MeV v4.8.1

December 16, 2011

Bug Fixes

• nEASE table viewer link shortcuts work properly.
• Handles error when saving large heatmaps from Experiment Viewer.
• Analysis options are customized to include data-specific options.

MeV v4.8

November 18, 2011

New CLValid Module
A new module for cluster validation, CLVALID, has been added to MeV’s existing clustering tools.  CLVALID uses the R package "clValid" to compare the relative properties of 10 different clustering methods across a several different numbers of clusters. This module aims to help choose a method that is most compact, well-separated, connected, and stable. It also optionally makes use of bioconductor annotation packages to biologically validate the results.

New Annotation Support Files
The MeV team has built a new pipeline for producing the annotation files that are used to support modules like EASE and to display chromosomal location information and GO terms in the various gene table views throughout MeV. The new annotations are collected from Bioconductor v2.6, and are more complete than before. The files also include many new arrays, such as Affymetrix's Exon (ST) arrays.

Complete list of supported arrays

In the future, we will be able to easily add new arrays as the Bioconductor team releases them, and to easily update these annotations when a new Bioconductor version is integrated into MeV. As a rule, MeV will provide annotation from the version of Bioconductor that is supported by MeV's currently-supported version of R. Currently, that version is R v2.11 and Bioconductor v2.6. This coordination is to ensure that annotation used internally by R modules is consistent with any annotation MeV displays.

MeV v4.7.3

July 15, 2011

New Features

* The EASE module has been re-enabled for use with RNA-Seq data.

MeV v4.7.2

July 11, 2011

New Features

* Custom annotation loading for RNA-Seq data.

Bugfixes

* Restored the MeV.exe icon.

* Fixed the broken link to HBGB Genome Browser.

* Fixed a Mac-specific bug in the GOSeq module that prevents the module from running.

* Fixed bug for zero-variance genes using Pearson Correlation.

* Enabled top-panel resizing.

Other Changes

* The GOSeq Module has been moved to the Meta Analysis toolbar.

MeV v4.7.1

May 19, 2011

Bugfixes

* A few new validation checks to RNA-Seq file loader

* Fixed a bug that showed up if the input RANseq file was incomplete.

MeV v4.7

May 16, 2011

New RNA-Seq Features

MeV is now capable of loading and analyzing RNA-Seq data.

New File Loader
MeV can now load summarized RNASeq data from a simple, tab-delimited file format. This format is fully described in the appendix of the MeV user manual. The loader can load count data, RPKM or FPKM, or combinations of the two data types.

GOSeq: GO term enrichment detection for RNASeq data (Young, et al, 2010).
GOSEQ is a technique for identifying differentially expressed sets of genes, such as GO terms while accounting for the biases inherent to sequencing data.

EdgeR: differential expression analysis of digital gene expression data  (Robinson et al., 2010).
EdgeR is a Bioconductor software package for examining differential expression of replicated count data. An over-dispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of over-dispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated.

DESeq: Digital gene expresion analysis based on the negative binomial distribution  (Anders and Huber, 2010).
The BioC package DESeq provides a powerful tool to estimate the variance in count data and test for differential expression. It can estimate variance-mean dependence in count data from high-throughput sequencing assays and test for differential expression based on a model using the negative binomial distribution.

DGESeq: An R package to identify differentially expressed genes from RNA-Seq data  (L. Wang et al., 2010).
Identify differentially expressed genes from RNA-seq data. RNA sequencing is modeled as a random sampling process, in which each read is sampled independently and uniformly from every possible nucleotide in the sample. Under this assumption the number of reads coming from a gene (or transcript isoform) follows a binomial distribution (and could be approximated by a Poisson distribution). Based on this statistical model, Fisher’s exact test, likelihood ratio test and 2 other methods were proposed to identify differentially expressed genes.

Other New Features

Expression Graphs
In the Sample Cluster Manager, a new graph view is available, called Expression Graphs. This option allows the creation of boxplots and bar charts of individual genes or groups of genes, compared across sample groups.

Major updates to GSEA user interface
Simpler, easier UI allows more intuitive use of the Gene Set Enrichment Module.  Several calculation improvements and algorithm fixes have been applied to the newest release.

Import File feature added to List Import option in Cluster Manager
Clusters can now be created by loading a file containing a gene list.

New MeV User Manual
We have updated the MEV manual to a web-based format. Now, the help buttons within MeV link directly to a local copy of the user manual. Full information about the linked module is available immediately.

