https://tempbioc.r-universe.devRecent package updates in tempbiocR-universehttps://github.com/tempbioc.pnghttps://tempbioc.r-universe.devThu, 11 Jun 2026 12:42:59 GMTlmweb012@gmail.com (Lukas M. Weber)This package contains source files for the "Orchestrating Spatial Transcriptomics Analysis with Bioconductor" online book. This book provides interactive examples and discussion on key principles of computational analysis workflows for spatial transcriptomics data using Bioconductor in R. The book contains chapters describing individual analysis steps as well as extended workflows, each with examples including R code and datasets.https://github.com/r-universe/tempbioc/actions/runs/27368685409Thu, 11 Jun 2026 12:42:59 GMTOSTA1.3.1failurehttps://tempbioc.r-universe.devhttps://github.com/bioc/OSTAhpages.on.github@gmail.com (Hervé Pagès)The package defines many S4 generic functions used in Bioconductor.https://github.com/r-universe/tempbioc/actions/runs/27059554909Sat, 06 Jun 2026 07:19:42 GMTBiocGenerics0.59.7successhttps://tempbioc.r-universe.devhttps://github.com/bioc/BiocGenericsmaintainer@bioconductor.org (Bioconductor Package Maintainer)Functions that are needed by many other packages or which replace R functions.https://github.com/r-universe/tempbioc/actions/runs/27059713764Tue, 28 Apr 2026 23:00:16 GMTBiobase2.73.1successhttps://tempbioc.r-universe.devhttps://github.com/bioc/BiobaseExpressionSetIntroduction.RnwExpressionSetIntroduction.pdfAn introduction to Biobase and ExpressionSets2013-10-18 21:56:022018-07-17 21:44:18esApply.RmdesApply.htmlesApply Introduction2022-10-22 20:39:122022-10-28 20:33:49BiobaseDevelopment.RmdBiobaseDevelopment.htmlBiobase development and the new eSet2022-10-31 20:04:222022-10-31 20:04:22stvjc@channing.harvard.edu (Vincent Carey)Support harvesting of diverse bioinformatic ontologies, making particular use of the ontologyIndex package on CRAN. We provide snapshots of key ontologies for terms about cells, cell lines, chemical compounds, and anatomy, to help analyze genome-scale experiments, particularly cell x compound screens. Another purpose is to strengthen development of compelling use cases for richer interfaces to emerging ontologies.https://github.com/r-universe/tempbioc/actions/runs/26628864807Tue, 28 Apr 2026 18:29:37 GMTontoProc2.7.0successhttps://tempbioc.r-universe.devhttps://github.com/bioc/ontoProcontoProc.RmdontoProc.htmlontoProc: Ontology interfaces for Bioconductor, with focus on cell type identification2017-09-10 15:22:232026-02-25 18:15:53owlents.Rmdowlents.htmlowlents: using OWL directly in ontoProc2023-11-17 13:28:192026-02-25 18:15:53jacquesserizay@gmail.com (Jacques Serizay)The primary aim of this book is to introduce the R user to Hi-C analysis. This book starts with key concepts important for the analysis of chromatin conformation capture and then presents Bioconductor tools that can be leveraged to process, analyze, explore and visualize Hi-C data.https://github.com/r-universe/tempbioc/actions/runs/26557760933Tue, 28 Apr 2026 12:53:51 GMTOHCA1.9.0successhttps://tempbioc.r-universe.devhttps://github.com/bioc/OHCAstub.Rmdstub.htmlLink to book2023-11-03 12:31:392024-01-22 11:34:44alan.ocallaghan@outlook.com (Alan OCallaghan)Deploys the multi-sample analysis chapters for the "Orchestrating Single Cell Analysis with Bioconductor" book. This describes the handling of multiple samples in a single-cell RNA-seq analysis, starting with integration of multiple datasets into a common space for consistent analyses, differential expression comparisons between conditions based on pseudo-bulk samples, and differential abundance analyses for cell subpopulations. It is intended for readers who are already familiar with basic single-cell analyses, possibly after reading some of the prior books in this collection.https://github.com/r-universe/tempbioc/actions/runs/26629664470Tue, 28 Apr 2026 12:53:46 GMTOSCA.multisample1.21.0failurehttps://tempbioc.r-universe.devhttps://github.com/bioc/OSCA.multisampleludwig_geistlinger@hms.harvard.edu (Ludwig Geistlinger)Deploys the advanced analysis chapters for the "Orchestrating Single Cell Analysis with Bioconductor" book. This describes the more complex steps of a single-cell RNA-seq analysis ranging from doublet detection, cell cycle assignment, specific steps for processing droplet data, nuclei-specific analyses, trajectory analyses, integrated analyses with protein abundances, and interactive visualization. It also elaborates on some of the