_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
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      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
r-normalyzerde 1.26.0
Propagated dependencies: r-vsn@3.76.0 r-summarizedexperiment@1.38.1 r-preprocesscore@1.70.0 r-matrixstats@1.5.0 r-mass@7.3-65 r-limma@3.64.0 r-ggplot2@3.5.2 r-ggforce@0.4.2 r-car@3.1-3 r-ape@5.8-1
Channel: guix-bioc
Location: guix-bioc/packages/n.scm (guix-bioc packages n)
Home page: https://github.com/ComputationalProteomics/NormalyzerDE
Licenses: Artistic License 2.0
Synopsis: Evaluation of normalization methods and calculation of differential expression analysis statistics
Description:

NormalyzerDE provides screening of normalization methods for LC-MS based expression data. It calculates a range of normalized matrices using both existing approaches and a novel time-segmented approach, calculates performance measures and generates an evaluation report. Furthermore, it provides an easy utility for Limma- or ANOVA- based differential expression analysis.

Total results: 1