Correction of scaling mismatches in oligonucleotide microarray data

摘要: Gene expression microarray data is notoriously subject to high signal variability. Moreover, unavoidable variation in the concentration of transcripts applied microarrays may result poor scaling summarized which can hamper analytical interpretations. This especially relevant a systems biology context, where systematic biases signals particular genes have severe effects on subsequent analyses. Conventionally it would be necessary replace mismatched arrays, but individual time points cannot rerun and inserted because experimental It therefore repeat whole series experiment, both impractical expensive. We explain how mismatches occur by popular MAS5 (GCOS; Affymetrix) algorithm, propose simple recursive algorithm correct them. Its principle identify set constant use this rescale signals. study properties using artificially generated apply data. show that generates used from other experiments, provided underlying system similar original. also demonstrate, example, method successfully existing imbalancesin The obtained for given experiment studied are sufficiently similar. type rescaling applications