale genomic data. It is a versatile microarray tool, incorporating sophisticated algorithms for clustering, visualization, classification, statistical analysis and biological theme discovery. Thousands of biologists have downloaded and used MeV to examine their data with an easy-to-use, graphical interface.