. Author manuscript; available in PMC: 2019 Jan 30.

Published in final edited form as: IEEE Int Conf Bioinform Biomed Workshops. 2011 Jan 28;2010:827–828. doi: 10.1109/BIBMW.2010.5703928

Table 1.

PCA and data quality evaluation

Table 1a. PCA-correction
Designed feature	GC-percentage		Date and scanner
Identified Component	1^st		2^nd
P-value	<1E-23		<1E-23
Table 1b. Evaluation of data quality
Data Quality	High noise		Low noise
	σ_LRR	N_{sub_ex}	σ_LRR	N_{sub_ex}
Uncorrected	0.30±0.03	76	0.25±0.03	10
Corrected (Comp. 1)	0.28±0.02	46	0.23±0.02	4
Corrected (Comp. 1, 2)	0.28±0.02	40	0.22±0.02	4
Table 1c. Detection Accuracy: PCA-correction
Total generated markers with CNVs: 75867
PennCNV results	Overall FPR		Overall FNR
Uncorrected	0.6220		0.1374
Corrected (comp. 1)	0.0389		0.0940
Corrected (comp. 1, 2)	0.0351		0.0886
Table 1d. Detection Accuracy: regression-based correction
PennCNV results	Overall FPR		Overall FNR
GC-percentage corrected	0.0389		0.0944

Note: High noise and low noise refer to the groups with high-SD and low-SD Gaussian noise, each group containing 100 samples; N_{sub_ex} denotes the number of bad samples that failed quality control; FPR and FNR are calculated using the total number of markers with CNVs.