The DaMiRseq package - Data Mining for RNA-Seq data: normalization, feature selection and classification

RNA-Seq is increasingly the method of choice for researchers studying the transcriptome. The strategies to analyze such complex high-dimensional data rely on data mining and statistical learning techniques. The DaMiRseq package offers a tidy pipeline that includes data mining procedures for data handling and implementation of prediction learning methods to build classification models. The package accepts any kind of data presented as a table of raw counts and allows the inclusion of variables that occur with the experimental setting. A series of functions enables data cleaning by filtering genomic features and samples, data adjustment 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 for assessing the effects of data management using diagnostic plots, such as clustering and heatmaps, RLE boxplots, MDS or correlation plots.