Tools for finding bumps in genomic data
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("bumphunter")bumphunter().clusterMaker().getSegments().regionFinder().limma because bumphunter is specifically designed to share information between nearby clustered locations.minfi because it provides a tailored wrapper around the bumphunter engine.bsseq because it adapts the bump hunting methodology specifically for bisulfite data.y) of genomic signals where rows represent genomic locations and columns represent biological replicates.X) representing the experimental covariates, created with standard R modeling functions.chr) and genomic positions (pos) corresponding to the rows of the signal matrix.clusterMaker().getSegments() and bumphunter().bumphunter().registerDoParallel().clusterMaker() before running segment-finding functions.doParallel package and registerDoParallel() to distribute permutation computations across multiple cores.X) contains an intercept term and the covariate of interest; avoid using permutation testing if adjusting for multiple confounders.foreachCleanup() after parallel execution to properly close connections.registerDoParallel() before calling bumphunter().chr vector is correctly passed to clusterMaker(), which strictly separates chromosomes.limma: Provides lmFit for linear modeling of biological replicates without the spatial smoothing and clustering steps.minfi: Offers a specialized implementation of the bumphunter methodology tailored specifically for Illumina 450k methylation arrays.bsseq: Adapts the bump hunting conceptual approach specifically for whole-genome bisulfite sequencing data.charm: Provides modifications of the bump hunting methodology for CHARM-like methylation microarrays.This skill is the knowledge layer — when, why, and how to use bumphunter. 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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