使用ggplot2的瓷砖图 - 同一瓷砖中的两个美学会导致图形故障

时间：2017-08-30 14:34:06

标签： r plot ggplot2

TL; DR - 创建平铺图，同一个平铺中的两个美学导致平铺颜色无法呈现，不确定它是否是我的代码或ggplot2的某些内容，有谁经历过这个？

当我尝试使用ggplot在同一个图块中绘制多个美学时，我得到了一个奇怪的绘图问题（不是错误，这默默地失败，我只注意到查看最终表示时的问题）。

我的数据：

satur_muts <- structure(list(id = c("0400136_T1", "0400136_T1", "0400136_T1", 
"0400136_T1", "0400171_T1", "0400171_T1", "0400171_T1", "0400171_T1", 
"0400179_T1", "0400179_T1", "0400179_T1", "0400195_T1", "0400195_T1", 
"0400195_T1", "0400195_T1"), gene = c("CDKN2A", "TP53", "KRAS", 
"KRAS", "CDKN2A", "TP53", "KRAS", "KRAS", "CDKN2A", "TP53", "KRAS", 
"CDKN2A", "TP53", "KRAS", "KRAS"), mut_type = structure(c(NA, 
NA, 1L, NA, NA, NA, NA, 1L, NA, NA, 1L, NA, NA, 1L, NA), .Label = "Missense", class = "factor"), 
    is_cnv = c(NA, NA, FALSE, TRUE, NA, NA, TRUE, FALSE, NA, 
    NA, FALSE, NA, NA, FALSE, TRUE)), row.names = c(NA, -15L), .Names = c("id", 
"gene", "mut_type", "is_cnv"), class = c("tbl_df", "tbl", "data.frame"
))

cnv_dat <- structure(list(id = structure(1:3, .Label = c("0400136_T1", "0400171_T1", 
"0400195_T1"), class = "factor"), gene = structure(c(1L, 1L, 
1L), .Label = "KRAS", class = "factor"), mut_type = structure(c(NA_integer_, 
NA_integer_, NA_integer_), .Label = character(0), class = "factor"), 
    cnv = structure(c(1L, 1L, 1L), .Label = "High-level amplification", class = "factor"), 
    is_cnv = c(TRUE, TRUE, TRUE)), row.names = c(NA, -3L), .Names = c("id", 
"gene", "mut_type", "cnv", "is_cnv"), class = c("tbl_df", "tbl", 
"data.frame"))

cb_palette <- c("Missense" = "#095481",
                "Splice site" = "#DE5423",
                "Frameshift" = "#179F30",
                "Nonsense" = "#F5B02C",
                "Nonstop" = "#FF69B4",
                "In-frame indel" = "#9C39B6")

mut_levels = c("Missense", "Splice site", "Frameshift", "Nonsense", "Nonstop", "In-frame indel")

我的代码：

library(dplyr); library(readr); library(ggplot2)
p1 <- ggplot(data = satur_muts) + 
  geom_tile(mapping = aes(x = id, y = gene, fill = mut_type), height=0.9, width=0.9) +
  scale_fill_manual(values = cb_palette, na.value = "Grey95", drop = F,
                    guide = guide_legend(title = "Somatic mutations", override.aes = list(size=4.5))  ,
                    breaks = unique(satur_muts$mut_type)[order(mut_levels)] %>% na.omit()) + 
  geom_tile(data = filter(satur_muts, gene == ""), 
            mapping = aes(x=id, y=gene), 
            fill = "white",
            height = 1, width = 1)

o <- levels(cnv_dat$cnv)
v_tuple <- c("High-level amplification"= 24, "Homozygous deletion" = 25)
b <- c("High-level amplification", "Homozygous deletion")
keep <- which( names(v_tuple) %in% unique(cnv_dat$cnv)[ order(o) ] )
p2 <- p1 + 
  geom_point(data = cnv_dat,  aes(x = id, y = gene, shape = cnv),
             fill = "grey", size = 3, stroke = 0.5, inherit.aes = F) +
  scale_shape_manual(values = v_tuple[keep], 
                     breaks = b[keep]) +
  guides(shape = guide_legend(title = "Somatic Copy Number Aberration"), override.aes = list(size=4.5), drop = T )
p2

enter image description here

根据下面的数据检查，最后一个蓝色和三角形瓷砖左侧的两个瓷砖也应该有蓝色背景。我不明白为什么一个样本会正确地绘制，而其他样本不会。

mt <- left_join(satur_muts, cnv_dat) %>% filter(!is.na(is_cnv))
t <- mt %>% filter(gene == "KRAS") 

# A tibble: 7 × 5
          id  gene mut_type is_cnv                      cnv
       <chr> <chr>    <chr>  <lgl>                   <fctr>
1 0400136_T1  KRAS Missense  FALSE                       NA
2 0400136_T1  KRAS     <NA>   TRUE High-level amplification
3 0400171_T1  KRAS     <NA>   TRUE High-level amplification
4 0400171_T1  KRAS Missense  FALSE                       NA
5 0400179_T1  KRAS Missense  FALSE                       NA
6 0400195_T1  KRAS Missense  FALSE                       NA
7 0400195_T1  KRAS     <NA>   TRUE High-level amplification

