A power law global error model for the identification of differentially expressed genes in microarray data

Norman Pavelka^* , Mattia Pelizzola^* , Caterina Vizzardelli^* , Monica Capozzoli , Andrea Splendiani , Francesca Granucci and Paola Ricciardi-Castagnoli

Department of Biotechnology and Bioscience, University of Milano-Bicocca, Piazza della Scienza 2, 20126 Milan, Italy

author email corresponding author email* Contributed equally

BMC Bioinformatics 2004, 5:203doi:10.1186/1471-2105-5-203


Published:	17 December 2004

Additional files

Additional File 1:

Performance of modeling method using different combinations of parameters p and q. The modeling method described in this study was tested on the 16iDC data set using different combinations of partitions (5, 10, 20, 50, 100, 200 and 500), and quantiles (0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 0.8, 0.9, 0.95, 0.98 and 0.99). For all 77 analyzed combinations of p and q regression lines were fitted to the data as described in the text. Goodness of fit was evaluated from the resulting slope (panel A), intercept (panel B) and adjusted r²(panel C).

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