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Related Concept Videos

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...

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Perturbations of Circulating miRNAs in Irritable Bowel Syndrome Detected Using a Multiplexed High-throughput Gene Expression Platform
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GTI: a novel algorithm for identifying outlier gene expression profiles from integrated microarray datasets.

John Patrick Mpindi1, Henri Sara, Saija Haapa-Paananen

  • 1FIMM, Institute of Molecular Medicine Finland, University of Helsinki, Helsinki, Finland. john.mpindi@helsinki.fi

Plos One
|March 3, 2011
PubMed
Summary
This summary is machine-generated.

A new bioinformatics method, the gene tissue index (GTI), effectively identifies outlier genes in cancer. This approach aids in discovering potential oncogenes and therapeutic targets in glioblastoma.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Cancer Research

Background:

  • Meta-analysis of gene expression data presents statistical challenges.
  • Identifying upregulated genes ('outlier genes') in tumor subsets is crucial for oncogene discovery.

Purpose of the Study:

  • To develop and validate a novel bioinformatic method for identifying outlier genes.
  • To assess the method's performance in detecting oncogenes and potential therapeutic targets in glioblastoma.

Main Methods:

  • Developed the gene tissue index (GTI) statistical method, adapting algorithms from economics.
  • Compared GTI against COPA, OS statistic, t-test, and ORT using simulated data.
  • Applied GTI to combined Affymetrix gene expression data from normal CNS, astrocytoma, and glioblastoma samples.

Main Results:

  • GTI performed comparably to existing methods in single-study simulations.
  • GTI identified known oncogenic outlier genes in glioblastoma more effectively than most previous methods.
  • Discovered 29 novel outlier genes in glioblastoma, including TYMS and CDKN2A, with 90% validated by immunohistochemistry.

Conclusions:

  • The GTI is a robust approach for identifying potential oncogene outliers and drug targets.
  • TYMS emerged as a potential therapeutic target, as its inhibition blocked glioblastoma cell proliferation.
  • The GTI algorithm is available as an R package.