| Title: | 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, which helps to capture coordinated gene expression patterns under different biological conditions. References where Surprisal analysis has been used for analyzing gene expression data (using the same methodology provided within this R package) are Kravchenko-Balasha et al (2014) <doi:10.1371/journal.pone.0108549>, Zadran et al. (2014) <doi:10.1073/pnas.1414714111>, Su et al. (2019) <doi:10.1371/journal.pcbi.1007034>, Bogaert et al. (2018) <doi:10.1371/journal.pone.0195142>. |
| Authors: | Annice Najafi [aut, cre] (ORCID: <https://orcid.org/0000-0003-0679-9397>) |
| Maintainer: | Annice Najafi <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 0.2 |
| Built: | 2026-06-08 08:06:27 UTC |
| Source: | https://github.com/annicenajafi/surprisalanalysis |
Perform Gene ontology analysis on a pattern of interest
GO_analysis_surprisal_analysis( transcript_weights, percentile_GO, lambda_no, key_type = "SYMBOL", flip = FALSE, species.db.str = "org.Hs.eg.db", ont = "BP", pAdjustMethod = "BH", top_GO_terms = 15 )GO_analysis_surprisal_analysis( transcript_weights, percentile_GO, lambda_no, key_type = "SYMBOL", flip = FALSE, species.db.str = "org.Hs.eg.db", ont = "BP", pAdjustMethod = "BH", top_GO_terms = 15 )
transcript_weights |
a dataframe containing the weight of transcripts in each pattern |
percentile_GO |
the percentile of transcript to be used for GO analysis, for example 95 will run GO on transcripts in the 95th percentile and above |
lambda_no |
the lambda pattern the user is interested in analyzing |
key_type |
type of transcripts which can be either SYMBOL, ENTREZID, ENSEMBL, or PROBEID |
flip |
a boolean variable which can either be true or false, if it is set to true, the lambda values will be multiplied by -1 |
species.db.str |
the type of species used for GO analysis, by default set to Homo sapiens, can be either 'org.Hs.eg.db' or 'org.Mm.eg.db' |
ont |
the ontology term for GO enrichment analysis. Can be either "BP", "MF" or "CC". They stand for "Biological Process", "Molecular Function" or "Cellular Component". Set to "BP" by default |
pAdjustMethod |
multiple testing correction method. Could be one of "BH", "bonferroni", "holm", "hochberg", "hommel", "BY", or "none". The default setting is "BH" |
top_GO_terms |
number of GO terms returns, by default set to 15 |
dataframe, the important GO terms related to a lambda gene pattern
csv.path <- system.file( "extdata", "helper_T_cell_0_test.csv", package = "SurprisalAnalysis" ) expr.df <- utils::read.csv(csv.path, check.names = FALSE) expr.df[1:700,]->expr.df sa.res <- surprisal_analysis(expr.df, zero.handling = "log1p") alph.all <- sa.res[[2]] go_top <- GO_analysis_surprisal_analysis( transcript_weights = alph.all, percentile_GO = 99, lambda_no = "lambda_1", key_type = "SYMBOL", flip = FALSE, species.db.str = "org.Hs.eg.db", ont = "BP", pAdjustMethod = "BH", top_GO_terms = 15 )csv.path <- system.file( "extdata", "helper_T_cell_0_test.csv", package = "SurprisalAnalysis" ) expr.df <- utils::read.csv(csv.path, check.names = FALSE) expr.df[1:700,]->expr.df sa.res <- surprisal_analysis(expr.df, zero.handling = "log1p") alph.all <- sa.res[[2]] go_top <- GO_analysis_surprisal_analysis( transcript_weights = alph.all, percentile_GO = 99, lambda_no = "lambda_1", key_type = "SYMBOL", flip = FALSE, species.db.str = "org.Hs.eg.db", ont = "BP", pAdjustMethod = "BH", top_GO_terms = 15 )
Launch the SurprisalAnalysis Shiny App
runSurprisalApp( port = getOption("shiny.port", 3838), host = getOption("shiny.host", "127.0.0.1"), launch.browser = getOption("shiny.launch.browser", TRUE), run = TRUE, ... )runSurprisalApp( port = getOption("shiny.port", 3838), host = getOption("shiny.host", "127.0.0.1"), launch.browser = getOption("shiny.launch.browser", TRUE), run = TRUE, ... )
port |
port to run the app on (passed to shiny::runApp) |
host |
host to listen on |
launch.browser |
should launch a browser? set to TRUE by default |
run |
boolean value, is set to TRUE by default. If set to FALSE it will not launch the graphical user interface |
... |
Further arguments passed along to shiny::runApp |
no return value, running the function will launch an application with graphical user interface
runSurprisalApp(port = httpuv::randomPort(), run = FALSE)runSurprisalApp(port = httpuv::randomPort(), run = FALSE)
This function performs surprisal analysis on transcriptomics data
surprisal_analysis(input.data, zero.handling = "pseudocount")surprisal_analysis(input.data, zero.handling = "pseudocount")
input.data |
transcriptomics data stores as dataframe |
zero.handling |
zero handling method. Can be either 'pseudocount' or 'log1p'. By default it is set to 'pseudocount' |
a list containing two matrix array objects, first one holding the lambda values representing the constraints or Lagrange multipliers and the second one holding the corresponding weights of transcripts stored (G matrix)
expr.df <- data.frame(gene_id = paste0("Gene", 1:6), S1 = c(0, 12, 3, 0, 50, 7), S2 = c(5, 0, 2, 9, 0, 4), S3 = c(8, 15, 0, 1, 25, 0), S4 = c(0, 7, 6, 0, 40, 3), check.names = FALSE) surprisal_analysis(expr.df, zero.handling = "pseudocount")expr.df <- data.frame(gene_id = paste0("Gene", 1:6), S1 = c(0, 12, 3, 0, 50, 7), S2 = c(5, 0, 2, 9, 0, 4), S3 = c(8, 15, 0, 1, 25, 0), S4 = c(0, 7, 6, 0, 40, 3), check.names = FALSE) surprisal_analysis(expr.df, zero.handling = "pseudocount")