可视化多个组中 presence/absence 事件的最佳方法

Question

我有一个数据集，其中记录了 40 个特定基因的 presence/absence 突变，比较了 20 种组织类型的正常组织（例如肺组织）与来自该组织的肿瘤（例如肺肿瘤）。我正在努力寻找可视化这些数据的最佳方式。

数据子集：

Gene    Lung_Normal Lung_Cancer Skin_Normal Skin_Cancer Brain_Normal    Brain_Cancer
Gene_1  TRUE    TRUE    TRUE    TRUE    TRUE    TRUE
Gene_2  TRUE    TRUE    TRUE    TRUE    TRUE    TRUE
Gene_3  FALSE   TRUE    FALSE   FALSE   FALSE   FALSE
Gene_4  FALSE   FALSE   FALSE   FALSE   FALSE   FALSE
Gene_5  FALSE   TRUE    FALSE   FALSE   FALSE   TRUE
Gene_6  FALSE   FALSE   TRUE    TRUE    TRUE    TRUE
Gene_7  FALSE   FALSE   FALSE   TRUE    FALSE   FALSE
Gene_8  FALSE   FALSE   FALSE   TRUE    FALSE   TRUE
Gene_9  FALSE   TRUE    FALSE   FALSE   FALSE   FALSE
Gene_10 FALSE   FALSE   FALSE   TRUE    FALSE   TRUE

我们想要传达的关键信息是，虽然相同的 3-4 个基因经常在正常组织中发生突变，但每个肿瘤都有更多的额外基因发生突变，并且肿瘤具有更多多样性。我可以像这样将其保留为 table，但我很想找到一种以清晰的方式可视化信息的好方法。

我想尝试制作一个图形，就像马戏团的情节一样，用一个圆圈和两个环代表所有数据。内环为正常组织，外环为癌组织，每段内环为相关正常组织，外环为相关癌组织。每个基因都会用颜色编码，只有在发生突变时才会显示。因此，对于所有正常组织，该片段将显示 2-3 个突变基因的 2-3 种颜色，而外部癌症片段将显示更多的颜色片段，代表更多的突变。

但是我还没有找到可以创建这种可视化效果的绘图软件。有谁知道制作这样的可视化的方法吗？即使只是将我指向 R 包也会非常有帮助。我研究了 circos 和雷达图，但我没有找到可以实现我想要的可视化类型的包，只显示每种情况下发生的事件。

如果有人认为不同类型的可视化可以表示此数据，请告诉我，我很乐意考虑可以清晰地表示数据的替代方案。

提前谢谢你。

Answer 1

不确定这是否是您要查找的内容，但我试了一下。另外，从上面的描述中我不能完全确定你想用不同类型的细胞做什么——肺、皮肤、脑？如果这不是您要查找的内容，也许您可以 post 一张描绘预期输出的图。

下图中，内圈是正常细胞，外圈是癌细胞。我这里的回答得益于this post.

## Make the data
tib <- tibble::tribble(
  ~Gene,    ~Lung_Normal, ~Lung_Cancer, ~Skin_Normal, ~Skin_Cancer, ~Brain_Normal,    ~Brain_Cancer,
"Gene_1", TRUE    , TRUE    , TRUE    , TRUE    , TRUE    , TRUE,
"Gene_2",   TRUE,     TRUE,     TRUE,     TRUE,     TRUE,     TRUE, 
"Gene_3", FALSE   , TRUE    , FALSE   , FALSE   , FALSE   , FALSE,
"Gene_4",   FALSE,    FALSE,    FALSE,    FALSE,    FALSE,    FALSE, 
"Gene_5", FALSE   , TRUE    , FALSE   , FALSE   , FALSE   , TRUE,
"Gene_6",   FALSE,    FALSE,    TRUE,     TRUE,     TRUE,     TRUE, 
"Gene_7", FALSE   , FALSE   , FALSE   , TRUE    , FALSE   , FALSE,
"Gene_8",   FALSE,    FALSE,    FALSE,    TRUE,     FALSE,    TRUE, 
"Gene_9", FALSE   , TRUE    , FALSE   , FALSE   , FALSE   , FALSE,
"Gene_10",  FALSE,    FALSE,    FALSE,    TRUE,     FALSE,    TRUE)

library(tidyr)
library(dplyr)

