RIdeogram
karyotype
Data
library(RIdeogram)
karyo_df = human_karyotype
dens_df = gene_density
rna_df = Random_RNAs_500
colA = c('green','white','red')
colB = c('blue','white','purple')
> head(karyo_df) ## CE: centriole
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 59829934
> head(dens_df) ## Value: e.g. SNP density in that window
Chr Start End Value
1 1 1 1000000 65
2 1 1000001 2000000 76
3 1 2000001 3000000 35
4 1 3000001 4000000 30
5 1 4000001 5000000 10
6 1 5000001 6000000 10
> head(rna_df)
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 ff7f00
Usage
ideogram(karyotype = karyo_df, output = "ideogram_out.svg")
svg2png("ideogram_out.svg",file = "1.png")
## anno type's input type:
## except heatmap, must have df$color in 'ff7f00' format
# marker - categorical
# heatmap - numeric
# line - numeric
# polygon - numeric
ideogram(
karyotype = karyo_df,
overlaid = dens_df, # numeric -> heatmap
label = dens_df, # # anno beside chromosomes
label_type = "heatmap", ## # anno type: marker, heatmap, line, polygon
colorset1 = colA, # overlaid color
colorset2 = colB, # # marker color: only for label_type = "heatmap"
width = 170,
Lx = 160, # position of legend x
Ly = 35,
output = "ideogram_out.svg"
)
svg2png("ideogram_out.svg",file = "2.png")
ideogram(
karyotype = karyo_df,
overlaid = dens_df,
label = rna_df, #
label_type = "marker", ##
colorset1 = colA,
colorset2 = NULL, #
width = 170,
Lx = 160,
Ly = 35,
output = "ideogram_out.svg"
)
svg2png("ideogram_out.svg",file = "3.png")
dens_df$color = 'ff7f00'
ideogram(
karyotype = karyo_df,
overlaid = dens_df,
label = dens_df, #
label_type = "polygon", ##
colorset1 = colA,
colorset2 = NULL, #
width = 170,
Lx = 160,
Ly = 35,
output = "ideogram_out.svg"
)
svg2png("ideogram_out.svg",file = "4.png")
tips
如果想画多个line或者polygon，可以修改overlaid的df
## single polygon
Chr Start End Value Color
1 1 1 2000000 0.00273566 fc8d62
2 1 1000001 3000000 0.00239580 fc8d62
3 1 2000001 4000000 0.00319407 fc8d62
4 1 3000001 5000000 0.00286900 fc8d62
5 1 4000001 6000000 0.00186596 fc8d62
6 1 5000001 7000000 0.00186182 fc8d62
## multi polygon
Chr Start End Value_1 Color_1 Value_2 Color_2
1 1 1 2000000 0.00273566 fc8d62 0.00385702 8da0cb
2 1 1000001 3000000 0.00239580 fc8d62 0.00331109 8da0cb
3 1 2000001 4000000 0.00319407 fc8d62 0.00374530 8da0cb
4 1 3000001 5000000 0.00286900 fc8d62 0.00339141 8da0cb
5 1 4000001 6000000 0.00186596 fc8d62 0.00305246 8da0cb
6 1 5000001 7000000 0.00186182 fc8d62 0.00323655 8da0cb
synteny
Dual
> levels(as.factor(karyotype_dual_comparison$species))
[1] "Grape" "Populus"
ideogram(
karyotype = karyotype_dual_comparison,
synteny = synteny_dual_comparison,
output = "ideogram_out.svg"
)
svg2png("ideogram_out.svg",file = "5.png")
Ternary
文件准备太复杂，且不确定species按照什么顺序排列；建议用circus
已知此例中有3个species按照如下顺序排列：A (Amborella), B (Grape) and C (Liriodendron)，type列：1=A_vs_B，2=A_vs_C，3=B_vs_C
> levels(as.factor(karyotype_ternary_comparison$species))
[1] "Amborella" "Grape" "Liriodendron"
> table(synteny_ternary_comparison$type)
1 2 3
192 210 188
ideogram(
karyotype = karyotype_ternary_comparison,
synteny = synteny_ternary_comparison,
output = "ideogram_out.svg"
)
svg2png("ideogram_out.svg",file = "6.png")
SVG to Img
ideogram输出强制保存为svg文件，需要手动转换
convertSVG(
svg = "ideogram_out.svg",
file = "ideogram_out.png", ## width height dpi
device = "png")
svg2tiff(...)
svg2pdf(...)
svg2jpg(...)
svg2png(...)
library("rsvg")
rsvg_pdf("ideogram_out.svg", "ideogram_out.pdf")
参考
RIdeogram: https://www.jianshu.com/p/07ae1fe18071 RIdeogram: https://cran.r-project.org/web/packages/RIdeogram/vignettes/RIdeogram.html
染色体显带及命名: https://zhuanlan.zhihu.com/p/284010321