R 2.11
All R-dependent MeV functions call R version 2.11 by default. 

R package auto-download
MeV now automatically downloads R support packages after installation. The packages no longer have to be included in the initial download.

"Set as Data Source" Option Removed
We have removed a feature of the MeV result tree. Previously, the right-click context menu for cluster nodes in the result tree contained an option called "Set as Data Source". Choosing this option would cause the genes in the selected cluster to be treated as the entire MeV source dataset. All subsequent modules and filters run in MeV would be applied only to that subset of the data. We have removed this option because it was redundant and not particularly stable. Users who want to work with only a subset of their data have two options, both of which are more robust and fit better with the MeV data metaphor.
Option 1. Launch as new Viewer:
Create a gene or sample cluster from the result node of interest, by right-clicking on the viewer window and choosing Store Entire Cluster.
Go to the appropriate Cluster Manager (Gene Cluster Manager or Sample Cluster Manager, in the result tree under the node Cluster Manager).
Right-click on the cluster you just created, and choose Open/Launch -> Launch MeV Session. This will create a new Multiple Array Viewer containing only the data from the selected cluster. All analyses executed in this MAV will only apply to the selected data.

Option 2. Select data cluster during module execution
Create a gene or sample cluster from the result node of interest, by right-clicking on the viewer window and choosing Store Entire Cluster.
In the module execution dialog, select that cluster from the cluster selection panel. The module will apply its analysis to only the genes/samples in that cluster.

Bugfixes

* The viewers for single-color Affymetrix data now handle the heatmap display of zero values properly
* GSEA bugfixes
* Missing HCL header bug resolved.
* NMF Plotviewer error fixed.
* The GSEA p-value graph viewer now saves and restores state correctly.
* The Windows version of MeV v4.6.2 shipped without a MeV executable. While the program was still usable with the tmev.bat file, it was annoying the MeV.exe file has been restored.
* Agilent file loader fixed for loading of 1-color data.
* Agilent file loader fixed for loading of multiple samples simultaneously.

MeV v4.6.2 bugfix release

November 23, 2010

• Hierarchical Clustering header trees now appear when HCL is auto-launched by another module.
• RHook default/cached prop loading and remote access and other Exception handling and forwarding.
• 64 Bit Java bypass added to TMEV.bat launch file.
• BN progress bar now updates properly when the module is run more than once.
• Added placeholder file to data/BN_files/kegg directory to ensure the folders always appear regardless of which unzip utility is used.
• Removed Cluster Analysis option for modules when Clusters have not been created.
• Several small fixes pertaining to loading and unloading data.
• Default setting changed in SAM when using R.
• Error handling in Non-Negative Matrix Factorization improved, Plotviewer label issues resolved.

MeV v4.6.1 bugfix release

August 12, 2010

• Errors fixed in selection of EASE file system.
• Default distance metric for HCL run after LIMMA is now Pearson Correlation.
• Loading annotation after expression data now stores organism and array data.
• Manually loaded of annotation files are now correctly flagged.
• Search function disabled when no data is loaded.
• MAGE-TAB file loader displays and processes files in preview window correctly.
• MeV manual link now redirects correctly.
• Toolbar resets to disabled correctly on clear data command.
• SAM default settings updated.
• Improvement to display of MeV banner in progress dialogs.
• Hierarchical tree view is no longer displayed for all nEASE sub-results.
• Improvement to "Save EASE table" menu options in EASE and nEASE results.
• State-saving improvements to EASE module.
• More compact, simpler nEASE results.
• Improvements to the Minet documentation.
• UI tweaks in BN dialog.

MeV v4.6 release

July 2, 2010

MeV v4.6 includes a large number of new features, including several new modules and large improvements to existing favorites.

Major additions

Attract Module

The Attract algorithm identifies the core gene expression modules that are differentially activated between cell types or different sample groups, and elucidates the set of expression profiles which describe the range of transcriptional behavior within each module.

Global Ancova Module

A technique for identifying differentially expressed gene sets based off of the calculation of an F-test between groups of samples. Analyses are typically run in a two-class format but may also be applied to additional groups.  Global Ancova fits linear models to the data and compares them using the extra sum of squares principle. The result table includes p-values, permutation p-values and asymptotic p-values.

