Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages.
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Provides basic utility functions for performing single-cell analyses, focusing on simple normalization, quality control and data transformations. Also provides some helper functions to assist development of other packages.
Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("scuttle")perFeatureQCMetrics or calculateAverage.uniquifyFeatureNames.data.frame for ggplot or model.matrix using makePerCellDF or makePerFeatureDF.medianSizeFactors), pooling normalization (computePooledFactors), or spike-in normalization (computeSpikeFactors).scran instead because many of scuttle's low-level utilities are legacy functions that have been superseded by more efficient implementations in other packages.quickCluster from scran instead because scuttle relies on external packages for the clustering step required prior to pooled normalization.SingleCellExperiment object.assay.type="counts").list(Empty=1:10)): Used in perFeatureQCMetrics to define control sets (e.g., empty wells) for calculating ratio metrics."counts"): Specifies which assay to extract data from in makePerCellDF and makePerFeatureDF."Tspan12"): Specifies the specific genes to extract expression data for in makePerCellDF.c(...)): Specifies the specific cells to extract expression profiles for in makePerFeatureDF.clusters): Used in computePooledFactors to apply normalization within predefined clusters before adjusting across clusters.uniquifyFeatureNames to safely convert Ensembl IDs to gene symbols for interactive analysis, ensuring duplicates are appended with identifiers and missing symbols are replaced.scran::quickCluster) prior to running computePooledFactors on heterogeneous populations to avoid violating the assumption that most genes are non-DE.computePooledFactors.uniquifyFeatureNames.computePooledFactors globally on a highly heterogeneous dataset can distort scaling factors; fix this by supplying a clusters argument to normalize within subpopulations first.quickCluster function required for pooled normalization.medianSizeFactors adapts for single-cell data.This skill is the knowledge layer — when, why, and how to use scuttle. 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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