Analysis of microarray gene expression data /
After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact o...
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Boston :
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[2004]
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| 100 | 1 | |a Lee, Mei-Ling Ting. |0 http://id.loc.gov/authorities/names/n2002158945 | |
| 245 | 1 | 0 | |a Analysis of microarray gene expression data / |c Mei-Ling Ting Lee. |
| 264 | 1 | |a Boston : |b Kluwer Academic, |c [2004] | |
| 264 | 4 | |c ©2004 | |
| 300 | |a 1 online resource (xvi, 371 pages) : |b illustrations (some color) | ||
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| 504 | |a Includes bibliographical references (pages 351-365) and indexes. | ||
| 505 | 0 | 0 | |g Part I |t Genome Probing Using Microarrays -- |g 2. |t DNA, RNA, Proteins, and Gene Expression |g 7 -- |g 2.1 |t The Molecules of Life |g 7 -- |g 2.2 |t Genes |g 8 -- |g 2.3 |t DNA |g 9 -- |g 2.4 |t RNA |g 12 -- |g 2.5 |t The Genetic Code |g 13 -- |g 2.6 |t Proteins |g 14 -- |g 2.7 |t Gene Expression and Microarrays |g 15 -- |g 2.8 |t Complementary DNA (cDNA) |g 16 -- |g 2.9 |t Nucleic Acid Hybridization |g 16 -- |g 3. |t Microarray Technology |g 19 -- |g 3.1 |t Transcriptional Profiling |g 20 -- |g 3.1.1 |t Sequencing-based Transcriptional Profiling |g 20 -- |g 3.1.2 |t Hybridization-based Transcriptional Profiling |g 22 -- |g 3.2 |t Microarray Technological Platforms |g 23 -- |g 3.3 |t Probe Selection and Synthesis |g 24 -- |g 3.4 |t Array Manufacturing |g 30 -- |g 3.5 |t Target Labeling |g 31 -- |g 3.6 |t Hybridization |g 34 -- |g 3.7 |t Scanning and Image Analysis |g 35 -- |g 3.8 |t Microarray Data |g 36 -- |g 3.8.1 |t Spotted Array Data |g 36 -- |g 3.8.2 |t In-situ Oligonucleotide Array Data |g 37 -- |g 3.9 |t So I Have My Microarray Data -- What's Next? |g 39 -- |g 3.9.1 |t Confirming Microarray Results |g 39 -- |g 3.9.2 |t Northern Blot Analysis |g 40 -- |g 3.9.3 |t Reverse-transcription PCR and Quantitative Real-time RT-PCR |g 40 -- |g 4. |t Inherent Variability in Array Data |g 45 -- |g 4.1 |t Genetic Populations |g 45 -- |g 4.2 |t Variability in Gene Expression Levels |g 47 -- |g 4.2.1 |t Variability Due to Specimen Sampling |g 47 -- |g 4.2.2 |t Variability Due to Cell Cycle Regulation |g 48 -- |g 4.2.3 |t Experimental Variability |g 48 -- |g 4.3 |t Test the Variability by Replication |g 50 -- |g 4.3.1 |t Duplicated Spots |g 50 -- |g 4.3.2 |t Multiple Arrays and Biological Replications |g 51 -- |g 5. |t Background Noise |g 53 -- |g 5.1 |t Pixel-by-pixel Analysis of Individual Spots |g 53 -- |g 5.2 |t General Models for Background Noise |g 56 -- |g 5.2.1 |t Additive Background Noise |g 57 -- |g 5.2.2 |t Correction for Background Noise |g 58 -- |g 5.2.3 |t Example: Replication Test Data Set |g 59 -- |g 5.2.4 |t Noise Models for GeneChip Arrays |g 62 -- |g 5.2.5 |t Elusive Nature of Background Noise |g 63 -- |g 6. |t Transformation and Normalization |g 67 -- |g 6.1 |t Data Transformations |g 67 -- |g 6.1.1 |t Logarithmic Transformation |g 67 -- |g 6.1.2 |t Square Root Transformation |g 68 -- |g 6.1.3 |t Box-Cox Transformation Family |g 69 -- |g 6.1.4 |t Affine Transformation |g 69 -- |g 6.1.5 |t The Generalized-log Transformation |g 71 -- |g 6.2 |t Data Normalization |g 72 -- |g 6.2.1 |t Normalization Across G Genes |g 74 -- |g 6.2.2 |t Example: Mouse Juvenile Cystic Kidney Data Set |g 75 -- |g 6.2.3 |t Normalization Across G Genes and N Samples |g 77 -- |g 6.2.4 |t Color Effects and MA Plots |g 78 -- |g 6.2.5 |t