Package: DaMiRseq
Type: Package
Date: 2021-11-20
Title: Data Mining for RNA-seq data: normalization,
    feature selection and classification
Version: 2.9.0
Author: Mattia Chiesa <>, 
    Luca Piacentini <>
Maintainer: Mattia Chiesa <>
Description: The DaMiRseq package offers a tidy pipeline of data mining
        procedures to identify transcriptional biomarkers and exploit 
		them for both binary and multi-class 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, ImmunoOncology
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, ineq, arm, pls, RSNNS, edgeR, plyr
Suggests: BiocStyle, knitr, testthat
Depends: R (>= 3.4), SummarizedExperiment, ggplot2
RoxygenNote: 7.1.1