Method CBS CNVFinder cnvPartition GLAD Nexus Rank Nexus SNPRank PennCNV QuantiSNP Parameter 5 SD 3 SD 1 SD 8,7,6,5 SDe 7,5,4,2 SDe 6,4,4,2 SDe Conﬁdence >10 Conﬁdence >5 Conﬁdence >0 Smoothed m >|0.4| Smoothed m >|0.3| Default Threshold 1E–4 Threshold 1E–3 Threshold 1E–2 Threshold 1E–4 Threshold 1E–3 Threshold 1E–2 Conﬁdence >17.5 Conﬁdence >10 SNPs 1 Log Bayes >10 Log Bayes >2.5 L = 2E6 4.41E–4 4.99E–4 9.62E–4 1.85E–4 3.76E–4 6.46E–4 6.98E–4 7.21E–4 7.33E–4 5.04E–4 5.87E–4 6.78E–4 0.00313 0.00432 0.00744 8.95E–4 0.00106 0.00122 7.35E–4 9.01E–4 9.65E–4 9.23E–4 0.0012 0.0015 6.91E–4 8.22E–4 0.00172 2.56E–4 5.10E–4 9.03E–4 0.00146 0.00148 0.00153 7.99E–4 9.59E–4 0.00107 0.00307 0.00386 0.00578 0.00128 0.00147 0.00174 0.00131 0.00155 0.00166 0.00159 0.00196 0.00228 0.0020 0.0023 0.0035 8.36E–4 0.0015 0.0024 0.0026 0.0027 0.0028 0.0022 0.0024 0.0026 0.00293 0.00816 0.03105 1.23E–4 1.69E–4 1.79E–4 0.0025 0.0030 0.0032 0.0030 0.0038 0.0043 e105 Nucleic Acids Research, 2010, Vol. 38, No. 9 PAGE 6 OF 14 Table 3. ROC curve residuals used to optimize parameters on the analysis dataset DGV ROC residual HapMap Asian ROC residual HapMap conﬁrmed ROC residual Maximal ROC curve residuals are optimal and indicated in bold. were created. First, a CNV detected by PennCNV within the GSE13372 dataset was randomly selected. Second, log R ratios were generated by drawing from a normal distribution with the mean and SD of the randomly selected CNV from the GEO dataset (Supplementary Table S3). CNV position and size and B allele frequency were assigned as in the neutral simulation. CNVs were placed every 1000 SNPs. SNP positions were spaced randomly from 1 to 5000 bp to model Aﬀymetrix 6.0 chip density. Since QuantiSNP only works on Illumina data, chromosome 1 SNP names from Illumina HumanHap 550 were used for QuantiSNP, the PennCNV Illumina HMM and Nexus Rank and SNP Rank. Aﬀymetrix SNP names were tested on the PennCNV gw6 and agre HMMs. RESULTS Optimization of method parameters The minimum NSR was observed for parameter settings of 1 SD for the CBS program, (6, 5, 4 and 2 SDe) for the CNVFinder program, 0 conﬁdence for the cnvPartition program, a = 0.001 for the GLAD program, 1 SNP length for the PennCNV program, signiﬁcance threshold = 0.01 for the Nexus Rank and Nexus SNPRank programs and L = 2 000 000 for the QuantiSNP programs using the training dataset (Table 1). These optimal parameters also lead to maximum ROC curve residuals for all but CBS. ROC residuals were calculated using our Asian HapMap CNV database. The same parameter settings were also observed as optimal for the analysis dataset and the large majority of parameter-DNA source combinations in the pilot dataset by ROC curve residuals (Tables 2 and 3). In simulation 1, the optimal parameters for ROC curve residuals are typically maximal at more conservative parameters than NSR optimal parameters, because NSR optimized parameters decreased speciﬁcity without increasing sensitivity (Supplementary Table S4). In all other simulations, sensitivity continued to increase more quickly than 1-speciﬁcity, and so optimal parameters concluded by NSR and ROC curve residuals agree with those concluded for the training dataset except for Nexus Rank (Supplementary Table S4). Overall, almost all datasets conﬁrm the validity of the NSR as a metric in choosing optimal parameters. The number of CNV SNPs detected at optimal param- eters was typically 150–350 per sample, with cnvPartition calling the fewest. Nexus Rank, however, called by far the most CNV SNPs per sample, over 10 000. Even so, it also had the best ROC residual of all methods (Table 1). QuantiSNP had double the number of CNV SNPs of most methods and also had good NSR and ROC statis- tics. GLAD had the lowest proportion of unique CNV SNPs (0.72), and QuantiSNP had the highest proportions (0.88). Optimal NSR parameters were identical for all methods over three training datasets varying in chip type and genotype rate. Method performance for multiple DNA sources Table 2 summarizes the average number of CNV SNPs per sample and ROC curve residuals for each method at NSR