Table 1.

Performance of rPCA for outlier detection on simulated data with 3 biological replicates in each treatment group using rlog transformation

ID of sample being added	outlier model	Error rate	Sample replicate	Outlier detected by PcaHubert	Number FP outlier called by PcaHubert	outlier detected by PcaGrid	Number FP outlier called by PcaGrid	SEN (%) by PcaGrid	SP (%) by PcaGrid
None (baseline)	NA	0.005		NA	NA	NA	NA	NA	NA
N-1	NA	0.01	1	NA	0	NA	0	NA	NA
N-2		0.05	1	NA	1	NA	0	NA	NA
N-3		0.1	1	NA	1	NA	0	NA	NA
N-4		0.2	1	NA	1	NA	0	NA	NA
L-1	outlierL	0.01	1	Yes	1	Yes	0	100	100
L-2		0.01	2	Yes	1	Yes	0	100	100
L-3		0.01	3	Yes	1	Yes	0	100	100
L-4		0.05	1	Yes	1	Yes	0	100	100
L-5		0.05	2	Yes	1	Yes	0	100	100
L-6		0.05	3	Yes	1	Yes	0	100	100
L-7		0.1	1	Yes	1	Yes	0	100	100
L-8		0.1	2	Yes	1	Yes	0	100	100
L-9		0.1	3	Yes	1	Yes	0	100	100
L-10		0.2	1	Yes	1	Yes	0	100	100
L-11		0.2	2	Yes	1	Yes	0	100	100
L-12		0.2	3	Yes	1	Yes	0	100	100
H-1	outlierH	0.01	1	Yes	1	Yes	0	100	100
H-2		0.01	2	Yes	1	Yes	0	100	100
H-3		0.01	3	Yes	1	Yes	0	100	100
H-4		0.05	1	Yes	1	Yes	0	100	100
H-5		0.05	2	Yes	1	Yes	0	100	100
H-6		0.05	3	Yes	1	Yes	0	100	100
H-7		0.1	1	Yes	1	Yes	0	100	100
H-8		0.1	2	Yes	1	Yes	0	100	100
H-9		0.1	3	Yes	1	Yes	0	100	100
H-10		0.2	1	Yes	1	Yes	0	100	100
H-11		0.2	2	Yes	1	Yes	0	100	100
H-12		0.2	3	Yes	1	Yes	0	100	100

SEN Sensitivity, SP Specificity, rlog Regularized log transformation, vst Variance Stabilizing Transformation, outlierL Outlier with low “outlierness”, outlierH Outlier with high “outlierness”