Normalization Based on LOWESS Function |g 80 -- |g 6.2.6 |t Normalization Based on Rank-invariant Genes |g 82 -- |g 6.2.7 |t Normalization Based on a Sample Pool |g 82 -- |g 6.2.8 |t Global Normalization Using ANOVA Models |g 82 -- |g 6.2.9 |t Other Normalization Issues |g 83 -- |g 7. |t Missing Values in Array Data |g 85 -- |g 7.1 |t Missing Values in Array Data |g 85 -- |g 7.1.1 |t Sources of Problem |g 85 -- |g 7.2 |t Statistical Classification of Missing Data |g 86 -- |g 7.3 |t Missing Values in Replicated Designs |g 88 -- |g 7.4 |t Imputation of Missing Values |g 89 -- |g 8. |t Saturated Intensity Readings |g 93 -- |g 8.1 |t Saturated Intensity Readings |g 93 -- |g 8.2 |t Multiple Power-levels for Spotted Arrays |g 93 -- |g 8.2.1 |t Imputing Saturated Intensity Readings |g 95 -- |g 8.3 |t High Intensities in Oligonucleotide Arrays |g 97 -- |g Part II |t Statistical Models and Analysis -- |g 9. |t Experimental Design |g 103 -- |g 9.1 |t Factors Involved in Experiments |g 103 -- |g 9.2 |t Types of Design Structures |g 106 -- |g 9.3 |t Common Practice in Microarray Studies |g 112 -- |g 9.3.1 |t Reference Design |g 112 -- |g 9.3.2 |t Time-course Experiment |g 114 -- |g 9.3.3 |t Color Reversal |g 115 -- |g 9.3.4 |t Loop Design |g 116 -- |g 9.3.5 |t Example: Time-course Loop Design |g 117 -- |g 10. |t ANOVA Models for Microarray Data |g 121 -- |g 10.1 |t A Basic Log-linear Model |g 121 -- |g 10.2 |t ANOVA With Multiple Factors |g 123 -- |g 10.2.1 |t Main Effects |g 123 -- |g 10.2.2 |t Interaction Effects |g 123 -- |g 10.3 |t A Generic Fixed-Effects ANOVA Model |g 124 -- |g 10.3.1 |t Estimation for Interaction Effects |g 126 -- |g 10.4 |t Two-stage Estimation Procedures |g 126 -- |g 10.5 |t Identifying Differentially Expressed Genes |g 130 -- |g 10.5.1 |t Standard MSE-based Approach |g 130 -- |g 10.5.2 |t Other Approaches |g 132 -- |g 10.5.3 |t Modified MSE-based Approach |g 132 -- |g 10.6 |t Mixed-effects Models |g 135 -- |g 10.7 |t ANOVA for Split-plot Design |g 136 -- |g 10.8 |t Log Intensity Versus Log Ratio |g 138 -- |g 11. |t Multiple Testing in Microarray Studies |g 143 -- |g 11.1 |t Hypothesis Testing for Any Individual Gene |g 143 -- |g 11.2 |t Multiple Testing for the Entire Gene Set |g 144 -- |g 11.2.1 |t Framework for Multiple Testing |g 144 -- |g 11.2.2 |t Test Statistic for Each Gene |g 145 -- |g 11.2.3 |t Two Error Control Criteria in Multiple Testing |g 146 -- |g 11.2.4 |t Implementation Algorithms |g 147 -- |g 11.2.5 |t Example of Multiple Testing Algorithms |g 152 -- |g 12. |t Permutation Tests in Microarray Data |g 157 -- |g 12.2 |t Permutation Tests in Microarray Studies |g 160 -- |g 12.2.1 |t Exchangeability in Microarray Designs |g 160 -- |g 12.2.2 |t Limitation of Having Few Permutations |g 162 -- |g 12.2.3 |t Pooling Test Results Across Genes |g 162 -- |g 12.3 |t Lipopolysaccharide-E. coli Data Set |g 163 -- |g 12.3.1 |t Statistical Model |g 164 -- |g 12.3.2 |t Permutation Testing and Results |g 166 -- |g 13. |t Bayesian Methods for Microarray Data |g 171 -- |g 13.1 |t Mixture Model for Gene Expression |g 171 -- |g 13.1.1 |t Variations on the Mixture Model |g 173 -- |g 13.1.2 |t Example of Gamma Models |g 175 -- |g 13.2 |t Mixture Model for Differential Expression |g 176 -- |g 13.2.1 |t Mixture Model for Color Ratio Data |g 176 -- |g 13.2.2 |t Relation of Mixture Model to ANOVA Model |g 180 -- |g 13.2.3 |t Bayes Interpretation of Mixture Model |g 182 -- |g 13.3 |t Empirical Bayes Methods |g 183 -- |g 13.3.1 |t Example of Empirical Bayes Fitting |g 184 -- |g 13.4 |t