## Re-arrange into long format
tib <- tib %>% 
  pivot_longer(cols=-Gene, names_pattern="(.*)_(.*)", names_to=c("type", ".value")) %>%  
  pivot_longer(c(Normal, Cancer), names_to = "diag", values_to="val") %>% 
  # code colors as the gene if it's mutated, otherwise Unmutated
  mutate(f = case_when(val ~ Gene, TRUE ~ "Unmutated")) %>% 
  group_by(Gene, f, diag) %>% 
  summarise(s = n()) %>% 
  mutate(diag = factor(diag, levels=c("Normal", "Cancer")), 
         f = factor(f, levels=c(paste("Gene", c(1,2,6,3,5,7,8,9,10,4), sep="_"), "Unmutated"))) 

library(ggplot2)
library(RColorBrewer)
ggplot(tib, aes(x=diag, 
                y = s, 
                fill=f)) + 
  geom_bar(stat="identity") + 
  coord_polar("y") + 
  theme_void() + 
  scale_fill_manual(values=c(brewer.pal(9, "Paired"), "gray75")) + 
  labs(fill = "Mutations")

编辑

这是艾伦建议的数据的样子。这种方法的缩放比例不高，而且需要大量颜色会使绘图的可读性降低。

df <- structure(list(genes = c("Gene1", "Gene2", "Gene3", "Gene4", 
"Gene5", "Gene6", "Gene7", "Gene8", "Gene9", "Gene10", "Gene11", 
"Gene12", "Gene13", "Gene14", "Gene15", "Gene16", "Gene17", "Gene18", 
"Gene19", "Gene20", "Gene21", "Gene22", "Gene23", "Gene24", "Gene25", 
"Gene26", "Gene27", "Gene28", "Gene29", "Gene30", "Gene31", "Gene32", 
"Gene33", "Gene34", "Gene35", "Gene36", "Gene37", "Gene38", "Gene39", 
"Gene40"), bone_cancer = c(FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, 
TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE), bone_normal = c(FALSE, 
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
TRUE, FALSE, TRUE), brain_cancer = c(TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE), brain_normal = c(FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, 
FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE), breast_cancer = c(FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE), breast_normal = c(TRUE, 
FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, 
FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, 
FALSE), colon_cancer = c(FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, TRUE, FALSE), colon_normal = c(FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
TRUE, TRUE, FALSE), kidney_cancer = c(FALSE, FALSE, FALSE, FALSE, 
                              FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, 
                              TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, 
                              FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
                              FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE), 
kidney_normal = c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, 
TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, 
TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE), liver_cancer = c(FALSE, 
FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, 
FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, 
FALSE, FALSE, FALSE), liver_normal = c(TRUE, FALSE, FALSE, 
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, 
TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, 
TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, 
FALSE), lung_cancer = c(TRUE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), 
lung_normal = c(FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, 
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), prostate_cancer = c(TRUE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, 
FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
TRUE, FALSE, TRUE), prostate_normal = c(TRUE, FALSE, FALSE, 
FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE), skin_cancer = c(FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, 
FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE), skin_normal = c(TRUE, 
FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, 
FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, 
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, 
FALSE, FALSE, FALSE), thyroid_cancer = c(FALSE, FALSE, FALSE, 
FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, 
FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, 
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE), thyroid_normal = c(FALSE, FALSE, TRUE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE)), 
class = "data.frame", row.names = c(NA, 40L))
names(df)[1] <- "Gene"
tib <- df %>% 
  pivot_longer(cols=-Gene, names_pattern="(.*)_(.*)", names_to=c("type", ".value")) %>%  
  pivot_longer(c(normal, cancer), names_to = "diag", values_to="val") %>% 
  # code colors as the gene if it's mutated, otherwise Unmutated
  mutate(f = case_when(val ~ Gene, TRUE ~ "Unmutated")) %>% 
  group_by(Gene, f, diag) %>% 
  summarise(s = n()) %>% 
  ungroup() %>% 
  group_by(Gene) %>% 
  mutate(diag = factor(diag, levels=c("normal", "cancer")))
         

