In venyao/shinyChromosomeR: the R package for shinyChromosome, an Shiny application for interactive creation of non-circular whole genome diagram

1 Introduction

shinyChromosome is an Shiny application for interactive creation of non-circular plots of whole genomes within the web browser. shinyChromosome is deployed at http://150.109.59.144:3838/shinyChromosome/, http://shinychromosome.ncpgr.cn/ and https://yimingyu.shinyapps.io/shinychromosome/, for online use.

shinyChromosomeR wraps the core script of shinyChromosome as an R package, allowing the creation of non-circular whole genome diagram from the R command line.

In this vignette, we will rely on several simple, illustrative example datasets to demonstrate the usage of shinyChromosomeR.

To use the shinyChromosomeR package, we need to load it into R first.

library(shinyChromosomeR)

2 Creation of single-genome plot using shinyChromosomeR

2.1 Essential steps to create a non-circular single genome plot

To create a non-circular single genome plot, we need a dataset to define the genome used in the single genome plot and other datasets to be displayed along all the chromosomes of the genome.

2.1.1 Read in the genome dataset

The genome dataset is compulsory and defines the frame of a non-circular plot. The genome dataset is basically a text file with 2 columns. The 1st column is the chromosome ID. The 2nd column is the chromosome length. The detailed format of the genome data is illustrated in the 'Input data format' menu (Under the 'Help' menu) of the shinyChromosome application (http://150.109.59.144:3838/shinyChromosome/).

data.chr <- read.table(system.file("examples/single_genome/genome_data.txt", 
                                   package="shinyChromosomeR"), 
                       header=TRUE, as.is=TRUE, sep="\t")
dim(data.chr)
head(data.chr, 2)

2.1.2 Read in other input datasets to be displayed along all chromosomes of the input genome

One or more datasets could be then read into R, which would be displayed along all chromosomes of the genome dataset in Step 2.1.1. These datasets can be used to create different types of plot, including 'point', 'line', 'bar', 'rect_gradual', 'rect_discrete', 'heatmap_gradual', 'heatmap_discrete', 'text', 'segment', 'vertical_line', 'horizontal_line' and 'ideogram'. Please check the 'Input data format' menu (Under the 'Help' menu) of the shinyChromosome application (http://150.109.59.144:3838/shinyChromosome/) for more details.

Please be noted that each dataset should be read into R as a data frame. Then each of the data frames should be stored as an element of a list. Please check the following example.

data.track.file <- system.file("examples/single_genome/point.txt", 
                                package="shinyChromosomeR")
data.track <- lapply(data.track.file, function(x){
   dt <- read.table(x, header=TRUE, as.is=TRUE, sep="\t")
   return(dt)
})
dim(data.track[[1]])
head(data.track[[1]], 2)

2.1.3 Make the plot using the single_genome_plot function

After all the input datasets has been prepared and read into R, we can call the single_genome_plot function to make the plot. By default, different datasets in step 2.1.2 would be displayed in different tracks. We can set the track of each dataset using the layer_index parameter. We also need to set the plot type for each dataset in step 2.1.2.

single_genome_plot(data.chr=data.chr, data.track=data.track, plot_type="point")

2.2 Create different types of single genome plot using shinyChromosome

2.2.1 Plot line

data.chr <- read.table(system.file("examples/single_genome/genome_data.txt", 
                                   package="shinyChromosomeR"), 
                       header=TRUE, as.is=TRUE, sep="\t")
head(data.chr, 2)

data.track.file <- system.file("examples/single_genome/line.txt", 
                                package="shinyChromosomeR")
data.track.file

data.track <- lapply(data.track.file, function(x){
   dt <- read.table(x, header=TRUE, as.is=TRUE, sep="\t")
   return(dt)
})
dim(data.track[[1]])
head(data.track[[1]], 2)

single_genome_plot(data.chr=data.chr, data.track=data.track, plot_type="line")

2.2.2 Plot point + line

data.chr <- read.table(system.file("examples/single_genome/genome_data.txt", 
                                   package="shinyChromosomeR"), 
                       header=TRUE, as.is=TRUE, sep="\t")
head(data.chr)

data.track.file <- c(system.file("examples/single_genome/point.txt", 
                                 package="shinyChromosomeR"), 
                     system.file("examples/single_genome/line.txt", 
                                 package="shinyChromosomeR"))
data.track.file

data.track <- lapply(data.track.file, function(x){
   dt <- read.table(x, header=TRUE, as.is=TRUE, sep="\t")
   return(dt)
})
dim(data.track[[1]])
head(data.track[[1]], 2)
dim(data.track[[2]])
head(data.track[[2]], 2)

single_genome_plot(data.chr=data.chr, data.track=data.track, plot_type=c("point", "line"))

