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Normalization Method for Transcriptional Studies of Heterogeneous Samples - Simultaneous Array Normalization and Identification of Equivalent Expression

  • Li-Xuan Qin and Jaya M Satagopan
Published/Copyright: February 4, 2009
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Statistical Applications in Genetics and Molecular Biology
From the journal Statistical Applications in Genetics and Molecular Biology Volume 8 Issue 1

Normalization is an important step in the analysis of microarray data of transcription profiles as systematic non-biological variations often arise from the multiple steps involved in any transcription profiling experiment. Existing methods for data normalization often assume that there are few or symmetric differential expression, but this assumption does not always hold. Alternatively, non-differentially expressed genes may be used for array normalization. However, it is unknown at the outset which genes are non-differentially expressed. In this paper we propose a hierarchical mixture model framework to simultaneously identify non-differentially expressed genes and normalize arrays using these genes. The Fisher's information matrix corresponding to array effects is derived, which provides useful intuition for guiding the choice of array normalization method. The operating characteristics of the proposed method are evaluated using simulated data. The simulations conducted under a wide range of parametric configurations suggest that the proposed method provides a useful alternative for array normalization. For example, the proposed method has better sensitivity than median normalization under modest prevalence of differentially expressed genes and when the magnitudes of over-expression and under-expression are not the same. Further, the proposed method has properties similar to median normalization when the prevalence of differentially expressed genes is very small. Empirical illustration of the proposed method is provided using a liposarcoma study from MSKCC to identify genes differentially expressed between normal fat tissue versus liposarcoma tissue samples.

Keywords: gene expression; normalization; mixture models; Fisher’s information
Published Online: 2009-2-4

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

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https://doi.org/10.2202/1544-6115.1339
Keywords for this article
gene expression; normalization; mixture models; Fisher’s information

Articles in the same Issue

  1. Article
  2. Sparse Canonical Correlation Analysis with Application to Genomic Data Integration
  3. Orthology-Based Multilevel Modeling of Differentially Expressed Mouse and Human Gene Pairs
  4. Sequential Analysis for Microarray Data Based on Sensitivity and Meta-Analysis
  5. Dimension Reduction of Microarray Data in the Presence of a Censored Survival Response: A Simulation Study
  6. A Nonlinear Mixed-Effects Model for Estimating Calibration Intervals for Unknown Concentrations in Two-Color Microarray Data with Spike-Ins
  7. Composite Likelihood Modeling of Neighboring Site Correlations of DNA Sequence Substitution Rates
  8. A Multiple Testing Approach to High-Dimensional Association Studies with an Application to the Detection of Associations between Risk Factors of Heart Disease and Genetic Polymorphisms
  9. Hypothesis Tests for Point-Mass Mixture Data with Application to `Omics Data with Many Zero Values
  10. Inferring Dynamic Genetic Networks with Low Order Independencies
  11. Normalization Method for Transcriptional Studies of Heterogeneous Samples - Simultaneous Array Normalization and Identification of Equivalent Expression
  12. A Bayesian Analysis Strategy for Cross-Study Translation of Gene Expression Biomarkers
  13. Modified FDR Controlling Procedure for Multi-Stage Analyses
  14. Detecting Outlier Samples in Microarray Data
  15. Survival Analysis with High-Dimensional Covariates: An Application in Microarray Studies
  16. Two-Stage Model-Based Clustering for Liquid Chromatography Mass Spectrometry Data Analysis
  17. Score Statistics for Mapping Quantitative Trait Loci
  18. Impact of Population Stratification on Family-Based Association Tests with Longitudinal Measurements
  19. A Multilocus Model for Constructing a Linkage Disequilibrium Map in Human Populations
  20. Testing of Chromosomal Clumping of Gene Properties
  21. Balanced Gradient Boosting from Imbalanced Data for Clinical Outcome Prediction
  22. Univariate Shrinkage in the Cox Model for High Dimensional Data
  23. Multilevel Comparison of Dendrograms: A New Method with an Application for Genetic Classifications
  24. Weighted Multiple Hypothesis Testing Procedures
  25. Incorporating Duplicate Genotype Data into Linear Trend Tests of Genetic Association: Methods and Cost-Effectiveness
  26. Increase of Rejection Rate in Case-Control Studies with the Differential Genotyping Error Rates
  27. A Parametric Model for Analyzing Anticipation in Genetically Predisposed Families
  28. Bayesian Unsupervised Learning with Multiple Data Types
  29. Extensions of Sparse Canonical Correlation Analysis with Applications to Genomic Data
  30. A Non-Homogeneous Hidden-State Model on First Order Differences for Automatic Detection of Nucleosome Positions
  31. Adaptive Transmission Disequilibrium Test for Family Trio Design
  32. Model Selection Based on FDR-Thresholding Optimizing the Area under the ROC-Curve
  33. Estimation of Selection Intensity under Overdominance by Bayesian Methods
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  35. Rotation Testing in Gene Set Enrichment Analysis for Small Direct Comparison Experiments
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  39. A Statistical Model for Genetic Mapping of Viral Infection by Integrating Epidemiological Behavior
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  41. Modeling Dependence in Methylation Patterns with Application to Ovarian Carcinomas
  42. M-quantile Regression Analysis of Temporal Gene Expression Data
  43. MC-Normalization: A Novel Method for Dye-Normalization of Two-Channel Microarray Data
  44. Characterizing the D2 Statistic: Word Matches in Biological Sequences
  45. Transmission Disequilibrium Test Power and Sample Size in the Presence of Locus Heterogeneity
  46. A Regularized Regression Approach for Dissecting Genetic Conflicts that Increase Disease Risk in Pregnancy
  47. Statistical Screening Method for Genetic Factors Influencing Susceptibility to Common Diseases in a Two-Stage Genome-Wide Association Study
  48. A Unified Mixed Effects Model for Gene Set Analysis of Time Course Microarray Experiments
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