e105 Nucleic Acids Research, 2010, Vol. 38, No. 9 Table 1. Results of NSR optimization search on the training datasets Method CBS CNVFinder cnvPartition GLAD Nexus Rank Nexus SNPRank PennCNV QuantiSNP Parameters 1 standard deviation 3 standard deviations 4 standard deviations 5 standard deviations SDe = 6, 4, 4, 2 SDe = 7, 5, 4, 2 SDe = 8, 7, 6, 5 Conﬁdence >0 Conﬁdence >5 Conﬁdence >10 Default Segment log R > |0.3| Segment log R > |0.4| Threshold <0.01 Threshold <0.001 Threshold <0.0001 Threshold <0.01 Threshold <0.001 Threshold <0.0001 All CNV calls SNPs >1 L = 2000000 L = 3000000 L = 4000000 Log Bayes 0 Log Bayes 2.5 CNV SNPs per Sample Unique CNV SNPs 275 270 265 130 630 446 258 195 193 186 262 243 192 235 206 185 271 248 332 314 283 311 268 10 126 2041 540 0.8050 0.8135 0.8169 0.9158 0.8476 0.8907 0.9037 0.8187 0.8185 0.8167 0.7245 0.7476 0.7724 0.8696 0.9385 0.9137 0.7862 0.7816 0.7889 0.7909 0.7677 0.8793 0.8423 0.7978 0.8312 0.8111 NSR 0.00293 0.00302 0.00309 0.00707 0.00135 0.00200 0.00350 0.00420 0.00425 0.00440 0.00276 0.00307 0.00402 8.588E–5 0.00046 0.00169 0.00335 0.00380 0.00426 0.00292 0.00310 0.00265 0.00268 0.00282 0.00267 0.00303 PAGE 4 OF 14 ROC residual 0.00284 0.00325 0.00316 0.00079 0.0016 0.00114 0.00061 0.00257 0.00253 0.00249 0.00208 0.00186 0.00111 0.00481 0.00349 0.00312 0.00288 0.00272 0.00246 0.00284 0.00282 0.00313 0.00311 0.00308 0.00308 0.00299 1 ± SD for one copy CNVs, 0.585 ± SD for three copy CNVs and 1 ± SD for four copy CNVs, where we prespeciﬁed both copy number, SD (0.1–0.3 of the base log R ratio) and the length (number of SNPs) at each CNV location (Figure 1). In simulation 2, CNVs were designed to reﬂect CNVs detected in the SCORM samples, from those detected by all seven methods (highest number of replicated CNVs) to those detected by three methods (moderate number of replicated CNVs). First, all CNVs to be simulated were spaced every 1000 SNPs apart as shown in Figure 1. We then designated the copy number of the CNV (sim2 copy: 0, 1, 3 or 4), the frequency of the detection methods that detected the same CNV (sim2 mm: 3–7) and CNV length in SNPs (sim2 size: 3–17) at each CNV location (Figure 1). Second, we created a pool of CNVs from the analysis dataset of SCORM, which were detected by three to seven of the methods tested herein and computed the mean and SD of each CNV. Finally, at each CNV location, we randomly chose a CNV from the pool that had the same copy number, frequency of the detection methods and CNV size as designated in Figure 1. At each CNV location, we simulated the same numbers of CNV SNPs with log R ratio of each SNP randomly drawn from the interval of mean ± SD. For each simu- lation, 100 replicates were generated. A neutral simulation was designed as a platform inde- pendent test of CNV detection methods. Log R ratios of all SNPs were initially assigned by drawing from a normal distribution with mean 0 and SD 0.25, which is between the Illumina and Aﬀymetrix SDs of datasets used in this study. Forty-eight CNVs, 12 for each of copy numbers 0, 1, 3 and 4 were created by replacing initial log R ratios with log R ratios from the CNV distribution as detailed in Supplementary Table S2. CNVs were placed every 1000 SNPs. For each sample, each CNV was given a random size of 3–15 SNPs and was randomly placed at one of 48 locations. B allele frequencies were randomly assigned from a range of 0–0.07 for AA, 0.52–0.68 for AB and 0.89–1 for BB, where all genotypes are equally likely. SNP positions were spaced randomly from 1–10 000 bp. Since QuantiSNP and PennCNV calls are dependent on platform data, SNP names from Illumina HumanHap 550 chromosomes 18–22 were used. A simulation based on Aﬀymetrix 6.0 chip data was designed using log R ratio and B allele frequency data generated by PennCNV-Aﬀymetrix (6) from Gene Expression Omnibus (GEO) (12) dataset GSE13372 (13). Log R ratios of all SNPs were initially assigned by drawing from a normal distribution with mean and SD equal to the mean of these two statistics in the GSE13372 samples. Forty CNVs, 10 for each of copy numbers 0, 1, 3 and 4