Graphics in the Context of Analyzing High-Throughput Data

Additional options for making graphics in the context of analyzing high-throughput data are available here. This includes automatic segmenting of the current device (eg window) to accommodate multiple new plots, automatic checking for optimal location of legends in plots, small histograms to insert as legends, histograms re-transforming axis labels to linear when plotting log2-transformed data, a violin-plot function for a wide variety of input-formats, principal components analysis (PCA) with bag-plots to highlight and compare the center areas for groups of samples, generic MA-plots (differential- versus average-value plots) , staggered count plots and generation of mouse-over interactive html pages.


Reference manual

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1.2.2 by Wolfgang Raffelsberger, a month ago

Browse source code at

Authors: Wolfgang Raffelsberger [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports graphics, grDevices, RColorBrewer, stats, wrMisc

Suggests dplyr, factoextra, FactoMineR, knitr, limma, rmarkdown, sm

Suggested by wrMisc, wrProteo, wrTopDownFrag.

See at CRAN