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README.md
<!-- README.md is generated from README.Rmd. Please edit that file --> # BioNERO <img src='man/figures/logo.png' align="right" height="139" /> <!-- badges: start --> [![GitHub issues](https://img.shields.io/github/issues/almeidasilvaf/BioNERO)](https://github.com/almeidasilvaf/BioNERO/issues) [![Lifecycle: stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https://lifecycle.r-lib.org/articles/stages.html#stable) [![R-CMD-check-bioc](https://github.com/almeidasilvaf/BioNERO/workflows/R-CMD-check-bioc/badge.svg)](https://github.com/almeidasilvaf/BioNERO/actions) [![Codecov test coverage](https://codecov.io/gh/almeidasilvaf/BioNERO/branch/devel/graph/badge.svg)](https://codecov.io/gh/almeidasilvaf/BioNERO?branch=devel) <!-- badges: end --> `BioNERO` aims to integrate all aspects of biological network inference in a single package, so users don’t have to learn the syntaxes of several packages and how to communicate among them. `BioNERO` features: - **Expression data preprocessing** using state-of-the-art techniques for network inference. - **Automated exploratory data analyses**, including principal component analysis (PCA) and heatmaps of gene expression or sample correlations. - **Inference of gene coexpression networks (GCNs)** using the popular WGCNA algorithm. - **Inference of gene regulatory networks (GRNs)** based on the “wisdom of the crowds” principle. This principle consists in inferring GRNs with multiple algorithms (here, CLR, GENIE3 and ARACNE) and calculating the average rank for each interaction pair. - **Exploration of network topology** of GCNs, GRNs, and protein-protein interaction networks. - **Network visualization**. - **Network comparison**, including identification of consensus modules across independent expression sets, and calculation of intra and interspecies module preservation statistics between different networks. ## Installation instructions Get the latest stable `R` release from [CRAN](http://cran.r-project.org/). Then install `BioNERO` from [Bioconductor](http://bioconductor.org/) using the following code: ``` r if (!requireNamespace("BiocManager", quietly = TRUE)) { install.packages("BiocManager") } BiocManager::install("BioNERO") ``` And the development version from [GitHub](https://github.com/almeidasilvaf/BioNERO) with: ``` r BiocManager::install("almeidasilvaf/BioNERO") ``` ## Citation Below is the citation output from using `citation('BioNERO')` in R. Please run this yourself to check for any updates on how to cite **BioNERO**. ``` r print(citation('BioNERO'), bibtex = TRUE) # # To cite BioNERO in publications use: # # Almeida-Silva, F., Venancio, T.M. BioNERO: an all-in-one # R/Bioconductor package for comprehensive and easy biological network # reconstruction. Funct Integr Genomics 22, 131-136 (2022). # https://doi.org/10.1007/s10142-021-00821-9 # # A BibTeX entry for LaTeX users is # # @Article{, # title = {BioNERO: an all-in-one R/Bioconductor package for comprehensive and easy biological network reconstruction}, # author = {Fabricio Almeida-Silva and Thiago M. Venancio}, # journal = {Functional And Integrative Genomics}, # year = {2022}, # volume = {22}, # number = {1}, # pages = {131-136}, # url = {https://link.springer.com/article/10.1007/s10142-021-00821-9}, # doi = {10.1007/s10142-021-00821-9}, # } ``` Please note that the `BioNERO` was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing this package.