Package: DaMiRseq
Type: Package
Date: 2017-03-03
Title: Data Mining for RNA-seq data: normalization, feature selection
        and classification
Version: 1.0.0
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
        covariates 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: 5.0.1
NeedsCompilation: no
Packaged: 2017-04-25 01:57:10 UTC; biocbuild
Built: R 3.4.0; ; 2017-04-25 03:25:04 UTC; windows
