'DIscBIO-classes.R' 'DIscBIO-generic-ClassVectoringDT.R'
'DIscBIO-generic-ClustDiffGenes.R' 'DIscBIO-generic-Clustexp.R'
'DIscBIO-generic-DEGanalysis.R'
'DIscBIO-generic-DEGanalysis2clust.R'
'DIscBIO-generic-Exprmclust.R'
'DIscBIO-generic-FinalPreprocessing.R'
'DIscBIO-generic-FindOutliers.R'
'DIscBIO-generic-NoiseFiltering.R'
'DIscBIO-generic-Normalizedata.R'
'DIscBIO-generic-PCAplotSymbols.R'
'DIscBIO-generic-PlotmclustMB.R'
'DIscBIO-generic-clusteringOrder.R'
'DIscBIO-generic-clustheatmap.R' 'DIscBIO-generic-comptSNE.R'
'DIscBIO-generic-plotExptSNE.R' 'DIscBIO-generic-plotGap.R'
'DIscBIO-generic-plotLabelstSNE.R'
'DIscBIO-generic-plotOrderTsne.R'
'DIscBIO-generic-plotSilhouette.R'
'DIscBIO-generic-plotSymbolstSNE.R'
'DIscBIO-generic-plottSNE.R'
'DIscBIO-generic-pseudoTimeOrdering.R' 'J48DT.R' 'J48DTeval.R'
'Jaccard.R' 'NetAnalysis.R' 'Networking.R' 'PPI.R'
'PlotMBpca.R' 'RpartDT.R' 'RpartEVAL.R' 'VolcanoPlot.R'
'customConverters.R' 'datasets.R'
'internal-functions-samr-adapted.R' 'internal-functions.R'

An open, multi-algorithmic pipeline for easy, fast and efficient
analysis of cellular sub-populations and the molecular signatures that
characterize them. The pipeline consists of four successive steps: data
pre-processing, cellular clustering with pseudo-temporal ordering, defining
differential expressed genes and biomarker identification. More details on
Ghannoum et. al. (2021) <doi:10.3390/ijms22031399>. This package implements
extensions of the work published by Ghannoum et. al. (2019)
<doi:10.1101/700989>.
methods, TSCAN, boot, httr, mclust, statmod, igraph, RWeka,
philentropy, NetIndices, png, grDevices, RColorBrewer, ggplot2,
rpart, fpc, cluster, rpart.plot, tsne, AnnotationDbi,
org.Hs.eg.db, graphics, stats, utils, impute

Distance Measure Based Judgment and Learning
Implements various decision support tools related to the new product development.
Subroutines include productivity evaluation using distance measures, benchmarking,
risk analysis, technology adoption model, inverse optimization, etc.