Minet Module

For a given dataset, minet infers the network in two steps. First, the mutual information between all pairs of variables in dataset is computed according to the estimator argument. Then the algorithm given by method considers the estimated mutual information in order to build the network.

SURV Module

The Survival (SURV) module contains two functions for the analysis of censored survival data. The first is a basic comparison of the survival curves of two groups of samples. The second feature of the module is the creation of a cox proportional hazards model based on the loaded gene expression data, using survival time as the reporting value.

EASE UI Rewrite

The EASE UI is simpler and easier to use now.

Updates to the BN module

Network Seed allows the user is to provide a file representing a network. Network seed can be used in one of the three ways:

1.    Using the user network seed alone and bypassing literature based network seeding altogether.

2.    Using the user network seed along with Literature mining seed.

3.    User provided network is used as a complete network and the network structure is not learned, only the Conditional Probability Tables (CPTs) associated with the network is learned for downstream exploration.

A node by the name of “CLASS” shows up in the network which captures the effect of sample groups on the network. Once the network is displayed the “CLASS” node behaves and can be treated as any other node in the network.

Updates to the GSEA module

Two new viewers are now provided, including a p-value graph viewer and a gene set membership plot. Gene sets can now be automatically downloaded from GeneSigDB and MSigDb.

Updates to the SAM module

A new addition to the SAM module integrates RHook to make a newer version of SAM available to users.  MeV’s SAM now makes use of serially correlated time-course data in the exploration of statistically significant gene expression.

Hierarchical Clustering Trees

MeV now displays hierarchical trees with meaningful and proportional node heights along with an optional scale tailored to the chosen distance metric used in constructing the tree.

Other Changes

Rama significance testing for spotted array data has been disabled, along with the Bridge module. These functions never worked properly and have been unsupported for some time. They are still available in older versions of MeV. The most recent version of MeV that contains these features is MeV v4.5.1.

We have also retired the Single Array Viewer.

Minor Additions

• FDR calculation is displayed in the TTEST module.
• Annotation can now be auto-loaded by MeV after expression data has already been loaded.
• Agilent file loader has been updated to work with the latest file formats.
• Pearson correlation coefficients are now the default distance metric for most analysis modules.

Known Issues

MeV's R-driven modules (LIMMA, Attract, Surv, SAM, etc) will not be accessible when MeV is launched via Java Webstart. This is due to difficulty with including the required R libraries with the Webstart download. Until we identify a solution to this problem, the workaround is to download MeV and run it locally.

MeV v4.5.1 bugfix release

December 17, 2009

Bugfixes

• 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.

MeV v4.5 release

November 13, 2009

MeV is now provided under the conditions of the Artistic License v2.0. It was previously released under the Artistic License v1.0.

Major additions

R project

MeV has integrated a R (CRAN) hook where by R functions and libraries can be called within the Java instance using shared libraries. Mev developed a library around the JRI (rForge) API which uses JNI, to make this happen. The user in *most cases would not have to set up or configure anything to run R dependent modules and it should be completely transparent. Please also note that MeV does not produce a command line interface to run R commands. This integration was done to leverage the R environment where many algorithms are readily available and does not need to be re-implemented in Java. MeV would use R internally as appropriate and the user should not expect to see any change in behavior of MeV.

**Notes**

1. On Windows and Mac OS X (10.6) where the default JVM is 64 Bit, the user will be thrown a warning to change to 32 bit JVM. This is required because the API is not ready for 64 Bit environment yet and we are working on a solution. However once the default JVM is set to 32 Bit, the R dependent modules would run. To help the users setup the 32 bit JVM we will be providing help and assistance to do the following:
   a. On Windows: To install 32 Bit Java if not already installed and to modify the launch script TMEV.sh to point to it.
   b. On Mac OS X we would provide instructions on how to set up 32bit JVM as default. The default in OS X 10.6 (Snow leopard is 64 bit).
2. On Mac we expect the user to have R 2.9.x universal binary installed in the Application Framework. We do not expect such for Windows and Linux systems.
3. Please use MeV SourceForge page for submitting queries, questions and issues. We have set up a page for this particular JVM issue named R MeV Integration, JVM issues.

LIMMA Module

Linear Models for Microarray Data, a statistical framework for the analysis of gene expression microarray data, using linear models for analyzing designed experiments and the assessment of differential expression, was implemented as a new module into MeV.

This module was implemented using the R framework described above and without writing a single line of Java code for the numerical analysis.

