Release Packages:
Bioconductor's stable, semi-annual release:
Comprehensive guide to using the SingleR Bioconductor package for automated cell annotation in single-cell RNA-seq datasets. Includes theoretical explanations, worked examples with code, and demonstrations of advanced functionality
Author: Aaron Lun Maintainer: Aaron Lun License: GPL-3
Describes how to use the csaw package to detect differentially bound regions in ChIP-seq data. This provides instructions on how to read in BAM files to obtain a per-window count matrix, filtering to obtain high-abundance windows of interest, normalization of sample-specific biases, testing for differential binding, consolidation of per-window results to obtain per-region statistics, and annotation and visualization of the DB results.
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.
Author: Jacques Serizay Maintainer: Jacques Serizay License: MIT + file LICENSE
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.
Authors: Robert Amezquita, Aaron Lun, Stephanie Hicks, Raphael Gottardo, Alan O'Callaghan Maintainer: Alan O'Callaghan License: CC BY 4.0
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.
Authors: Helena L. Crowell, Mark D. Robinson, Stephanie C. Hicks, Lukas M. Weber Maintainer: Lukas M. Weber License: CC BY 4.0
Demonstrates some basic analysis of single-cell RNA-seq data with scrapper. This includes quality control, normalization, feature selection, various forms of dimensionality reduction, clustering into subpopulations, and detection of marker genes. It is intended for users who already have some familiarity with R and want to get hands-on with some basic single-cell analyses.
Author: Aaron Lun Maintainer: Aaron Lun License: MIT + file LICENSE
A demo of a BiocBook online book.
