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Related Concept Videos

Sample Preparation for Analysis: Advanced Techniques01:08

Sample Preparation for Analysis: Advanced Techniques

Accurate analysis of complex samples often requires advanced preparation techniques to achieve reliable and reproducible results. Samples containing inorganic or organic materials can be challenging to dissolve or decompose effectively. Standard sample preparation methods include acid digestion, fusion, dry ashing, and wet digestion.
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Rapid High Throughput Amylose Determination in Freeze Dried Potato Tuber Samples
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Published on: October 14, 2013

Thawing Frozen Robust Multi-array Analysis (fRMA).

Matthew N McCall1, Rafael A Irizarry

  • 1Department of Biostatistics and Computational Biology, University of Rochester Medical Center, Rochester, NY, USA. mccallm@gmail.com

BMC Bioinformatics
|September 20, 2011
PubMed
Summary
This summary is machine-generated.

The frmaTools R package enables users to create custom frozen parameter vectors for Frozen Robust Multi-array Analysis (fRMA) preprocessing. This enhances fRMA applicability beyond widely used platforms, even with limited data.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Frozen Robust Multi-array Analysis (fRMA) is a novel microarray preprocessing method.
  • fRMA offers individual array preprocessing with multi-array benefits.
  • Creating fRMA frozen parameters typically requires a large, curated database, limiting its use to common platforms.

Purpose of the Study:

  • To introduce the frmaTools R package for creating custom fRMA frozen parameter vectors.
  • To assess the training dataset size needed for reliable fRMA parameter estimation.
  • To demonstrate fRMA's utility in scenarios with limited public array data.

Main Methods:

  • Development of the frmaTools R package.
  • Exploration of training dataset sizes for parameter estimation.
  • Evaluation of custom fRMA implementations in specific scenarios.

Main Results:

  • The frmaTools package allows rapid generation of user-defined frozen parameter vectors.
  • fRMA demonstrates robust performance even with smaller training datasets in certain situations.
  • The package integrates seamlessly into existing microarray preprocessing workflows.

Conclusions:

  • The frmaTools package significantly broadens the applicability of the fRMA algorithm.
  • Users can now implement fRMA independently, overcoming platform limitations.
  • frmaTools is freely available via the Bioconductor project, promoting wider adoption.