RIdeogram绘图

RIdeogram

1、加载包

> require(RIdeogram)

2、数据

（1）包内就有的数据集human_karyotype

> data(human_karyotype, package="RIdeogram")
> 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  59829934

（2）包内就有的数据集gene_density

> data(gene_density, package="RIdeogram")
> head(gene_density)
  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

（3）包内就有的数据集Random_RNAs_500

> data(Random_RNAs_500, package="RIdeogram")
> 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 ff7f00

3、绘图

（1）只画出人类染色体组型的图

> ideogram(karyotype = human_karyotype)
> convertSVG("chromosome.svg", device = "png")

chromosome.png

（2）不要中间的“拦腰斩” ---- 着丝粒位置

> human_karyotype <- human_karyotype[,1:3]   # 所以只要前3列的chr， start， end 就行了
> ideogram(karyotype = human_karyotype)
> convertSVG("chromosome.svg", device = "png")

chromosome.png

（3）可视化整个人类基因组的基因密度

> ideogram(karyotype = human_karyotype, overlaid = gene_density)
> convertSVG("chromosome.svg", device = "png")

chromosome.png

换个颜色：

> ideogram(karyotype = human_karyotype, overlaid = gene_density, colorset1 = c("#fc8d59", "#ffffbf", "#91bfdb"))
> convertSVG("chromosome.svg", device = "png")

chromosome.png

（4）染色体表象旁边的轨道标签来映射一些全基因组数据

> ideogram(karyotype = human_karyotype, label = Random_RNAs_500, label_type = "marker")
> convertSVG("chromosome.svg", device = "png")

chromosome.png
仔细查看Random_RNAs_500数据会发现里面是

可以自定义
     起个名儿    标注形状                                         颜色
     Type           Shape          Chr   Start      End         color
1   名儿     circle/box/triangle    6    69204486   69204568    6a3d9a

这个形状除了这3种还能不能换其他的？？？不懂不懂

（5）laber还可以是其他类型
label_type = "heatmap"

> ideogram(karyotype = human_karyotype, overlaid = gene_density, label = LTR_density, label_type = "heatmap", colorset1 = c("#f7f7f7", "#e34a33"), colorset2 = c("#f7f7f7", "#2c7fb8"))
> convertSVG("chromosome.svg", device = "png")

chromosome.png

label_type = "line"
 这个类型是用来在染色体旁边画折线图的，换一组数据，像如下有 value 的

> data(liriodendron_karyotype, package="RIdeogram")
> head(liriodendron_karyotype)
  Chr Start       End
1   1     0 118073833
2   2     0  98364873
3   3     0 207093695
4   4     0  50051714
5   5     0  45443526
6   6     0  35772468

> data(Fst_between_CE_and_CW, package="RIdeogram")
> head(Fst_between_CE_and_CW)
  Chr   Start     End     Value
1   1       1 2000000 0.0646357
2   1 1000001 3000000 0.0626714
3   1 2000001 4000000 0.0582397
4   1 3000001 5000000 0.0679570
5   1 4000001 6000000 0.0965196
6   1 5000001 7000000 0.0934111

> data(Pi_for_CE, package="RIdeogram")
> head(Pi_for_CE)
  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

> data(Pi_for_CE_and_CW, package="RIdeogram")
> head(Pi_for_CE_and_CW)
  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

 1个value

> ideogram(karyotype = liriodendron_karyotype, overlaid = Fst_between_CE_and_CW, label = Pi_for_CE, label_type = "line", colorset1 = c("#e5f5f9", "#99d8c9", "#2ca25f"))
> convertSVG("chromosome.svg", device = "png")

chromosome.png
 2个value

> ideogram(karyotype = liriodendron_karyotype, overlaid = Fst_between_CE_and_CW, label = Pi_for_CE_and_CW, label_type = "line", colorset1 = c("#e5f5f9", "#99d8c9", "#2ca25f"))
> convertSVG("chromosome.svg", device = "png")

chromosome.png

label_type = "polygon" 多边形
  1个value

> ideogram(karyotype = liriodendron_karyotype, overlaid = Fst_between_CE_and_CW, label = Pi_for_CE, label_type = "polygon", colorset1 = c("#e5f5f9", "#99d8c9", "#2ca25f"))
> convertSVG("chromosome.svg", device = "png")

chromosome.png
  2个value

> ideogram(karyotype = liriodendron_karyotype, overlaid = Fst_between_CE_and_CW, label = Pi_for_CE_and_CW, label_type = "polygon", colorset1 = c("#e5f5f9", "#99d8c9", "#2ca25f"))
> convertSVG("chromosome.svg", device = "png")

chromosome.png
（6）可视化两个或三个基因组之间的基因组同线性结果
 先略过 https://cran.r-project.org/web/packages/RIdeogram/vignettes/RIdeogram.html 最下面

4、输出

convertSVG("chromosome.svg", device = "png", dpi = 600)
                  名儿   格式：“tiff”、“pdf”、“jpg”    分辨率