Parsing SwissLipids into a network for LipiNet¶
import lipinet.databases # Import the module
import importlib
# Reload the module to ensure changes are picked up
importlib.reload(lipinet)
# Now can use the functions after reloading the module
from lipinet.databases import get_prior_knowledge
from lipinet.utils import split_and_expand_large, create_nodedf_from_edgedf, check_for_split_characters
import pandas as pd
Parsing the manual way¶
LipiNet offers conventient functions to parse prior knowledge resources straight into networks. But to show what is happening behind the scenes, this notebook goes through the data and each of the steps. Which may also be particularly helpful to you if you need to customise the networks in a way that is not yet supported by LipiNet directly.
df_swisslipids = get_prior_knowledge('swisslipids', verbose=True)
df_swisslipids
File found locally at /Users/agjanyunlu/Documents/Metabolomics/lipinet/lipinet/.data/downloaded/swisslipids_lipids.tsv. Loading data...
Before cleaning, number of values in lipid class column with trailing space: Lipid class*
False 779171
True 76
Name: count, dtype: int64
After cleaning, number of values in lipid class column with trailing space: Lipid class*
False 779247
Name: count, dtype: int64
| Lipid ID | Level | Name | Abbreviation* | Synonyms* | Lipid class* | Parent | Components* | SMILES (pH7.3) | InChI (pH7.3) | ... | Exact m/z of [M+Li]+ | Exact m/z of [M+NH4]+ | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | SLM:000000002 | Class | Ceramide (iso-d17:1(4E)) | Cer(iso-d17:1(4E)) | N-acyl-15-methylhexadecasphing-4-enine | SLM:000399814 | NaN | NaN | CC(C)CCCCCCCCC\C=C\[C@@H](O)[C@H](CO)NC([*])=O | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 70846 | NaN | NaN | MNXM97012 | | 11443131 | 14685263 | 18390550 | 21325339 |... |
| 1 | SLM:000000003 | Isomeric subspecies | 15-methylhexadecasphing-4-enine | NaN | NaN | SLM:000390097 | NaN | NaN | CC(C)CCCCCCCCC\C=C\[C@@H](O)[C@@H]([NH3+])CO | InChI=1S/C17H35NO2/c1-15(2)12-10-8-6-4-3-5-7-9... | ... | 292.282235 | 303.300605 | 284.259503 | 320.236181 | 344.280632 | 70771 | NaN | NaN | MNXM57784 | 19372430 |
| 2 | SLM:000000006 | Isomeric subspecies | 15-methylhexadecasphinganine | NaN | NaN | SLM:000390097 | NaN | NaN | CC(C)CCCCCCCCCCC[C@@H](O)[C@@H]([NH3+])CO | InChI=1S/C17H37NO2/c1-15(2)12-10-8-6-4-3-5-7-9... | ... | 294.297885 | 305.316255 | 286.275153 | 322.251831 | 346.296282 | 70829 | NaN | NaN | MNXM97029 | 19372430 |
| 3 | SLM:000000007 | Class | Sphingomyelin (iso-d17:1(4E)) | SM(iso-d17:1(4E)) | N-acyl-15-methylhexadecasphing-4-enine-1-phosp... | SLM:000001000 | NaN | NaN | CC(C)CCCCCCCCC\C=C\[C@@H](O)[C@H](COP([O-])(=O... | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 70775 | NaN | NaN | MNXM97113 | 14685263 | 21926990 | 9603947 |
| 4 | SLM:000000035 | Isomeric subspecies | sphinganine | NaN | NaN | SLM:000390097 | NaN | NaN | CCCCCCCCCCCCCCC[C@@H](O)[C@@H]([NH3+])CO | InChI=1S/C18H39NO2/c1-2-3-4-5-6-7-8-9-10-11-12... | ... | 308.313535 | 319.331905 | 300.290803 | 336.267481 | 360.311932 | 57817 | LMSP01020001 | HMDB00269 | MNXM302 | 10652340 | 10702247 | 10751414 | 10802064 | 10... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 779244 | SLM:000782324 | NaN | apo carotenoid | NaN | NaN | SLM:000508864 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 53183 | NaN | NaN | NaN | NaN |
| 779245 | SLM:000782325 | NaN | terpenoid | NaN | NaN | SLM:000508864 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 26873 | NaN | NaN | NaN | NaN |
| 779246 | SLM:000782326 | NaN | C-45 isoprenoid | NaN | NaN | SLM:000508864 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 87168 | NaN | NaN | NaN | NaN |
| 779247 | SLM:000782327 | NaN | gamma-lactone | NaN | NaN | SLM:000782238 | NaN | NaN | O1C(C(C(C1=O)*)*)* | NaN | ... | NaN | NaN | NaN | NaN | NaN | 37581 | NaN | NaN | NaN | NaN |
| 779248 | SLM:000782328 | NaN | oxidized 2-acylglycerol | NaN | NaN | SLM:000000355 | NaN | NaN | OCC(CO)OC(=O)* | NaN | ... | NaN | NaN | NaN | NaN | NaN | 167117 | NaN | NaN | NaN | NaN |
779249 rows × 29 columns
If we take a closer look into the data, especially the Lipid class* column, we will see that some of the values have multiple entries. For example Ceramide phosphoinositol is a Class level entry that itself belongs to both the SLM:000000834 and SLM:000399815 classes.
df_swisslipids.dropna(subset='Lipid class*')[df_swisslipids['Lipid class*'].dropna().str.contains('|', regex=False)]
| Lipid ID | Level | Name | Abbreviation* | Synonyms* | Lipid class* | Parent | Components* | SMILES (pH7.3) | InChI (pH7.3) | ... | Exact m/z of [M+Li]+ | Exact m/z of [M+NH4]+ | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | SLM:000000392 | Class | Ceramide phosphoinositol | IPC | Inositol-1-phosphoceramide | SLM:000000834 | SLM:000399815 | NaN | NaN | O[C@H]([*])[C@H](COP([O-])(=O)O[C@H]1[C@H](O)[... | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 64916 | NaN | NaN | NaN | 10888667 | 20727985 |
| 234 | SLM:000000509 | Isomeric subspecies | All-trans-retinyl hexadecanoate | NaN | all-trans-retinyl palmitate | SLM:000000982 | SLM:000508854 | NaN | NaN | CCCCCCCCCCCCCCCC(=O)OCC=C(C)C=CC=C(C)C=CC1=C(C... | InChI=1S/C36H60O2/c1-7-8-9-10-11-12-13-14-15-1... | ... | NaN | NaN | NaN | NaN | NaN | 17616 | NaN | HMDB03648 | NaN | 10769148 | 10819989 | 12230550 | 15550674 | 15... |
| 315 | SLM:000000612 | NaN | tetracosenoyl-CoA | NaN | NaN | SLM:000390051 | SLM:000782334 | NaN | NaN | CC(C)(COP([O-])(=O)OP([O-])(=O)OC[C@H]1O[C@H](... | NaN | ... | NaN | NaN | NaN | NaN | NaN | 74146 | NaN | NaN | NaN | 18541923 | 20110363 | 20937905 |
| 317 | SLM:000000614 | NaN | hexacosenoyl-CoA | NaN | NaN | SLM:000390051 | SLM:000782334 | NaN | NaN | CC(C)(COP([O-])(=O)OP([O-])(=O)OC[C@H]1O[C@H](... | NaN | ... | NaN | NaN | NaN | NaN | NaN | 74161 | NaN | NaN | NaN | 18165233 |
| 319 | SLM:000000621 | NaN | 2-hydroxy-tetracosenoyl-CoA | NaN | NaN | SLM:000390051 | SLM:000782334 | NaN | NaN | CC(C)(COP([O-])(=O)OP([O-])(=O)OC[C@H]1O[C@H](... | NaN | ... | NaN | NaN | NaN | NaN | NaN | 74215 | NaN | NaN | NaN | 18541923 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 755324 | SLM:000758294 | Class | Globoside | Globo | Globo-series | SLM:000000834 | SLM:000399813 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 61360 | NaN | NaN | NaN | NaN |
| 755325 | SLM:000758295 | Class | Isogloboside | Isoglobo | Isoglobo-series | SLM:000000834 | SLM:000399813 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 78257 | NaN | NaN | NaN | NaN |
| 779141 | SLM:000782221 | NaN | Resolvin E | RvE | NaN | SLM:000501332 | SLM:000508853 | NaN | NaN | NaN | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | NaN | LMFA0314 | NaN | NaN | NaN |
| 779142 | SLM:000782222 | NaN | Resolvin D | RvD | NaN | SLM:000501331 | SLM:000508853 | NaN | NaN | NaN | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | NaN | LMFA0403 | NaN | NaN | NaN |
| 779157 | SLM:000782237 | NaN | an N-(omega-(9Z,12Z-octadecadienoyloxy)-ultra-... | NaN | NaN | SLM:000000413 | SLM:000782274 | NaN | NaN | [C@H]([C@@H](/C=C/CCCCCCCCCCCCC)O)(NC(=O)*COC(... | NaN | ... | NaN | NaN | NaN | NaN | NaN | 157662 | NaN | NaN | NaN | NaN |
119 rows × 29 columns
What about other IDs?