Hierarchical Bayes Models |g 187 -- |g 13.4.1 |t Example of Hierarchical Modeling |g 189 -- |g 14. |t Power and Sample Size Considerations |g 193 -- |g 14.1 |t Test Hypotheses in Microarray Studies |g 194 -- |g 14.2 |t Distributions of Estimated Differential Expression |g 196 -- |g 14.3 |t Summary Measures of Estimated Differential Expression |g 196 -- |g 14.4 |t Multiple Testing Framework |g 197 -- |g 14.5 |t Dependencies of Estimation Errors |g 199 -- |g 14.6 |t Familywise Type I Error Control |g 200 -- |g 14.6.1 |t Type I Error Control: the Sidak Approach |g 201 -- |g 14.6.2 |t Type I Error Control: the Bonferroni Approach |g 203 -- |g 14.7 |t Familywise Type II Error Control |g 204 -- |g 14.7.1 |t Type II Error Control: the Sidak Approach |g 206 -- |g 14.7.2 |t Type II Error Control: the Bonferroni Approach |g 206 -- |g 14.8 |t Contrast of Planning and Implementation in Multiple Testing |g 207 -- |g 14.9 |t Power Calculations for Different Summary Measures |g 208 -- |g 14.9.1 |t Designs with Linear Summary Measure |g 208 -- |g 14.9.2 |t Numerical Example for Linear Summary |g 210 -- |g 14.9.3 |t Designs with Quadratic Summary Measure |g 211 -- |g 14.9.4 |t Numerical Example for Quadratic Summary |g 213 -- |g 14.10 |t A Bayesian Perspective on Power and Sample Size |g 214 -- |g 14.10.1 |t Connection to Local Discovery Rates |g 215 -- |g 14.10.2 |t Representative Local True Discovery Rate |g 215 -- |g 14.10.3 |t Numerical Example for TDR and FDR |g 216 -- |g 14.11 |t Applications to Standard Designs |g 216 -- |g 14.11.1 |t Treatment-control Designs |g 217 -- |g 14.11.2 |t Sample Size for a Treatment-control Design |g 218 -- |g 14.11.3 |t Multiple-treatment Designs |g 221 -- |g 14.11.4 |t Power Table for a Multiple-treatment Design |g 224 -- |g 14.11.5 |t Time-course and Similar Multiple-treatment Designs |g 227 -- |g 14.12 |t Relation Between Power, Replication and Design |g 228 -- |g 14.12.1 |t Effects of Replication |g 228 -- |g 14.12.2 |t Controlling Sources of Variability |g 229 -- |g 14.13 |t Assessing Power from Microarray Pilot Studies |g 230 -- |g 14.13.1 |t Example 1: Juvenile Cystic Kidney Disease |g 230 -- |g 14.13.2 |t Example 2: Opioid Dependence |g 231 -- |g Part III |t Unsupervised Exploratory Analysis -- |g 15. |t Cluster Analysis |g 237 -- |g 15.1 |t Distance and Similarity Measures |g 238 -- |g 15.2 |t Distance Measures |g 239 -- |g 15.2.1 |t Properties of Distance Measures |g 239 -- |g 15.2.2 |t Minkowski Distance Measures |g 240 -- |g 15.2.3 |t Mahalanobis Distance |g 241 -- |g 15.3 |t Similarity Measures |g 241 -- |g 15.3.1 |t Inner Product |g 241 -- |g 15.3.2 |t Pearson Correlation Coefficient |g 242 -- |g 15.3.3 |t Spearman Rank Correlation Coefficient |g 243 -- |g 15.4 |t Inter-cluster Distance |g 243 -- |g 15.4.1 |t Mahalanobis Inter-cluster Distance |g 244 -- |g 15.4.2 |t Neighbor-based Inter-cluster Distance |g 244 -- |g 15.5 |t Hierarchical Clustering |g 244 -- |g 15.5.1 |t Single Linkage Method |g 245 -- |g 15.5.2 |t Complete Linkage Method |g 245 -- |g 15.5.3 |t Average Linkage Clustering |g 245 -- |g 15.5.4 |t Centroid Linkage Method |g 246 -- |g 15.5.5 |t Median Linkage Clustering |g 246 -- |g 15.5.6 |t Ward's Clustering Method |g 246 |
