NetWeaver

Current version 0.0.9

Description

NetWeaver is motivated towards developing a simple and flexible pipeline for visualizing the complex features of enrichment and correlation of gene coexpression network modules. While circos style 2D track plot is one natural choice for such practice, existing packages are designed primarily for handling genome structure and intervals. They are either too complicated to use, requiring certain level of knowledge of scripting, or limited in applications to only genomic structure data. To address these issues, particularly extend beyond applications in genomic structure data, NetWeaver offers a lightweight implementation of circular track plot, providing simple and flexible R function utilities and pipelines to generate circular images for visualizing different types of structure/relationship data.

Installation

NetWeaver is available from CRAN so the simplest way to install in R is by running install.packages("NetWeaver").

To install the latest development from here in github, run devtools::install_github("mw201608/NetWeaver") in R.

Example 1: circular track plot showing characteristics of coexpression network modules

A slice view of the full 360 degree circular visualization when plotting only the top 15 modules (set slice.size=90 at parameter initialization):

The above figures are generated from a real data analysis featuring coexpression network modules. The numbers in the out-most ring show the module id 1-50. The barplot in track #1 shows the module ranking score. The heat-map in track #2-5 shows module-trait correlation. The heat-map in the remaining tracks #6-29 show the P value significance of enrichment for various gene signatures. Detailed description about the data and sample code is available from vignette("netweaver") (link), ?Modules or ?rc.initialize within R after loading the package.

In general, a typical workflow of a circular track plot includes:

1. initialize plot parameters

rc.initialize(.....)

2. prepare plot canvas

rc.plot.area(.....)

3. plot chromosome cytoband (optional)

rc.plot.ideogram(.....)

4. plot heat-map, histogram, barchart, etc.

rc.plot.histogram(....)
rc.plot.heatmap(....)

5. plot links/ribbons in the middle

rc.plot.link(....)
rc.plot.ribbon(....)

Another example pipeline for plotting a hypothetical data is shown in the sample code of function rc.initialize.

Example 2: donut pie-chart

By Erica Liu

Load libraries

library(NetWeaver)
library(RColorBrewer)
options(stringsAsFactors=FALSE)

Set up example data and colors

dat=c(Perl=0.2, Bash=0.3, Python=0.1, Mathematica=0.1, MySQL=0.15, Latex=0.15)*100
cols=brewer.pal(length(dat)+1,'Dark2')
Cyto=data.frame(Chr=names(dat), Start=1, End=dat, BandColor=cols[1:length(dat)])

Initialize

rc.initialize(Cyto, num.tracks=3, params=list(chr.padding=0.1, track.padding=0, track.height=0.29))

Make plot area

rc.plot.area()

Plot outer layer

rc.plot.histogram(Cyto, track.id=2, color.col='BandColor', track.border=NA, polygon.border=NA)
textData=data.frame(Chr=Cyto$Chr, Pos=Cyto$End/2, Label=names(dat))
rc.plot.text(textData, track.id=2.5, col='white', cex=0.6)

Print circle and text in the middle

x2 <- c(seq(-0.4, 0.4, 0.002), seq(-0.4, 0.4, 0.002))
y2 <- c((  1 * (0.16 - x2[1:401]^2)^0.5 ), ( -1 * (0.16 - x2[402:802]^2)^0.5 ))
y2[1]=y2[401]=y2[402]=y2[802]=0
polygon(x2,y2, col = cols[1+length(dat)],border = NA)
text(0,0,labels='R',cex=2,col='white')

Example 3: Sunburst plot application

Please see https://github.com/mw201608/sunburst.shiny