deco

This package is for version 3.9 of Bioconductor. This package has been removed from Bioconductor. For the last stable, up-to-date release version, see deco.

Decomposing Heterogeneous Cohorts using Omic Data Profiling

DOI: 10.18129/B9.bioc.deco

Bioconductor version: 3.9

This package discovers differential features in hetero- and homogeneous omic data by a two-step method including subsampling LIMMA and NSCA. DECO reveals feature associations to hidden subclasses not exclusively related to higher deregulation levels.

Author: Francisco Jose Campos-Laborie, Jose Manuel Sanchez-Santos and Javier De Las Rivas. Bioinformatics and Functional Genomics Group. Cancer Research Center (CiC-IBMCC, CSIC/USAL). Salamanca. Spain.

Maintainer: Francisco Jose Campos Laborie <fjcamlab at gmail.com>

Citation (from within R, enter citation("deco")):

Installation

To install this package, start R (version "3.6") and enter:


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

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

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("deco")

deco HTML R Script

Reference Manual PDF

NEWS Text

Need some help? Ask on the Bioconductor Support site!

Details

biocViews Bayesian, BiomedicalInformatics, Clustering, DifferentialExpression, ExonArray, FeatureExtraction, GeneExpression, MicroRNAArray, Microarray, MultipleComparison, Proteomics, RNASeq, Sequencing, Software, Transcription, Transcriptomics, mRNAMicroarray

Version 1.0.0

In Bioconductor since BioC 3.9 (R-3.6) (5 years)

License GPL (>=3)

Depends R (>= 3.5.0), AnnotationDbi, BiocParallel, SummarizedExperiment, limma

Imports stats, methods, ggplot2, foreign, graphics, BiocStyle, Biobase, cluster, gplots, RColorBrewer, locfit, made4, ade4, sfsmisc, scatterplot3d, gdata, grDevices, utils, reshape2, gridExtra

System Requirements

URL https://github.com/fjcamlab/deco

Suggests knitr, curatedTCGAData, MultiAssayExperiment, Homo.sapiens

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