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Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?

  • Lev Klebanov and Andrei Yakovlev
Published/Copyright: March 24, 2006
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 5 Issue 1

One of the prevailing ideas in the literature on microarray data analysis is to pool the expression measures across genes and treat them as a sample drawn from some distribution. Several universal laws were proposed to analytically describe this distribution. This idea raises a number of concerns. The expression levels of genes are not identically distributed random variables so that treating them as a sample amounts to sampling from a mixture of equally weighted distributions, each being associated with a different gene. The expression levels of different genes are heavily dependent random variables so that the law of large numbers and statistical goodness-of-fit tests are normally inapplicable to this kind of data. This dependence represents a very serious pitfall in microarray data analysis.

Keywords: microarray analysis; gene expression; pooled data; stochastic dependence
Published Online: 2006-3-24

©2011 Walter de Gruyter GmbH & Co. KG, Berlin/Boston

Articles in the same Issue

  1. Article
  2. Low-Order Conditional Independence Graphs for Inferring Genetic Networks
  3. A Generalized Clustering Problem, with Application to DNA Microarrays
  4. A Bayes Regression Approach to Array-CGH Data
  5. Statistical Selection of Maintenance Genes for Normalization of Gene Expressions
  6. Predicting the Strongest Domain-Domain Contact in Interacting Protein Pairs
  7. Dimension Reduction for Classification with Gene Expression Microarray Data
  8. A New Type of Stochastic Dependence Revealed in Gene Expression Data
  9. A New Order Estimator for Fixed and Variable Length Markov Models with Applications to DNA Sequence Similarity
  10. Quality Optimised Analysis of General Paired Microarray Experiments
  11. Issues of Processing and Multiple Testing of SELDI-TOF MS Proteomic Data
  12. Cross-Validated Bagged Prediction of Survival
  13. Treatment of Uninformative Families in Mean Allele Sharing Tests for Linkage
  14. Quantile-Function Based Null Distribution in Resampling Based Multiple Testing
  15. Combining Results of Microarray Experiments: A Rank Aggregation Approach
  16. Model Selection for Mixtures of Mutagenetic Trees
  17. Pseudo-likelihood for Non-reversible Nucleotide Substitution Models with Neighbour Dependent Rates
  18. A Method to Increase the Power of Multiple Testing Procedures Through Sample Splitting
  19. Bayesian Hierarchical Model for Correcting Signal Saturation in Microarrays Using Pixel Intensities
  20. Using Complexity for the Estimation of Bayesian Networks
  21. Detecting Local High-Scoring Segments: a First-Stage Approach for Genome-Wide Association Studies
  22. Examining Protein Structure and Similarities by Spectral Analysis Technique
  23. Parameter Estimation for the Exponential-Normal Convolution Model for Background Correction of Affymetrix GeneChip Data
  24. Approximate Sample Size Calculations with Microarray Data: An Illustration
  25. Numerical Solutions for Patterns Statistics on Markov Chains
  26. A Heuristic Bayesian Method for Segmenting DNA Sequence Alignments and Detecting Evidence for Recombination and Gene Conversion
  27. A Two-Step Multiple Comparison Procedure for a Large Number of Tests and Multiple Treatments
  28. Validation in Genomics: CpG Island Methylation Revisited
  29. An Improved Nonparametric Approach for Detecting Differentially Expressed Genes with Replicated Microarray Data
  30. Letter to the Editor
  31. Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?
  32. Reader's Reaction
  33. Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)
https://doi.org/10.2202/1544-6115.1185
Keywords for this article
microarray analysis; gene expression; pooled data; stochastic dependence

Articles in the same Issue

  1. Article
  2. Low-Order Conditional Independence Graphs for Inferring Genetic Networks
  3. A Generalized Clustering Problem, with Application to DNA Microarrays
  4. A Bayes Regression Approach to Array-CGH Data
  5. Statistical Selection of Maintenance Genes for Normalization of Gene Expressions
  6. Predicting the Strongest Domain-Domain Contact in Interacting Protein Pairs
  7. Dimension Reduction for Classification with Gene Expression Microarray Data
  8. A New Type of Stochastic Dependence Revealed in Gene Expression Data
  9. A New Order Estimator for Fixed and Variable Length Markov Models with Applications to DNA Sequence Similarity
  10. Quality Optimised Analysis of General Paired Microarray Experiments
  11. Issues of Processing and Multiple Testing of SELDI-TOF MS Proteomic Data
  12. Cross-Validated Bagged Prediction of Survival
  13. Treatment of Uninformative Families in Mean Allele Sharing Tests for Linkage
  14. Quantile-Function Based Null Distribution in Resampling Based Multiple Testing
  15. Combining Results of Microarray Experiments: A Rank Aggregation Approach
  16. Model Selection for Mixtures of Mutagenetic Trees
  17. Pseudo-likelihood for Non-reversible Nucleotide Substitution Models with Neighbour Dependent Rates
  18. A Method to Increase the Power of Multiple Testing Procedures Through Sample Splitting
  19. Bayesian Hierarchical Model for Correcting Signal Saturation in Microarrays Using Pixel Intensities
  20. Using Complexity for the Estimation of Bayesian Networks
  21. Detecting Local High-Scoring Segments: a First-Stage Approach for Genome-Wide Association Studies
  22. Examining Protein Structure and Similarities by Spectral Analysis Technique
  23. Parameter Estimation for the Exponential-Normal Convolution Model for Background Correction of Affymetrix GeneChip Data
  24. Approximate Sample Size Calculations with Microarray Data: An Illustration
  25. Numerical Solutions for Patterns Statistics on Markov Chains
  26. A Heuristic Bayesian Method for Segmenting DNA Sequence Alignments and Detecting Evidence for Recombination and Gene Conversion
  27. A Two-Step Multiple Comparison Procedure for a Large Number of Tests and Multiple Treatments
  28. Validation in Genomics: CpG Island Methylation Revisited
  29. An Improved Nonparametric Approach for Detecting Differentially Expressed Genes with Replicated Microarray Data
  30. Letter to the Editor
  31. Treating Expression Levels of Different Genes as a Sample in Microarray Data Analysis: Is it Worth a Risk?
  32. Reader's Reaction
  33. Reader's Reaction to "Dimension Reduction for Classification with Gene Expression Microarray Data" by Dai et al (2006)
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