basic analysis steps, focusing on alternative strategies and theoretical considerations. It is intended for readers who are already familiar with basic single-cell analyses, possibly after reading some of the prior books in this collection.https://github.com/r-universe/tempbioc/actions/runs/26581193540Tue, 28 Apr 2026 12:53:44 GMTOSCA.advanced1.21.0failurehttps://tempbioc.r-universe.devhttps://github.com/bioc/OSCA.advancedpeter.hickey@gmail.com (Peter Hickey)Deploys the workflows of the "Orchestrating Single Cell Analysis with Bioconductor" book. This contains worked case studies of analyses of a variety of single-cell datasets, each proceeding from a SingleCellExperiment object. Exposition is generally minimal other than for dataset-specific justifications for parameter tweaks; refer to the other books in the OCSA collection for a detailed explanation of the theoretical basis of each step. It is intended for readers who already know the background and just want some code to copy and paste into their own analyses.https://github.com/r-universe/tempbioc/actions/runs/26629457578Tue, 28 Apr 2026 12:53:42 GMTOSCA.workflows1.21.0failurehttps://tempbioc.r-universe.devhttps://github.com/bioc/OSCA.workflowsludwig_geistlinger@hms.harvard.edu (Ludwig Geistlinger)Deploys the introduction to the "Orchestrating Single Cell Analysis with Bioconductor" book. This describes how to install R and Bioconductor packages, links out to some resources to learn R, describes how to load datasets into an R session, provides an overview of the SingleCellExperiment class, and performs a "quick start" demonstration for basic single-cell RNA-seq analyses. It is intended for readers with little-to-no computational background who are just getting started with analyses in R.https://github.com/r-universe/tempbioc/actions/runs/26629063020Tue, 28 Apr 2026 12:53:40 GMTOSCA.intro1.21.0failurehttps://tempbioc.r-universe.devhttps://github.com/bioc/OSCA.intropeter.hickey@gmail.com (Peter Hickey)Deploys basic analysis chapters for the "Orchestrating Single Cell Analysis with Bioconductor" book. This describes the steps of a simple single-cell RNA-seq analysis, involving quality control, normalization, various forms of dimensionality reduction, clustering into subpopulations, detection of marker genes, and annotation of cell types. It is intended for users who already have some familiarity with R and want to get hands-on with some basic single-cell analyses.https://github.com/r-universe/tempbioc/actions/runs/26581191315Tue, 28 Apr 2026 12:53:37 GMTOSCA.basic1.21.0successhttps://tempbioc.r-universe.devhttps://github.com/bioc/OSCA.basicstub.Rmdstub.htmlLink to book2021-01-18 07:54:482021-01-18 07:54:48alan.ocallaghan@outlook.com (Alan OCallaghan)Online book for orchestrating single cell analysis with Bioconductor. Contains plenty of worked examples, theoretical background and some philosophical discussions on the nature of single-cell data analysis.https://github.com/r-universe/tempbioc/actions/runs/26872111260Tue, 28 Apr 2026 12:53:30 GMTOSCA1.23.0successhttps://tempbioc.r-universe.devhttps://github.com/bioc/OSCAstub.Rmdstub.htmlLink to book2020-10-04 08:23:202021-01-31 02:44:58hpages.on.github@gmail.com (Hervé Pagès)This package provides version 1.18 of the 'HTSlib' C library for high-throughput sequence analysis. The package is primarily useful to developers of other R packages who wish to make use of HTSlib. Motivation and instructions for use of this package are in the vignette, vignette(package="Rhtslib", "Rhtslib").https://github.com/r-universe/tempbioc/actions/runs/26628910649Tue, 28 Apr 2026 12:40:40 GMTRhtslib3.9.0successhttps://tempbioc.r-universe.devhttps://github.com/bioc/RhtslibRhtslib.RmdRhtslib.htmlMotivation and use of Rhtslib2015-03-04 00:49:462024-11-27 01:24:54mtmorgan.xyz@gmail.com (Martin Morgan)Dirichlet-multinomial mixture models can be used to describe variability in microbial metagenomic data. This package is an interface to code originally made available by Holmes, Harris, and Quince, 2012, PLoS ONE 7(2): 1-15, as discussed further in the man page for this package, ?DirichletMultinomial.https://github.com/r-universe/tempbioc/actions/runs/27059714194Tue, 28 Apr 2026 12:36:30 GMTDirichletMultinomial1.55.0successhttps://tempbioc.r-universe.devhttps://github.com/bioc/DirichletMultinomialDirichletMultinomial.RmdDirichletMultinomial.htmlDirichletMultinomial for Clustering and Classification of Microbiome Data2024-10-19 16:42:232024-10-19 16:42:23