Information Theoretic Analysis of Gene Expression Data
Overview
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.
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- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK13 OK2026-04-30
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Documentation
- Help pages
- 3
- Examples that run
- 100%3 run in a plain check (1 of them under a condition)
- Exported functions with examples
- 100%
- Documented parameters
- 93%
- Return-value docs
- 100%
- Help pages citing references
- 0%
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Package metadata
- First published
- 2025-09-10
- Total releases
- 3 / 1 yrs
- License
- MIT + file LICENSE OSI
- Download size
- 2.2 MB
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