NMF Module

Non-negative Matrix Factorization, a technique which makes use of an algorithm based on decomposition by parts of an extensive data matrix into a small number of relevant metagenes, has been implemented as a new module into MeV. NMF’s ability to identify expression patterns and make class discoveries has been shown to able to have greater robustness over popular clustering techniques such as HCL and SOM.

GSEA UI Rewrite

GSEA user interface has undergone a major revamp in this release. The slick new UI is more intuitive and user friendly. Two additional viewers “Leading Edge Graph” --displaying the subset of genes contributing most to the gene set level metric and “Test Statistic Graph” --showing how genes within a gene set contribute to the overall gene-set-level metric have been also been added.

Nested EASE

Nested EASE (nEASE) is an extension of the EASE module. The nEASE algorithm includes a second, sub-level, iterative Fisher’s Exact Test on significantly enriched GO terms identified in a first-level EASE analysis. This sub-classification approach provides increased sensitivity for detecting enriched GO terms and thus affords a deeper understanding of possible mechanisms underlying a given condition under study.

A tutorial describing how to use the new nested EASE feature is available on the MeV website, mev.tm4.org.

Venn Diagrams

A new addition to the Cluster Manager allows users to viewer relationships between two or three clusters of samples or genes in the form of a Venn Diagram. This interactive addition also displays a p-value for two-cluster diagrams representing the likelihood of the given overlap occurring in a random sampling of identical size.

Minor Additions

• EASE analysis results now save much more efficiently.
• Most File Choosers now default to the last-used data directory.
• The Gaggle interface now supports metadata as part of its broadcast. See the Gaggle page on the MeV website for details.
• Chromosomal location annotation will now be rendered as links to the UCSC Genome browser in MeV’s standard table views.
• Expression data can be viewed in the Institute for Systems Biology’s Genome Browser, through a new right-click menu option in many result viewers. This feature requires that chromosomal location information has been loaded.
• Enabled multiple selecting for automatic cluster creation.

Bugfixes

• USC no longer throws an error when a result set of size zero is returned.
• PCA, COA and TRN modules can now save and load analysis results regardless of whether Java3D is installed.
• The title bar in error message dialogs read an appropriate “Algorithm Exception” instead of “Out of Memory Exception”.
• A bug in EASE prevented the use of the Trim Options checkbox. This has been fixed.
• The Save Analysis progress dialog now responds to the Cancel and close window buttons.
• The state-saving functions of the SAM and TTEST modules have been re-written for greater speed and stability.
• Cluster Manager heatmaps and expression viewers no longer fail when clusters are created on data that has been filtered.
• Rank Products no longer misreports p-values for datasets larger than 40 samples.
• Inverted “Show Color” checkbox in Cluster Manager is fixed.
• Fixed null pointer exception in Original Data viewer when launching new session.
• PPI seeding in BN module was not reading the ppi file correctly leading to missed interactions. It is now been fixed.
• BN module would not run on Windows if install path had spaces in them (e.g. ‘Documents Settings’, ‘Program Files’ etc). This problem has been fixed.
• GEO GDS and Series Matrix file loaders have Affymetrix as default selection
• Null pointer exception no longer thrown on clicking heat map cells after loading GEO files
• MeV file loader loads data even if annotations are missing

Known Issues

• The LIMMA module will not be accessible when MeV is launched via Java Webstart. This is due to difficulty with including the required R libraries with the Webstart download. We expect to address this problem for the next major MeV release in May. Until then, the workaround is to download MeV and run it from the local desktop.

MeV v4.4.1 release

June 30, 2009

• State-saving optimizations in the TTEST and SAM modules.
• Fixed problem in Rank Products analysis that prevented two-class unpaired from running more than 40 samples.
• Fixed GUI issues with Cluster Manager.
• Changed display of node heights for Hierarchical Clustering.
• Fixed cluster storing dialog box bug- dialog box could not be cancelled.
• When launched via Java Webstart, MeV now loads data with correct row indexing
• Table links to external GO databases now support multiple terms.
• Chromosomal location end-coordinates are now loaded fully, rather than with a truncated last digit
• Annotation fields are now re-loaded in the same order they were saved in.
• Hypergeometrc distribution is now calculated correctly.

