Evaluating the performance of microarray segmentation algorithms.
BACKGROUND
Although numerous algorithms have been developed for microarray segmentation, extensive comparisons between the algorithms have acquired far less attention. In this study, we evaluate the performance of nine microarray segmentation algorithms. Using both simulated and real microarray experiments, we overcome the challenges in performance evaluation, arising from the lack of ground-truth information. The usage of simulated experiments allows us to analyze the segmentation accuracy on a single pixel level as is commonly done in traditional image processing studies. With real experiments, we indirectly measure the segmentation performance, identify significant differences between the algorithms, and study the characteristics of the resulting gene expression data.


RESULTS
Overall, our results show clear differences between the algorithms. The results demonstrate how the segmentation performance depends on the image quality, which algorithms operate on significantly different performance levels, and how the selection of a segmentation algorithm affects the identification of differentially expressed genes.


BACKGROUND
Supplementary results and the microarray images used in this study are available at the companion web site http://www.cs.tut.fi/sgn/csb/spotseg/
Lehmussola
Antti
A
Institute of Signal Processing, Tampere University of Technology PO Box 553, 33101 Tampere, Finland. antti.lehmussola@tut.fi
Ruusuvuori
Pekka
P
Yli-Harja
Olli
O
D000465:Algorithms; D020869:Gene Expression Profiling / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012986:Software Validation