| Title: | Drawing SVG Graphics to Visualize and Map Genome-Wide Data on Idiograms |
|---|---|
| Description: | For whole-genome analysis, idiograms are virtually the most intuitive and effective way to map and visualize the genome-wide information. RIdeogram was developed to visualize and map whole-genome data on idiograms with no restriction of species. |
| Authors: | Zhaodong Hao [aut, cre], Dekang Lv [aut], Ying Ge [aut], Jisen Shi [aut], Weijers Dolf [aut], Guangchuang Yu [aut], Jinhui Chen [aut] |
| Maintainer: | Zhaodong Hao <[email protected]> |
| License: | Artistic-2.0 |
| Version: | 0.2.2 |
| Built: | 2024-11-01 04:39:38 UTC |
| Source: | https://github.com/tickingclock1992/rideogram |
convert svg to png or other format
convertSVG(svg, file = "chromosome", device = NULL, width = 8.2677, height = 11.6929, dpi = 300) svg2pdf(svg, file = "chromosome", width = 8.2677, height = 11.6929, dpi = 300) svg2png(svg, file = "chromosome", width = 8.2677, height = 11.6929, dpi = 300) svg2tiff(svg, file = "chromosome", width = 8.2677, height = 11.6929, dpi = 300) svg2jpg(svg, file = "chromosome", width = 8.2677, height = 11.6929, dpi = 300)convertSVG(svg, file = "chromosome", device = NULL, width = 8.2677, height = 11.6929, dpi = 300) svg2pdf(svg, file = "chromosome", width = 8.2677, height = 11.6929, dpi = 300) svg2png(svg, file = "chromosome", width = 8.2677, height = 11.6929, dpi = 300) svg2tiff(svg, file = "chromosome", width = 8.2677, height = 11.6929, dpi = 300) svg2jpg(svg, file = "chromosome", width = 8.2677, height = 11.6929, dpi = 300)
svg |
svg file |
file |
output file name |
device |
target format |
width |
output width |
height |
output height |
dpi |
output dpi |
invisible grob object
Zhaodong Hao, Dekang Lv, Ying Ge, Jisen Shi, Dolf Weijers, Guangchuang Yu, Jinhui Chen
Fst values between China east and west groups of Liriodendron chinense and being calculated in a 2-Mb sliding window with a 1-Mb step
data(Fst_between_CE_and_CW)data(Fst_between_CE_and_CW)
data frame
Nature Plants, doi: 10.1038/s41477-018-0323-6
Gene numbers was counted with a window of 1 Mb
data(gene_density)data(gene_density)
data frame
Gencode (https://www.gencodegenes.org/)
extract some specific feature information from a gff file
GFFex(input, karyotype, feature = "gene", window = 1000000)GFFex(input, karyotype, feature = "gene", window = 1000000)
input |
gff file |
karyotype |
karyotype file |
feature |
feature format |
window |
window size |
dataframe
Zhaodong Hao, Dekang Lv, Ying Ge, Jisen Shi, Dolf Weijers, Guangchuang Yu, Jinhui Chen
The version of this geome is gencode.v29.
data(human_karyotype)data(human_karyotype)
data frame
Gencode (https://www.gencodegenes.org/)
ideogram with overlaid heatmap annotation and optional track label
ideogram(karyotype, overlaid = NULL, label = NULL, synteny = NULL, colorset1 = c("#4575b4", "#ffffbf", "#d73027"), colorset2 = c("#b35806", "#f7f7f7", "#542788"), width = 170, Lx = 160, Ly = 35, output = "chromosome.svg")ideogram(karyotype, overlaid = NULL, label = NULL, synteny = NULL, colorset1 = c("#4575b4", "#ffffbf", "#d73027"), colorset2 = c("#b35806", "#f7f7f7", "#542788"), width = 170, Lx = 160, Ly = 35, output = "chromosome.svg")
karyotype |
karyotype data |
overlaid |
overlaid data |
label |
track label data |
synteny |
synteny data |
colorset1 |
overlaid heatmap-1 color |
colorset2 |
overlaid heatmap-2 color |
width |
width of plot region |
Lx |
position of legend (x) |
Ly |
position of legend (y) |
output |
output file, only svg is supported |
output file
Zhaodong Hao, Dekang Lv, Ying Ge, Jisen Shi, Dolf Weijers, Guangchuang Yu, Jinhui Chen
# Loading the package require(RIdeogram) # Loading the testing data data(human_karyotype, package="RIdeogram") data(gene_density, package="RIdeogram") data(Random_RNAs_500, package="RIdeogram") # Checking the data format head(human_karyotype) head(gene_density) head(Random_RNAs_500) # Running the function ideogram(karyotype = human_karyotype) convertSVG("chromosome.svg", device = "png") # Then, you will find a SVG file and a PNG file in your Working Directory.# Loading the package require(RIdeogram) # Loading the testing data data(human_karyotype, package="RIdeogram") data(gene_density, package="RIdeogram") data(Random_RNAs_500, package="RIdeogram") # Checking the data format head(human_karyotype) head(gene_density) head(Random_RNAs_500) # Running the function ideogram(karyotype = human_karyotype) convertSVG("chromosome.svg", device = "png") # Then, you will find a SVG file and a PNG file in your Working Directory.
Grape and Populus genomes
data(karyotype_dual_comparison)data(karyotype_dual_comparison)
data frame
MCscan
Amborella, Grape and Liriodendron genomes
data(karyotype_ternary_comparison)data(karyotype_ternary_comparison)
data frame
MCscan
Liriodendron chinense genome
data(liriodendron_karyotype)data(liriodendron_karyotype)
data frame
Nature Plants, doi: 10.1038/s41477-018-0323-6
LTR numbers was counted with a window of 1 Mb
data(LTR_density)data(LTR_density)
data frame
UCSC (http://genome.ucsc.edu/index.html)
Pi values of the China east group of Liriodendron chinense and being calculated in a 2-Mb sliding window with a 1-Mb step
data(Pi_for_CE)data(Pi_for_CE)
data frame
Nature Plants, doi: 10.1038/s41477-018-0323-6
Pi values of the China east and west groups of Liriodendron chinense and being calculated in a 2-Mb sliding window with a 1-Mb step
data(Pi_for_CE_and_CW)data(Pi_for_CE_and_CW)
data frame
Nature Plants, doi: 10.1038/s41477-018-0323-6
500 RNAs randomly selected from all tRNAs, rRNAs and miRNA in the human genome.
data(Random_RNAs_500)data(Random_RNAs_500)
data frame
Gencode (https://www.gencodegenes.org/)
Genome Synteny between Grape and Populus
data(synteny_dual_comparison)data(synteny_dual_comparison)
data frame
MCscan
Genome Synteny among Amborella, Grape and Liriodendron
data(synteny_ternary_comparison)data(synteny_ternary_comparison)
data frame
MCscan
Genome Synteny among Amborella, Grape and Liriodendron with gradient fill
data(synteny_ternary_comparison_graident)data(synteny_ternary_comparison_graident)
data frame
MCscan