In windforclouds/PSV2N: This is a shortest path-based algorithm named PS-V2N.

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)

PSV2N

This is a shortest path-based algorithm named PS-V2N (Proximity Score of Vertex to Network) which was proposed for the target identification.

Installation

You can install the released version of PSV2N from github with:

install.packages("devtools")
library(devtools)
github_install("windforclouds/PSV2N")
library(PSV2N)

Example

GOplot

library(PSV2N)
## basic example code
GOplot(vertex_sample[,1])

KEGGplot

KEGGplot(
        vertex_sample[1:20,1],
        enrich.pvalue = 0.01,
        low.color = "green",
        high.color = "red",
        labs.x = "Pvalue",
        labs.y = "Pathway Name",
        titlesize.y   = 8,
        labs.title = "Pathway Enrichment"
        )

annotation_gene(vertex_sample[1:5,1])

You'll still need to render README.Rmd regularly, to keep README.md up-to-date. devtools::build_readme() is handy for this. You could also use GitHub Actions to re-render README.Rmd every time you push. An example workflow can be found here: https://github.com/r-lib/actions/tree/master/examples.

In that case, don't forget to commit and push the resulting figure files, so they display on GitHub and CRAN.

windforclouds/PSV2N documentation built on Dec. 23, 2021, 5:15 p.m.