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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...
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Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
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Rup (RNA-seq Usability Assessment Pipeline) - Quality Control for Bulk RNA-seq Experiments in Eukaryotes
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Including probe-level measurement error in robust mixture clustering of replicated microarray gene expression.

Xuejun Liu1, Magnus Rattray

  • 1Nanjing University of Aeronautics and Astronautics. xuejun.liu@nuaa.edu.cn

Statistical Applications in Genetics and Molecular Biology
|January 4, 2011
PubMed
Summary

We developed a robust Student's t-mixture model for clustering noisy gene expression data. This new method accurately handles replicated data and measurement errors, yielding more biologically meaningful gene function clusters.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Statistical Genetics

Background:

  • Probabilistic mixture models are used for clustering gene expression data to understand gene function.
  • Microarray data often contains technical and biological noise, necessitating the use of replicated experiments.
  • Existing mixture models often fail to account for correlations in replicated data or probe-level measurement errors, and are sensitive to non-Gaussian data distributions.

Purpose of the Study:

  • To propose a robust Student's t-mixture model for analyzing noisy gene expression data.
  • To explicitly handle replicated measurements and incorporate probe-level measurement error.
  • To automatically determine the optimal number of clusters using a minimum message length criterion.

Main Methods:

  • Developed a robust Student's t-mixture model incorporating replicated gene expression data and probe-level measurement error.
  • Utilized a minimum message length criterion for automatic model selection (number of components).
  • Applied the model to synthetic and yeast time-course gene expression datasets, using Affymetrix probe-level data with uncertainty estimates.

Main Results:

  • The Student's t-mixture model demonstrated robust performance on synthetic data with realistic noise compared to Gaussian mixture models and other methods.
  • Analysis of yeast time-course data yielded more biologically relevant clusters, evidenced by improved Gene Ontology (GO) category enrichment and transcription factor-gene interactions.
  • Automatic component selection proved computationally efficient, enabling application to larger datasets.

Conclusions:

  • The proposed Student's t-mixture model offers a robust and effective approach for clustering noisy gene expression data, outperforming existing methods.
  • The model's ability to handle replicated data, measurement error, and non-Gaussian distributions leads to more biologically interpretable results.
  • Automatic model selection enhances computational efficiency, making the method scalable for large-scale genomic studies.