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 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.
Kreil
David P
DP
Dept of Genetics, Univ. of Cambridge, Downing Street, Cambridge CB2 3EH, UK. D.Kreil@gen.cam.ac.uk
Russell
Roslin R
RR
D000465:Algorithms; D016477:Artifacts; D002138:Calibration / Q000592:standards; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000295:instrumentation / Q000379:methods* / Q000592:standards*; D014644:Genetic Variation / Q000235:genetics; D008957:Models, Genetic; D015233:Models, Statistical; D009716:Numerical Analysis, Computer-Assisted; D020411:Oligonucleotide Array Sequence Analysis / Q000295:instrumentation / Q000379:methods* / Q000592:standards*; D017422:Sequence Analysis, DNA / Q000295:instrumentation / Q000379:methods* / Q000592:standards*