cols_with_split_chars = check_for_split_characters(df_swisslipids, delimiter='|')
Checking split characters (|) in Lipid ID
No rows found
Checking split characters (|) in Level
No rows found
Checking split characters (|) in Name
No rows found
Checking split characters (|) in Abbreviation*
Found 9768 rows with split characters
| Lipid ID | Level | Name | Abbreviation* | Synonyms* | Lipid class* | Parent | Components* | SMILES (pH7.3) | InChI (pH7.3) | ... | Exact m/z of [M+Li]+ | Exact m/z of [M+NH4]+ | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 56 | SLM:000000262 | Class | 1,2-diacyl-sn-glycerol | 1,2-sn-DAG | DAG | DG | Diacylglycerol | SLM:000000423 | NaN | NaN | OC[C@@H](COC([*])=O)OC([*])=O | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 17815 | NaN | NaN | MNXM59 | 10336610 | 10685032 | 10888667 | 10931938 | 11... |
| 114 | SLM:000000341 | Class | 1-acyl-sn-glycerol | MAG | MG | Monoacylglycerol | SLM:000117130 | NaN | NaN | OC[C@H](O)COC([*])=O | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 64683 | NaN | NaN | MNXM2963 | 10685032 | 15939762 | 18037386 | 8663293 | 960... |
| 122 | SLM:000000355 | Class | 2-acylglycerol | MAG | MG | Monoacylglycerol | SLM:000000403 | NaN | NaN | OCC(CO)OC([*])=O | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 17389 | NaN | NaN | MNXM335 | NaN |
| 146 | SLM:000000400 | Class | Triacylglycerol | TAG | TG | NaN | SLM:000117141 | NaN | NaN | [*]C(=O)OCC(COC([*])=O)OC([*])=O | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 17855 | NaN | NaN | MNXM248 | 12682047 | 16135509 | 16150821 | 21704635 | 27... |
| 147 | SLM:000000401 | Class | Diacylglycerol | DAG | DG | NaN | SLM:000117140 | NaN | NaN | [*]OCC(CO[*])O[*] | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 18035 | NaN | NaN | MNXM59 | 12682047 | 16135509 | 16150821 | 27247428 | 29... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 505694 | SLM:000508489 | Molecular subspecies | Phosphatidylglycerol (O-17:1_0:0) | LPG(O-17:1_0:0) | PG(O-17:1_0:0) | Lysophosphatidylglycerol (O-17:1_0:0) | SLM:000508807 | SLM:000508779 | SLM:000001333 (sn1 or sn2 or sn3) | OCC(O)COP([O-])(=O)OCC(CO[*])O[*] | InChI=none | ... | 489.316311 | 500.334681 | 481.293579 | 517.270257 | 541.314708 | NaN | NaN | NaN | MNXM629334 | NaN |
| 505695 | SLM:000508490 | Molecular subspecies | Phosphatidylglycerol (O-15:1_0:0) | LPG(O-15:1_0:0) | PG(O-15:1_0:0) | Lysophosphatidylglycerol (O-15:1_0:0) | SLM:000508807 | SLM:000508775 | SLM:000001331 (sn1 or sn2 or sn3) | OCC(O)COP([O-])(=O)OCC(CO[*])O[*] | InChI=none | ... | 461.285011 | 472.303381 | 453.262279 | 489.238957 | 513.283408 | NaN | NaN | NaN | MNXM628940 | NaN |
| 505696 | SLM:000508491 | Molecular subspecies | Phosphatidylglycerol (O-13:1_0:0) | LPG(O-13:1_0:0) | PG(O-13:1_0:0) | Lysophosphatidylglycerol (O-13:1_0:0) | SLM:000508807 | SLM:000508771 | SLM:000001329 (sn1 or sn2 or sn3) | OCC(O)COP([O-])(=O)OCC(CO[*])O[*] | InChI=none | ... | 433.253711 | 444.272081 | 425.230979 | 461.207657 | 485.252108 | NaN | NaN | NaN | MNXM628548 | NaN |
| 595061 | SLM:000597889 | Isomeric subspecies | 7-oxoresolvin D2 | 7-oxo-RvD2| 7-keto-RvD2 | (16R,17S)-dihydroxy-7-oxo-(4Z,8E,10Z,12E,14E,1... | SLM:000508853 | SLM:000782222 | NaN | NaN | C(C/C=C\CC(/C=C/C=C\C=C\C=C\[C@H]([C@H](C/C=C\... | InChI=1S/C22H30O5/c1-2-3-9-16-20(24)21(25)17-1... | ... | 381.224780 | 392.243150 | 373.202048 | 409.178725 | 433.223177 | 137497 | NaN | NaN | NaN | 22844113 |
| 595062 | SLM:000597890 | Isomeric subspecies | 16-oxoresolvin D2 | 16-oxo-RvD2| 16-keto-RvD2 | (7S,17S)-dihydroxy-16-oxo-(4Z,8E,10Z,12E,14E,1... | SLM:000508853 | SLM:000782222 | NaN | NaN | C(C/C=C\C[C@@H](\C=C\C=C/C=C/C=C/C([C@H](C/C=C... | InChI=1S/C22H30O5/c1-2-3-9-16-20(24)21(25)17-1... | ... | 381.224780 | 392.243150 | 373.202048 | 409.178725 | 433.223177 | 137498 | NaN | NaN | NaN | 22844113 |
9768 rows × 29 columns
Checking split characters (|) in Synonyms*
Found 19853 rows with split characters
| Lipid ID | Level | Name | Abbreviation* | Synonyms* | Lipid class* | Parent | Components* | SMILES (pH7.3) | InChI (pH7.3) | ... | Exact m/z of [M+Li]+ | Exact m/z of [M+NH4]+ | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 11 | SLM:000000101 | Class | 1,2-diacyl-sn-glycero-3-phospho-(1'-sn-glycero... | PA | 1,2-diacyl-sn-glycero-3-phospho-(1'-sn-glycero... | SLM:000477285 | NaN | NaN | O[C@@H](COP([O-])([O-])=O)COP([O-])(=O)OC[C@@H... | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 60110 | NaN | NaN | MNXM871 | 20485265 | 9880566 |
| 17 | SLM:000000147 | Isomeric subspecies | N-(9Z-octadecenoyl)-ethanolamine | NAE (18:1(9Z)) | (9Z-octadecenoyl)-ethanolamide | N-(9Z-octadec... | SLM:000000378 | NaN | NaN | CCCCCCCC\C=C/CCCCCCCC(=O)NCCO | InChI=1S/C20H39NO2/c1-2-3-4-5-6-7-8-9-10-11-12... | ... | 332.313535 | 343.331905 | 324.290803 | 360.267481 | 384.311932 | 71466 | NaN | HMDB02088 | MNXM107386 | 14634025 | 16527816 | 17015445 | 17626977 | 17... |
| 18 | SLM:000000149 | Isomeric subspecies | N-hexadecanoyl-ethanolamine | NAE (16:0) | hexadecanoyl-ethanolamide | N-hexadecanoyl eth... | SLM:000000378 | NaN | NaN | CCCCCCCCCCCCCCCC(=O)NCCO | InChI=1S/C18H37NO2/c1-2-3-4-5-6-7-8-9-10-11-12... | ... | 306.297885 | 317.316255 | 298.275153 | 334.251831 | 358.296282 | 71464 | NaN | HMDB02100 | MNXM107548 | 12824167 | 14634025 | 15655246 | 15760304 | 16... |
| 19 | SLM:000000178 | Isomeric subspecies | N-(docosanoyl)-15-methylhexadecasphing-4-enine | Cer(iso-d17:1(4E)/22:0) | Ceramide (iso-d17:1(4E)/22:0) | N-docosanoyl-1... | SLM:000000002 | SLM:000392021 | SLM:000000827 (n-acyl) | CCCCCCCCCCCCCCCCCCCCCC(=O)N[C@@H](CO)[C@H](O)\... | InChI=1S/C39H77NO3/c1-4-5-6-7-8-9-10-11-12-13-... | ... | 614.605801 | 625.624171 | 606.583069 | 642.559747 | 666.604198 | 71377 | NaN | NaN | MNXM107026 | 19372430 |
