LC-MS Data Pre-Processing. Xiuxia Du, Ph.D. Department of Bioinformatics and Genomics University of North Carolina at Charlotte

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1 LC-MS Data Pre-Processing Xiuxia Du, Ph.D. Department of Bioinformatics and Genomics University of North Carolina at Charlotte

2 Outline Raw LC-MS data - Profile and centroid data - Mass vs. retention time map - TIC - EIC - Feature Data pre-processing - Feature detection - Feature grouping - Feature alignment Feature identification 2

3 Raw LC-MS data 3

4 List of mass spectra list of scans in raw files MS scans in blue MS/MS scans in red # sequential retention time MS level type of spectrum p = profile c = centroid t = thresholded polarity of ionization + = positive - = negative? = unknown 4

5 One mass spectrum 5

6 One mass spectrum 6

7 One mass spectrum 7

8 Zoom in one mass spectrum profile mode 8

9 Conversion to centroid mode 9

10 Conversion to centroid mode click here to bring up the parameter window 10

11 Conversion to centroid mode 11

12 Conversion to centroid mode Mass detection in progress 12

13 Mass spectra in centroid mode 13

14 Mass spectra in centroid mode 14

15 Mass spectra in centroid mode 15

16 Spectrum in centroid mode Data files are much smaller than files in profile mode. We will use the centroid data for practicing data preprocessing using XCMS in R. 16

17 LC-MS raw data in 3D 17

18 Raw data in 3D 18

19 Raw data in 3D 19

20 Raw data in 3D 20

21 3D to 2D Direct processing of the 3D data is NOT trivial Instead, we examine 2D - Mass vs. retention time - Total ion current vs. retention time: TIC - Ion current vs. retention time for a particular mass: EIC (Extracted Ion Chromatogram) 21

22 Mass vs. retention time map 22

23 Mass vs. retention time map 23

24 Mass vs. retention time map 24

25 TIC 25

26 TIC 26

27 TIC 27

28 EIC 28

29 EIC 29

30 EIC 30

31 EIC 31

32 Feature 32

33 Feature Feature: A 3D signal induced by a single ion species (e.g. [M+H] + or [M-H] - of a compound) How to detect features? - by peak picking from EICs Data processing and analysis workflow raw MS data feature detection feature filtering feature grouping feature identification stats alignment feature annotation 33

34 Data Pre-processing 34

35 Detection of chromatographic features Use wavelet transform ψ s,τ (t) t s = 1 s = 2 s = 8 Scale Intensity * Chromatogram Gaussian Fit mexican hat wavelet Seconds Implemented in XCMS as the centwave method Tautenhahn, R.; Bottcher, C.; Neumann, S., Highly sensitive feature detection for high resolution LC/MS. BMC bioinformatics 2008, 9,

36 Detection of chromatographic features 36

37 Detection of chromatographic features 37

38 Feature grouping and alignment 38

39 Feature grouping and alignment zoom in 39

40 Feature grouping and alignment 40

41 Feature grouping and alignment 41

42 Result of data pre-processing DB Name Mass RT platform IN1 IN2 IN3 IN4 IN5 IN6 HMDB 1-Phenylethylamin ES HMDB 2-Ethylacrylic acid ES HMDB Canavanine ES HMDB Diketogulonic acid ES HMDB Iso-Valeraldehyde ES in-house 3,4-Dehydro-Dprol ES in-house 4-hydroxy-proline ES in-house Malic acid ES in-house 2,3,4-Trihydroxybu ES in-house 2,3-Diaminopropio ES in-house 4-Methy2-oxovaler ES in-house 5-Aminopentanoic ES in-house Acetylcarnitine ES HMDB 11-beta-hydroxyand ES HMDB 13-Hydroperoxylin ES HMDB 17-Hydroxylinolen ES HMDB 2,4-Diaminobutyric ES HMDB 2,6 dimethylheptan ES HMDB 2-Ethylhydracrylic ES HMDB 2-Ketohexanoic aci ES

43 Feature identification Apply statistics and machine learning to detect discriminating peaks Identify discriminating peaks 43

44 Feature identification 44

45 Feature identification 45

46 Feature identification 46

47 Feature identification 47

48 Feature identification Information we have for identification - M+H - Experimental isotopic identification - MS/MS 48

49 Feature identification 49

50 Feature identification 50

51 Feature identification 51

52 Feature identification 52

53 Feature identification 53

54 Feature identification 54

55 Feature identification 55

56 Feature identification 56

57 Feature identification 57

58 Feature identification 58

59 Feature identification 59

60 Feature identification Compare isotopic distributions theoretical experimental 60

61 Feature identification 61

62 CompareMS/MS Feature identification library experimental 62

63 Thank you! 63

Advance XCMS Data Processing. H. Paul Benton

Advance XCMS Data Processing. H. Paul Benton Advance XCMS Data Processing H. Paul Benton Reminder of what we re trying to do Peak Detection Grouping Groups similar Peaks across replicates Retention Time Alignment Statistical Analysis of Classes Total