The FDA Adverse Event Reporting System (FAERS) is a database used for the spontaneous reporting of adverse events and medication errors related to human drugs and therapeutic biological products. faers pacakge serves as the interface between the FAERS database and R. Furthermore, faers pacakge offers a standardized approach for performing pharmacovigilance analysis.
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library(faers)
# 1. Check metadata of FAERS
faers_meta(internal = TRUE)
# 2. Download and parse quarterly data files
data2 <- faers(
c(2004, 2017),
c("q1", "q2"),
dir = system.file("extdata", package = "faers"),
compress_dir = tempdir()
)
# 3. Retrieve specific field data (e.g., demographic data)
demo_data <- faers_get(data2, "demo")
# 4. Load standardized data and retrieve MedDRA hierarchy
data_std <- readRDS(system.file("extdata", "standardized_data.rds", package = "faers"))
meddra_hier <- faers_meddra(data_std, use = "hierarchy")
Input/Output Note: Inputs are year, quarter, and directory paths for FAERS raw data; outputs are standardized FAERS data tables and MedDRA hierarchy mappings.
faers_get.faers_standardize and faers_meddra.FALSE): Logical indicating whether to use internal cached FAERS metadata in faers_meta."hierarchy"): Component of MedDRA to retrieve (e.g., "hierarchy" or "SMQ").faers_meta(internal = TRUE) to verify available quarters and file sizes before downloading.faers_combine to merge multiple quarters of parsed FAERS data into a single object.faers_standardize with a valid MedDRA path to ensure consistent terminology.meddra_path is correctly specified.openfda: For querying the OpenFDA API directly instead of parsing raw quarterly files.还没有评价。欢迎分享你的使用感受。
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This tool uses the label-tree function from HyPhy to annotate a phylogenetic tree. It allows users to select a subset of leaves using either a regular expression or a list of sequence names, and then apply a specified label to these selecte
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This package provides a client for the Bioconductor AnnotationHub web resource. The AnnotationHub web resource provides a central location where genomic files (e.g., VCF, bed, wig) and other resources from standard locations (e.g., UCSC, En
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apeglm provides Bayesian shrinkage estimators for effect sizes for a variety of GLM models, using approximation of the posterior for individual coefficients.
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ASURAT is a software for single-cell data analysis. Using ASURAT, one can simultaneously perform unsupervised clustering and biological interpretation in terms of cell type, disease, biological process, and signaling pathway activity. Inputting a single-cell RNA-seq data and knowledge-based databases, such as Cell Ontology, Gene Ontology, KEGG, etc., ASURAT transforms gene expression tables into original multivariate tables, termed sign-by-sample matrices (SSMs).
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