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Table 2 Results of the example data sets

From: IPO: a tool for automated optimization of XCMS parameters

 

Metabolite fingerprinting

Lipidomics

Central carbon metabolism

pooled sample injections

   

training set:

12

4

6

test set:

11

4

6

DoEs peakpicking

4

3

2

DoEs retcor + grouping

5

5

4

time for peakpicking optimization

3.8 h

1.5 h

0.9 h

time for retcor + grouping optimization

0.8 h

0.7 h

0.6 h

overall time

4.6 h

2.2 h

1.5 h

 

default

optimized

default

optimized

default

optimized

#peaks

      

training set:

55,845

57,075

33,298

31,710

24,247

24,230

test set:

65,851

53,205

34,415

32,397

27,539

25,609

#RPa

      

training set:

6,999

8,433

12,606

14,367

2,710

3,351

test set:

7,587

7,903

12,999

14,594

1,582

1,869

#LIPb

      

training set:

15,497

11,645

15,245

17,284

11,327

11,490

test set:

11,163

10,855

15,643

17,680

12,646

10,962

PPSc

      

training set:

1,214

1,565

8,802

14,308

568

881

test set:

1,053

1,475

9,001

14,472

168

238

RCSd

      

training set:

12.3

144.8

67.8

575.4

92.8

311.8

test set:

9.4

142.4

37.6

580.4

48.1

206.7

#reliable groups

      

training set:

536

990

3,669

5,343

1,504

2,424

test set:

314

759

1,564

5,639

793

1,855

#non-reliable groups

      

training set:

2,636

82

3,605

151

1,217

101

test set:

2,740

70

3,248

110

1,150

69

GSe

      

training set:

109

11,952

3,734

189,057

1,859

58,176

test set:

36

8,230

753

289,076

547

49,870

  1. areliable peaks; blow intensity peaks; cpeak picking score; dretention time correction; score; egrouping score
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