Usage and Examples
This is a simple package with only two functions ideogram and convertSVG.
First, you need to load the package after you installed it.
Then, you need to load the data from the RIdeogram package.
data(human_karyotype, package="RIdeogram")
data(gene_density, package="RIdeogram")
data(Random_RNAs_500, package="RIdeogram")You can use the function “head()” to see the data format.
head(human_karyotype)
#> Chr Start End CE_start CE_end
#> 1 1 0 248956422 122026459 124932724
#> 2 2 0 242193529 92188145 94090557
#> 3 3 0 198295559 90772458 93655574
#> 4 4 0 190214555 49712061 51743951
#> 5 5 0 181538259 46485900 50059807
#> 6 6 0 170805979 58553888 59829934Specifically, the ‘karyotype’ file contains the karyotype information and has five columns (or three, see below). The first column is Chromosome ID, the second and thrid columns are start and end positions of corresponding chromosomes and the fourth and fifth columns are start and end positions of corresponding centromeres.
head(gene_density)
#> Chr Start End Value
#> 1 1 1 1000000 65
#> 2 1 1000000 2000000 76
#> 3 1 2000000 3000000 35
#> 4 1 3000000 4000000 30
#> 5 1 4000000 5000000 10
#> 6 1 5000000 6000000 10The ‘mydata’ file contains the heatmap information and has four columns. The first column is Chromosome ID, the second and thrid columns are start and end positions of windows in corresponding chromosomes and the fourth column is a characteristic value in corresponding windows, such as gene number.
head(Random_RNAs_500)
#> Type Shape Chr Start End color
#> 1 tRNA circle 6 69204486 69204568 6a3d9a
#> 2 rRNA box 3 68882967 68883091 33a02c
#> 3 rRNA box 5 55777469 55777587 33a02c
#> 4 rRNA box 21 25202207 25202315 33a02c
#> 5 miRNA triangle 1 86357632 86357687 ff7f00
#> 6 miRNA triangle 11 74399237 74399333 ff7f00The ‘mydata_interval’ file contains the label information and has six columns. The first column is the label type, the second column is the shape of label with three available options of box, triangle and circle, the third column is Chromosome ID, the fourth and fifth columns are the start and end positions of corresponding labels in the chromosomes and the sixth column is the color of the label.
Or, you can also load your own data by using the function “read.table”, such as
human_karyotype <- read.table("karyotype.txt", sep = "\t", header = T, stringsAsFactors = F)
gene_density <- read.table("data_1.txt", sep = "\t", header = T, stringsAsFactors = F)
Random_RNAs_500 <- read.table("data_2.txt", sep = "\t", header = T, stringsAsFactors = F)The “karyotype.txt” file contains karyotype information; the “data_1.txt” file contains heatmap data; the “data_2.txt” contains track label data.
These three files are all you need, now you can visualize these information using the ideogram function.
Basic usage
ideogram(karyotype, overlaid = NULL, label = NULL, colorset1, colorset2, width, Lx, Ly, output = "chromosome.svg")
convertSVG(svg, device, width, height, dpi)Now, let’s begin.
First, we draw a idiogram with no mapping data.
Then, you will find a SVG file and a PNG file in your Working Directory.
Next, we can map genome-wide data on the chromosome idiogram. In this case, we visulize the gene density across the human genome.
ideogram(karyotype = human_karyotype, overlaid = gene_density)
convertSVG("chromosome.svg", device = "png")
Alternatively, we can map some genome-wide data with track labels next to the chromosome idiograms.
ideogram(karyotype = human_karyotype, label = Random_RNAs_500)
convertSVG("chromosome.svg", device = "png")
We can also map the overlaid heatmap and track labels on the chromosome idiograms at the same time.
ideogram(karyotype = human_karyotype, overlaid = gene_density, label = Random_RNAs_500)
convertSVG("chromosome.svg", device = "png")
If you want to change the color of heatmap, you can modify the argument ‘colorset1’ (default set is colorset1 = c(“#4575b4”, “#ffffbf”, “#d73027”)). You can use either color names as listed by colors() or hexadecimal strings of the form “#rrggbb” or “#rrggbbaa”.
ideogram(karyotype = human_karyotype, overlaid = gene_density, label = Random_RNAs_500, colorset1 = c("#fc8d59", "#ffffbf", "#91bfdb"))
convertSVG("chromosome.svg", device = "png")
If you don not know the centromere information in your species, you don not need to modify the script. In this case, the ‘karyotype’ file has only three columns.
To simulate this case, we deleted the last two columns of the ‘human_karyotype’ file.
human_karyotype <- human_karyotype[,1:3]
ideogram(karyotype = human_karyotype, overlaid = gene_density, label = Random_RNAs_500)
convertSVG("chromosome.svg", device = "png")
If there are only ten chromosomes in your species, maybe you need to motify the argument ‘width’ (default value is “170”).
To simulate this case, we only keep the first ten columns of the ‘human_karyotype’ file.
Before
human_karyotype <- human_karyotype[1:10,]
ideogram(karyotype = human_karyotype, overlaid = gene_density, label = Random_RNAs_500)
convertSVG("chromosome.svg", device = "png")
After
human_karyotype <- human_karyotype[1:10,]
ideogram(karyotype = human_karyotype, overlaid = gene_density, label = Random_RNAs_500, width = 100)
convertSVG("chromosome.svg", device = "png")
If you want to move the Legend, then you need to modify the arguments ‘Lx’ and ‘Ly’(default values are “160” and “35”, separately).
‘Lx’ means the distance between upper-left point of the Legend and the left margin; ‘Ly’ means the distance between upper-left point of the Legend and the upper margin.
ideogram(karyotype = human_karyotype, overlaid = gene_density, label = Random_RNAs_500, width = 100, Lx = 80, Ly = 25)
convertSVG("chromosome.svg", device = "png")
If you have two sets of heatmap data, such as gene density and LTR density, you can use the following scripts to map and visualize these data in idiograms.
data(human_karyotype, package="RIdeogram") #reload the karyotype data
ideogram(karyotype = human_karyotype, overlaid = gene_density, label = LTR_density, colorset1 = c("#f7f7f7", "#e34a33"), colorset2 = c("#f7f7f7", "#2c7fb8")) #use the arguments 'colorset1' and 'colorset2' to set the colors for gene and LTR heatmaps, separately.
convertSVG("chromosome.svg", device = "png")
In addition, you can use the argument “device” (default value is “png”)to set the format of output file, such as, “tiff”, “pdf”, “jpg”, etc. And, you can use the argument “dpi” (default value is “300”) to set the resolution of the output image file.
Also, there are four shortcuts to convert the SVG images to these optional image formats with no necessary to set the argument “device”, such as
svg2tiff("chromosome.svg")
svg2pdf("chromosome.svg")
svg2jpg("chromosome.svg")
svg2png("chromosome.svg")