| 20 | SLM:000000179 | Isomeric subspecies | N-(heneicosanoyl)-15-methylhexadecasphing-4-enine | Cer(iso-d17:1(4E)/21:0) | Ceramide (iso-d17:1(4E)/21:0) | N-henicosanoyl... | SLM:000000002 | SLM:000392020 | SLM:000001207 (n-acyl) | CCCCCCCCCCCCCCCCCCCCC(=O)N[C@@H](CO)[C@H](O)\C... | InChI=1S/C38H75NO3/c1-4-5-6-7-8-9-10-11-12-13-... | ... | 600.590151 | 611.608521 | 592.567419 | 628.544097 | 652.588548 | 71375 | NaN | NaN | MNXM107036 | 19372430 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 745092 | SLM:000747954 | Isomeric subspecies | CDP-1,2-di-(13-methyltetradecanoyl)-sn-glycerol | CDP-DAG (iso15:0/iso15:0) | 1,2-di-(13-methyltetradecanoyl)-sn-glycero-3-c... | SLM:000000084 | NaN | SLM:000000047 (sn1 or sn2) | [H]Nc1ccn([C@@H]2O[C@H](COP([O-])(=O)OP([O-])(... | InChI=1S/C42H77N3O15P2/c1-32(2)23-19-15-11-7-5... | ... | 932.498448 | 943.516818 | 924.475716 | 960.452394 | 984.496846 | NaN | NaN | HMDB0116214 | NaN | NaN |
| 745093 | SLM:000747955 | Isomeric subspecies | CDP-1-(13-methyltetradecanoyl)-2-(15-methylhex... | CDP-DAG (iso15:0/iso17:0) | 1-(13-methyltetradecanoyl)-2-(15-methylhexadec... | SLM:000000084 | NaN | SLM:000000047 (sn1) / SLM:000000048 (sn2) | [H]Nc1ccn([C@@H]2O[C@H](COP([O-])(=O)OP([O-])(... | InChI=1S/C44H81N3O15P2/c1-34(2)25-21-17-13-9-6... | ... | 960.529748 | 971.548118 | 952.507016 | 988.483694 | 1012.528146 | NaN | NaN | HMDB0116216 | NaN | NaN |
| 745175 | SLM:000748037 | Isomeric subspecies | CDP-1-(15-methylhexadecanoyl)-2-(11-methyldode... | CDP-DAG (iso17:0/iso13:0) | 1-(15-methylhexadecanoyl)-2-(11-methyldodecano... | SLM:000000084 | NaN | SLM:000000048 (sn1) / SLM:000001197 (sn2) | [H]Nc1ccn([C@@H]2O[C@H](COP([O-])(=O)OP([O-])(... | InChI=1S/C42H77N3O15P2/c1-32(2)23-19-15-11-8-6... | ... | 932.498448 | 943.516818 | 924.475716 | 960.452394 | 984.496846 | NaN | NaN | HMDB0116248 | NaN | NaN |
| 745176 | SLM:000748038 | Isomeric subspecies | CDP-1-(15-methylhexadecanoyl)-2-(13-methyltetr... | CDP-DAG (iso17:0/iso15:0) | 1-(15-methylhexadecanoyl)-2-(13-methyltetradec... | SLM:000000084 | NaN | SLM:000000047 (sn2) / SLM:000000048 (sn1) | [H]Nc1ccn([C@@H]2O[C@H](COP([O-])(=O)OP([O-])(... | InChI=1S/C44H81N3O15P2/c1-34(2)25-21-17-13-9-6... | ... | 960.529748 | 971.548118 | 952.507016 | 988.483694 | 1012.528146 | NaN | NaN | HMDB0116250 | NaN | NaN |
| 745177 | SLM:000748039 | Isomeric subspecies | CDP-1,2-di-(15-methylhexadecanoyl)-sn-glycerol | CDP-DAG (iso17:0/iso17:0) | 1,2-di-(15-methylhexadecanoyl)-sn-glycero-3-cy... | SLM:000000084 | NaN | SLM:000000048 (sn1 or sn2) | [H]Nc1ccn([C@@H]2O[C@H](COP([O-])(=O)OP([O-])(... | InChI=1S/C46H85N3O15P2/c1-36(2)27-23-19-15-11-... | ... | 988.561049 | 999.579419 | 980.538317 | 1016.514994 | 1040.559446 | NaN | NaN | HMDB0116252 | NaN | NaN |
19853 rows × 29 columns
Checking split characters (|) in Lipid class*
Found 119 rows with split characters
| Lipid ID | Level | Name | Abbreviation* | Synonyms* | Lipid class* | Parent | Components* | SMILES (pH7.3) | InChI (pH7.3) | ... | Exact m/z of [M+Li]+ | Exact m/z of [M+NH4]+ | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 142 | SLM:000000392 | Class | Ceramide phosphoinositol | IPC | Inositol-1-phosphoceramide | SLM:000000834 | SLM:000399815 | NaN | NaN | O[C@H]([*])[C@H](COP([O-])(=O)O[C@H]1[C@H](O)[... | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 64916 | NaN | NaN | NaN | 10888667 | 20727985 |
| 234 | SLM:000000509 | Isomeric subspecies | All-trans-retinyl hexadecanoate | NaN | all-trans-retinyl palmitate | SLM:000000982 | SLM:000508854 | NaN | NaN | CCCCCCCCCCCCCCCC(=O)OCC=C(C)C=CC=C(C)C=CC1=C(C... | InChI=1S/C36H60O2/c1-7-8-9-10-11-12-13-14-15-1... | ... | NaN | NaN | NaN | NaN | NaN | 17616 | NaN | HMDB03648 | NaN | 10769148 | 10819989 | 12230550 | 15550674 | 15... |
| 315 | SLM:000000612 | NaN | tetracosenoyl-CoA | NaN | NaN | SLM:000390051 | SLM:000782334 | NaN | NaN | CC(C)(COP([O-])(=O)OP([O-])(=O)OC[C@H]1O[C@H](... | NaN | ... | NaN | NaN | NaN | NaN | NaN | 74146 | NaN | NaN | NaN | 18541923 | 20110363 | 20937905 |
| 317 | SLM:000000614 | NaN | hexacosenoyl-CoA | NaN | NaN | SLM:000390051 | SLM:000782334 | NaN | NaN | CC(C)(COP([O-])(=O)OP([O-])(=O)OC[C@H]1O[C@H](... | NaN | ... | NaN | NaN | NaN | NaN | NaN | 74161 | NaN | NaN | NaN | 18165233 |
| 319 | SLM:000000621 | NaN | 2-hydroxy-tetracosenoyl-CoA | NaN | NaN | SLM:000390051 | SLM:000782334 | NaN | NaN | CC(C)(COP([O-])(=O)OP([O-])(=O)OC[C@H]1O[C@H](... | NaN | ... | NaN | NaN | NaN | NaN | NaN | 74215 | NaN | NaN | NaN | 18541923 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 755324 | SLM:000758294 | Class | Globoside | Globo | Globo-series | SLM:000000834 | SLM:000399813 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 61360 | NaN | NaN | NaN | NaN |
| 755325 | SLM:000758295 | Class | Isogloboside | Isoglobo | Isoglobo-series | SLM:000000834 | SLM:000399813 | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | 78257 | NaN | NaN | NaN | NaN |
| 779141 | SLM:000782221 | NaN | Resolvin E | RvE | NaN | SLM:000501332 | SLM:000508853 | NaN | NaN | NaN | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | NaN | LMFA0314 | NaN | NaN | NaN |
| 779142 | SLM:000782222 | NaN | Resolvin D | RvD | NaN | SLM:000501331 | SLM:000508853 | NaN | NaN | NaN | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | NaN | LMFA0403 | NaN | NaN | NaN |
| 779157 | SLM:000782237 | NaN | an N-(omega-(9Z,12Z-octadecadienoyloxy)-ultra-... | NaN | NaN | SLM:000000413 | SLM:000782274 | NaN | NaN | [C@H]([C@@H](/C=C/CCCCCCCCCCCCC)O)(NC(=O)*COC(... | NaN | ... | NaN | NaN | NaN | NaN | NaN | 157662 | NaN | NaN | NaN | NaN |
119 rows × 29 columns
Checking split characters (|) in Parent
No rows found
Checking split characters (|) in Components*
No rows found
Checking split characters (|) in SMILES (pH7.3)
No rows found
Checking split characters (|) in InChI (pH7.3)
No rows found
Checking split characters (|) in InChI key (pH7.3)
No rows found
Checking split characters (|) in Formula (pH7.3)
No rows found
Checking split characters (|) in Charge (pH7.3)
Not a string column
Checking split characters (|) in Mass (pH7.3)
Not a string column
Checking split characters (|) in Exact Mass (neutral form)
Not a string column
Checking split characters (|) in Exact m/z of [M.]+
Not a string column
Checking split characters (|) in Exact m/z of [M+H]+
Not a string column
