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

BMC Systems Biology

Table 1 Simulation results compared the number of true positives among different methods

Positive^a				1%			5%			10%
%^b	σ ²_x ^c	σ ²_z ^d	α ^e	SIS^f	SKI^g	P^h	SIS	SKI	P	SIS	SKI	P
0.0	1	1	0.075	38.96	38.94	36.36	45.78	45.72	43.63	47.66	47.63	45.63
0.5	1	1	0.275	38.53	43.06	45.22	45.66	47.65	48.54	47.53	48.85	49.13
1.0	1	1	0.384	38.5	46.34	47.99	45.65	48.9	49.58	47.49	49.51	49.83
0.0	1	3	0.090	39.10	38.97	35.01	45.81	45.80	42.94	47.71	47.72	44.03
0.5	1	3	0.249	38.92	42.55	43.85	45.80	47.31	48.28	47.57	48.55	49.10
1.0	1	3	0.368	39.04	45.81	47.58	45.88	48.60	49.44	47.65	49.21	49.73
0.0	3	1	0.113	36.84	36.43	35.77	44.61	44.01	43.37	46.69	46.57	46.19
0.5	3	1	0.261	37.27	42.16	44.90	45.15	47.36	48.34	47.07	48.56	49.03
1.0	3	1	0.374	36.91	46.01	48.89	44.76	49.42	49.51	47.12	49.86	49.90
0.0	3	3	0.104	37.84	37.48	35.19	45.73	45.43	44.07	47.63	47.53	45.93
0.5	3	3	0.264	37.26	42.52	44.48	45.03	47.35	48.26	47.19	48.58	49.00
1.0	3	3	0.355	37.05	45.20	47.37	45.1	48.6	49.39	47.05	49.36	49.76

^aTop 1, 5 and 10% variables were selected respectively under different settings
^bthe percentage of non-zero β’s overlapped with each other in two datasets
^c σ ²_x : the variance added in internal dataset to generate response Y _x
^d σ ²_z : the variance added in external dataset to generate response Y _z
^e α: the estimated value of α which control the weight of two ranks in geometric mean
^fSIS: variables were sorted by marginal correlation using only internal dataset
^gSKI: variables were sorted by weighted geometric mean of two marginal correlation based ranks using two dataset
^hPool: two dataset were pooled together and treated as a single dataset, and then variables were sorted by marginal correlation

ISSN: 1752-0509