The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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The R implementation for the Grammar of Succint Lipid Nomenclature parses different short hand notation dialects for lipid names. It normalizes them to a standard name. It further provides calculated monoisotopic masses and sum formulas for each successfully parsed lipid name and supplements it with LIPID MAPS Category and Class information. Also, the structural level and further structural details about the head group, fatty acyls and functional groups are returned, where applicable.
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The R implementation for the Grammar of Succint Lipid Nomenclature parses different shorthand notation dialects for lipid names, normalizes them, and calculates chemical properties.
library(rgoslin)
# Check which grammars are supported
listAvailableGrammars()
# Check whether a given lipid name can be parsed
isValidLipidName("PC 32:1")
# Parse a single lipid name to return a data frame of properties
df <- parseLipidNames("PC 32:1")
# Parse a lipid name with a manually specified grammar
tagDf <- parseLipidNames("TG(16:1(5E)/18:0/20:2(3Z,6Z))", grammar = "LipidMaps")
# Parse multiple lipid names using a vector
multipleLipidNamesDf <- parseLipidNames(c("PC 32:1", "LPC 34:1", "TG(18:1_18:0_16:1)"))
Input: A character vector of lipid shorthand names. Output: A data frame containing parsed lipid properties (e.g., normalized name, mass, sum formula, category).
parseLipidNames().isValidLipidName().parseLipidNames(..., grammar = "FattyAcids").xcms instead.MSnbase or Spectra instead."PC 32:1", "TG(16:1(5E)/18:0/20:2(3Z,6Z))")."Shorthand2020", "Goslin", "FattyAcids", "LipidMaps", "SwissLipids", and "HMDB"."Goslin", "LipidMaps", "FattyAcids"). If omitted, all available parsers are tested sequentially.listAvailableGrammars() to check supported grammars before parsing.grammar parameter in parseLipidNames() when parsing large vectors to avoid testing all parsers and improve performance.isValidLipidName() to quickly filter out unparseable names before running full parsing.grammar argument on large datasets: Causes slow execution because all grammars are tested sequentially. Fix: Specify grammar = "Goslin" or another specific grammar.(d9)): Causes parsing errors. Fix: Preprocess names using string manipulation (e.g., str_match or str_replace_all) to remove or convert suffixes before parsing.lipidr: For downstream analysis, visualization, and QC of lipidomics data (can be integrated with rgoslin).MSnbase: For low-level mass spectrometry data container handling.Spectra: For raw mass spectra representation and processing.아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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The package contains functions for exploratory oligonucleotide array analysis. The dependence on tkWidgets only concerns few convenience functions. 'affy' is fully functional without it.
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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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AUCell allows to identify cells with active gene sets (e.g. signatures, gene modules...) in single-cell RNA-seq data. AUCell uses the "Area Under the Curve" (AUC) to calculate whether a critical subset of the input gene set is enriched with
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