如何调整Biomarker可视化过程中，显示label的数量

[liuninghua521]

老师好，我想知道在这一步该怎么调整label的数量，调整成显示丰度前10的门，其余可以合并为other之类的显示格式

display the high light of phylum clade.

p2 <- p1 + geom_hilight( data = td_filter(nodeClass == "Phylum"), mapping = aes(node = node, fill = label) )

我在可视化自己的数据，这一步显示出来的门太多了

[liuninghua521]

补充 老师，我还想知道该怎么调整样本和分组的因子顺序，就是将sample和time按自己想要的顺序排列

display the relative abundance of features(OTU)

p3 <- p2 + ggnewscale::new_scale("fill") + geom_fruit( data = td_unnest(RareAbundanceBySample), geom = geom_star, mapping = aes( x = fct_reorder(Sample, time, .fun=min), size = RelRareAbundanceBySample, fill = time, subset = RelRareAbundanceBySample > 0 ), starshape = 13, starstroke = 0.25, offset = 0.04, pwidth = 0.8, grid.params = list(linetype=2) ) + scale_size_continuous( name="Relative Abundance (%)", range = c(.5, 3) ) + scale_fill_manual(values=c("#1B9E77", "#D95F02"))

我尝试使用 taxa.tree@extraInfo$RareAbundanceBySample <- lapply(taxa.tree@extraInfo$RareAbundanceBySample, function(df) { df$Sample <- factor(df$Sample, levels = sample_order)
return(df) }) 的方式直接修改，但是没有效果。 麻烦老师了。

[xiangpin]

我在可视化自己的数据，这一步显示出来的门太多了

可以用mutate先生成一列新的label（调整后的phylum的名字或者其他水平都可以）然后再画，比如

> taxa.tree
'treedata' S4 object'.

...@ phylo:

Phylogenetic tree with 218 tips and 186 internal nodes.

Tip labels:
  OTU_67, OTU_231, OTU_188, OTU_150, OTU_207, OTU_5, ...
Node labels:
  r__root, k__Bacteria, p__Actinobacteria, p__Bacteroidetes, p__Cyanobacteria,
p__Deinococcus-Thermus, ...

Rooted; no branch lengths.

with the following features available:
  'nodeClass', 'nodeDepth', 'RareAbundanceBySample', 'RareAbundanceBytime'.

# The associated data tibble abstraction: 404 × 7
# The 'node', 'label' and 'isTip' are from the phylo tree.
    node label   isTip nodeClass nodeDepth RareAbundanceBySample
   <int> <chr>   <lgl> <chr>         <dbl> <list>
 1     1 OTU_67  TRUE  OTU               8 <tibble [19 × 4]>
 2     2 OTU_231 TRUE  OTU               8 <tibble [19 × 4]>
 3     3 OTU_188 TRUE  OTU               8 <tibble [19 × 4]>
 4     4 OTU_150 TRUE  OTU               8 <tibble [19 × 4]>
 5     5 OTU_207 TRUE  OTU               8 <tibble [19 × 4]>
 6     6 OTU_5   TRUE  OTU               8 <tibble [19 × 4]>
 7     7 OTU_1   TRUE  OTU               8 <tibble [19 × 4]>
 8     8 OTU_2   TRUE  OTU               8 <tibble [19 × 4]>
 9     9 OTU_3   TRUE  OTU               8 <tibble [19 × 4]>
10    10 OTU_4   TRUE  OTU               8 <tibble [19 × 4]>
# ℹ 394 more rows
# ℹ 1 more variable: RareAbundanceBytime <list>
# ℹ Use `print(n = ...)` to see more rows
> taxa.tree %>% dplyr::mutate(new.label=dplyr::if_else(label %in% c("p__Actinobacteria", "p__Bacteroidetes", "p__Cyanobacteria"), "Others", label)) %>% ggtree(layout='cir') + geom_hilight(data=td_filter(nodeClass=='Phylum'), mapping=aes(node = node, fill=new.label)) -> p1
> taxa.tree %>% ggtree(layout='cir') + geom_hilight(data = td_filter(nodeClass=='Phylum'), mapping=aes(node = node, fill=label)) -> p2
> p1 / p2

|> 我还想知道该怎么调整样本和分组的因子顺序，就是将sample和time按自己想要的顺序排列

可以在geom_fruit图层里先用td_mutate去调整sample的factor

> taxa.tree %>% tidyr::unnest(RareAbundanceBySample) %>% dplyr::pull(Sample) %>% unique() %>% sample() -> s1
# A tbl_df is returned for independent data analysis.
> s1
 [1] "F3D1"   "F3D147" "F3D5"   "F3D145" "F3D143" "F3D150" "F3D148" "F3D146"
 [9] "F3D141" "F3D2"   "F3D142" "F3D0"   "F3D7"   "F3D9"   "F3D8"   "F3D3"
[17] "F3D149" "F3D6"   "F3D144"
> p <- ggtree(taxa.tree, layout = 'cir')
> p + geom_fruit(data = td_mutate(Sample=factor(Sample, levels=s1), .f=td_unnest(RareAbundanceBySample)), geom = geom_tile, mapping = aes(subset=RareAbundance>0, fill=time, x=Sample))

![Uploading 捕获.PNG…]()

如果你觉得有点陌生，td_mutate(Sample = factor(Sample, levels= s1), .f = td_unnest(RareAbundanceBySample))(p$data)看看数据。td_unnest, td_filter, td_mutate这三个函数主要就是传到图层里的data参数，然后用这个函数去整理p$data里的数据。

[liuninghua521]

谢谢老师！