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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

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...
Comparing Copy Number Variations and SNPs02:26

Comparing Copy Number Variations and SNPs

Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
Copy number variations or CNVs are the structural variations that cover more than 1kb of DNA sequence. The single nucleotide polymorphism (SNP), on the other hand, is a single nucleotide change or a point mutation that is found in more than 1%...

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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
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Comparing microarray studies.

Mayte Suárez-Fariñas1, Marcelo O Magnasco

  • 1Center for Studies in Physics and Biology, The Rockefeller University, New York, NY, USA.

Methods in Molecular Biology (Clifton, N.J.)
|July 20, 2007
PubMed
Summary

Comparing microarray studies requires careful consideration of technology, protocols, and lab variability. This guide offers methods and pitfalls for reliable data integration and analysis.

Area of Science:

  • Bioinformatics
  • Genomics
  • Biostatistics

Background:

  • Microarray technology enables large-scale gene expression profiling.
  • Integrating data from diverse microarray studies presents significant challenges.
  • Variability in experimental design and analysis can impact study agreement.

Purpose of the Study:

  • To provide a practical guide for comparing and integrating different microarray studies.
  • To identify key factors influencing agreement between microarray studies.
  • To offer methods and highlight common pitfalls in microarray data analysis.

Main Methods:

  • Discussion of factors affecting study agreement: technology, platforms, statistical criteria, protocols, lab variability.
  • Presentation of methods for comparing and refining microarray study integration.

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  • Illustrative example case study.

    • Main Results:

    • Identification of critical variables (technology, protocols, lab variability) impacting microarray study comparability.
    • Detailed methods for robust data integration and comparison.
    • Common pitfalls in microarray analysis are highlighted.

    Conclusions:

    • Careful consideration of technical and analytical factors is crucial for successful microarray study comparison.
    • Standardized approaches and awareness of potential pitfalls enhance the reliability of integrated microarray data.
    • This guide facilitates more accurate and meaningful cross-study analyses in genomics.