Package: DaMiRseq
Type: Package
Date: 2017-09-22
Title: Data Mining for RNA-seq data: normalization, feature selection
        and classification
Version: 1.2.0
Author: Mattia Chiesa <mattia.chiesa@hotmail.it>, 
    Luca Piacentini <luca.piacentini@cardiologicomonzino.it>
Maintainer: Mattia Chiesa <mattia.chiesa@hotmail.it>
Description: The DaMiRseq package offers a tidy pipeline of data mining
        procedures to identify transcriptional biomarkers and exploit 
		them for classification purposes. The package accepts any kind
		of data presented as a table of raw counts and allows including
        both continous and factorial variables that occur with the 
		experimental setting. A series of functions enable the user 
		to clean up the data by filtering genomic features and samples,
		to adjust data by identifying and removing the unwanted source
		of variation (i.e. batches and confounding factors) 
		and to select the best predictors for modeling.
		Finally, a "Stacking" ensemble learning technique is applied
		to build a robust classification model. Every step
    includes a checkpoint that the user may exploit to assess the
    effects of data management by looking at diagnostic plots, such
    as clustering and heatmaps, RLE boxplots, MDS or correlation
    plot.
License: GPL (>= 2)
Encoding: UTF-8
LazyData: true
biocViews: Sequencing, RNASeq, Classification
VignetteBuilder: knitr
Imports: DESeq2, limma, EDASeq, RColorBrewer, sva, Hmisc, pheatmap,
        FactoMineR, corrplot, randomForest, e1071, caret, MASS,
        lubridate, plsVarSel, kknn, FSelector, methods, stats, utils,
        graphics, grDevices, reshape2
Suggests: BiocStyle, knitr, testthat
Depends: R (>= 3.4), SummarizedExperiment, ggplot2
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2017-10-31 02:11:14 UTC; biocbuild
Built: R 3.4.2; ; 2017-10-31 04:03:37 UTC; windows
