{"doi":"10.1021/pr300231n","title":"Bayesian Independent Component Analysis Recovers Pathway Signatures from Blood Metabolomics Data","abstract":"Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the source matrix of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a mixing matrix, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field.","journal":"Journal of Proteome Research","year":2012,"id":2210,"datarank":0.47670807455219194,"base_score":3.1780538303479458,"endowment":3.1780538303479458,"self_citation_contribution":0.47670807455219194,"citation_network_contribution":0.0,"self_endowment_contribution":0.47670807455219194,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":23,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.0398,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2012-07-17","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":2350,"name":"Karsten Suhre","orcid":"0000-0001-9638-3912","position":1,"is_corresponding":false},{"id":2344,"name":"Thomas Illig","orcid":"0000-0003-4284-5389","position":2,"is_corresponding":false},{"id":2342,"name":"Jerzy Adamski","orcid":"0000-0001-9259-0199","position":3,"is_corresponding":false},{"id":42,"name":"Fabian Joachim Theis","orcid":"0000-0002-2419-1943","position":4,"is_corresponding":false},{"id":2337,"name":"Jan Krumsiek","orcid":"0000-0003-4734-3791","position":0,"is_corresponding":true}],"reference_count":50,"raw_metadata":null,"created_at":"2026-03-01T18:20:47.508186Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}