Description
Information Theoretic Analysis of Gene Expression Data.
Description
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>.
README.md
SurprisalAnalysis R package guidelines
🖥️ Installation
To install the R package:
install.packages('devtools')
devtools::install_github('AnniceNajafi/SurprisalAnalysis')
Usage
To use the R package you should follow the steps below:
I. Store gene expression data in a csv file with the first row holding the sample names and the first column holding the gene names.
II. Read the csv file and run the following code:
input.data <- read.csv('expression_data.csv')
results <- surprisal_analysis(input.data)
III. To run GO analysis on the patterns simply use the code below:
results[[2]]-> transcript_weights
percentile_GO <- 0.95 #change based on your preference
lambda_no <- 1 #change based on your preference
GO_analysis_surprisal_analysis(transcript_weights, percentile_GO, lambda_no, key_type = "SYMBOL", flip = FALSE, species.db.str = "org.Hs.eg.db", top_GO_terms=15)
Use GUI from R package
Simply run the following code:
runSurprisalApp()
Web-based application
A web-based application based on the above has been deployed on this link.