MeV v4.4 release

May 28, 2009

Major additions

BN Predict Cytoscape Plugin as MeV Extension
As an extension to the already published BN method, we have developed a Cytoscape plug-in that does predictive modeling of the BN described above. For each network loaded with an associated conditional probability table (CPT), the plugin calculates probabilities of expression (bin/state probabilities) for each network node.  Documentation can be found at http://www.tm4.org/BnPredict

MAGE-TAB File Loader
The MeV MAGE-TAB file loader allows a user to select and load expression data from MAGE-TAB (version 1.0) formatted files.  The MAGE-TAB specification can be found at http://www.mged.org/mage-tab/spec1.0.html. This format includes files that describe an experimental series, the samples used in the experimental series, optionally the array design of the microarray chips employed in the study, and a data matrix file containing gene expression data values. 

New Cluster Manager Tool
The new Cluster Manager allows users to create, visualize, and manipulate clusters and includes numerous new features and enhancements to the previously existing clustering system. 

RP Two-Class Unpaired and Two-Class Paired experimental designs.
MeV now supports the use of the Rank Products module for two-class experimental designs.  Previously, only one-class functionality existed for this method.  As with most other modules, the Cluster Selector feature is compatible with this addition.

Automated Linking to Online Databases
MeV can now automatically link to online databases, such as NCBI, AmiGO and others. When gene annotation is loaded using MeV’s new annotation file loader (under the “annotation” subsection of the file loader) links will automatically be generated from some of these annotations to the appropriate online resource. The specific resources used can be customized for individual users, and expanded to other annotation types not loaded by the MeV automated annotation loader. See the MeV manual for more details.

Annotation Support for New Arrays
The MeV team has developed a new tool for creating the annotation files everyone depends on for their data analysis. We will be providing support for many new arrays in the future. These arrays will be available from the ftp site as well as via direct download from within MeV’s file loaders. We have also generated new EASE datafiles that provide better, more complete EASE results.

Java WebStart Improvements
MeV can now accept a wider range of command-line arguments, improving its usefulness as a webstarted application. Instructions for getting started with webstart are available at http://www.tm4.org/mev_webstart.html

Assignment File Saving Synchronization
MeV’s assignment file system has been synchronized between virtually all modules and experimental designs.  Files saved in each module are compatible with the loaders of other modules.  Additionally, the files themselves are saved in a human-readable format that can be opened, read and edited by users.  See the MeV manual for more information.

Minor Additions

• The entire dataset is now displayed in each of the standard viewers – heatmap, expression graph, centroid and table view – immediately upon loading.
• New Proportional branch length feature added to Hierarchical Clustering.
• A new p-value viewer has been added to GSEA.
• Node rotation feature added to Hierarchical Clustering.
• Sample reordering feature added to heatmap viewers.
• Five standard viewers added to “Original Data” immediately after data is loaded.
• Cluster Selection feature added to SAM module for “One-Class”, “Two-class unpaired”, and “Multi-class”.
• New feature allowing multiple clustering by binning.
• A new interface to the annotation downloader makes annotation loading easier.
• Excel-style copying of data from result tables is now available.
• Behind-the-scenes work on state-saving makes it much faster and more robust.

Bugfixes

• Fixed missing columns in One-Condition BETR output.
• Improved handling of missing data in BETR analysis.
• Tweaks to make MeV behave better without an internet connection.
• SAM no longer tags genes with zero variance as “Negative Significant”.

Known Issues

Results returned by the RP module in two-class unpaired analysis can become uninformative for datasets with greater than about 40 samples. When the number of samples is too high, the module will report RP values of zero for many of the genes. We recommend using the SAM module instead of RP for these datasets, as RP was designed to work best with small numbers of samples. We plan to have this issue addressed for the next MeV release. 

MeV v4.3.02

February 05, 2009

• ResourceManager downloads via HTTP now have a more reasonable timeout setting.
• The ResourceManager will now download files provided by servlets.
• The EASE initialization dialog box would not initialize when custom annotation files were loaded.
• The color-coding of result nodes in TEASE were not set properly.
• Tav file loader was not loading preferences files. This has been fixed.
• Launching new session from a cluster bug repaired.

MeV v4.3.01

December 02, 2008
We were notified by the Broad Institute that we were directly linking to their MSigDB database without following their licensing procedure. MeV v4.3.01 was built with the automated dataset downloading feature in GSEA disabled. A future release will re-enable this automated downloading, if possible.

