SurprisalAnalysis: Information Theoretic Analysis of Gene Expression Data

Implements Surprisal analysis for gene expression data such as RNA-seq or microarray experiments. Surprisal analysis is an information-theoretic method that decomposes gene expression data into a baseline state and constraint-associated deviations, capturing coordinated gene expression patterns under different biological conditions. References: Kravchenko-Balasha N. et al. (2014) <doi:10.1371/journal.pone.0108549>. Zadran S. et al. (2014) <doi:10.1073/pnas.1414714111>. Su Y. et al. (2019) <doi:10.1371/journal.pcbi.1007034>. Bogaert K. A. et al. (2018) <doi:10.1371/journal.pone.0195142>.

Version: 0.2
Published: 2025年09月10日
Author: Annice Najafi ORCID iD [aut, cre]
Maintainer: Annice Najafi <annicenajafi27 at gmail.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README

Documentation:

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Windows binaries: r-devel: SurprisalAnalysis_0.2.zip, r-release: SurprisalAnalysis_0.2.zip, r-oldrel: SurprisalAnalysis_0.2.zip
macOS binaries: r-release (arm64): SurprisalAnalysis_0.2.tgz, r-oldrel (arm64): not available, r-release (x86_64): not available, r-oldrel (x86_64): SurprisalAnalysis_0.2.tgz

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