A GAM based framework for analysis of ChIP-Seq data
Released Sep 14, 2017 by Georg Stricker
data.table 1.11.4 DESeq2 1.20.0 GenomeInfoDb 1.16.0 futile.logger S4Vectors 0.18.3 BiocParallel 1.14.2 SummarizedExperiment 1.10.1 Rsamtools 1.32.3 reshape2 1.4.3 GenomicAlignments 1.16.0 Biostrings 2.48.0 GenomicRanges 1.32.6 IRanges 2.14.10 mgcv 1.8-24
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.