MeV v4.3 release

December 01, 2008

Major additions

Gene Set Enrichment Analysis (GSEA) Module
GSEA has been developed recently to capture changes in the expression of pre-defined sets of genes. GSEA implementation in MeV is based on the paper by Jiang and Gentleman. From Jiang, Z., Gentleman, R., 2007. Extensions to gene set enrichment analysis. Bioinformatics. 23(3):306-13.
Instructions: http://www.tm4.org/mev/gsea/gseadoc.html

Bayesian Estimation of Temporal Regulation (BETR) Module
BETR is a flexible linear random-effects modeling framework that takes into account correlations between samples and sampling times.   It is a new empirical Bayes approach that has shown improved results over currently used time-course techniques. From Aryee, M., J. Gutierrez-Pabello, I. Kramnik, T., Maiti, J. Quackenbush 2008. An Improved Empirical Bayes Approach to Estimating Differential Expression in Microarray Time-Course Data:  BETR (Bayesian Estimation of Temporal Regulation). Submitted.

Rank Products Analysis (RP) Module
Rank Products is a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments.   RP provides a fast and efficient technique that is highly resistant to noise and can reduce the number of replicates needed for reliable results. From Breitling, R., P. Armengaud, A. Amtmann, P. Herzyk 2004. Rank products: a simple, yet powerful, new method to detect differentially regulated genes in replicated microarray experiments.  Federation of European Biochemical Societies. 83-92.

Support Data File Downloads
Many of the components of MeV require data in addition to the usual expression and annotation data. Previously, users of MeV have had to provide the data files required by the various modules. This could be a hassle, since downloading, validating and keeping track of these files was a chore. A new feature of MeV allows modules to automatically download the support files they need, provided they are freely available online. These files are cached on the local file system so that MeV can continue to function if an internet connection is not available.

Currently, these functions are used by the modules EASE, TEASE, GSEA and BN. The annotation selection components of MeV's file loaders now also use these functions to download annotation for selected arrays.

Minor Additions

• MeV can now accept the broadcasts of name lists from the Gaggle. For more information on Gaggle, see the Gaggle website (http://gaggle.systemsbiology.net/).
• MeV can now be launched via Java WebStart, with a pre-selected data file loaded. See the MeV page on the TM4 Website for details (http://mev.tm4.org/).
• Many display options, such as element size and color range, are now stored between MeV sessions.
• The TDMS file loader now allows users to select between two-color and single-color array types, rather than "two-color" and "affy or single-color". 
• File loaders containing a tabular display of the annotation file to be loaded will color-code the cells of the table. The colors represent the data types the loader will assign to each cell.
• File loaders will attempt to guess the first expression value of a data file, before applying the above color-coding. This guess may be wrong, so users should verify the selection.
• Parser for annotation files provided by Affymetrix  integrated with the TDMS file loader
• The Cluster Utilities now allows the option of creating clusters by binning values together.

Bugfixes

• The Search functions will now search annotations loaded with the new annotation model.
• The Cluster Import tool will now allow matching of annotation values from the new annotation model.
• Gaggle broadcasts from R are now correctly received.
• Spot Information Box displays annotations already present in the expression file.

MeV v4.2.02 bugfix release

September 30th, 2008

Bugfixes

• Mac and Linux: "Save" dialogs allow input of a new filename.
• Improved sample datafiles are included in the download.
• Added auto color selection to cluster operations selection dialog.
• Fixed One-Way ANOVA saving bug.
• Improved cluster operations in Cluster Manager.
• Cytoscape can now be launched via Java WebStart from the BN module in the absence of an internet connection, provided that the application has been cached. The application is launched using cached web properties, if Cytoscape webstart jars exist from previous downloads.
• Changed default number of BN bootstrap iterations to 20.

MeV v4.2.01 bugfix release

August 20th, 2008

Bugfixes

• When the data source was changed using the "Set as Datasource" option in the result tree, the Gaggle broadcast functions would send the wrong data, or occasionally throw an exception. This has now been fixed.
• Initialization dialog of t-Test and ANOVA required an existing cluster repository to run analyses, even when using button selection.  This has now been fixed.
• The Cluster Selection tool failed when clusters had been previously selected using different clustering sources.  This has now been fixed.

New Bayesian Networks & Literature Mining Network Format for Cytoscape

• Uses XGMML format for creating graphs when gene annotation is available.
• Uses MeV Annotation model to provide node annotation as attributes in XGMML format.
• When gene annotation is not available, it resorts to SIF format for Cytoscape networks.

