Table 2

Comparison of the number of quantitative reproducible features between apLCMS and xMSanalyzer
Datasets apLCMS xMSanalyzer xMSanalyzer
P{12,0.5}: default P1{3,0.3} υ P2{3,0.8} P1{12,0.5} υ P2{3,0.8}
Sample Set 1 (Column A) 839 out of1454 (57.7%) 1208 out of 2384 (50.6%) 1115 out of 1816 (61.3%)
Sample Set 1 (Column B) 791 out of1238 (63.89%) 1236 out of 2201 (56.1%) 1081 out of 1615 (66.9%)
Sample Set 2 (Column A) 134 out of1324 (10.1%) 470 out of 2677 (17.5%) 424 out of 2256 (18.7%)
Sample Set 2 (Column B) 474 out of1573 (30.1%) 966 out of 2969 (32.5%) 897 out of 2546 (35.2%)
Average over all datasets 560 (40%) 970 (37.9%) 879 (42.7%)

The number of reproducible features (median PID < 30%) identified by apLCMS at min.run = 12 and min.pres = 0.5 and xMSanalyzer at P1{3,0.3} υ P2{3,0.8} that weighs more importance to the number of features as compared to quality, and at P1{12,0.5} υ P2{3,0.8} that gives balanced importance to the quality and quantity of features.

Uppal et al.

Uppal et al. BMC Bioinformatics 2013 14:15   doi:10.1186/1471-2105-14-15

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