Calibration of microarray gene-expression data.
Calibration of microarray measurements aims at removing systematic biases from the probe-level data to get expression estimates that linearly correlate with the transcript abundance in the studied samples. The improvement of calibration methods is an essential prerequisite for estimating absolute expression levels, which, in turn, are required for quantitative analyses of transcriptional regulation, for example, in the context of gene profiling of diseases. We address hybridization on microarrays as a reaction process in a complex environment and express the measured intensities as a function of the input quantities of the experiment. Popular calibration methods such as MAS5, dChip, RMA, gcRMA, vsn, and PLIER are briefly reviewed and assessed in light of the hybridization model and of previous benchmark studies. We present our hook method, a new calibration approach that is based on a graphical summary of the actual hybridization characteristics of a particular microarray. Although single-chip related, hook performs as well as the multi-chip-related gcRMA, presently one of the best state-of-the-art methods for estimating expression values. The hook method, in addition, provides a set of chip summary characteristics that evaluate the performance of a given hybridization. The algorithm of the method is briefly described and its performance is exemplified.
Binder
Hans
H
Interdisciplinary Centre for Bioinformatics, University of Leipzig, Leipzig, Germany.
Preibisch
Stephan
S
Berger
Hilmar
H
D000465:Algorithms; D002138:Calibration; D019295:Computational Biology / Q000379:methods; D015870:Gene Expression; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D015233:Models, Statistical; D008967:Molecular Biology / Q000379:methods*; D009693:Nucleic Acid Hybridization; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods; D012313:RNA / Q000378:metabolism; D012333:RNA, Messenger / Q000378:metabolism
D012333:RNA, Messenger; D012313:RNA