LEADER 05432nam 2200649Ia 450 001 9910831169203321 005 20230721023407.0 010 $a1-282-37950-X 010 $a9786612379505 010 $a0-470-68598-0 010 $a0-470-68599-9 035 $a(CKB)1000000000822292 035 $a(EBL)470724 035 $a(OCoLC)609849754 035 $a(SSID)ssj0000335040 035 $a(PQKBManifestationID)11251332 035 $a(PQKBTitleCode)TC0000335040 035 $a(PQKBWorkID)10271961 035 $a(PQKB)10593108 035 $a(MiAaPQ)EBC470724 035 $a(EXLCZ)991000000000822292 100 $a20091008d2009 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aBatch effects and noise in microarray experiments, sources, and solutions$b[electronic resource] /$fedited by Andreas Scherer 210 $aChichester, West Sussex ;$aHoboken $cJ. Wiley$d2009 215 $a1 online resource (282 p.) 225 1 $aWiley Series in Probability and Statistics ;$vv.868 300 $aDescription based upon print version of record. 311 $a0-470-74138-4 320 $aIncludes bibliographical references and index. 327 $aBatch Effects and Noise in Microarray Experiments; Contents; List of Contributors; Foreword; Preface; 1 Variation, Variability, Batches and Bias in Microarray Experiments: An Introduction; 2 Microarray Platforms and Aspects of Experimental Variation; 2.1 Introduction; 2.2 Microarray Platforms; 2.2.1 Affymetrix; 2.2.2 Agilent; 2.2.3 Illumina; 2.2.4 Nimblegen; 2.2.5 Spotted Microarrays; 2.3 Experimental Considerations; 2.3.1 Experimental Design; 2.3.2 Sample and RNA Extraction; 2.3.3 Amplification; 2.3.4 Labeling; 2.3.5 Hybridization; 2.3.6 Washing; 2.3.7 Scanning 327 $a2.3.8 Image Analysis and Data Extraction2.3.9 Clinical Diagnosis; 2.3.10 Interpretation of the Data; 2.4 Conclusions; 3 Experimental Design; 3.1 Introduction; 3.2 Principles of Experimental Design; 3.2.1 Definitions; 3.2.2 Technical Variation; 3.2.3 Biological Variation; 3.2.4 Systematic Variation; 3.2.5 Population, Random Sample, Experimental and Observational Units; 3.2.6 Experimental Factors; 3.2.7 Statistical Errors; 3.3 Measures to Increase Precision and Accuracy; 3.3.1 Randomization; 3.3.2 Blocking; 3.3.3 Replication; 3.3.4 Further Measures to Optimize Study Design 327 $a3.4 Systematic Errors in Microarray Studies3.4.1 Selection Bias; 3.4.2 Observational Bias; 3.4.3 Bias at Specimen/Tissue Collection; 3.4.4 Bias at mRNA Extraction and Hybridization; 3.5 Conclusion; 4 Batches and Blocks, Sample Pools and Subsamples in the Design and Analysis of Gene Expression Studies; 4.1 Introduction; 4.1.1 Batch Effects; 4.2 A Statistical Linear Mixed Effects Model for Microarray Experiments; 4.2.1 Using the Linear Model for Design; 4.2.2 Examples of Design Guided by the Linear Model; 4.3 Blocks and Batches; 4.3.1 Complete Block Designs; 4.3.2 Incomplete Block Designs 327 $a4.3.3 Multiple Batch Effects4.4 Reducing Batch Effects by Normalization and Statistical Adjustment; 4.4.1 Between and Within Batch Normalization with Multi-array Methods; 4.4.2 Statistical Adjustment; 4.5 Sample Pooling and Sample Splitting; 4.5.1 Sample Pooling; 4.5.2 Sample Splitting: Technical Replicates; 4.6 Pilot Experiments; 4.7 Conclusions; Acknowledgements; 5 Aspects of Technical Bias; 5.1 Introduction; 5.2 Observational Studies; 5.2.1 Same Protocol, Different Times of Processing; 5.2.2 Same Protocol, Different Sites (Study 1); 5.2.3 Same Protocol, Different Sites (Study 2) 327 $a5.2.4 Batch Effect Characteristics at the Probe Level5.3 Conclusion; 6 Bioinformatic Strategies for cDNA-Microarray Data Processing; 6.1 Introduction; 6.1.1 Spike-in Experiments; 6.1.2 Key Measures - Sensitivity and Bias; 6.1.3 The IC Curve and MA Plot; 6.2 Pre-processing; 6.2.1 Scanning Procedures; 6.2.2 Background Correction; 6.2.3 Saturation; 6.2.4 Normalization; 6.2.5 Filtering; 6.3 Downstream Analysis; 6.3.1 Gene Selection; 6.3.2 Cluster Analysis; 6.4 Conclusion; 7 Batch Effect Estimation of Microarray Platforms with Analysis of Variance; 7.1 Introduction 327 $a7.1.1 Microarray Gene Expression Data 330 $aBatch Effects and Noise in Microarray Experiments: Sources and Solutions looks at the issue of technical noise and batch effects in microarray studies and illustrates how to alleviate such factors whilst interpreting the relevant biological information. Each chapter focuses on sources of noise and batch effects before starting an experiment, with examples of statistical methods for detecting, measuring, and managing batch effects within and across datasets provided online. Throughout the book the importance of standardization and the value of standard operating procedures in the devel 410 0$aWiley Series in Probability and Statistics 606 $aDNA microarrays 606 $aDNA microarrays$xExperiments 615 0$aDNA microarrays. 615 0$aDNA microarrays$xExperiments. 676 $a572.8 676 $a572.8636 676 $a611.01816 701 $aScherer$b Andreas$f1966-$01602707 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910831169203321 996 $aBatch effects and noise in microarray experiments, sources, and solutions$93926741 997 $aUNINA