「R」使用ggpolar绘制生存关联网络图

之前课题组一个师妹有需要绘制一个带相关性又能展示生存分析显著性的极坐标图，所以造了ggpolar这个包，今天分享给大家，感兴趣的读者不妨使用自己的数据模仿下，应用到自己的分析项目中去。

Source: `vignettes/surv-corr-connection.Rmd`[1]

library(ggpolar)
#> Loading required package: ggplot2
library(survival)
library(ezcox)
#> Welcome to 'ezcox' package!
#> =======================================================================
#> You are using ezcox version 1.0.2
#> 
#> Project home : https://github.com/ShixiangWang/ezcox
#> Documentation: https://shixiangwang.github.io/ezcox
#> Cite as      : arXiv:2110.14232
#> =======================================================================
#> 

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data = survival::lung
head(data)
#>   inst time status age sex ph.ecog ph.karno pat.karno meal.cal wt.loss
#> 1    3  306      2  74   1       1       90       100     1175      NA
#> 2    3  455      2  68   1       0       90        90     1225      15
#> 3    3 1010      1  56   1       0       90        90       NA      15
#> 4    5  210      2  57   1       1       90        60     1150      11
#> 5    1  883      2  60   1       0      100        90       NA       0
#> 6   12 1022      1  74   1       1       50        80      513       0

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Pick several variables.

vars = c("age", "sex", "ph.ecog", "ph.karno", "pat.karno", "meal.cal", "wt.loss")

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Univariable Cox analysis

df_cox = ezcox(data, vars)
df_cox
#> # A tibble: 7 × 12
#>   Variable  is_control contrast_level ref_level n_contrast n_ref      beta    HR
#>   <chr>     <lgl>      <chr>          <chr>          <int> <int>     <dbl> <dbl>
#> 1 age       FALSE      age            age              228   228  0.0187   1.02 
#> 2 sex       FALSE      sex            sex              228   228 -0.531    0.588
#> 3 ph.ecog   FALSE      ph.ecog        ph.ecog          227   227  0.476    1.61 
#> 4 ph.karno  FALSE      ph.karno       ph.karno         227   227 -0.0164   0.984
#> 5 pat.karno FALSE      pat.karno      pat.karno        225   225 -0.0199   0.98 
#> 6 meal.cal  FALSE      meal.cal       meal.cal         181   181 -0.000124 1    
#> 7 wt.loss   FALSE      wt.loss        wt.loss          214   214  0.00132  1    
#> # … with 4 more variables: lower_95 <dbl>, upper_95 <dbl>, p.value <dbl>,
#> #   global.pval <dbl>

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Correlation analysis

vars_comb = combn(vars, 2, simplify = FALSE)
cor_value = sapply(vars_comb, function(x) {
  cor(data[[x[1]]], data[[x[2]]], use = "pairwise")
})

df_cor = cbind(as.data.frame(t(sapply(vars_comb, function(x) x))), cor_value)
colnames(df_cor) = c("var1", "var2", "correlation")
df_cor$size = abs(df_cor$correlation)
df_cor$way = ifelse(df_cor$correlation > 0, "positive", "negative")
df_cor
#>         var1      var2 correlation       size      way
#> 1        age       sex -0.12216709 0.12216709 negative
#> 2        age   ph.ecog  0.19323604 0.19323604 positive
#> 3        age  ph.karno -0.20318207 0.20318207 negative
#> 4        age pat.karno -0.12616688 0.12616688 negative
#> 5        age  meal.cal -0.23141071 0.23141071 negative
#> 6        age   wt.loss  0.03814787 0.03814787 positive
#> 7        sex   ph.ecog -0.02060379 0.02060379 negative
#> 8        sex  ph.karno  0.01138505 0.01138505 positive
#> 9        sex pat.karno  0.04607145 0.04607145 positive
#> 10       sex  meal.cal -0.16835976 0.16835976 negative
#> 11       sex   wt.loss -0.12907708 0.12907708 negative
#> 12   ph.ecog  ph.karno -0.80726666 0.80726666 negative
#> 13   ph.ecog pat.karno -0.51122086 0.51122086 negative
#> 14   ph.ecog  meal.cal -0.09851018 0.09851018 negative
#> 15   ph.ecog   wt.loss  0.18758944 0.18758944 positive
#> 16  ph.karno pat.karno  0.52029737 0.52029737 positive
#> 17  ph.karno  meal.cal  0.04223324 0.04223324 positive
#> 18  ph.karno   wt.loss -0.17543452 0.17543452 negative
#> 19 pat.karno  meal.cal  0.16575874 0.16575874 positive
#> 20 pat.karno   wt.loss -0.17199064 0.17199064 negative
#> 21  meal.cal   wt.loss -0.10257242 0.10257242 negative

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Visualization

df_cox$role = ifelse(
  df_cox$p.value > 0.05, "non-signf",
  ifelse(df_cox$HR < 1, "protector", "risker")
)
df_cox$`-log10(p)` = -log10(df_cox$p.value)

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p = polar_init(df_cox, x = Variable, aes(color = role, size = `-log10(p)`))
p

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p + 
  ggnewscale::new_scale("color") +
  polar_connect(df_cor, x1 = var1, x2= var2, size = size, color = way, alpha = 0.3) + 
  scale_size(range = c(0.1, 4))
#> Scale for 'size' is already present. Adding another scale for 'size', which
#> will replace the existing scale.

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参考资料

[1]

vignettes/surv-corr-connection.Rmd: https://github.com/ShixiangWang/polar/blob/master/vignettes/surv-corr-connection.Rmd