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Related Concept Videos

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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Sample Preparation to Bioinformatics Analysis of DNA Methylation: Association Strategy for Obesity and Related Trait Studies
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The dChip survival analysis module for microarray data.

Samir B Amin1, Parantu K Shah, Aimin Yan

  • 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard School of Public Health, 450 Brookline Ave, Boston, MA 02215, USA.

BMC Bioinformatics
|March 11, 2011
PubMed
Summary
This summary is machine-generated.

A new dChip software module enables genome-wide survival analysis using gene expression and copy number data. This tool facilitates interactive visualization of survival curves and identification of genomic markers for patient outcomes.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Genome-wide expression signatures are emerging as potential markers for overall survival and disease recurrence risk.
  • Existing software lacks capabilities for survival analysis using SNP array data or interactive survival curve visualization for expression-based clusters.

Purpose of the Study:

  • To develop a survival analysis module for dChip software.
  • To enable genome-wide survival analysis using both gene expression and copy number alteration data.
  • To provide interactive visualization of survival analysis results.

Main Methods:

  • Developed a survival analysis module integrated into the dChip software.
  • Incorporated functions for chromosome display, clustering, and interactive Kaplan-Meier (K-M) plot exploration.
  • Implemented survival p-value computation using log-rank test and Cox models.
  • Utilized permutation analysis to identify significant genomic regions associated with survival.

Main Results:

  • The dChip survival module performs genome-wide survival analysis for gene expression and copy number microarray data.
  • Interactive exploration of K-M plots is possible using expression or copy number data.
  • The module identifies significant genomic regions associated with survival through permutation testing.

Conclusions:

  • The dChip survival module offers a user-friendly approach to survival analysis and result visualization.
  • It requires no coding expertise and has a minimal learning curve for existing dChip users.
  • Fast computation is achieved through Visual C++ implementation, and the software is freely available.