Checking split characters (|) in Exact m/z of [M+K]+
Not a string column
Checking split characters (|) in Exact m/z of [M+Na]+
Not a string column
Checking split characters (|) in Exact m/z of [M+Li]+
Not a string column
Checking split characters (|) in Exact m/z of [M+NH4]+
Not a string column
Checking split characters (|) in Exact m/z of [M-H]-
Not a string column
Checking split characters (|) in Exact m/z of [M+Cl]-
Not a string column
Checking split characters (|) in Exact m/z of [M+OAc]-
Not a string column
Checking split characters (|) in CHEBI
Found 3 rows with split characters
| Lipid ID | Level | Name | Abbreviation* | Synonyms* | Lipid class* | Parent | Components* | SMILES (pH7.3) | InChI (pH7.3) | ... | Exact m/z of [M+Li]+ | Exact m/z of [M+NH4]+ | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 465 | SLM:000000784 | Isomeric subspecies | 1,2-di-(9Z-octadecenoyl)-sn-glycero-3-phosphate | PA(18:1(9Z)/18:1(9Z)) | Phosphatidate (18:1(9Z)/18:1(9Z)) | SLM:000000329 | SLM:000082169 | SLM:000000418 (sn1 or sn2) | CCCCCCCC\C=C/CCCCCCCC(=O)OC[C@H](COP([O-])([O-... | InChI=1S/C39H73O8P/c1-3-5-7-9-11-13-15-17-19-2... | ... | 707.519775 | 718.538147 | 699.497009 | 735.473694 | 759.518188 | 74546 | 82922 | LMGP10010962 | HMDB07865 | MNXM51075 | 11309392 | 14634025 | 14665624 | 15164764 | 15... |
| 387185 | SLM:000389154 | NaN | (14Z,17Z,20Z,23Z,26Z)-dotriacontapentaenoate | NaN | Fatty acid 32:5(14Z,17Z,20Z,23Z,26Z) | SLM:000389801 | NaN | NaN | CCCCC\C=C/C\C=C/C\C=C/C\C=C/C\C=C/CCCCCCCCCCCC... | InChI=1S/C32H54O2/c1-2-3-4-5-6-7-8-9-10-11-12-... | ... | 477.427836 | 488.446207 | 469.405105 | 505.381782 | 529.426234 | 82731 | CHEBI:82731 | LMFA01030848 | NaN | NaN | NaN |
| 595221 | SLM:000598072 | NaN | all-trans-retinol--[retinol-binding protein] | NaN | NaN | SLM:000000982 | NaN | NaN | [*][C@H](N-*)C(-*)=O | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 17336 | 83228 | NaN | NaN | NaN | 20628054 | 28758396 |
3 rows × 29 columns
Checking split characters (|) in LIPID MAPS
No rows found
Checking split characters (|) in HMDB
No rows found
Checking split characters (|) in MetaNetX
No rows found
Checking split characters (|) in PMID
Found 1318 rows with split characters
| Lipid ID | Level | Name | Abbreviation* | Synonyms* | Lipid class* | Parent | Components* | SMILES (pH7.3) | InChI (pH7.3) | ... | Exact m/z of [M+Li]+ | Exact m/z of [M+NH4]+ | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | SLM:000000002 | Class | Ceramide (iso-d17:1(4E)) | Cer(iso-d17:1(4E)) | N-acyl-15-methylhexadecasphing-4-enine | SLM:000399814 | NaN | NaN | CC(C)CCCCCCCCC\C=C\[C@@H](O)[C@H](CO)NC([*])=O | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 70846 | NaN | NaN | MNXM97012 | | 11443131 | 14685263 | 18390550 | 21325339 |... |
| 3 | SLM:000000007 | Class | Sphingomyelin (iso-d17:1(4E)) | SM(iso-d17:1(4E)) | N-acyl-15-methylhexadecasphing-4-enine-1-phosp... | SLM:000001000 | NaN | NaN | CC(C)CCCCCCCCC\C=C\[C@@H](O)[C@H](COP([O-])(=O... | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 70775 | NaN | NaN | MNXM97113 | 14685263 | 21926990 | 9603947 |
| 4 | SLM:000000035 | Isomeric subspecies | sphinganine | NaN | NaN | SLM:000390097 | NaN | NaN | CCCCCCCCCCCCCCC[C@@H](O)[C@@H]([NH3+])CO | InChI=1S/C18H39NO2/c1-2-3-4-5-6-7-8-9-10-11-12... | ... | 308.313535 | 319.331905 | 300.290803 | 336.267481 | 360.311932 | 57817 | LMSP01020001 | HMDB00269 | MNXM302 | 10652340 | 10702247 | 10751414 | 10802064 | 10... |
| 5 | SLM:000000042 | Isomeric subspecies | cholesta-5,7-dien-3beta-ol | NaN | NaN | SLM:000501263 | NaN | NaN | [H][C@@]1(CC[C@@]2([H])C3=CC=C4C[C@@H](O)CC[C@... | InChI=1S/C27H44O/c1-18(2)7-6-8-19(3)23-11-12-2... | ... | 391.354671 | 402.373042 | 383.331940 | 419.308617 | 443.353069 | 17759 | LMST01010069 | HMDB00032 | MNXM710 | 10329655 | 10344195 | 10786622 | 11230174 | 16... |
| 6 | SLM:000000043 | Isomeric subspecies | lathosterone | NaN | NaN | SLM:000501263 | NaN | NaN | [H][C@@]12CC=C3[C@]4([H])CC[C@]([H])([C@H](C)C... | InChI=1S/C27H44O/c1-18(2)7-6-8-19(3)23-11-12-2... | ... | 391.354671 | 402.373042 | 383.331940 | 419.308617 | 443.353069 | 71550 | NaN | NaN | MNXM97065 | 19531354 | 22505847 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 595221 | SLM:000598072 | NaN | all-trans-retinol--[retinol-binding protein] | NaN | NaN | SLM:000000982 | NaN | NaN | [*][C@H](N-*)C(-*)=O | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 17336 | 83228 | NaN | NaN | NaN | 20628054 | 28758396 |
| 595222 | SLM:000598073 | NaN | all-trans-retinyl heptanoate | NaN | NaN | SLM:000000982 | NaN | NaN | C1(C)(C)C(\C=C\C(=C\C=C\C(=C\COC(CCCCCC)=O)\C)... | InChI=1S/C27H42O2/c1-7-8-9-10-16-26(28)29-21-1... | ... | NaN | NaN | NaN | NaN | NaN | 138724 | NaN | NaN | NaN | 20628054 | 28758396 |
| 595223 | SLM:000598074 | NaN | 2-heptanoyl-sn-glycero-3-phosphocholine | NaN | NaN | SLM:000000724 | NaN | NaN | P(OC[C@@H](CO)OC(=O)CCCCCC)(=O)(OCC[N+](C)(C)C... | InChI=1S/C15H32NO7P/c1-5-6-7-8-9-15(18)23-14(1... | ... | NaN | NaN | NaN | NaN | NaN | 138266 | NaN | NaN | NaN | 20628054 | 22605381 | 28758396 |
| 595230 | SLM:000598083 | NaN | 12-hydroxy-(9Z)-octadecenoyl-CoA | NaN | NaN | SLM:000389958 | SLM:000390051 | NaN | NaN | S(C(CCCCCCC/C=C\C[C@@H](CCCCCC)O)=O)CCNC(CCNC(... | InChI=1S/C39H68N7O18P3S/c1-4-5-6-13-16-27(47)1... | ... | NaN | NaN | NaN | NaN | NaN | 139559 | NaN | NaN | NaN | 17084870 | 27758859 |
| 595245 | SLM:000598101 | NaN | a mannosylinositol-1-phospho-N-(2-hydroxyacyl)... | NaN | NaN | SLM:000000835 | NaN | NaN | OC[C@H]1OC(O[C@@H]2[C@@H](O)[C@H](O)[C@@H](O)[... | InChI=none | ... | NaN | NaN | NaN | NaN | NaN | 74994 | NaN | NaN | NaN | 12954640 | 9368028 |
1318 rows × 29 columns
Okay wow! So these are all the columns we have found with split characters…
cols_with_split_chars
['Abbreviation*', 'Synonyms*', 'Lipid class*', 'CHEBI', 'PMID']
We can also check for different types of characters if we know that they will be present. For instance SL uses the / character for Components*, but this is also used by another of columns like the lipid names themselves or smiles and inchi.
