Tools for parsing Illumina's microarray output files, including IDAT.
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Package-intrinsic requirements from the Bioconductor landing page — reproduce in any R environment.
BiocManager::install("illuminaio")readIDAT.MeanBinData, NumBeadsBinData, DevBinData) directly from Illumina BeadChip platforms.illuminaio is strictly designed for raw file parsing and leaves data retention choices to the user.affy or oligo instead because illuminaio is exclusively built for Illumina BeadArray platforms..idat files (e.g., _Grn.idat)..idat file to be parsed by readIDAT.readIDAT; the function will automatically determine the IDAT format and call the appropriate internal reading routine.illuminaio output to GenomeStudio, remember to identify and remove internal control bead-types, as GenomeStudio excludes these automatically.illuminaio.illuminaio output than in GenomeStudio exports. Fix: Filter out the unannotated internal control bead-types that illuminaio extracts by default.illuminaio under the hood.This skill is the knowledge layer — when, why, and how to use illuminaio. 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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