levs <- tib %>% 
  dplyr::select(f, s) %>% 
  summarise(pct_mutated = sum(s*(f!= "Unmutated"))/sum(s)) %>% 
  arrange(-pct_mutated)  %>% 
  dplyr::select(Gene) %>% 
  pull()


tib<- tib %>% 
  mutate(f = factor(f, levels=c(levs, "Unmutated")))



library(ggplot2)
library(RColorBrewer)
ggplot(tib, aes(x=diag, 
                y = s, 
                fill=f)) + 
  geom_bar(stat="identity") + 
  coord_polar("y") + 
  theme_void() + 
  scale_fill_manual(values=c(rainbow(length(levels(tib$f))-1), "gray75")) + 
  labs(fill = "Mutations")

Answer 2

另一种方法是热图。您可以通过对癌症与正常进行分面来做到这一点，或者调整填充以仅反映癌症突变、仅反映正常组织突变、两者都反映，或两者都不反映。

对于这两种情况，首先需要重塑您的数据：

选项 1

library(tidyr)
library(dplyr)
library(ggplot2)

df %>% 
  pivot_longer(-1) %>%
  separate(name, into = c("tissue", "state"), sep = "_") %>%
  mutate(genes = factor(genes, paste0("Gene", 1:40))) %>%
  ggplot(aes(tissue, genes, fill = value)) + geom_tile(color = "black") +
  facet_grid(.~state) +
  scale_x_discrete(guide = guide_axis(n.dodge = 2))

选项 2

df %>% 
  pivot_longer(-1) %>%
  separate(name, into = c("tissue", "state"), sep = "_") %>%
  mutate(genes = factor(genes, paste0("Gene", 1:40))) %>%
  group_by(genes, tissue) %>%
  summarize(mutations = factor(2 + diff(value) + 2 * all(value))) %>%
  ggplot(aes(tissue, genes, fill = mutations)) + geom_tile(color = "black") +
  scale_x_discrete(guide = guide_axis(n.dodge = 2)) +
  scale_fill_discrete(labels = c("Neither", "Cancer only", "Healthy only", "Both"))

对于这些，我使用了应该近似于您的数据结构的样本数据：

df <- structure(list(genes = c("Gene1", "Gene2", "Gene3", "Gene4", 
"Gene5", "Gene6", "Gene7", "Gene8", "Gene9", "Gene10", "Gene11", 
"Gene12", "Gene13", "Gene14", "Gene15", "Gene16", "Gene17", "Gene18", 
"Gene19", "Gene20", "Gene21", "Gene22", "Gene23", "Gene24", "Gene25", 
"Gene26", "Gene27", "Gene28", "Gene29", "Gene30", "Gene31", "Gene32", 
"Gene33", "Gene34", "Gene35", "Gene36", "Gene37", "Gene38", "Gene39", 
"Gene40"), bone_cancer = c(FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, 
TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE), bone_normal = c(FALSE, 
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
TRUE, FALSE, TRUE), brain_cancer = c(TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE), brain_normal = c(FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, 
FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE), breast_cancer = c(FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE), breast_normal = c(TRUE, 
FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, 
FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, 
FALSE), colon_cancer = c(FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, TRUE, FALSE), colon_normal = c(FALSE, 
TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
TRUE, TRUE, FALSE), kidney_cancer = c(FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, 
TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE), 
    kidney_normal = c(FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
    FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, 
    TRUE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, 
    TRUE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, TRUE, 
    TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE), liver_cancer = c(FALSE, 
    FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, 
    FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, TRUE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, 
    FALSE, FALSE, FALSE), liver_normal = c(TRUE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, 
    TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, 
    TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, 
    FALSE), lung_cancer = c(TRUE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), 
    lung_normal = c(FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, TRUE, 
    TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE), prostate_cancer = c(TRUE, 
    FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, 
    FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    TRUE, FALSE, TRUE), prostate_normal = c(TRUE, FALSE, FALSE, 
    FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
    FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, TRUE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE), skin_cancer = c(FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, 
    TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, FALSE, FALSE), skin_normal = c(TRUE, 
    FALSE, TRUE, TRUE, TRUE, FALSE, TRUE, FALSE, FALSE, TRUE, 
    FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, 
    TRUE, FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, TRUE, TRUE, 
    TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, TRUE, 
    FALSE, FALSE, FALSE), thyroid_cancer = c(FALSE, FALSE, FALSE, 
    FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, TRUE, TRUE, 
    FALSE, FALSE, TRUE, FALSE, TRUE, TRUE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, TRUE, FALSE, 
    FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE), thyroid_normal = c(FALSE, FALSE, TRUE, FALSE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, TRUE, TRUE, FALSE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, 
    FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, 
    FALSE, FALSE, FALSE, FALSE, FALSE, FALSE, TRUE, FALSE)), class = "data.frame", row.names = c(NA, 
40L))

