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Metabolite Set Enrichment Analysis (ChemRICH)
- 1. Unit 5.3 & 5.6 Metabolite set enrichment analysis (ChemRICH) Dinesh Barupal dinkumar@ucdavis.edu
- 2. DATA ACQUISITION Separation Detection SAMPLING EXTRACTION DATA PROCESSING File Conversion Baseline Correction Peak Detection Deconvolution Adduct Annotation Alignment Gap Filling STATISTICS Normalization Multivariate Analysis (Parametric, Nonparametric) Univariate Analysis (Unsupervised, Supervised) BIOLOGICAL INTERPRETATION Pathway Mapping Network Enrichment STUDY DESIGN VALIDATION COMPOUND IDENTIFICATION Molecular Formula ID Structure ID MS Library Search Database Search In silico Fragmentation WCMC UC Davis
- 3. Questions : • How to group metabolites into sets? • Which statistical method to use for set enrichment ? • Which sets are significantly different among two study groups ?
- 4. High quality metabolomics data is a commodity http://metabolomics.ucdavis.edu/ + Raw LC/GC MS data files + Quality control reports + ~ 5000 high quality unknown metabolites ~800 known metabolites for $280 only ! By 2020, blood metabolomics datasets will have 1500 identified compounds.
- 5. How to groups metabolites into sets ? Pros Cons Pathway maps • Well-known definitions and accepted by biologists. • Canonical maps • Easy interpretation • Manual boundaries • Poor coverage • Overlapping maps • Lack on consensus among databases Chemical classes • Well-known classes, accepted by epidemiologists • Good coverage • Non-overlapping sets Network modules • Study specific • Non-overlapping • All identified compounds are covered. • Interpretation is difficult Correlation modules • Study specific • Non overlapping • Unknowns are included • Interpretation is difficult
- 6. 385 173 MeSH NCBI BioSystems All 187 KEGG 135 Example Metabolomics dataset: non-obese diabetic mice (http://www.metabolomicsworkbench.org/data/DRCCMetadata.php?Mode=Study&StudyID=ST000075) 385 identified primary metabolites, oxylipins, complex lipids. Argument 1 : Biochemical databases are incomplete for metabolomics
- 7. Argument 2: Pathway definitions are manual and vary across different databases Major pathway databases 0 500 1000 1500 2000 2500 3000 Pathwaycount
- 8. Which Krebs Cycle definitions ? KEGG Reactome SMPDB MetaCyc
- 9. Argument 3: Pathway definitions are overlapping 1 2 3 4 5 6 7 8 910111213141516171819202123262829303235367096 Number shows the count of pathway maps Compounds from NCBI Biosystems Database
- 10. What is enrichment analysis ? http://jura.wi.mit.edu/bio/education/hot_topics/ > 50,000 papers report use of enrichment or overrepresentation for lists of genes, transcripts, proteins or metabolites. An very hot area of research for building new bioinformatics software. Tons of opportunities for development in the field of metabolomics.
- 11. http://www.metaboanalyst.ca/ A typical pathway enrichment report N L K M pvalue = phyper(M,L,N-L,K) All CPDs in HMDB with pathway annotations (~1600) A pathway altered compounds What is the probability of having n metabolites of the a pathway in the input list ? Pathways are often used for enrichment analysis Why we need another enrichment analysis approach ?
- 12. Argument 4: Hypergeometric or fisher exact test is inappropriate for metabolomics
- 13. • expected compounds – entire HMDB (~110,000) • compounds with pathway annotations – ~2000 for human • compound with reaction annotations - ~4000 for human • compound with literature annotations – ~15000 for human blood • detected known compounds – varies between 500- 1000 • detected all compounds - ~ 3000 Argument 5: Background database size is not defined for metabolomics
- 14. • Not all metabolites from a pathway map are present in a metabolomics dataset • Not all detected metabolites have pathway annotations • Pathway boundaries are arbitrary and over-lapping • Pathway maps vary across biochemical databases • Background database size is varying over time for a hypergeometric test A pathway-independent method that uses all identified metabolites uses non-overlapping set definitions that does not depend on any background databases ChemRICH : Chemical Similarity Enrichment Analysis Better: Major problems in pathway based analysis
- 15. What are alternative set definitions and statistics ?