check_for_split_characters(df_swisslipids.drop(columns=['Name','Abbreviation*','Synonyms*','SMILES (pH7.3)','InChI (pH7.3)']), delimiter='/')
Checking split characters (/) in Lipid ID
No rows found
Checking split characters (/) in Level
No rows found
Checking split characters (/) in Lipid class*
No rows found
Checking split characters (/) in Parent
No rows found
Checking split characters (/) in Components*
Found 708725 rows with split characters
| Lipid ID | Level | Lipid class* | Parent | Components* | InChI key (pH7.3) | Formula (pH7.3) | Charge (pH7.3) | Mass (pH7.3) | Exact Mass (neutral form) | ... | Exact m/z of [M+Li]+ | Exact m/z of [M+NH4]+ | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 164 | SLM:000000422 | Isomeric subspecies | SLM:000000329 | SLM:000081844 | SLM:000000418 (sn2) / SLM:000000510 (sn1) | InChIKey=OPVZUEPSMJNLOM-QEJMHMKOSA-L | C37H69O8P | -2.0 | 672.913818 | 674.488647 | ... | 681.504089 | 692.522461 | 673.481384 | 709.458069 | 733.502502 | 64839 | LMGP10010032 | HMDB07859 | MNXM66476 | 10359651 | 11788596 | 12963729 | 16620771 | 17... |
| 229 | SLM:000000498 | Isomeric subspecies | SLM:000000324 | SLM:000105249 | SLM:000000296 (sn2) / SLM:000000826 (sn1) | InChIKey=KRTOMQDUKGRFDJ-ZAHDIIMDSA-M | C47H82O13P | -1.0 | 886.120483 | 886.557129 | ... | 893.572571 | 904.590942 | 885.549866 | 921.526550 | 945.570984 | 133606 | LMGP06010010 | HMDB09815 | MNXM75683 | 22942276 | 23097495 | 23472195 | 8300559 |
| 269 | SLM:000000557 | Isomeric subspecies | SLM:000000261 | SLM:000088147 | SLM:000000510 (sn1) / SLM:000000826 (sn2) | InChIKey=PZNPLUBHRSSFHT-RRHRGVEJSA-N | C42H84NO8P | 0.0 | 762.091980 | 761.593445 | ... | 768.608887 | 779.627258 | NaN | 796.562866 | 820.607300 | 73000 | LMGP01010573 | HMDB07970 | MNXM69304 | 18195019 | 19416660 | 22923616 | 27399000 |
| 332 | SLM:000000636 | Isomeric subspecies | SLM:000000329 | SLM:000082164 | SLM:000000418 (sn1) / SLM:000000510 (sn2) | InChIKey=ZSXHMDPHNCOWSV-QEJMHMKOSA-L | C37H69O8P | -2.0 | 672.913818 | 674.488647 | ... | 681.504089 | 692.522461 | 673.481384 | 709.458069 | 733.502502 | 74551 | LMGP10010964 | NaN | MNXM66662 | 16620771 | 18606822 | 19318427 | 19801371 | 20... |
| 333 | SLM:000000637 | Isomeric subspecies | SLM:000000329 | SLM:000082168 | SLM:000000418 (sn1) / SLM:000000826 (sn2) | InChIKey=XIERONXOJKEALF-PXYGFXEISA-L | C39H73O8P | -2.0 | 700.966980 | 702.519958 | ... | 709.535400 | 720.553772 | 701.512695 | 737.489380 | 761.533813 | 74552 | LMGP10010963 | NaN | MNXM66667 | 16620771 | 18606822 | 19318427 | 19801371 | 21... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 745172 | SLM:000748034 | Isomeric subspecies | SLM:000000084 | NaN | SLM:000000048 (sn1) / SLM:000001195 (sn2) | InChIKey=LJSBNBPNSBKZCI-JNOBRDIFSA-L | C33H57N3O15P2 | -2.0 | NaN | 799.342142 | ... | 806.357598 | 817.375968 | 798.334866 | 834.311543 | 858.355995 | NaN | NaN | NaN | NaN | NaN |
| 745173 | SLM:000748035 | Isomeric subspecies | SLM:000000084 | NaN | SLM:000000048 (sn1) / SLM:000001196 (sn2) | InChIKey=ODNYDZLXLRZPCJ-GPTQCAHZSA-L | C35H61N3O15P2 | -2.0 | NaN | 827.373442 | ... | 834.388898 | 845.407268 | 826.366166 | 862.342844 | 886.387295 | NaN | NaN | NaN | NaN | NaN |
| 745174 | SLM:000748036 | Isomeric subspecies | SLM:000000084 | NaN | SLM:000000048 (sn1) / SLM:000000853 (sn2) | InChIKey=FJIBTCUXUBRYKG-QOTCTSOZSA-L | C37H65N3O15P2 | -2.0 | NaN | 855.404743 | ... | 862.420198 | 873.438568 | 854.397466 | 890.374144 | 914.418595 | NaN | NaN | NaN | NaN | NaN |
| 745175 | SLM:000748037 | Isomeric subspecies | SLM:000000084 | NaN | SLM:000000048 (sn1) / SLM:000001197 (sn2) | InChIKey=AIBKQADSQWEVSS-HUKRWTLJSA-L | C42H75N3O15P2 | -2.0 | NaN | 925.482993 | ... | 932.498448 | 943.516818 | 924.475716 | 960.452394 | 984.496846 | NaN | NaN | HMDB0116248 | NaN | NaN |
| 745176 | SLM:000748038 | Isomeric subspecies | SLM:000000084 | NaN | SLM:000000047 (sn2) / SLM:000000048 (sn1) | InChIKey=PIZFKSVTEGNINS-BQUKFSKHSA-L | C44H79N3O15P2 | -2.0 | NaN | 953.514293 | ... | 960.529748 | 971.548118 | 952.507016 | 988.483694 | 1012.528146 | NaN | NaN | HMDB0116250 | NaN | NaN |
708725 rows × 24 columns
Checking split characters (/) in InChI key (pH7.3)
No rows found
Checking split characters (/) in Formula (pH7.3)
No rows found
Checking split characters (/) in Charge (pH7.3)
Not a string column
Checking split characters (/) in Mass (pH7.3)
Not a string column
Checking split characters (/) in Exact Mass (neutral form)
Not a string column
Checking split characters (/) in Exact m/z of [M.]+
Not a string column
Checking split characters (/) in Exact m/z of [M+H]+
Not a string column
Checking split characters (/) in Exact m/z of [M+K]+
Not a string column
Checking split characters (/) in Exact m/z of [M+Na]+
Not a string column
Checking split characters (/) in Exact m/z of [M+Li]+
Not a string column
Checking split characters (/) in Exact m/z of [M+NH4]+
Not a string column
Checking split characters (/) in Exact m/z of [M-H]-
Not a string column
Checking split characters (/) in Exact m/z of [M+Cl]-
Not a string column
Checking split characters (/) in Exact m/z of [M+OAc]-
Not a string column
Checking split characters (/) in CHEBI
No rows found
Checking split characters (/) in LIPID MAPS
No rows found
Checking split characters (/) in HMDB
No rows found
Checking split characters (/) in MetaNetX
No rows found
Checking split characters (/) in PMID
No rows found
['Components*']
These double entries for the classes will be important to take into account for our class hierarchy, because if we don’t many of these Class level entries will become disjointed in the ontology.
To help us handle this connection we will split it into two using the split_and_expand_large utility function, but we will come back to this a bit later…
For now we will also add another column for components, so that later we can have both the actual component with location (e.g. sn) and a parsed version where we just have the SL
df_swisslipids['Components_parsed'] = df_swisslipids['Components*']
Now we can melt to start creating the edges df
Building the edges df¶
# # Split the 'Lipid class*' column into multiple rows
# df_swisslipids_splitexp = split_and_expand_large(
# df_swisslipids, #.assign(from_layer_col='swisslipids')
# split_col='Lipid class*',
# expand_cols=['Lipid ID', 'Level', 'Name', 'Abbreviation*',
# 'CHEBI', 'LIPID MAPS', 'HMDB', 'MetaNetX', 'PMID','Synonyms*','Parent','Components*','Components_parsed'], #'from_layer_col'
# delimiter='|'
# )
df_swisslipids_edges = pd.melt(df_swisslipids, #df_swisslipids_splitexp
id_vars=['Lipid ID'],
value_vars=['CHEBI','LIPID MAPS','HMDB','MetaNetX','PMID','Lipid class*','Abbreviation*','Synonyms*','Parent','Components*','Components_parsed'],
var_name='melted_column', value_name='value')
df_swisslipids_edges
| Lipid ID | melted_column | value | |
|---|---|---|---|
| 0 | SLM:000000002 | CHEBI | 70846 |
| 1 | SLM:000000003 | CHEBI | 70771 |
| 2 | SLM:000000006 | CHEBI | 70829 |
| 3 | SLM:000000007 | CHEBI | 70775 |
| 4 | SLM:000000035 | CHEBI | 57817 |
| ... | ... | ... | ... |
| 8571734 | SLM:000782324 | Components_parsed | NaN |
| 8571735 | SLM:000782325 | Components_parsed | NaN |
| 8571736 | SLM:000782326 | Components_parsed | NaN |
| 8571737 | SLM:000782327 | Components_parsed | NaN |
| 8571738 | SLM:000782328 | Components_parsed | NaN |
8571739 rows × 3 columns
Because this melt operation also resulted in a large number of null values, which probably mean nothing to us in this case, we will drop instances where the value is null
df_swisslipids_edges = df_swisslipids_edges.dropna(subset='value')
