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Frequently Asked Questions
Q. I am trying to run TMEV in Linux. I have Java installed, but when I try to run the program, I get this error:
Failed to load Main-Class manifest attribute from TMEV.jar
A. Execute this command from the MEV root directory:
Q. How do I get TMEV to run on MacOSX?
A. Try double-clicking on the file called TMEV_MacOSX. If the program does not launch, you may have an older version of the program with a broken application file. You can download a new one from the website.
Q. When I try to launch TMEV in Windows, a black window opens and closes again very quickly.
A. There are two possibilities. The first one is that you do not have Java 1.6.x installed on your computer. You can download it from the Sun website. Run the installer and follow the prompts. For a simple installation, accept the defaults that are presented.
If Java v1.6.x is already installed, you probably have an older version of the TMEV.bat file. Changes in Java made for version 1.6.x made the older versions of the TMEV.bat file unuseable. Simply download a new version from the TM4 web site.
Q. When loading my data into a Multiple Array Viewer, I get the error
A. This error usually occurs when something is wrong with the input file format. Be sure the file is saved as tab-delimited text. Each item on a line should be separated from the others by only one tab character - be especially careful that there are no spaces next to the tabs and extra tab characters at the ends of the lines. Open your input data file in a program like Excel and check that none of the cells are empty. Fill empty cells with values like "0" or N/A, depending on the type of data in them.
Q. When I save a cluster I can't re-open it in the same Multiple Array Viewer window. I get an error like:
A. The preferences file you are using for your original data will not always work with the cluster data you have saved. You need to create a new preferences file which matches the format of the saved cluster file. See the MeV manual for a description of the preferences file format and instructions on how to make a custom preferences file.
Q. When I start TMEV, I get the following error message:
A. TMEV cannot find the file algorithms.jar. This file should be located in the TMEV/lib directory. If this file is missing, just download TMEV again and reinstall. If re-downloading doesn't help, the MeV zipfile is probably being corrupted when it is unzipped. Try another unzip utility.
Q. When I try to run the PCA module, I get an error like this:
A. The problem is likely with the version of the JRE Java3D that you are using. Try reinstalling or updating Java3D.
Q. Why are the Eigenvalues and Eigenvectors from PCAG (PCA from genes) and PCAE (PCA from experiments) the same?
A. To compute the principal components, the m Eigenvalues and their corresponding Eigenvectors are calculated from the (m x m) distance matrix using Singular Value Decomposition (SVD). m = number of experiments. n = number o genes. Genesis uses this method for both cases (genes AND experiments)! See also answer to question (3).
Q. PCAE (PCA from experiments): If I load a data set with 30 genes and 5 conditions, I expect to see 30 components with 30 dimensions each and not only 5 components. Furthermore, the Eigenvalues should be different from those that I get with PCAG (PCA form genes).
A. This is correct, and the straightforward implementation did exactly calculate the SVD of the (n x n) distance matrix. In this case we get the expected n components, where n is the number of genes. However, the disadvantage of this method is, that it is very computational intensive, since n is usually quite large.
And here is the trick. It is due to a mathematical tick possible to calculate the result using the small (m x m, m = number of experiments) distance matrix and than calculate with the SVD of this matrix back to the required solution (PCAE).
The results of this shortcut are the same as using the straightforward method, however since we are calculating the SVD also form the small matrix, you have of course the same Eigenvalues as calculating PCA from genes.
The gain is remarkable. Lets say we have 10000 genes and 100 experiments. In this case you have to calculate the SVD of a 100x100 distance matrix (5000 elements) instead of a 10000x10000 matrix (50.000.000 elements), or in other words: 1 sec instead of a couple of hours!
Q. When I load my affy data, most of the data in the main display is red. I have tried to adjust the ratio scale but it does not seem to help. Why is that?
A. The reason it is red is that affy data is positive and only uses the right side (black --> red side) of the gradient. You will need to rescale your data because of the magnitude of the values. What is important to note is that it takes a log base 2 of the intensity so when you adjust the color range in the display menu use about +-10 or 15 and your data should show as a nice gradient in the black to red range.
Q. Whenever I try to run the PCA, I am unable to view the results. I do have Java 3d installed in the right directory.
A. Make sure you have installed Java3D open GL version instead of Java3D DirectX version.
Q. How do I fix this Java.lang.outofmemory error?
A. Sometimes huge data sets can cause out of memory errors. For some algorithms (notably HCL), we get out of memory errors if the number of genes is larger than about 10,000.
If you are using a PC or a Linux machine, you can edit the TMEV.bat file (the file that you double-click on to start MeV) in a text editor and change the memory allocation. To this, find the text "java -Xmx512m" in the file, and replace 512 with 768 or 1024.
If you are using mac, click on Mev_MacOSX_2_2. Select "show package contents". Open 'contents' folder. Click on Info.plist. Open it in a text editor. You will see the following key and related string in the file:
Just change the 512 to 768 or 1024 and save the file.
Whatever platform you are using, be sure to allocate less memory to Java than the total amount of memory on your system. If you try to give Java 1024Mb of memory on a system that has only 512, MeV will not start.
Q. What are the hardware/software spec required to run MeV?
A. Supported Platforms: PC, Unix, Mac.
Q. When loading .mev and annotation files, the progress bar stalls. The black console window shows the error
A. Try checking the "Remove Annotation Quotes" box in the mev file loader. This option will remove any quotation marks that may have been added to your mev file by a certain popular spreadsheet program. These quotation marks can cause problems in MeV. If your version of MeV doesn't have this option, either upgrade to version 3.0 or higher, or edit your annotation file so that it contains no quotation marks in the Date field at the top. You will not be able to see these quotes if you open the file in the aforementioned spreadsheet program - use a text editor like Wordpad or Simpletext instead.
Q.In the EASE module, the results report something called "List Hits" and "Population Hits". What are these numbers and how do they differ?
A. The 4 columns related to list size, list hits, population hits, and population size are related to account for how many genes in the population are members or not members of the cluster and how many genes are members or not members of the annotated biological role. These numbers go toward building a 2x2 contingency matrix that captures this accounting.
In EASE there are annotation categories such as GO Biological Process or KEGG Pathway which map loci to these types of annotation. The population size is the number of data items (genes or more generically data rows) that contain a particular category of annotation. We use the original EASE implementation policy of only considering data items that are annotated in the annotation category as part of the population.
The population hits are a subset of the population size that correspond to a particular annotation in that system or annotation category.
The list is related to the set of genes that are being investigated for over represented biological roles. By over represented I mean that the biological role is more prevalent in the cluster than would be expected by chance relative to the prevalence of the role in the entire population. We’re looking for biological roles that are disproportionately represented in the cluster of interest.
The list hits number indicates the number of data features in the cluster that are specifically annotated with the biological role on that line.
Analyzed cluster size = 500 genes.
Array size = 25000 (total # data rows)
The Fisher’s Exact test takes all 4 numbers (or numbers derived from these) to report a p-value. 50 of the 52 genes related to TCA are in the cluster so that’s would be a significant result.