- 16. Alternative A : MetaMapp clusters http://metamapp.fiehnlab.ucdavis.edu/ Limitations Cluster labels Similarity cutoff
- 17. Alternative B : Chemical similarity clusters Distance matrix is Tanimoto coefficient Limitation Cluster labels
- 18. Alternative C : Chemical Ontologies Medical Subject Headings ontology Lipidmaps ontology 110K compounds with mesh annotations MeSH is linked to PubMed automated text mining on identified ontology groups. Limitation Not every detected metabolite is covered 50K compounds 385 173 MeSH NCBI BioSystems All 187 KEGG 135
- 19. KS test is a better statistical method for metabolomics enrichment Parameter Fisher Exact Hypergeo metric Bionomial K-S Background database Yes Yes No No p-value cutoff Yes Yes Yes No K-S :Kolmogorov–Smirnov test is a nonparametric test of the equality of continuous, one- dimensional probability distributions that can be used to compare a sample with a reference probability distribution (one-sample K–S test)
- 20. MeSH PubChem Name CID SMILES MeSH IDs Name CID SMILES MeSH IDs Fingerprint PubChem fingerprint rCDK package (91,444 unique structures & 2768 MeSH classes) ChemRICH database Name CID SMILES p-value effect size Metabolomics dataset statistics lookup Tanimoto MeSH IDs Classes Name Class Non-overlapping classes KS Test Class P-value Generation of the ChemRICH database ChemRICH analysis NC >0.9 HC STR SMILES Class Enriched Sets HC New compounds ChemRICH impact plot ChemRICH combines MeSH, Chemical similarity and KS Test
- 21. Start All metabolites ChemRICH lookup No Yes Label found No Tanimoto Similarity Yes TM score >0.90 Yes No Detection of new Clusters New Cluster ? Yes No TM score >0.75 Yes No Reported individually Generation of non- overlapping class annotation p-values SMILES regex search Similarity matrix HCL ChemRICH enrichment plot END Effect sizes Classes found (68) (385) (317) (151) (166) (147) (19) (0) (19) (5) (14) Set size >2 Yes (325) No (55) (50 sets) KS-test ChemRICH combines MeSH, Chemical similarity and KS Test Precise steps in the ChemRICH analysis for a metabolomics dataset
- 22. A 1 2 2 ` ` ` disaccharides hexose- phosphates pentoses hexoses sugar alcohols sugar acids tricarboxylic acids butyrates hydroxybutyrates amino acids, sulfur amino acids, branched-chain cholesterol esters pyridines amino acids, aromatic indoles sphingomyelins Unsaturated_lysophosphatidylcholines phosphatidylcholines phosphatidyl- inositols plasmalogens phosphatidyl- ethanolamines DiHODE oxo-ETE HETrE HETE Unsaturated_triglycerides Saturated FA Saturated_triglycerides Saturated_ lysophosphatidylcholines cluster order on Tanimoto similarity tree -log(pvalue) 0 10 20 30 0 10 20 30 40 50 Cluster name cluster size pvalues adjusted pvalue total changed increased decreased UnSaturated PC 38 5.18E-10 2.54E-08 25 2 23 UnSaturated TG 35 7.38E-09 1.81E-07 22 21 1 UnSaturated SM 17 8.30E-06 0.000135 12 0 12 UnSaturated LPC 9 1.10E-05 0.000135 9 0 9 Butyrates 7 9.14E-05 0.000896 7 6 1 Disaccharides 8 0.00021 0.001712 7 6 1 PUFA TG 12 0.000266 0.001862 8 8 0 Hexoses 7 0.000597 0.003656 6 6 0 Sugar Acids 10 0.001707 0.009296 6 6 0 PUFA PI 4 0.002339 0.010419 4 0 4 Saturated TG 4 0.002339 0.010419 4 4 0 OH-FA_20 17 0.003475 0.014191 6 1 5 OH-FA_18 10 0.004912 0.018513 5 0 5 PUFA PC 11 0.005484 0.019193 5 0 5 Amino Acids, Branched-Chain 3 0.007153 0.019472 3 3 0 Pentoses 3 0.007153 0.019472 3 3 0 PUFA LPC 3 0.007153 0.019472 3 0 3 PUFA PE 6 0.007153 0.019472 4 0 4 Sugar Alcohols 12 0.01423 0.036698 4 3 1 Amino Acids, Sulfur 3 0.041632 0.081599 2 0 2 Hexosephosphates 3 0.041632 0.081599 2 2 0 Indoles 3 0.041632 0.081599 2 2 0 O=FA_20 3 0.041632 0.081599 2 0 2 Pyridines 3 0.041632 0.081599 2 2 0 Tricarboxylic Acids 3 0.041632 0.081599 2 2 0 Using the ontology/chemistry clusters to compute p-values for significant metabolic differences
- 23. ChemRICH app Interactive cluster plot compound level data table cluster level data table chemical similarity tree Result downloads as xlsx, pptx, png , pdf ChemRICH is available online www.ChemRICH.us
- 24. ChemRICH analysis for the NAFLD study
- 25. ChemRICH : Data preparation Example dataset available in the chemrich example folder spring_2018_metabolomics_course_chemrich_example Use PubChem Identified Exchange Service to obtain identifiers, InchiKeys and SMILES for compound names.
- 26. ChemRICH input file errors - • Duplicate PubChem CIDs • Duplicate names • Missing SMILES codes • Missing p-value or fold-change • Headers mismatch • > 1000 compounds
- 27. Perform ChemRICH analysis www.ChemRICH.us Paste your data in this box
- 28. Explanation of results Editable power-point slide
- 30. Download/interact with results Imino Acids Saturated_Lysophosphatidylcholines Lysophospholipids Unsaturated_Lysophosphatidylcholines NewCluster_32 Cholestenes Phosphatidylethanolamines NewCluster_14 Unsaturated_Phosphatidylcholines Sphingomyelins Diglycerides Plasmalogens Unsaturated_Ceramides Galactosylceramides Cholesterol Esters 0 10 20 30 0 5 10 15 20 median XlogP of clusters -log(pvalue)
- 33. User provided classes http://chemrich.fiehnlab.ucdavis.edu/ocpu/library/ChemRICHTest3/www/class.html
- 34. Github https://github.com/barupal/chemrich Docker image and source codes https://bitbucket.org/barupal/chemrich-docker Bitbucket https://hub.docker.com/r/barupal/chemrich-docker/ Docker docker pull barupal/chemrich-docker
- 35. Main advantages of the ChemRICH method • mapping of up to 95% of the known compounds in a metabolomics dataset. • non-overlapping clusters. • background database independent statistics. • can map compounds that are not yet in any database, such as in-silico compounds. • utilizes existing knowledge from chemical ontologies to enable straightforward literature mining. • allows identification of new chemical clusters that are not yet covered in ontologies yet. • cluster impact plot visualize the chemical diversity. • inclusion of well known chemical classes as well room for clustering of other chemical classes. Barupal Dinesh & Fiehn Oliver. ChemRICH : Chemical Similarity Enrichment Analysis for metabolomics datasets. Scientific Report (2017) Publication Conclusions
Editor's Notes
- Pathway count KEGG 495 HMDB 613 Wikipathways 789 Reactome 2000 MetaCyc 2453