Package: GNET2 1.28.0

Chen Chen

GNET2: Constructing gene regulatory networks from expression data through functional module inference

Cluster genes to functional groups with E-M process. Iteratively perform TF assigning and Gene assigning, until the assignment of genes did not change, or max number of iterations is reached.

Authors:Chen Chen, Jie Hou and Jianlin Cheng

GNET2_1.28.0.tar.gz
GNET2_1.28.0.zip(r-4.7-x86_64)GNET2_1.28.0.zip(r-4.7-arm64)GNET2_1.28.0.zip(r-4.6-x86_64)GNET2_1.28.0.zip(r-4.6-arm64)GNET2_1.28.0.zip(r-4.5-x86_64)
GNET2_1.28.0.tgz(r-4.6-x86_64)GNET2_1.28.0.tgz(r-4.6-arm64)GNET2_1.28.0.tgz(r-4.5-x86_64)GNET2_1.28.0.tgz(r-4.5-arm64)
GNET2_1.28.0.tar.gz(r-4.7-arm64)GNET2_1.28.0.tar.gz(r-4.7-x86_64)GNET2_1.28.0.tar.gz(r-4.6-arm64)GNET2_1.28.0.tar.gz(r-4.6-x86_64)
GNET2_1.28.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
GNET2/json (API)

# Install 'GNET2' in R:

install.packages('GNET2', repos = c('https://bioc-release.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/chrischen1/gnet2/issues

Uses libs:

c++– GNU Standard C++ Library v3

On CRAN:

geneexpression regression network networkinference software cpp

2.60 score 2 stars 8 scripts 440 downloads 1 mentions 14 exports 86 dependencies

Last updated from:13b58eec1d (on RELEASE_3_23). Checks:15 ERROR, 1 OK. Indexed: no.

Target	Result	Time
bioc-checks	ERROR	173
linux-devel-arm64	ERROR	228
linux-devel-x86_64	ERROR	310
source / vignettes	ERROR	263
linux-release-arm64	ERROR	227
linux-release-x86_64	ERROR	319
macos-release-arm64	ERROR	214
macos-release-x86_64	ERROR	535
macos-oldrel-arm64	ERROR	179
macos-oldrel-x86_64	ERROR	356
windows-devel-arm64	ERROR	305
windows-devel-x86_64	ERROR	257
windows-release-arm64	ERROR	296
windows-release-x86_64	ERROR	218
windows-oldrel-x86_64	ERROR	211
wasm-release	OK	135

Exports:build_module build_moduleR build_moduleR_heuristic build_split_table calc_likelihood_score extract_edges get_correlation_list gnet kneepointDetection plot_gene_group plot_group_correlation plot_tree save_gnet similarity_score

Dependencies:abind base64enc Biobase BiocGenerics bit bit64 bslib cachem cli clipr cpp11 crayon data.table DelayedArray DiagrammeR digest dplyr evaluate farver fastmap fontawesome fs generics GenomicRanges ggplot2 glue gtable highr hms htmltools htmlwidgets igraph IRanges isoband jquerylib jsonlite knitr labeling lattice lifecycle magrittr Matrix MatrixGenerics matrixStats memoise mime pillar pkgconfig plyr prettyunits progress purrr R6 rappdirs RColorBrewer Rcpp readr reshape2 rlang rmarkdown rstudioapi S4Arrays S4Vectors S7 sass scales Seqinfo SparseArray stringi stringr SummarizedExperiment tibble tidyr tidyselect tinytex tzdb utf8 vctrs viridisLite visNetwork vroom withr xfun xgboost XVector yaml

Vignettes and/or manual for this package failed to build, some documents may be unavailable. Please inspect thebuild logs for more information.

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Fit a regression tree.	build_module
Build regression tree.	build_moduleR
Build regression tree with splits are detemined by K-means heuristicly.	build_moduleR_heuristic
Build split table by K-means heuristicly.	build_split_table
Calculate Gaussian Likelihood score.	calc_likelihood_score
Extract the network from the gnet result	extract_edges
Calculate correlation within each group.	get_correlation_list
Run GNET2	gnet
Knee point detection.	kneepointDetection
Plot a module	plot_gene_group
Plot the correlation of each group	plot_group_correlation
Plot the regression tree.	plot_tree
Save the GNET2 results	save_gnet
Compute the similarity from a predefined condition group	similarity_score