df_swisslipids_edges
| Lipid ID | melted_column | value | |
|---|---|---|---|
| 0 | SLM:000000002 | CHEBI | 70846 |
| 1 | SLM:000000003 | CHEBI | 70771 |
| 2 | SLM:000000006 | CHEBI | 70829 |
| 3 | SLM:000000007 | CHEBI | 70775 |
| 4 | SLM:000000035 | CHEBI | 57817 |
| ... | ... | ... | ... |
| 8571494 | SLM:000781997 | Components_parsed | SLM:000000856 (n-acyl) |
| 8571495 | SLM:000781998 | Components_parsed | SLM:000389154 (n-acyl) |
| 8571496 | SLM:000781999 | Components_parsed | SLM:000485643 (n-acyl) |
| 8571497 | SLM:000782000 | Components_parsed | SLM:000485644 (n-acyl) |
| 8571498 | SLM:000782001 | Components_parsed | SLM:000485645 (n-acyl) |
4678499 rows × 3 columns
There are still some things we need to tidy up so that it is in a suitable format for OnionNet
df_swisslipids_edges = df_swisslipids_edges.copy()
df_swisslipids_edges['source_layer'] = 'swisslipids'
df_swisslipids_edges.rename(columns={'Lipid ID':'source_id', 'melted_column':'target_layer', 'value':'target_id'}, inplace=True)
df_swisslipids_edges = df_swisslipids_edges[['source_layer','source_id','target_layer','target_id']]
df_swisslipids_edges['target_layer'] = df_swisslipids_edges['target_layer'].map(lambda x: 'swisslipids' if x=='Lipid class*' else f"sl_{str(x).replace(' ','').strip('*').lower()}")
#df_swisslipids_edges['target_layer'] = df_swisslipids_edges['target_layer'].map(lambda x: )
df_swisslipids_edges
| source_layer | source_id | target_layer | target_id | |
|---|---|---|---|---|
| 0 | swisslipids | SLM:000000002 | sl_chebi | 70846 |
| 1 | swisslipids | SLM:000000003 | sl_chebi | 70771 |
| 2 | swisslipids | SLM:000000006 | sl_chebi | 70829 |
| 3 | swisslipids | SLM:000000007 | sl_chebi | 70775 |
| 4 | swisslipids | SLM:000000035 | sl_chebi | 57817 |
| ... | ... | ... | ... | ... |
| 8571494 | swisslipids | SLM:000781997 | sl_components_parsed | SLM:000000856 (n-acyl) |
| 8571495 | swisslipids | SLM:000781998 | sl_components_parsed | SLM:000389154 (n-acyl) |
| 8571496 | swisslipids | SLM:000781999 | sl_components_parsed | SLM:000485643 (n-acyl) |
| 8571497 | swisslipids | SLM:000782000 | sl_components_parsed | SLM:000485644 (n-acyl) |
| 8571498 | swisslipids | SLM:000782001 | sl_components_parsed | SLM:000485645 (n-acyl) |
4678499 rows × 4 columns
For rows where it is swisslipids to swisslipids, we actually want to correct this from target_layer to source_layer, because currently the target_layer in this case is actually the parent class, and ideally it would be better to have the parent point towards the children, so that way the root node should be the one with multiple outgoing edges and no incoming edges…
Be sure to only run this once, otherwise it will switch back again…
# Identify rows where both source_layer and target_layer are 'swisslipids'
condition = (df_swisslipids_edges["source_layer"] == "swisslipids") & (df_swisslipids_edges["target_layer"] == "swisslipids")
# Swap the columns for rows satisfying the condition
df_swisslipids_edges.loc[condition, ["source_layer", "source_id", "target_layer", "target_id"]] = df_swisslipids_edges.loc[condition, ["target_layer", "target_id", "source_layer", "source_id"]].values
# Output the modified DataFrame
df_swisslipids_edges
| source_layer | source_id | target_layer | target_id | |
|---|---|---|---|---|
| 0 | swisslipids | SLM:000000002 | sl_chebi | 70846 |
| 1 | swisslipids | SLM:000000003 | sl_chebi | 70771 |
| 2 | swisslipids | SLM:000000006 | sl_chebi | 70829 |
| 3 | swisslipids | SLM:000000007 | sl_chebi | 70775 |
| 4 | swisslipids | SLM:000000035 | sl_chebi | 57817 |
| ... | ... | ... | ... | ... |
| 8571494 | swisslipids | SLM:000781997 | sl_components_parsed | SLM:000000856 (n-acyl) |
| 8571495 | swisslipids | SLM:000781998 | sl_components_parsed | SLM:000389154 (n-acyl) |
| 8571496 | swisslipids | SLM:000781999 | sl_components_parsed | SLM:000485643 (n-acyl) |
| 8571497 | swisslipids | SLM:000782000 | sl_components_parsed | SLM:000485644 (n-acyl) |
| 8571498 | swisslipids | SLM:000782001 | sl_components_parsed | SLM:000485645 (n-acyl) |
4678499 rows × 4 columns
df_swisslipids_edges['target_layer'].value_counts()
target_layer
swisslipids 779247
sl_abbreviation 776464
sl_components 765323
sl_components_parsed 765323
sl_synonyms 548163
sl_metanetx 505003
sl_parent 493491
sl_hmdb 26026
sl_lipidmaps 12117
sl_chebi 4276
sl_pmid 3066
Name: count, dtype: int64
Now let’s return to two items on our todo list:
splitting values that have multi-identifiers
trimming/parsing the components col
edges_with_multilinks = df_swisslipids_edges[df_swisslipids_edges['target_id'].str.contains('|', regex=False, na=False)]
edges_with_multilinks
| source_layer | source_id | target_layer | target_id | |
|---|---|---|---|---|
| 465 | swisslipids | SLM:000000784 | sl_chebi | 74546 | 82922 |
| 387185 | swisslipids | SLM:000389154 | sl_chebi | 82731 | CHEBI:82731 |
| 595221 | swisslipids | SLM:000598072 | sl_chebi | 17336 | 83228 |
| 3116996 | swisslipids | SLM:000000002 | sl_pmid | | 11443131 | 14685263 | 18390550 | 21325339 |... |
| 3116999 | swisslipids | SLM:000000007 | sl_pmid | 14685263 | 21926990 | 9603947 |
| ... | ... | ... | ... | ... |
| 6199835 | swisslipids | SLM:000747954 | sl_synonyms | 1,2-di-(13-methyltetradecanoyl)-sn-glycero-3-c... |
| 6199836 | swisslipids | SLM:000747955 | sl_synonyms | 1-(13-methyltetradecanoyl)-2-(15-methylhexadec... |
| 6199918 | swisslipids | SLM:000748037 | sl_synonyms | 1-(15-methylhexadecanoyl)-2-(11-methyldodecano... |
| 6199919 | swisslipids | SLM:000748038 | sl_synonyms | 1-(15-methylhexadecanoyl)-2-(13-methyltetradec... |
| 6199920 | swisslipids | SLM:000748039 | sl_synonyms | 1,2-di-(15-methylhexadecanoyl)-sn-glycero-3-cy... |
30942 rows × 4 columns
edges_with_multilinks.value_counts('target_layer')
target_layer
sl_synonyms 19853
sl_abbreviation 9768
sl_pmid 1318
sl_chebi 3
Name: count, dtype: int64
edges_with_multilinks_split = split_and_expand_large(edges_with_multilinks,
split_col='target_id',
expand_cols=['source_layer','source_id','target_layer'],
delimiter='|').drop_duplicates()
edges_with_multilinks_split
| source_layer | source_id | target_layer | target_id | |
|---|---|---|---|---|
| 0 | swisslipids | SLM:000000784 | sl_chebi | 74546 |
| 1 | swisslipids | SLM:000000784 | sl_chebi | 82922 |
| 2 | swisslipids | SLM:000389154 | sl_chebi | 82731 |
| 3 | swisslipids | SLM:000389154 | sl_chebi | CHEBI:82731 |
| 4 | swisslipids | SLM:000598072 | sl_chebi | 17336 |
| ... | ... | ... | ... | ... |
| 68383 | swisslipids | SLM:000748037 | sl_synonyms | CDP-DG(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/18:1(11Z)) |
| 68384 | swisslipids | SLM:000748038 | sl_synonyms | 1-(15-methylhexadecanoyl)-2-(13-methyltetradec... |
| 68385 | swisslipids | SLM:000748038 | sl_synonyms | CDP-DG(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/18:1(9Z)) |
| 68386 | swisslipids | SLM:000748039 | sl_synonyms | 1,2-di-(15-methylhexadecanoyl)-sn-glycero-3-cy... |
| 68387 | swisslipids | SLM:000748039 | sl_synonyms | CDP-DG(22:6(4Z,7Z,10Z,13Z,16Z,19Z)/18:2(9Z,12Z)) |
68380 rows × 4 columns
This is good, but we also need to remember the separators in the components column
edges_with_multilinks2 = df_swisslipids_edges[df_swisslipids_edges['target_id'].str.contains('/', regex=False, na=False) &
df_swisslipids_edges['target_layer'].str.contains('sl_components', regex=False, na=False)]
edges_with_multilinks2
| source_layer | source_id | target_layer | target_id | |
|---|---|---|---|---|
| 7013405 | swisslipids | SLM:000000422 | sl_components | SLM:000000418 (sn2) / SLM:000000510 (sn1) |
