preprocessing

Microarray technology is a powerful technique to measure the expression levels of large numbers of genes simultaneously. Microarray data contains many noise sources; therefore, several preprocessing steps are necessary to convert the raw data to achieve accurate analyzing results. Preprocessing of microarray data includes background correction, data normalization, and summarization steps each can be performed by a large variety of methods. However, the relative impact of these methods on the detection of differentially expressed genes remains to be determined. The aim of this study was to compare the effects of different methods of preprocessing on the results of differentially expressed gene detection. The used data was downloaded from the NCBI GEO database. The series (GSE) accession number, platform (GPL) accession number, and platform name of the data were GSE56589, GPL18534, and Affymetrix Bovine Genome Array, respectively. Two background correction methods (MAS.5 and RMA.2), two normalization methods (Scaling normalization and Quantile normalization), and two summarization methods (Tukey biweight and Medianpolish) were evaluated. The results showed that the number and types of differentially expressed genes could be mainly affected by background correction and normalization methods, but the summarization method showed a small impact.