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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...
Sample Size Calculation01:19

Sample Size Calculation

Knowledge of the sample size is the first requirement to conduct random sampling or an experiment. The sample size is the total number of units, observations, or groups (in some cases) used to get the data to estimate a population parameter. As the name suggests, the sample size is that of the sample drawn from the population and differs from the population size.
The sample size for the given experiment or sampling effort is fundamental to any study design. Sample size decides the number of...

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Related Experiment Video

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

How large a training set is needed to develop a classifier for microarray data?

Kevin K Dobbin1, Yingdong Zhao, Richard M Simon

  • 1Biometric Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, NIH, Rockville, Maryland 20852, USA. dobbinke@mail.nih.gov

Clinical Cancer Research : an Official Journal of the American Association for Cancer Research
|January 4, 2008
PubMed
Summary
This summary is machine-generated.

Determining the right number of samples for training gene expression classifiers is crucial. This study presents a model-based method to calculate adequate sample sizes for high-dimensional microarray data, ensuring reliable classifier development.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene expression microarray studies aim to develop predictive classifiers for patient prognoses or treatment responses.
  • Training classifiers requires a sufficient number of samples, but determining this number for high-dimensional data is challenging.

Purpose of the Study:

  • To present a model-based approach for determining the necessary sample size to adequately train a classifier.
  • To address the challenge of sample size determination in high-dimensional gene expression data analysis.

Main Methods:

  • A model-based approach was developed to calculate required sample sizes.
  • The method considers standardized fold change, class prevalence, and the number of genes or features.

Main Results:

  • Sample size determination is feasible using standardized fold change, class prevalence, and gene count.
  • The method can assess the adequacy of existing training set sizes.
  • An interactive web tool is available for sample size calculations.

Conclusions:

  • Sample size calculations for developing classifiers from high-dimensional microarray data are feasible.
  • The study provides a practical method and discusses important experimental design considerations.