There is no silver bullet: a guide to low-level data transforms and normalisation methods for microarray data
To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficul...
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          | Published in | Briefings in bioinformatics Vol. 6; no. 1; pp. 86 - 97 | 
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| Main Authors | , | 
| Format | Journal Article | 
| Language | English | 
| Published | 
        Oxford
          Oxford University Press
    
        01.03.2005
     Oxford Publishing Limited (England)  | 
| Subjects | |
| Online Access | Get full text | 
| ISSN | 1467-5463 1477-4054  | 
| DOI | 10.1093/bib/6.1.86 | 
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| Abstract | To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application. | 
    
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| AbstractList | To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application.To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application. To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application. To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application. Keywords: microarrays, experimental bias, data normalisation, low-level datatransforms, microarray data analysis To overcome random experimental variation, even for simple screens, data from multiple microarrays have to be combined. There are, however, systematic differences between arrays, and any bias remaining after experimental measures to ensure consistency needs to be controlled for. It is often difficult to make the right choice of data transformation and normalisation methods to achieve this end. In this tutorial paper we review the problem and a selection of solutions, explaining the basic principles behind normalisation procedures and providing guidance for their application. [PUBLICATION ABSTRACT] Keywords: microarrays, experimental bias, data normalisation, low-level datatransforms, microarray data analysis  | 
    
| Author | KREIL, David P RUSSELL, Roslin R  | 
    
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| Keywords | Data analysis Experimental data Bias Prediction DNA chip low-level data- transforms data normalisation Review Microarray experimental bias Clusterin microarrays Statistical test Computer program microarray data analysis Classification Robustness Bioinformatics  | 
    
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| SubjectTerms | Algorithms Bioinformatics Biological and medical sciences Calibration - standards Data analysis Data Interpretation, Statistical Design of experiments Fundamental and applied biological sciences. Psychology Gene expression Gene Expression Profiling - instrumentation Gene Expression Profiling - methods Gene Expression Profiling - standards General aspects Genetic Variation - genetics Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Genetic Models, Statistical Numerical Analysis, Computer-Assisted Oligonucleotide Array Sequence Analysis - instrumentation Oligonucleotide Array Sequence Analysis - methods Oligonucleotide Array Sequence Analysis - standards Sequence Analysis, DNA - instrumentation Sequence Analysis, DNA - methods Sequence Analysis, DNA - standards  | 
    
| Title | There is no silver bullet: a guide to low-level data transforms and normalisation methods for microarray data | 
    
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