The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assa
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("QFeatures")aggregateFeatures.plot or exploring the data interactively with display.readQFeatures.MultiAssayExperiment directly.SummarizedExperiment instead.GenomicRanges or IRanges instead, as QFeatures is designed for quantitative feature matrices.data.frame) containing quantitative columns and feature annotations.DataFrame to the colData argument, containing at least the quantitative column names."psms" or "peptides").aggregateFeatures (e.g., colMeans)."FileName").DataFrame containing sample annotations to be matched with the quantitative columns.NAs.plot to explore the hierarchy of assays through an interactive plotly graph.readQFeatures and specifying the quantCols.aggregateFeatures with an appropriate summary function like colMeans.plot to visualize the hierarchical relationships between assays via the internal AssayLinks.assay() and metadata using rowData() or colData() for downstream exploration.longForm() to convert the QFeatures object into a long table format, which is ideal for ggplot2 visualization.aggregateFeatures will warn if row data contain missing values. Fix: Read the manual page regarding the effects of missing values on data aggregation before proceeding.plot. Fix: Use plot(hl, interactive = TRUE) to navigate the tree interactively.NAs can cause issues. Fix: Set removeEmptyCols = TRUE in readQFeatures to automatically drop them.runCol to readQFeatures to split the table into multiple sets.QFeatures builds upon and extends for MS data.QFeatures sets.readQFeatures before converting the data to a QFeatures object.This skill is the knowledge layer — when, why, and how to use qfeatures. 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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