| 505 | 0 | 0 | |t -- |g 15.5.7 |t Applications |g 246 -- |g 15.5.8 |t Comparisons of Clustering Algorithms |g 247 -- |g 15.6 |t K-means Clustering |g 247 -- |g 15.7 |t Bayesian Cluster Analysis |g 248 -- |g 15.8 |t Two-way Clustering Methods |g 248 -- |g 15.9 |t Reliability of Clustering Patterns for Microarray Data |g 249 -- |g 16. |t Principal Components and Singular Value Decomposition |g 251 -- |g 16.1 |t Principal Component Analysis |g 251 -- |g 16.1.1 |t Applications of Dominant Principal Components |g 253 -- |g 16.2 |t Singular-value Decomposition |g 254 -- |g 16.3 |t Computational Procedures for SVD |g 255 -- |g 16.4 |t Eigengenes and Eigenarrays |g 256 -- |g 16.5 |t Fraction of Eigenexpression |g 256 -- |g 16.6 |t Generalized Singular Value Decomposition |g 257 -- |g 16.7 |t Robust Singular Value Decomposition |g 257 -- |g 17. |t Self-Organizing Maps |g 261 -- |g 17.1 |t The Basic Logic of a SOM |g 261 -- |g 17.2 |t The SOM Updating Algorithm |g 265 -- |g 17.3 |t Program GENECLUSTER |g 267 -- |g 17.4 |t Supervised SOM |g 268 -- |g 17.5 |t Applications |g 268 -- |g 17.5.1 |t Using SOM to Cluster Genes |g 268 -- |g 17.5.2 |t Using SOM to Cluster Tumors |g 269 -- |g 17.5.3 |t Multiclass Cancer Diagnosis |g 270 -- |g Part IV |t Supervised Learning Methods -- |g 18. |t Discrimination and Classification |g 277 -- |g 18.1 |t Fisher's Linear Discriminant Analysis |g 278 -- |g 18.2 |t Maximum Likelihood Discriminant Rules |g 279 -- |g 18.3 |t Bayesian Classification |g 280 -- |g 18.4 |t k-Nearest Neighbor Classifier |g 281 -- |g 18.5 |t Neighborhood Analysis |g 282 -- |g 18.6 |t A Gene-casting Weighted Voting Scheme |g 283 -- |g 18.7 |t Example: Classification of Leukemia Samples |g 284. |
| 520 | |a After genomic sequencing, microarray technology has emerged as a widely used platform for genomic studies in the life sciences. Microarray technology provides a systematic way to survey DNA and RNA variation. With the abundance of data produced from microarray studies, however, the ultimate impact of the studies on biology will depend heavily on data mining and statistical analysis. The contribution of this book is to provide readers with an integrated presentation of various topics on analyzing microarray data. | ||
| 538 | |a Master and use copy. Digital master created according to Benchmark for Faithful Digital Reproductions of Monographs and Serials, Version 1. Digital Library Federation, December 2002. |u http://purl.oclc.org/DLF/benchrepro0212 |5 MiAaHDL | ||
| 583 | 1 | |a digitized |c 2011 |h HathiTrust Digital Library |l committed to preserve |2 pda |5 MiAaHDL | |
| 588 | 0 | |a Print version record. | |
| 500 | |a Electronic resource. | ||
| 650 | 0 | |a DNA microarrays |x Statistical methods. | |
| 650 | 0 | |a Gene expression |x Statistical methods. | |
| 650 | 2 | 4 | |a Statistics, general. |
| 650 | 2 | 4 | |a Cancer Research. |
| 650 | 2 | 4 | |a Human Genetics. |
| 650 | 2 | 4 | |a Mathematical Biology in General. |
| 650 | 2 | 4 | |a Evolutionary Biology. |
| 650 | 6 | |a Puces à ADN |x Méthodes statistiques. | |
| 650 | 6 | |a Expression génique |x Méthodes statistiques. | |
| 650 | 7 | |a SCIENCE |x Life Sciences |x Genetics & Genomics. |2 bisacsh | |
| 650 | 0 | 7 | |a DNA microarrays |x Statistical methods. |2 cct |
| 650 | 0 | 7 | |a Gene expression |x Statistical methods. |2 cct |
| 650 | 2 | |a Oligonucleotide Array Sequence Analysis |x methods. |0 https://id.nlm.nih.gov/mesh/D020411Q000379 | |
| 650 | 2 | |a Gene Expression. |0 https://id.nlm.nih.gov/mesh/D015870 | |
| 650 | 2 | |a Oligonucleotide Array Sequence Analysis |x statistics & numerical data. |0 https://id.nlm.nih.gov/mesh/D020411Q000706 | |
| 655 | 7 | |a Electronic books. |2 local | |
| 710 | 2 | |a SpringerLink (Online service) |0 http://id.loc.gov/authorities/names/no2005046756 | |
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