Answer 3

这是一个替代方案。类似于热图，但使用彩色点代替。我还选择隐藏 non-mutations，因为我认为我们更感兴趣的是可视化突变发生的位置。如果 FALSE 值也带有颜色，它会通过为我们的大脑解释提供额外的强调来增加认知负荷。

# Creating the data.
gene_data <- structure(list(Gene = c("Gene_1", "Gene_2", "Gene_3", "Gene_4", 
"Gene_5", "Gene_6", "Gene_7", "Gene_8", "Gene_9", "Gene_10"), 
    Lung_Normal = c(TRUE, TRUE, FALSE, FALSE, FALSE, FALSE, FALSE, 
    FALSE, FALSE, FALSE), Lung_Cancer = c(TRUE, TRUE, TRUE, FALSE, 
    TRUE, FALSE, FALSE, FALSE, TRUE, FALSE), Skin_Normal = c(TRUE, 
    TRUE, FALSE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE
    ), Skin_Cancer = c(TRUE, TRUE, FALSE, FALSE, FALSE, TRUE, 
    TRUE, TRUE, FALSE, TRUE), Brain_Normal = c(TRUE, TRUE, FALSE, 
    FALSE, FALSE, TRUE, FALSE, FALSE, FALSE, FALSE), Brain_Cancer = c(TRUE, 
    TRUE, FALSE, FALSE, TRUE, TRUE, FALSE, TRUE, FALSE, TRUE)), row.names = c(NA, 
-10L), class = c("tbl_df", "tbl", "data.frame"))

稍微处理一下数据：

mutation_data <- gene_data %>%
  pivot_longer(-Gene, values_to = "Mutation") %>%  # making long form
  separate(name, into = c("Type", "Status")) %>% # splitting cell type and cancer status
  mutate(Mutation = as.numeric(Mutation), # cleaning data for plotting
         Type = factor(Type, levels = c("Cancer", "Normal")),
         Gene = factor(Gene, levels = paste0("Gene_", 1:10))) %>% #genes appear in order
  filter(Mutation == 1) # Removing FALSE for cleaner plot

构建情节

 ggplot() +
    # plotting all cancer mutations in red
    geom_point(data = mutation_data %>% filter(Status == "Cancer"), 
               aes(x = Type, y = Gene, color = Status),  size = 4)  +
    # plotting all mutations present in normal and cancer in black over top of the red
    geom_point(data = mutation_data %>% filter(Status == "Normal"), 
             aes(x = Type, y = Gene, color = Status), size = 4) +
    # formatting the color scale and legend
    scale_color_manual(name = "Mutation locations", values = c("Cancer" = "Red", "Normal" = "Black"),
                     labels = c("Cancer cells only", "Cancer and normal cells")) +
    # providing title, subtitle, and x label
    labs(title = "Gene mutations in brain, lung, and skin cells",
         subtitle = "Cancer cells vs. normal cells",
         x = "Cell type") +
    theme_bw() # setting a theme I like

可视化多个组中 presence/absence 事件的最佳方法

Best approach to visualise presence/absence of events in multiple groups

r

data-visualization

radar-chart

circos