mzR provides a unified API to the common file formats and parsers available for mass spectrometry data. It comes with a subset of the proteowizard library for mzXML, mzML and mzIdentML. The netCDF reading code has previously been used in XC
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("mzR")openMSfile().runInfo(), instrumentInfo(), and header().peaks() function.openIDfile().MSnbase instead because mzR is strictly a low-level data access API.xcms instead because mzR does not provide these algorithms natively..mzML, .mzXML, .cdf (netCDF), or .mzid (for identifications).openMSfile() or openIDfile().close() when not needed anymore to release the memory of cached content.header() to retrieve a summary of all scans (retention time, MS level, precursor m/z) before loading heavy raw spectral arrays with peaks().peaksCount() to determine the number of peaks in a spectrum before extracting the full m/z and intensity matrices.close() on the opened file object when finished.mzR pointers. Fix: Use MSnbase (with on-disk mode) which uses mzR internally but offers a coherent, high-level S4 interface.This skill is the knowledge layer — when, why, and how to use mzr. To run this analysis on your own data with managed compute, automated QC, and reproducible outputs, use BioMate — free to start.
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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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