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Table 2 Comparison of the number of quantitative reproducible features between apLCMS and xMSanalyzer

From: xMSanalyzer: automated pipeline for improved feature detection and downstream analysis of large-scale, non-targeted metabolomics data

Datasets

apLCMS

xMSanalyzer

xMSanalyzer

 

P{12,0.5}: default

P1{3,0.3} Ï… P2{3,0.8}

P1{12,0.5} Ï… 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%)

  1. 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.
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BMC Bioinformatics

ISSN: 1471-2105

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