GenePattern Team Blog

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


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:

  1. 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.
  2. 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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Computing SNP Copy Number and Loss of Heterozygosity

Posted on Saturday, September 15, 2012 at 12:29PM by The GenePattern Team


In cancer genomics, copy number change is one of the hallmarks of the genetic instability common to most human cancers and loss of heterozygosity (LOH) of tumor suppressor genes is a crucial step in the development of sporadic and hereditary cancer (Monti, 2005). Using modules available in GenePattern, you can compute SNP copy number and LOH based on Affymetrix SNP chip data for paired target/normal samples and then view them in the Integrative Genomics Viewer (IGV). The following modules are used for this computation, with IGV at the end for viewing the results:

  • SNPFileCreator
  • XChromosomeCorrect
  • CopyNumberDivideByNormals
  • LOHPaired
  • IGV


SNPFileCreator converts the .CEL files from an Affymetrix array into a GenePattern .SNP file. Raw data for the probes in each SNP probe set are converted to a single intensity value per SNP using one of four modeling algorithms: Average Difference, PM/MM Difference Model (dChip, the default), Median Probe, or Trimmed Mean....

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