- Introduction to metabolomics
- Types of metabolomics data
- Handling and processing metabolomics data
- Annotation of metabolomics data
CSAMA 2022
Sample is dissolved in a fluid (mobile phase).
Mobile phase carries analytes through column (stationary phase).
Sample is dissolved in a fluid (mobile phase).
Mobile phase carries analytes through column (stationary phase).
Separation based on affinity for the column’s stationary phase.
Sample is dissolved in a fluid (mobile phase).
Mobile phase carries analytes through column (stationary phase).
Separation based on affinity for the column’s stationary phase.
Commonly used: RPLC (Reversed Phase LC). HILIC (hyrophilic liquid interaction chromatography)
We gain an additional dimension:
We gain an additional dimension:
We gain an additional dimension:
We gain an additional dimension:
We gain an additional dimension:
We gain an additional dimension:
We gain an additional dimension:
ms <- readMSData(fl, mode = "onDisk")
sps <- Spectra(fl, backend = MsBackendMzR())
## DataFrame with 779 rows and 6 columns ## mz rt sample_1 sample_2 sample_3 sample_4 ## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> ## FT001 326.378 25.409 4654.057 4755.993 4750.997 4671.01 ## FT002 134.096 16.513 767.239 926.769 791.133 852.95 ## ... ... ... ... ... ... ... ## FTM936 501.383 137.340 7767.00 7871.74 7869.51 7697.32 ## FTM937 612.404 28.094 1667.49 1640.29 1676.65 1652.04
## DataFrame with 779 rows and 6 columns ## mz rt sample_1 sample_2 sample_3 sample_4 ## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> ## FT001 326.378 25.409 4654.057 4755.993 4750.997 4671.01 ## FT002 134.096 16.513 767.239 926.769 791.133 852.95 ## ... ... ... ... ... ... ... ## FTM936 501.383 137.340 7767.00 7871.74 7869.51 7697.32 ## FTM937 612.404 28.094 1667.49 1640.29 1676.65 1652.04
## DataFrame with 779 rows and 6 columns ## mz rt sample_1 sample_2 sample_3 sample_4 ## <numeric> <numeric> <numeric> <numeric> <numeric> <numeric> ## FT001 326.378 25.409 4654.057 4755.993 4750.997 4671.01 ## FT002 134.096 16.513 767.239 926.769 791.133 852.95 ## ... ... ... ... ... ... ... ## FTM936 501.383 137.340 7767.00 7871.74 7869.51 7697.32 ## FTM937 612.404 28.094 1667.49 1640.29 1676.65 1652.04
name | formula | exactmass |
---|---|---|
Caffeine | C8H10N4O2 | 194.1 |
name | formula | exactmass | [M+H]+ |
---|---|---|---|
Caffeine | C8H10N4O2 | 194.1 | 195.1 |
name | formula | exactmass | [M+H]+ | [M+Na]+ |
---|---|---|---|---|
Caffeine | C8H10N4O2 | 194.1 | 195.1 | 217.1 |
name | formula | exactmass | [M+H]+ | [M+Na]+ |
---|---|---|---|---|
Caffeine | C8H10N4O2 | 194.1 | 195.1 | 217.1 |
mtch <- matchValues(query, target, Mass2MzParam(c("[M+H]+", "[M+Na]+")))
name | formula | exactmass | [M+H]+ | [M+Na]+ |
---|---|---|---|---|
Caffeine | C8H10N4O2 | 194.1 | 195.1 | 217.1 |
mtch <- matchValues(query, target, Mass2MzParam(c("[M+H]+", "[M+Na]+")))
query
: experimental m/z values.target
: reference masses.name | formula | exactmass | [M+H]+ | [M+Na]+ |
---|---|---|---|---|
Caffeine | C8H10N4O2 | 194.1 | 195.1 | 217.1 |
Enprofylline | C8H10N4O2 | 194.1 | 195.1 | 217.1 |
name | formula | exactmass | [M+H]+ | [M+Na]+ |
---|---|---|---|---|
Caffeine | C8H10N4O2 | 194.1 | 195.1 | 217.1 |
Enprofylline | C8H10N4O2 | 194.1 | 195.1 | 217.1 |
mtch <- matchValues(query, target, Mass2MzRtParam(c("[M+H]+", "[M+Na]+")))
simmat <- compareSpectra(a, b)
mtch <- matchSpectra(query, target, CompareSpectraParam())
SpectriPy: integrate python MS libraries (matchms, MS2DeepScore) into Spectra-based workflows.
SpectraTutorials
: introduction to MS data handling and processing using Spectra.
MetaboAnnotationTutorials
: annotation of untargeted metabolomics data.
Thank you for your attention