激情信息

R version 3.3.2 (2016-10-31)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: macOS Sierra 10.12

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] parallel  grid      stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] viridis_0.3.4      RColorBrewer_1.1-2 doMC_1.3.4         iterators_1.0.8    foreach_1.4.3      cowplot_0.7.0      lattice_0.20-34    gridExtra_2.2.1    readxl_0.1.1      
[10] forcats_0.2.0      stringr_1.2.0      devtools_1.12.0    dplyr_0.5.0        purrr_0.2.2        readr_1.0.0        tidyr_0.6.2        tibble_1.3.0       ggplot2_2.2.1     
[19] tidyverse_1.1.1   

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.10     plyr_1.8.4       tools_3.3.2      digest_0.6.12    jsonlite_1.4     lubridate_1.6.0  memoise_1.0.0    gtable_0.2.0     nlme_3.1-131     psych_1.6.12    
[11] DBI_0.5-1        haven_1.0.0      withr_1.0.2      httr_1.2.1       xml2_1.1.1       hms_0.3          R6_2.2.0         foreign_0.8-67   reshape2_1.4.2   modelr_0.1.0    
[21] magrittr_1.5     codetools_0.2-15 scales_0.4.1     assertthat_0.1   mnormt_1.5-5     rvest_0.3.2      colorspace_1.3-2 labeling_0.3     stringi_1.1.5    lazyeval_0.2.0  
[31] munsell_0.4.3    broom_0.4.2

和Rstudio版

$version
[1] ‘1.0.136’

1 个答案:

答案 0 :(得分：2)

我注意到，通过这个练习制作一个代表传递satur_muts，一个包含非唯一（x，y）配对的表格，导致ggplot到＆＃34;猜测＆＃34 ;在哪个配对使用，即使指定了单一美学（按照上面的代码）。

首先获取适当的输入数据以制作图块背景（satur_muts）：

genes <- structure(c(2L, 3L, 1L), .Label = c("CDKN2A", "KRAS", "TP53"), class = "factor")

ids <- structure(1:4, .Label = c("0400136_T1", "0400171_T1", "0400179_T1", 
    "0400195_T1"), class = "factor")

the_muts <- structure(list(id = c("0400136_T1", "0400171_T1", "0400179_T1", 
"0400195_T1"), gene = c("KRAS", "KRAS", "KRAS", "KRAS"), mut_type = structure(c(1L, 
1L, 1L, 1L), .Label = "Missense", class = "factor"), is_cnv = c(FALSE, 
FALSE, FALSE, FALSE), cnv = c("No High-level CNV", "No High-level CNV", 
"No High-level CNV", "No High-level CNV")), row.names = c(NA, 
-4L), .Names = c("id", "gene", "mut_type", "is_cnv", "cnv"), class = c("tbl_df", 
"tbl", "data.frame"))

制作正确的satur_muts：

library(dplyr)
satur_muts_expand <- expand.grid(gene = genes, id = ids) %>% as_data_frame()
new_satur_muts <- left_join(satur_muts_expand, the_muts %>% filter(!is.na(mut_type), !is_cnv) %>% distinct(), by = c("gene", "id"))

不：

satur_muts <- left_join(satur_muts_expand, the_muts, by = c("gene", "id"))

你应该得到这个：

new_satur_muts <- structure(list(gene = c("KRAS", "TP53", "CDKN2A", "KRAS", "TP53", 
"CDKN2A", "KRAS", "TP53", "CDKN2A", "KRAS", "TP53", "CDKN2A"), 
    id = c("0400136_T1", "0400136_T1", "0400136_T1", "0400171_T1", 
    "0400171_T1", "0400171_T1", "0400179_T1", "0400179_T1", "0400179_T1", 
    "0400195_T1", "0400195_T1", "0400195_T1"), mut_type = structure(c(1L, 
    NA, NA, 1L, NA, NA, 1L, NA, NA, 1L, NA, NA), .Label = "Missense", class = "factor")), row.names = c(NA, 
-12L), .Names = c("gene", "id", "mut_type"), class = c("tbl_df", "tbl","data.frame"))

并使用来自OP和cnv_dat的{​​{1}}，运行与上述相同的代码可解决问题（通过一些satur_muts <- new_satur_muts调整和theme）fixed! < / p>

基本上，如果您尝试在同一个图块中绘制多个美学（颜色，符号等），就会出现此问题。所以在这个例子中，我必须确保每个样本 - 基因配对在用于绘制突变（颜色）的表中是唯一的，然后覆盖附加信息（cnv）是一个SEPARATE表。所以事实证明这不是一个图形问题，而是在绘图之前包含输入数据的质量。感谢大家的帮助！

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