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Description: Fits Weighted Quantile Sum (WQS) regressions for continuous or binomial outcomes.
Usage: gwqs(formula, mix_name, data, q = 4, validation = 0.6, valid_var = NULL, b = 100, b1_pos = TRUE, family = "gaussian", seed = NULL, wqs2 = FALSE, plots = FALSE, tables = FALSE)
Metabolomics involves the identification and measurement of small-molecule metabolites of endogenous and exogenous origin in a biospecimen. These metabolites represent a diverse group of low-molecular-weight structures, such as lipids, amino acids, peptides, nucleic acids, organic acids, vitamins, thiols, carbohydrates, environmental chemicals, and dietary compounds. Different approaches and analytical platforms are used to detect, characterize, and quantify metabolites and related metabolic pathways, including untargeted and targeted liquid chromatography-mass spectrometry (LC-MS), gas chromatography-MS (GC-MS), and nuclear magnetic resonance (NMR). In CHEAR, most metabolomics studies use a LC-MS platform to perform untargeted metabolomics. Therefore, the purpose of the tutorial is to provide a basic overview for non-experts of how LC-MS-based untargeted metabolomics datasets are generated, which should aid in data analysis and interpretation.
This User Manual outlines the Major Functions and Processes supported by the CHEAR Data Submission and Review Portal, and how to use them. This manual is intended for use by Primary Investigators (i.e., “PI”s) and their CoInvestigators. These users will be accessing the portal to upload their study results data, generate CHEAR Participant IDs (PIDs) and Specimen IDs (SIDs), map CHEAR SIDs to PIDs, retrieving lab result data, and other related activities.
If you are not sure how to verify the SHA-512 hash of the file that you downloaded or uploaded, you can use this in-browser file hasher. This will calculate the hash without uploading.
The Data Center is responsible for creating and maintaining the HHEAR Ontology—a common vocabulary for use in the HHEAR program. The Ontology is evolving with the program and will connect to best-in-class existing vocabularies, thus facilitating the integration of data from multiple studies.
Facilitating the mapping of variables from data dictionaries into terms consistent with the HHEAR Ontology
Incorporating the study's data into the HHEAR Ontology to support collaborative research across the HHEAR consortium, including pooled analyses from health research studies participating in CHEAR and HHEAR
Developing methods and services for comparing similar variables from different data dictionaries, starting with very basic mappings of equivalent terms and moving into more sophisticated analyses of relationships among variables
Providing tools and services to manage the HHEAR Ontology evolution