Skip to main content

Table 3 Comparison of the number of features detected (total and known) using apLCMS, xMSanalyzer, and XCMS

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

Datasets

apLCMS

xMSanalyzer-apLCMS

XCMS v1.20.1

 

{default}

{3,0.3} Ï… {3,0.8}

{step = 0.001, snthresh = 3, max = 5, mzdiff = 0.1}

Sample Set 1 (Column A)

1454

2384

1027

 

MMCD: 314 (21.6%)

MMCD: 534 (22.3%)

MMCD: 222 (21.6%)

 

Metlin: 292 (20.1%)

Metlin: 433 (18.1%)

Metlin: 230 (22.4%)

Sample Set 1 (Column B)

1238

2201

998

 

MMCD: 309 (25%)

MMCD: 557 (25.3%)

MMCD: 261 (26.1%)

 

Metlin: 279 (22.5%)

Metlin: 468 (21.2%)

Metlin: 252 (25.2%)

Sample Set 2 (Column A)

1324

2677

1262

 

MMCD: 408 (30.8%)

MMCD: 732 (27.3%)

MMCD: 324 (25.7%)

 

Metlin: 497 (37.5%)

Metlin: 705 (26.3%)

Metlin: 431 (34.2%)

Sample Set 2 (Column B)

1573

2969

1395

 

MMCD: 508 (32.3%)

MMCD: 794 (26.7%)

MMCD: 359 (25.7%)

 

Metlin: 693 (44.1%)

Metlin: 848 (28.5%)

Metlin: 514 (36.8%)

Average over all datasets

Total: 1397

Total: 2558

Total: 1171

 

Known metabolites: 413 (29.6%)

Known metabolites: 634 (24.8%)

Known metabolites: 324 (27.7%)