README.md

In SydneyBioX/LCN2G: LC-N2G: A Local Consistency Approach for Nutrigenomics Data Analysis

LC-N2G

Shiny app(LC-N2G) explores the relationship between nutrition and its corresponding gene expression data. The default dataset comes from the mouse nutrition study (GSE85998)[1]. The overall workflow of LC-N2G is as follows and for a full description, we refer to our paper.

Installation

Packages Requirements

• shiny ≥ 1.4.0.2
• shinythemes ≥ 1.1.2
• shinyjs ≥ 1.1
• DT ≥ 0.13
• directlabels ≥ 2020.1.31
• mclust ≥ 5.4.5
• GA ≥ 3.2
• WGCNA ≥ 1.69
• tidyverse ≥ 1.3.0
• plotly ≥ 4.9.2
• visNetwork ≥ 2.0.9
• limma ≥ 3.42.2
• dynamicTreeCut ≥ 1.63-1
• psych ≥ 1.9.12.31
• fields ≥ 10.3
• plotly ≥ 4.9.2
• reshape2 ≥ 1.4.3

Get started

To use this shiny app you can either:

• visit our webpage http://shiny.maths.usyd.edu.au/LC-N2G/ , or

• install it through

  r remotes::install_github("SydneyBioX/LCN2G") library(LCN2G) run_App()

  Vignette

You can find the vignette at our website: https://sydneybiox.github.io/LCN2G/.

Our paper

Xu, X.N., Solon-Biet, S.M. et al: LC-N2G: A Local Consistency Approach for Nutrigenomics Data Analysis. (Submitted to BMC Bioinformatics)

Reference

[1] Solon-Biet, S.M., Cogger, V.C. et al: Defining the nutritional and metabolic context of fgf21 using the geometric framework. Cell Metabolism 24, 555–565 (2016)

[2] Raubenheimer, D., Simpson, S.J.: Nutritional ecology and human health. Annual Review of Nutrition 36, 603–626 (2016)

SydneyBioX/LCN2G documentation built on Oct. 3, 2020, 6:09 a.m.