| 7013470 | swisslipids | SLM:000000498 | sl_components | SLM:000000296 (sn2) / SLM:000000826 (sn1) |
| 7013510 | swisslipids | SLM:000000557 | sl_components | SLM:000000510 (sn1) / SLM:000000826 (sn2) |
| 7013573 | swisslipids | SLM:000000636 | sl_components | SLM:000000418 (sn1) / SLM:000000510 (sn2) |
| 7013574 | swisslipids | SLM:000000637 | sl_components | SLM:000000418 (sn1) / SLM:000000826 (sn2) |
| ... | ... | ... | ... | ... |
| 8537662 | swisslipids | SLM:000748034 | sl_components_parsed | SLM:000000048 (sn1) / SLM:000001195 (sn2) |
| 8537663 | swisslipids | SLM:000748035 | sl_components_parsed | SLM:000000048 (sn1) / SLM:000001196 (sn2) |
| 8537664 | swisslipids | SLM:000748036 | sl_components_parsed | SLM:000000048 (sn1) / SLM:000000853 (sn2) |
| 8537665 | swisslipids | SLM:000748037 | sl_components_parsed | SLM:000000048 (sn1) / SLM:000001197 (sn2) |
| 8537666 | swisslipids | SLM:000748038 | sl_components_parsed | SLM:000000047 (sn2) / SLM:000000048 (sn1) |
1417450 rows × 4 columns
edges_with_multilinks2_split = split_and_expand_large(edges_with_multilinks2,
split_col='target_id',
expand_cols=['source_layer','source_id','target_layer'],
delimiter='/').drop_duplicates()
edges_with_multilinks2_split
| source_layer | source_id | target_layer | target_id | |
|---|---|---|---|---|
| 0 | swisslipids | SLM:000000422 | sl_components | SLM:000000418 (sn2) |
| 1 | swisslipids | SLM:000000422 | sl_components | SLM:000000510 (sn1) |
| 2 | swisslipids | SLM:000000498 | sl_components | SLM:000000296 (sn2) |
| 3 | swisslipids | SLM:000000498 | sl_components | SLM:000000826 (sn1) |
| 4 | swisslipids | SLM:000000557 | sl_components | SLM:000000510 (sn1) |
| ... | ... | ... | ... | ... |
| 3592487 | swisslipids | SLM:000748036 | sl_components_parsed | SLM:000000853 (sn2) |
| 3592488 | swisslipids | SLM:000748037 | sl_components_parsed | SLM:000000048 (sn1) |
| 3592489 | swisslipids | SLM:000748037 | sl_components_parsed | SLM:000001197 (sn2) |
| 3592490 | swisslipids | SLM:000748038 | sl_components_parsed | SLM:000000047 (sn2) |
| 3592491 | swisslipids | SLM:000748038 | sl_components_parsed | SLM:000000048 (sn1) |
3592492 rows × 4 columns
Now let’s also parse the brackets from the parsed components so that these can be linked directly to the other SLMs if needed
# Apply transformation only for rows where target_layer equals 'sl_components_parsed'
mask = edges_with_multilinks2_split['target_layer'] == 'sl_components_parsed'
edges_with_multilinks2_split.loc[mask, 'target_id'] = edges_with_multilinks2_split.loc[mask, 'target_id'].str.split('(').str[0].str.strip()
edges_with_multilinks2_split
| source_layer | source_id | target_layer | target_id | |
|---|---|---|---|---|
| 0 | swisslipids | SLM:000000422 | sl_components | SLM:000000418 (sn2) |
| 1 | swisslipids | SLM:000000422 | sl_components | SLM:000000510 (sn1) |
| 2 | swisslipids | SLM:000000498 | sl_components | SLM:000000296 (sn2) |
| 3 | swisslipids | SLM:000000498 | sl_components | SLM:000000826 (sn1) |
| 4 | swisslipids | SLM:000000557 | sl_components | SLM:000000510 (sn1) |
| ... | ... | ... | ... | ... |
| 3592487 | swisslipids | SLM:000748036 | sl_components_parsed | SLM:000000853 |
| 3592488 | swisslipids | SLM:000748037 | sl_components_parsed | SLM:000000048 |
| 3592489 | swisslipids | SLM:000748037 | sl_components_parsed | SLM:000001197 |
| 3592490 | swisslipids | SLM:000748038 | sl_components_parsed | SLM:000000047 |
| 3592491 | swisslipids | SLM:000748038 | sl_components_parsed | SLM:000000048 |
3592492 rows × 4 columns
Now we need a way to change these original rows where they had multilinks and add back the corrected ones.
# Identify rows with multilinks (either '|' or '/' with the specific target_layer condition)
mask_pipe = df_swisslipids_edges['target_id'].str.contains('|', regex=False, na=False)
mask_slash = (
df_swisslipids_edges['target_id'].str.contains('/', regex=False, na=False) &
df_swisslipids_edges['target_layer'].str.contains('sl_components', regex=False, na=False)
)
mask_problem = mask_pipe | mask_slash
# Remove these rows from the original df
df_clean = df_swisslipids_edges[~mask_problem].copy()
# Now, combine the cleaned df with the corrected edges dataframes.
# These corrected dataframes are assumed to be:
# - edges_with_multilinks_split
# - edges_with_multilinks2_split
df_swisslipids_edges = pd.concat([df_clean, edges_with_multilinks_split, edges_with_multilinks2_split], ignore_index=True)
# (Optional) Drop any duplicate rows that might arise
df_swisslipids_edges = df_swisslipids_edges.drop_duplicates()
# df_final now contains the original "good" rows plus the corrected edges.
df_swisslipids_edges
| source_layer | source_id | target_layer | target_id | |
|---|---|---|---|---|
| 0 | swisslipids | SLM:000000002 | sl_chebi | 70846 |
| 1 | swisslipids | SLM:000000003 | sl_chebi | 70771 |
| 2 | swisslipids | SLM:000000006 | sl_chebi | 70829 |
| 3 | swisslipids | SLM:000000007 | sl_chebi | 70775 |
| 4 | swisslipids | SLM:000000035 | sl_chebi | 57817 |
| ... | ... | ... | ... | ... |
| 6890974 | swisslipids | SLM:000748036 | sl_components_parsed | SLM:000000853 |
| 6890975 | swisslipids | SLM:000748037 | sl_components_parsed | SLM:000000048 |
| 6890976 | swisslipids | SLM:000748037 | sl_components_parsed | SLM:000001197 |
| 6890977 | swisslipids | SLM:000748038 | sl_components_parsed | SLM:000000047 |
| 6890978 | swisslipids | SLM:000748038 | sl_components_parsed | SLM:000000048 |
6890979 rows × 4 columns
Now we will determine whether the edge is within the same layer (intralayer) or between different layers (interlayer)
def assess_edge_layertype(df):
interlayer = df['source_layer']!=df['target_layer']
df['interlayer'] = interlayer
return df
df_swisslipids_edges = assess_edge_layertype(df_swisslipids_edges)
df_swisslipids_edges
| source_layer | source_id | target_layer | target_id | interlayer | |
|---|---|---|---|---|---|
| 0 | swisslipids | SLM:000000002 | sl_chebi | 70846 | True |
| 1 | swisslipids | SLM:000000003 | sl_chebi | 70771 | True |
| 2 | swisslipids | SLM:000000006 | sl_chebi | 70829 | True |
| 3 | swisslipids | SLM:000000007 | sl_chebi | 70775 | True |
| 4 | swisslipids | SLM:000000035 | sl_chebi | 57817 | True |
| ... | ... | ... | ... | ... | ... |
| 6890974 | swisslipids | SLM:000748036 | sl_components_parsed | SLM:000000853 | True |
| 6890975 | swisslipids | SLM:000748037 | sl_components_parsed | SLM:000000048 | True |
| 6890976 | swisslipids | SLM:000748037 | sl_components_parsed | SLM:000001197 | True |
| 6890977 | swisslipids | SLM:000748038 | sl_components_parsed | SLM:000000047 | True |
| 6890978 | swisslipids | SLM:000748038 | sl_components_parsed | SLM:000000048 | True |
6890979 rows × 5 columns
Now we will build the node df
Building the node df¶
df_swisslipids_nodes = create_nodedf_from_edgedf(edge_df=df_swisslipids_edges, props=['layer', 'id'], cols=['layer', 'node_id'])
df_swisslipids_nodes
| layer | node_id | |
|---|---|---|
| 0 | swisslipids | SLM:000000002 |
| 1 | swisslipids | SLM:000000003 |
| 2 | swisslipids | SLM:000000006 |
| 3 | swisslipids | SLM:000000007 |
| 4 | swisslipids | SLM:000000035 |
| ... | ... | ... |
| 13781953 | sl_components_parsed | SLM:000000853 |
| 13781954 | sl_components_parsed | SLM:000000048 |
| 13781955 | sl_components_parsed | SLM:000001197 |
| 13781956 | sl_components_parsed | SLM:000000047 |
| 13781957 | sl_components_parsed | SLM:000000048 |
13781958 rows × 2 columns
Let’s also see how many are duplicates
df_swisslipids_nodes.value_counts(dropna=True)
layer node_id
swisslipids SLM:000000353 132660
SLM:000000377 98800
SLM:000000102 80218
SLM:000117148 46826
SLM:000000400 38525
...
