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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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Published on: October 11, 2019

Visualizing multidimensional cancer genomics data.

Michael P Schroeder1, Abel Gonzalez-Perez1, Nuria Lopez-Bigas2

  • 1Research Program on Biomedical Informatics - GRIB, Universitat Pompeu Fabra (UPF), Parc de Recerca Biomèdica de Barcelona (PRBB), Dr. Aiguader 88, E-08003 Barcelona, Spain.

Genome Medicine
|February 1, 2013
PubMed
Summary
This summary is machine-generated.

Exploring cancer genomics data requires effective visualization tools. This review highlights common techniques and platforms for analyzing somatic alterations and clinical data in oncogenomics research.

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Area of Science:

  • Oncology
  • Genomics
  • Bioinformatics

Background:

  • Cancer genomics projects generate vast amounts of data on somatic alterations across the genome, transcriptome, and epigenome.
  • Large-scale initiatives like the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA) have produced extensive oncogenomic datasets.
  • Extracting meaningful insights from these complex datasets necessitates expert exploration of alterations and their relationships.

Purpose of the Study:

  • To review effective and common visualization techniques for exploring oncogenomics data.
  • To discuss tools that enable researchers to visualize multidimensional oncogenomic datasets.
  • To highlight the importance of intuitive visualization for integrating different alteration types with clinical data.

Main Methods:

  • Review of established visualization techniques in cancer genomics.
  • Discussion of software tools and platforms for oncogenomic data visualization.
  • Categorization of visualization methods based on their application in exploring somatic alterations.

Main Results:

  • Identification of key visualization methods used in cancer genomics.
  • Presentation of a selection of tools including Circos, Gitools, Integrative Genomics Viewer, Cytoscape, Savant Genome Browser, and StratomeX.
  • Overview of platforms such as cBio Cancer Genomics Portal, IntOGen, UCSC Cancer Genomics Browser, Regulome Explorer, and Cancer Genome Workbench.

Conclusions:

  • Intuitive visualization tools are essential for the effective exploration of cancer genomics data.
  • A variety of tools and platforms exist to support the visualization of multidimensional oncogenomic datasets.
  • Integrating different types of alterations with clinical data through visualization aids knowledge extraction in cancer research.