Use of Mixture Models in a Microarray-Based Screening Procedure for Detecting Differentially Represented Yeast Mutants
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Rafael A Irizarry
We describe the use of a statistical model in a genome-wide microarray-based yeast genetic screen performed by imposing different genetic selections on thousands of yeast mutants in parallel. A mixture model is fitted to data obtained from oligonucleotide arrays hybridized to 20-mer oligonucleotide ``barcodes'' and a procedure based on the fitted model is used to search for mutants differentially represented under experimental and control conditions. The fitted stochastic model provides a way to assess uncertainty. We demonstrate the usefulness of the model by applying it to the problem of screening for components of the nonhomologous end joining (NHEJ) pathway and identified known components of the NHEJ pathway.
©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston
Artikel in diesem Heft
- Article
- Use of Mixture Models in a Microarray-Based Screening Procedure for Detecting Differentially Represented Yeast Mutants
- Sampling Correction in Pedigree Analysis
- Parameter estimation for the calibration and variance stabilization of microarray data
- Transformations for cDNA Microarray Data
- Supervised Detection of Regulatory Motifs in DNA Sequences
- Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot
- On the Power of Profiles for Transcription Factor Binding Site Detection
- An Empirical Bayesian Method for Differential Expression Studies Using One-Channel Microarray Data
Artikel in diesem Heft
- Article
- Use of Mixture Models in a Microarray-Based Screening Procedure for Detecting Differentially Represented Yeast Mutants
- Sampling Correction in Pedigree Analysis
- Parameter estimation for the calibration and variance stabilization of microarray data
- Transformations for cDNA Microarray Data
- Supervised Detection of Regulatory Motifs in DNA Sequences
- Visualisation of Gene Expression Data - the GE-biplot, the Chip-plot and the Gene-plot
- On the Power of Profiles for Transcription Factor Binding Site Detection
- An Empirical Bayesian Method for Differential Expression Studies Using One-Channel Microarray Data