MeV v4.2 release

August 01, 2008

Major additions

Main Toolbar Redesign
A new main toolbar utilizing drop-down menus was designed to accommodate a growing number of MeV functions and replaces a long list of module buttons.  Modules are now grouped into seven categories on the main toolbar – Clustering, Statistics, Classification, Data Reduction, Meta Analysis, Visualization and Miscellaneous.


Bayesian Networks & Literature Mining Module Updates

• Addition of KEGG pathway based interactions as priors in generating complex interaction networks.
• Cytoscape is no longer included as part of the release to display networks. Instead Cystoscape is launched via Java Webstart and networks are loaded as files or as broadcasts through Gaggle.
• New viewers to display network files that are created.
• New viewers support right-click pop-up menu to launch Cytoscape with networks listed in the viewers. Earlier, once Cytoscape was closed the networks could not be viewed anymore.
• After state saving there was no way to view the networks in Cytoscape. Now that feature is added to launch Cytoscape from the viewers after state saved data is loaded back to MeV.
• Improved User Interface with lot of validtions.

• Improved error and exception handling for following situations:

  No interactions found.
  Too many interactions found.
  Cluster Size to big to support.
  Out of Memory Error.
  Miscellaneous previously un-handled exceptions.

Clustering interface improvements
New cluster creation tool, rework of cluster displays, multi-cluster display, cluster ordering

• Multi-cluster display and rearrangement
• Automatic cluster assignment based on annotation
• Cluster selection feature added to t-Test, ANOVA and 2-factor ANOVA
• Removal of dependence on config directory and included files
• New properties file storage

Gaggle Interface
MeV now implements the Gaggle interface. The Gaggle is a bioinformatics data-sharing protocol developed at the Institute for Systems Biology in Seattle, Washington. The purpose of the Gaggle is to allow the movement of biological data between applications on a desktop computer and between the desktop and various websites. More information about the Gaggle is available from the ISB Gaggle website, http://gaggle.systemsbiology.net/.

MeV can now accept broadcasts of data matrices from other Gaggle-enabled applications, and can broadcast expression matrices and gene lists from module result viewers, such as the expression viewer. Also, Gaggle is used by the Relevance Networks and Bayesian Networks modules to broadcast network data to Cytoscape.

Bug Fixes/Minor Additions

• Addition of full-length PCA calculation, in addition to current estimation
• HCL optimal leaf-node ordering
• Module nodes in the result tree can now be renamed
• NonpaR init dialogs open file choosers to the MeV data directory by default.
• NonpaR init dialog calls new HCL dialog instead of old one.
• PCA dialog now has "median" centering selected as default
• Refactoring of viewer classes
• Improvements to EASE initialization dialog
• Integration of EASE with new annotation model
• Default location set for browsing annotation files from the file loaders
• Cluster-creation dialog now auto-selects an unused color for new clusters
• HCL memory assessment tool predetermines user’s system capabilities and displays a warning if they do not have enough memory

MeV v4.1 release

Jan 18, 2008

Major additions

Annotation Model Update
MeV now uses a new annotation model. Users can now retrieve annotation for their affymetrix data from the publicly available Resourcerer database through MeV.

New File Loaders
MeV now supports two additional GEO file formats namely, GEO Series Matrix and GEO GDS. These additions can be found under MeV’s 'Load Data' menu.  The user interfaces of all the file loaders have been revamped.  See the MeV manual for more details. 

NonPaR
The NonpaR module in MeV consists of four nonparametric tests that can be used to analyze several common experimental designs.  The Wilcoxon Rank Sum, the Kruskal Test, the Mack-Skillings Test and the Fisher Exact Test were implemented as described in Nonparametric Statistical Methods  by Hollander and Wolfe.

Charm   
aCGH or Array Based Comparative Genomic Hybridization uses Cy5 (red) labeled tumor DNA and Cy3 (green) labeled normal DNA to determine gene copy number increases or losses on spotted cDNA or oligo arrays. The CGH module of MeV provides functionalities to load, visualize & analyze such datasets with a rich set of graphical tools & log ratio threshold based analysis algorithms. 