2.2.3 Plot bar

data.chr <- read.table(system.file("examples/single_genome/genome_data.txt", 
                                   package="shinyChromosomeR"), 
                       header=TRUE, as.is=TRUE, sep="\t")
head(data.chr, 2)

data.track.file <- system.file("examples/single_genome/bar.txt", 
                                package="shinyChromosomeR")
data.track.file

data.track <- lapply(data.track.file, function(x){
   dt <- read.table(x, header=TRUE, as.is=TRUE, sep="\t")
   return(dt)
})
dim(data.track[[1]])
head(data.track[[1]], 2)

single_genome_plot(data.chr=data.chr, data.track=data.track, plot_type="bar")

2.2.4 Plot heatmap_discrete

data.chr <- read.table(system.file("examples/single_genome/genome_data.txt", 
                                   package="shinyChromosomeR"), 
                       header=TRUE, as.is=TRUE, sep="\t")
head(data.chr, 2)

data.track.file <- system.file("examples/single_genome/heatmap_discrete.txt", 
                                package="shinyChromosomeR")
data.track.file

data.track <- lapply(data.track.file, function(x){
   dt <- read.table(x, header=TRUE, as.is=TRUE, sep="\t")
   return(dt)
})
dim(data.track[[1]])
head(data.track[[1]], 2)

single_genome_plot(data.chr=data.chr, data.track=data.track, 
                   plot_type="heatmap_discrete", chr_plotype=2,
                   margin_layer=0.01)

2.2.5 Plot heatmap_gradual

data.chr <- read.table(system.file("examples/single_genome/genome_data.txt", 
                                   package="shinyChromosomeR"), 
                       header=TRUE, as.is=TRUE, sep="\t")
head(data.chr, 2)

data.track.file <- system.file("examples/single_genome/heatmap_gradual.txt", 
                                package="shinyChromosomeR")
data.track.file

data.track <- lapply(data.track.file, function(x){
   dt <- read.table(x, header=TRUE, as.is=TRUE, sep="\t")
   return(dt)
})
dim(data.track[[1]])
head(data.track[[1]], 2)

single_genome_plot(data.chr=data.chr, data.track=data.track, 
                   plot_type="heatmap_gradual", chr_plotype=2,
                   margin_layer=0.01)

2.2.6 Plot ideogram

data.chr <- read.table(system.file("examples/single_genome/genome_data.txt", 
                                   package="shinyChromosomeR"), 
                       header=TRUE, as.is=TRUE, sep="\t")
head(data.chr, 2)

data.track.file <- system.file("examples/single_genome/ideogram.txt", 
                                package="shinyChromosomeR")
data.track.file

data.track <- lapply(data.track.file, function(x){
   dt <- read.table(x, header=TRUE, as.is=TRUE, sep="\t")
   return(dt)
})
dim(data.track[[1]])
head(data.track[[1]], 2)

single_genome_plot(data.chr=data.chr, data.track=data.track, 
                   plot_type="ideogram", chr_plotype=2, 
                   margin_layer=0.01)

2.2.7 Plot bar + vertical_line

The user can tune the appearance of the generated plot by setting the values of diverse parameters of the single_genome_plot function.

data.chr <- read.table(system.file("examples/single_genome/genome_data.txt", 
                                   package="shinyChromosomeR"), 
                       header=TRUE, as.is=TRUE, sep="\t")
head(data.chr, 2)

data.track.file <- c(system.file("examples/single_genome/bar.txt", 
                                 package="shinyChromosomeR"), 
                     system.file("examples/single_genome/vertical_line.txt", 
                                 package="shinyChromosomeR"))
data.track.file

data.track <- lapply(data.track.file, function(x){
   dt <- read.table(x, header=TRUE, as.is=TRUE, sep="\t")
   return(dt)
})
dim(data.track[[1]])
head(data.track[[1]], 2)
dim(data.track[[2]])
head(data.track[[2]], 2)

single_genome_plot(data.chr=data.chr, data.track=data.track, 
                   plot_type=c("bar", "vertical_line"), chr_plotype=1, 
                   margin_layer=0.01, layer_index=c(1, 1),
                   col_type=c(2, 2), color_cus=c("gold", "grey50"))

