October 2012 Java 6 update and GenePattern visualizer issues
Posted on Friday, October 26, 2012 at 02:47PM by David Eby
UPDATE MARCH 17, 2017 : Java applet visualizers no longer work in any browser. Please click here to read the blog post.
(Originally posted 2012-11-09)
Oracle recently released an update to their Java 6 platform which could cause problems for GenePattern users. The update disables the underlying technologies used by several of GenePattern components, specifically the visualization modules (such as ComparativeMarkerSelectionViewer and HeatMapViewer) and the file uploader. The GenePattern team is currently evaluating options to address this issue.
This update came out within the last couple of weeks, so you may or may not have already applied this change depending on the update policies of your organization and the settings on your own computer. It is known by several different names, including at least the following:
- Java SE 6 Update 37
- JRE 6u37 or JDK 6u37
- Java for Mac OS X 10.6 Update 11
- Java for OS X 2012-006 (Mac OS X 10.7 or Later)
- JRE...
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GenePattern blog
Posted on Friday, October 26, 2012 at 01:49PM by David Eby
Welcome to the GenePattern blog! We are launching this as a place where we can post important news and announcements for the GenePattern community with more detail than can fit into a system announcement or tweet. Feel free to give us feedback and ask questions using the Comments section below.
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Using ComparativeMarkerSelection for Differential Expression Analysis
Posted on Sunday, September 30, 2012 at 12:32PM by The GenePattern Team
Overview
In GenePattern, you use the ComparativeMarkerSelection module to identify the genes (if any) that are differentially expressed between two phenotype classes. Typically, this is a three-step process:
- Run the PreprocessDataset module to preprocess the expression data.
PreprocessDataset removes platform noise and genes that have little variation. It takes an expression data file and generates a new, modified expression data file. - Run the ComparativeMarkerSelection module to compute differential gene expression.
For each gene, ComparativeMarkerSelection first uses a test statistic to calculate the difference in gene expression between the samples in the first class and the samples in the second class and then estimates the significance (p-value) of the test statistic score. Because testing tens of thousands of genes simultaneously increases the possibility of mistakenly identifying a non-marker gene as a marker gene, ComparativeMarkerSelection corrects for multiple hypothesis...
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