
<!-- README.md is generated from README.Rmd file. Please edit that file -->

# influential <a href='https://github.com/asalavaty/influential'><img src='man/figures/Symbol.png' align="right" height="221" /></a>

<!-- badges: start -->

[![AppVeyor build
status](https://img.shields.io/appveyor/build/asalavaty/influential/master.svg)](https://ci.appveyor.com/project/asalavaty/influential)
[![](https://www.r-pkg.org/badges/version/influential?color=blue)](https://cran.r-project.org/package=influential)
[![](http://cranlogs.r-pkg.org/badges/grand-total/influential?color=green)](https://cran.r-project.org/package=influential)
![CRAN Downloads](https://cranlogs.r-pkg.org/badges/influential)
[![](https://img.shields.io/badge/Integrated%20Value%20of%20Influence-IVI-blue.svg)](https://doi.org/10.1016/j.patter.2020.100052)
[![](https://img.shields.io/badge/SIR--based%20Influence%20Ranking-SIRIR-green.svg)](https://doi.org/10.1016/j.patter.2020.100052)
[![](https://img.shields.io/badge/Experimental%20data--based%20Integrative%20Ranking-ExIR-blue.svg)](https://www.biorxiv.org/content/10.1101/2022.10.03.510585v1.abstract)
<!-- badges: end -->

## Overview

The goal of `influential` is to help identification of the most
`influential` nodes in a network as well as the classification and
ranking of top candidate features. This package contains functions for
the classification and ranking of features, reconstruction of networks
from adjacency matrices and data frames, analysis of the topology of the
network and calculation of centrality measures as well as a novel and
powerful `influential` node ranking. The **Experimental data-based
Integrative Ranking (ExIR)** is a sophisticated model for classification
and ranking of the top candidate features based on only the experimental
data. The first integrative method, namely the **Integrated Value of
Influence (IVI)**, that captures all topological dimensions of the
network for the identification of network most `influential` nodes is
also provided as a function. Also, neighborhood connectivity, H-index,
local H-index, and collective influence (CI), all of which required
centrality measures for the calculation of **IVI**, are for the first
time provided in an R package. Additionally, a function is provided for
running **SIRIR** model, which is the combination of leave-one-out cross
validation technique and the conventional SIR model, on a network to
unsupervisedly rank the true influence of vertices. Furthermore, some
functions have been provided for the assessment of dependence and
correlation of two network centrality measures as well as the
conditional probability of deviation from their corresponding means in
opposite directions.

Check out [**our paper**](https://doi.org/10.1016/j.patter.2020.100052)
for a more complete description of the IVI formula and all of its
underpinning methods and analyses.

The **Experimental data-based Integrative Ranking (ExIR)** model is
described in our [**iScience article**](https://asalavaty.com/).

## Author

The `influential` package was written by [Adrian
Salavaty](https://asalavaty.com/)

## Advisors

Mirana Ramialison and Peter D. Currie

## How to Install

You can install the official [CRAN
release](https://cran.r-project.org/package=influential) of the
`influential` with the following code:

``` r
install.packages("influential")
```

Or the development version from GitHub:

``` r
## install.packages("remotes")
remotes::install_github("asalavaty/influential", build_vignettes = TRUE)
```

## Vignettes

A comprehensive introduction to `influential` and all of its functions
is available in the
[vignette](https://cran.r-project.org/package=influential/vignettes/Vignettes.html).

You may browse Vignettes from within R using the following code.

``` r
browseVignettes("influential")
```

## Shiny apps

- [Influential Software Package web
  portal](https://influential.erc.monash.edu/)

- [IVI Shiny App](https://influential.erc.monash.edu/IVI/): A shiny app
  for the calculation of the Integrated Value of Influence (IVI) of
  network nodes as well as IVI-based visualization of the network.

You can also access the IVI shiny app offline from within R and run it
on your local machine using the following command.

``` r
influential::runShinyApp("IVI")
```

- [ExIR Shiny App](https://influential.erc.monash.edu/ExIR/): A shiny
  app for running the Experimental-data-based Integrative Ranking (ExIR)
  model as well as visualization of its results.

You can also access the ExIR shiny app offline from within R and run it
on your local machine using the following command.

``` r
influential::runShinyApp("ExIR")
```

## How to cite `influential`

If you use `influential`, please cite the relevant associated
publication(s).

For the **Integrated Value of Influence (IVI)** and network influence
analysis, please cite:

- Salavaty A, Ramialison M, Currie PD. Integrated Value of Influence: An
  Integrative Method for the Identification of the Most Influential
  Nodes within Networks. *Patterns*. 2020.08.14. [Read
  online](https://doi.org/10.1016/j.patter.2020.100052).

For the **Experimental data-based Integrative Ranking (ExIR)** model,
please cite:

- Salavaty A, Douek AM, Kaslin J, Ramialison M, Currie PD. ExIR enables
  prioritizing driver and biomarker genes from omics data in a reference
  free manner. *iScience*. In press.

You can also access the package citation information from within R:

``` r
citation("influential")
```

## How to contribute

Please don’t hesitate to report any bugs/issues and request for
enhancement or any other contributions. To submit a bug report or
enhancement request, please use the [`influential` GitHub issues
tracker](https://github.com/asalavaty/influential/issues).