Bug Fixes/Minor Additions

• PCA module data-centering
• Change in the format of GEO files had caused two of the existing GEO file loaders to stop functioning. This has been fixed.
• Tab-delimited multiple-sample (TDMS) file loader now allows users to select the experiment platform (Affymetrix, Spotted arrays or other).  This solves the problem of MeV guessing the data platform based on the percentage of negative values, and scaling the colors based on the guess.
• Cluster color selection has been enhanced. Users can now choose colors from a specially-chosen accessible color palette. The colors in this palette can be distinguished by people with a wide range of color insensitivities.
• Various bug-fixes and enhancements

MeV v4.0 release

July 06, 2006

Major additions

 
RAMA: Robust Analysis of MicroArrays (MeV Manual Section 5.2)
RAMA can be found under the Adjust Data -> Replicate Analysis menu.  Use it for robust estimation of cDNA microarray intensities with replicates.  The package uses a Bayesian hierarchical model for the robust estimation.  Outliers are modeled explicitly using a t-distribution, and the model also addresses classical issues such as design effects, normalization, transformation, and nonconstant variance.
 
Analysis-Saving Rewrite
The analysis-saving functions of MeV have been rewritten and made more robust for the purposes of transferring saved analyses between computers.  Most of the changes are 'under the hood' and only of interest to developers.  However, users should keep in mind a few caveats:  
 
Because of the changes to the format of saved analysis files (*.anl files), MeV 4.0 cannot open saved analysis files created by MeV 3.1 and 4.0b.  In order to make saving analyses  seamless and efficient, we implemented a different mechanism for saving *.anl files, and have had to sacrifice backwards-compatibility with previous versions.  Future versions of MeV after v4.0 will be able to open analysis (*.anl) files created by MeV v4.0 and later.  
 
New File Loaders
File loaders for a variety of file formats have been added to MeV's 'Load Data' menu.  Some examples include GEO SOFT and dChip.  See the MeV manual for more details.  
 
CGH Viewer & Analyzer (MeV Manual Section 13)
aCGH or Array Based Comparative Genomic Hybridization uses Cy5 (red) labeled tumor DNA and Cy3 (green) labeled normal DNA to determine gene copy number increases or losses on spotted cDNA or oligo arrays. The CGH module of MeV provides functionalities to load, visualize & analyze such datasets with a rich set of graphical tools & log ratio threshold based analysis algorithms.  

New Module Development

 
Linear Expression Map (LEM) Module (MeV Manual Section 11.20)
The Linear Expression Map module produces an expression viewer which maps gene expression to chromosomal location.  Navigational aides and expression graphs allow one to visualize and track the expression of neighboring loci over the experimental conditions under study.
 
TEASE: Tree-EASE (MeV Manual Section 11.2)
TEASE is an improvement to the existing HCL module. Use it to find over-represented GO-term categories in each branch of a HCL tree.  
 
BRIDGE: Bayesian Robust Inference for Differential Gene Expression (MeV Manual Section 11.28)
The package fits a robust Bayesian hierarchical model for testing for differential expression.  This package is written in the R programming language and therefore requires access to an RServe server.  See the Appendix of the MeV manual for instructions in installing R and RServe.
 
USC:Uncorrelated Shrunken Centroids (MeV Manual Section 11.29)
USC is an integrated classification and feature selection algorithm applicable to microarray data with any number of classes.  The algorithm is motivated by the shrunken centroid (SC) algorithm (Tibshirani et al. 2002) with the following key modification: USC exploits the inter-dependence of genes by removing highly correlated genes. 
Bug Fixes/Minor Additions 

•  New Affymetrix-, Bioconductor- and GenePix- specific data filters are available under Adjust Data -> Data Filters.
•  choose File-> Customized Application Menubar to create a new MultipleArrayViewer window with only a subset of the available module buttons shown.
•  A direct link to the MeV manual is available under Help -> MeV Manual.
•  PubMed references found in Credits->Papers/Publications are now linked to the PubMed repository.
•  Two Factor ANOVA fix to handle possible machine precision rounding error.
•  Support Tree (ST) repair to restore selection of distance metric.
•  Template Matching (PTM) repair to handle cases where the cluster manager has clusters from more than one source algorithm.
•  EASE repair to handle European number formatting conventions.
•  EASE updates are now handled more robustly.
•  RN annotation mapping fix: previously, displayed annotation in RNViewer was incorrect if a filter had been applied to the data before running RN
•  PTM fix to handle problem when the cluster repository contained clusters from multiple algorithms.  Empty clusters are also handled correctly.