Package: EGRNi 0.1.6

EGRNi: Ensemble Gene Regulatory Network Inference

Gene regulatory network constructed using combined score obtained from individual network inference method. The combined score measures the significance of edges in the ensemble network. Fisher's weighted method has been implemented to combine the outcomes of different methods based on the probability values. The combined score follows chi-square distribution with 2n degrees of freedom. <doi:10.22271/09746315.2020.v16.i3.1358>.

Authors:Chiranjib Sarkar [aut, cre, ctb], Dipayan Sarkar [aut], Rajender Parsad [aut], Dwijesh Mishra [aut]

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EGRNi.pdf |EGRNi.html✨
EGRNi/json (API)

# Install 'EGRNi' in R:
install.packages('EGRNi', repos = c('https://chiranjibsbioinfo.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • Edgescore - Edge score obtained from 4 different methods for Ensemble Gene Regulatory Network Inference
  • gene_exp - Gene expression data for Ensemble Gene Regulatory Network Inference
  • pvalue - Probability values for Ensemble Gene Regulatory Network Inference
  • weight - Weights for Ensemble Gene Regulatory Network Inference

On CRAN:

Conda-Forge:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

1.18 score 15 scripts 240 downloads 12 exports 29 dependencies

Last updated 2 years agofrom:07b2e2ed77. Checks:8 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKFeb 11 2025
R-4.5-winOKFeb 11 2025
R-4.5-macOKFeb 11 2025
R-4.5-linuxOKFeb 11 2025
R-4.4-winOKFeb 11 2025
R-4.4-macOKFeb 11 2025
R-4.3-winOKFeb 11 2025
R-4.3-macOKFeb 11 2025

Exports:CRNEdg2FwEdgescoreEGRNF_scoregene_expIntsctEdg2FwPCNPLSNpvalueRidgNweight

Dependencies:bitbit64clicliprcpp11crayonfansifdrtoolgdatagluegtoolshmslifecyclemagrittrMASSpillarpkgconfigprettyunitsprogressR6readrrlangtibbletidyselecttzdbutf8vctrsvroomwithr