sl_metanetx MNXM311776 1
MNXM311777 1
MNXM311778 1
MNXM311779 1
swisslipids SLM:000782332 1
Name: count, Length: 2783345, dtype: int64
Now let’s merge the nodes with the information from earlier to create richer node attributes
df_swisslipids_nodes = pd.merge(df_swisslipids_nodes, df_swisslipids.assign(from_layer_col='swisslipids'),
left_on=['layer','node_id'], right_on=['from_layer_col','Lipid ID'],
how='outer')
df_swisslipids_nodes
| layer | node_id | Lipid ID | Level | Name | Abbreviation* | Synonyms* | Lipid class* | Parent | Components* | ... | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | Components_parsed | from_layer_col | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | sl_abbreviation | (5S)-HpHEPE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | sl_abbreviation | 15-KETE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | sl_abbreviation | (10,11S,12R)-TriHETrE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | sl_abbreviation | (10R)-H-(11S,12S)-EpETrE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | sl_abbreviation | (10R)-H-(8S,9S)-EpETrE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 13781953 | swisslipids | SLM:000782330 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 13781954 | swisslipids | SLM:000782331 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 13781955 | swisslipids | SLM:000782331 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 13781956 | swisslipids | SLM:000782331 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 13781957 | swisslipids | SLM:000782332 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
13781958 rows × 33 columns
This has a lot of duplicates in it, so lets remove them, along with the from_layer_col which means nothing in this case and is just a relic of our join back with the initial df we used to create the edges (which could probably be tidied up)
df_swisslipids_nodes = df_swisslipids_nodes.drop_duplicates()
df_swisslipids_nodes = df_swisslipids_nodes.drop(columns='from_layer_col')
df_swisslipids_nodes
| layer | node_id | Lipid ID | Level | Name | Abbreviation* | Synonyms* | Lipid class* | Parent | Components* | ... | Exact m/z of [M+NH4]+ | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | Components_parsed | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | sl_abbreviation | (5S)-HpHEPE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 1 | sl_abbreviation | 15-KETE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 2 | sl_abbreviation | (10,11S,12R)-TriHETrE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 3 | sl_abbreviation | (10R)-H-(11S,12S)-EpETrE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 4 | sl_abbreviation | (10R)-H-(8S,9S)-EpETrE | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 13781947 | swisslipids | SLM:000782328 | SLM:000782328 | NaN | oxidized 2-acylglycerol | NaN | NaN | SLM:000000355 | NaN | NaN | ... | NaN | NaN | NaN | NaN | 167117 | NaN | NaN | NaN | NaN | NaN |
| 13781950 | swisslipids | SLM:000782329 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 13781953 | swisslipids | SLM:000782330 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 13781954 | swisslipids | SLM:000782331 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
| 13781957 | swisslipids | SLM:000782332 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN |
2783345 rows × 32 columns
Now we have the nodes and edges dfs for swisslipids and understand how we have arrived at them. In reality you don’t have to go through this process every time, LipiNet offers a convenient function to do just this if you are interested in this same network setup.
Using the LipiNet parse_swisslipids function¶
The LipiNet parse_swisslipids function automatically runs through all of the same steps as we have just covered.
from lipinet.parse_swisslipids import parse_swisslipids_data
sl_results = parse_swisslipids_data(verbose=False)
df_sl_nodes = sl_results['df_nodes']
df_sl_edges = sl_results['df_edges']
We can also check to make sure these are equal here for an individual entry
df_swisslipids_nodes.iloc[0]
layer sl_abbreviation
node_id (5S)-HpHEPE
Lipid ID NaN
Level NaN
Name NaN
Abbreviation* NaN
Synonyms* NaN
Lipid class* NaN
Parent NaN
Components* NaN
SMILES (pH7.3) NaN
InChI (pH7.3) NaN
InChI key (pH7.3) NaN
Formula (pH7.3) NaN
Charge (pH7.3) NaN
Mass (pH7.3) NaN
Exact Mass (neutral form) NaN
Exact m/z of [M.]+ NaN
Exact m/z of [M+H]+ NaN
Exact m/z of [M+K]+ NaN
Exact m/z of [M+Na]+ NaN
Exact m/z of [M+Li]+ NaN
Exact m/z of [M+NH4]+ NaN
Exact m/z of [M-H]- NaN
Exact m/z of [M+Cl]- NaN
Exact m/z of [M+OAc]- NaN
CHEBI NaN
LIPID MAPS NaN
HMDB NaN
MetaNetX NaN
PMID NaN
Components_parsed NaN
Name: 0, dtype: object
df_sl_nodes.iloc[0]
layer sl_abbreviation
node_id (5S)-HpHEPE
Lipid ID NaN
Level NaN
Name NaN
Abbreviation* NaN
Synonyms* NaN
Lipid class* NaN
Parent NaN
Components* NaN
SMILES (pH7.3) NaN
InChI (pH7.3) NaN
InChI key (pH7.3) NaN
Formula (pH7.3) NaN
Charge (pH7.3) NaN
Mass (pH7.3) NaN
Exact Mass (neutral form) NaN
Exact m/z of [M.]+ NaN
Exact m/z of [M+H]+ NaN
Exact m/z of [M+K]+ NaN
Exact m/z of [M+Na]+ NaN
Exact m/z of [M+Li]+ NaN
Exact m/z of [M+NH4]+ NaN
Exact m/z of [M-H]- NaN
Exact m/z of [M+Cl]- NaN
Exact m/z of [M+OAc]- NaN
CHEBI NaN
LIPID MAPS NaN
HMDB NaN
MetaNetX NaN
PMID NaN
Components_parsed NaN
Name: 0, dtype: object
For the first entry it looks good, what about for the entire df? We can use the pd.testing.assert_frame_equal function to do this.
First we will use a null test to test equality between df_swisslipids_nodes and df_swisslipids_edges, which should obviously be False.
try:
pd.testing.assert_frame_equal(df_swisslipids_nodes, df_swisslipids_edges)
print('DataFrames are equal')
except AssertionError as e:
print(e)
DataFrame are different
DataFrame shape mismatch
[left]: (2783345, 32)
[right]: (6890979, 5)
Now let’s test between df_swisslipids_nodes and df_sl_nodes, which should hopefully be True and not throw an error. We will also test the edges df while we’re at it too.
try:
pd.testing.assert_frame_equal(df_swisslipids_nodes, df_sl_nodes)
print('DataFrames for nodes are equal')
except AssertionError as e:
print(e)
DataFrames for nodes are equal
try:
pd.testing.assert_frame_equal(df_swisslipids_edges, df_sl_edges)
print('DataFrames for edges are equal')
except AssertionError as e:
print(e)
DataFrames for edges are equal
Great! It looks like both approaches achieve the same df. We will use these dfs in other parts of the package.
If they are different, we can inspect the exact rows here
diff = df_sl_edges.merge(df_swisslipids_edges, how='outer', indicator=True)
diff_rows_edges = diff[diff['_merge'] != 'both']
diff_rows_edges
| source_layer | source_id | target_layer | target_id | interlayer | _merge |
|---|
diff = df_sl_nodes.merge(df_swisslipids_nodes, how='outer', indicator=True)
diff_rows_nodes = diff[diff['_merge'] != 'both']
diff_rows_nodes
| layer | node_id | Lipid ID | Level | Name | Abbreviation* | Synonyms* | Lipid class* | Parent | Components* | ... | Exact m/z of [M-H]- | Exact m/z of [M+Cl]- | Exact m/z of [M+OAc]- | CHEBI | LIPID MAPS | HMDB | MetaNetX | PMID | Components_parsed | _merge |
|---|
0 rows × 33 columns
These should also be the same
df_sl_edges[df_sl_edges['source_id']=='SLM:000389145']
| source_layer | source_id | target_layer | target_id | interlayer | |
|---|---|---|---|---|---|
| 1640 | swisslipids | SLM:000389145 | sl_chebi | 18059 | True |
| 429400 | swisslipids | SLM:000389145 | sl_metanetx | MNXM12117 | True |
| 549344 | swisslipids | SLM:000389145 | swisslipids | SLM:000000436 | False |
| 549407 | swisslipids | SLM:000389145 | swisslipids | SLM:000000525 | False |
| 549887 | swisslipids | SLM:000389145 | swisslipids | SLM:000001193 | False |
| 665828 | swisslipids | SLM:000389145 | swisslipids | SLM:000117142 | False |
| 936914 | swisslipids | SLM:000389145 | swisslipids | SLM:000390054 | False |
| 1046948 | swisslipids | SLM:000389145 | swisslipids | SLM:000500463 | False |
| 1055230 | swisslipids | SLM:000389145 | swisslipids | SLM:000508860 | False |
| 1328368 | swisslipids | SLM:000389145 | swisslipids | SLM:000782283 | False |
df_swisslipids_edges[df_swisslipids_edges['source_id']=='SLM:000389145']
| source_layer | source_id | target_layer | target_id | interlayer | |
|---|---|---|---|---|---|
| 1640 | swisslipids | SLM:000389145 | sl_chebi | 18059 | True |
| 429400 | swisslipids | SLM:000389145 | sl_metanetx | MNXM12117 | True |
| 549344 | swisslipids | SLM:000389145 | swisslipids | SLM:000000436 | False |
| 549407 | swisslipids | SLM:000389145 | swisslipids | SLM:000000525 | False |
| 549887 | swisslipids | SLM:000389145 | swisslipids | SLM:000001193 | False |
| 665828 | swisslipids | SLM:000389145 | swisslipids | SLM:000117142 | False |
| 936914 | swisslipids | SLM:000389145 | swisslipids | SLM:000390054 | False |
| 1046948 | swisslipids | SLM:000389145 | swisslipids | SLM:000500463 | False |
| 1055230 | swisslipids | SLM:000389145 | swisslipids | SLM:000508860 | False |
| 1328368 | swisslipids | SLM:000389145 | swisslipids | SLM:000782283 | False |