2.2.8 Plot ideogram + bar

data.chr <- read.table(system.file("examples/single_genome/genome_data.txt", 
                                   package="shinyChromosomeR"), 
                       header=TRUE, as.is=TRUE, sep="\t")
head(data.chr, 2)

data.track.file <- c(system.file("examples/single_genome/ideogram.txt", 
                                 package="shinyChromosomeR"), 
                     system.file("examples/single_genome/bar.txt", 
                                 package="shinyChromosomeR"))
data.track.file

data.track <- lapply(data.track.file, function(x){
   dt <- read.table(x, header=TRUE, as.is=TRUE, sep="\t")
   return(dt)
})
dim(data.track[[1]])
head(data.track[[1]], 2)
dim(data.track[[2]])
head(data.track[[2]], 2)

single_genome_plot(data.chr=data.chr, data.track=data.track, 
                   plot_type=c("ideogram", "bar"), chr_plotype=1, 
                   layer_index=c(1, 2),
                   col_type=c(2, 2), height_layer=c(0.006, 0.08),
                   margin_layer=c(0.001, 0.01))

3 Create two genomes plot using shinyChromosomeR

3.1 Essential steps to create a non-circular two-genomes plot

To create a non-circular two genomes plot, we need three datasets. The first dataset defines the genome aligned along the horizontal axis. The second dataset defines the genome aligned along the vertical axis. The third dataset is the main dataset used to create the two genomes plot.

3.1.1 Read in the genome dataset aligned along the horizontal axis

The format of the genome dataset aligned along the horizontal axis should be the same as the genome dataset illustrated in section 2.1.1.

data.chr1 <- read.table(system.file("examples/two_genome/genome1_data.txt", 
                                    package="shinyChromosomeR"), 
                        header=TRUE, as.is=TRUE, sep="\t")
dim(data.chr1)
head(data.chr1, 2)

3.1.2 Read in the genome dataset aligned along the vertical axis

The format of the genome dataset aligned along the vertical axis should be the same as the genome dataset illustrated in section 2.1.1.

data.chr2 <- read.table(system.file("examples/two_genome/genome2_data.txt", 
                                    package="shinyChromosomeR"), 
                        header=TRUE, as.is=TRUE, sep="\t")
dim(data.chr2)
head(data.chr2, 2)

3.1.3 Read in the main dataset

The main dataset can be used to create different types of plot, including 'point_discrete', 'point_gradual', 'rect_gradual', 'rect_discrete' and 'segment'. Please check the 'Input data format' menu (Under the 'Help' menu) of the shinyChromosome application (http://150.109.59.144:3838/shinyChromosome/) for more details.

data.2geno.plot <- read.table(system.file("examples/two_genome/point_gradual.txt", 
                                          package="shinyChromosomeR"), 
                              header=TRUE, as.is=TRUE, sep="\t")
head(data.2geno.plot, 2)

3.1.4 Make the plot using the two_genomes_plot function

two_genomes_plot(data.chr1=data.chr1, data.chr2=data.chr2, 
                 data.2geno.plot=data.2geno.plot, plot_type="point_gradual")

3.2 Create different types of two-genomes plot using shinyChromosome

3.2.1 Plot rect_discrete

data.chr1 <- read.table(system.file("examples/two_genome/genome1_data.txt", 
                                    package="shinyChromosomeR"), 
                        header=TRUE, as.is=TRUE, sep="\t")
head(data.chr1, 2)

data.chr2 <- read.table(system.file("examples/two_genome/genome2_data.txt", 
                                    package="shinyChromosomeR"), 
                        header=TRUE, as.is=TRUE, sep="\t")
head(data.chr2, 2)

data.2geno.plot <- read.table(system.file("examples/two_genome/rect_discrete.txt", 
                                          package="shinyChromosomeR"), 
                              header=TRUE, as.is=TRUE, sep="\t")
head(data.2geno.plot, 2)

two_genomes_plot(data.chr1=data.chr1, data.chr2=data.chr2, 
                 data.2geno.plot=data.2geno.plot, plot_type="rect_discrete", 
                 theme_sty="theme6", vertical=1, horizontal=1)

3.2.2 Plot segment

data.chr1 <- read.table(system.file("examples/two_genome/genome1_data.txt", 
                                    package="shinyChromosomeR"), 
                        header=TRUE, as.is=TRUE, sep="\t")
head(data.chr1, 2)

data.chr2 <- read.table(system.file("examples/two_genome/genome2_data.txt", 
                                    package="shinyChromosomeR"), 
                        header=TRUE, as.is=TRUE, sep="\t")
head(data.chr2, 2)

data.2geno.plot <- read.table(system.file("examples/two_genome/segment.txt", 
                                          package="shinyChromosomeR"), 
                              header=TRUE, as.is=TRUE, sep="\t")
head(data.2geno.plot, 2)

two_genomes_plot(data.chr1=data.chr1, data.chr2=data.chr2, 
                 data.2geno.plot=data.2geno.plot, plot_type="segment", 
                 theme_sty="theme6", vertical=1, horizontal=1)

4 Session Information

The version number of R and packages loaded for generating the vignette were:

sessionInfo()

venyao/shinyChromosomeR documentation built on June 10, 2019, 11:33 a.m.