BMC Bioinformatics

Table 2 Candidate copy number variants from synthetic data

From: Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data

Total from Xba data
Method ^a	# CNVs	# Duplications	%	#Deletions	%	# True CNVs	# Unique True CNVs ^b	True Positive Rate (power) ^c	# False Positive CNVs	False Discovery Rate
CNAG-GLAD	334	20	6	314	94	331	58	0.58	3	0.009
dChip	381	166	44	215	56	213	32	0.32	168	0.44
dChip-GLAD	70	0	0	70	100	70	31	0.31	0	0
CNAT-GLAD	111	10	9	101	90	101	36	0.36	10	0.09
Total from Hind data
Method	# CNVs	# Duplications	%	#Deletions	%	# True CNVs	# Unique True CNVs	True Positive Rate (Power)	# False Positive CNVs	False Discovery Rate
CNAG-GLAD	70	11	16	59	84	70	42	0.42	0	0
dChip	269	91	34	178	66	155	26	0.26	114	0.42
dChip-GLAD	101	5	5	96	95	94	33	0.33	7	0.07
CNAT-GLAD	49	0	0	49	100	48	23	0.23	1	0.02

^aWhere two software packages are listed, the first one was used for normalization and the second for CNV detection.
^bCNVs with different chromosomal locations and breakpoints.
^cThe number of true (synthetic) CNVs per array is 100.

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ISSN: 1471-2105

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