High-dimensional omics data analysis using a variable screening protocol with prior knowledge integration (SKI)

BMC Systems Biology

Table 2 Simulation results compared the number of true positives among iterative and non-iterative approaches when top 1% variables were selected

%^a	ρ ^b	α ^c	SIS^d	SKI^e	iSIS^f	iSKI^g
0	0.3	0.061	23.32	23.12	25.22	22.53
0.5	0.3	0.342	24.83	33.20	26.13	34.43
1	0.3	0.443	23.14	34.41	26.33	38.85
0	0.6	0.044	37.35	36.34	41.11	36.17
0.5	0.6	0.392	36.47	41.67	39.67	44.83
1	0.6	0.453	37.12	45.83	40.44	49.40

^a%: the percentage of non-zero β’s overlapped with each other in two datasets
^b ρ: correlation coefficients between two neighbor variables in each cluster
^c α: the estimated value of α which control the weight of two ranks in geometric mean
^dSIS: variables were sorted by marginal correlation using only internal dataset
^eiSIS: iterative version of SIS
^fSKI: variables were sorted by weighted geometric mean of two marginal correlation based ranks using two dataset
^giSKI: iterative version of SKI

ISSN: 1752-0509