GenoGAM

DOI: 10.18129/B9.bioc.GenoGAM    

A GAM based framework for analysis of ChIP-Seq data

Bioconductor version: Release (3.14)

This package allows statistical analysis of genome-wide data with smooth functions using generalized additive models based on the implementation from the R-package 'mgcv'. It provides methods for the statistical analysis of ChIP-Seq data including inference of protein occupancy, and pointwise and region-wise differential analysis. Estimation of dispersion and smoothing parameters is performed by cross-validation. Scaling of generalized additive model fitting to whole chromosomes is achieved by parallelization over overlapping genomic intervals.

Author: Georg Stricker [aut, cre], Alexander Engelhardt [aut], Julien Gagneur [aut]

Maintainer: Georg Stricker <georg.stricker at protonmail.com>

Installation

To install this package, start R (version "4.1") and enter:

if (!require("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("GenoGAM")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

PDF		Reference Manual

Details

biocViews	ChIPSeq, ChipOnChip, DifferentialExpression, DifferentialPeakCalling, Epigenetics, Genetics, ImmunoOncology, Regression, Software, WholeGenome
Version	2.12.0
In Bioconductor since	BioC 3.3 (R-3.3) (6 years)
License	GPL-2
Depends	R (>= 3.5), SummarizedExperiment(>= 1.1.19), HDF5Array(>= 1.8.0), rhdf5(>= 2.21.6), S4Vectors(>= 0.23.18), Matrix (>= 1.2-8), data.table (>= 1.9.4)
Imports	Rcpp (>= 0.12.14), sparseinv (>= 0.1.1), Rsamtools(>= 1.18.2), GenomicRanges(>= 1.23.16), BiocParallel(>= 1.5.17), DESeq2(>= 1.11.23), futile.logger (>= 1.4.1), GenomeInfoDb(>= 1.7.6), GenomicAlignments(>= 1.7.17), IRanges(>= 2.5.30), Biostrings(>= 2.39.14), DelayedArray(>= 0.3.19), methods, stats
LinkingTo	Rcpp, RcppArmadillo
Suggests	BiocStyle, chipseq(>= 1.21.2), LSD (>= 3.0.0), genefilter(>= 1.54.2), ggplot2 (>= 2.1.0), testthat, knitr, rmarkdown
SystemRequirements
Enhances
URL	https://github.com/gstricker/GenoGAM
BugReports	https://github.com/gstricker/GenoGAM/issues
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Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package
Windows Binary
macOS 10.13 (High Sierra)
Source Repository	git clone https://git.bioconductor.org/packages/GenoGAM
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/GenoGAM
Package Short Url	https://bioconductor.org/packages/GenoGAM/
Package Downloads Report	Download Stats