使用ggmaplot函数绘制火山图

加载所需R包

  1. library(ggpubr)

基本用法:

Usage

  1. ggmaplot(data, fdr = 0.05, fc = 1.5, genenames = NULL, detection_call = NULL, size = NULL,
  2.   font.label = c(12, "plain", "black"), label.rectangle = FALSE, palette = c("#B31B21", "#1465AC", "darkgray"), top = 15, select.top.method = c("padj", "fc"), main = NULL, xlab = "Log2 mean expression",
  3.   ylab = "Log2 fold change", ggtheme = theme_classic(), ...)

常用参数：

Arguments

  1. data    #差异基因分析结果 an object of class DESeqResults, get_diff, DE_Results, matrix or data frame containing the columns baseMean, log2FoldChange, and padj. Rows are genes.
  2.         #baseMean: the mean expression of genes in the two groups.
  3.         #log2FoldChange: the log2 fold changes of group 2 compared to group 1
  4.         #padj: the adjusted p-value of the used statiscal test.
  5. fdr     #设定fdr阈值 Accepted false discovery rate for considering genes as differentially expressed.
  6. fc      #设定fc阈值 the fold change threshold. Only genes with a fold change >= fc and padj <= fdr are considered as significantly differentially expressed.
  7. genenames    #基因名 a character vector of length nrow(data) specifying gene names corresponding to each row. Used for point labels.
  8. detection_call    #选出特定表达模式的基因进行展示 a numeric vector with length = nrow(data), specifying if the genes is expressed (value = 1) or not (value = 0). For example detection_call = c(1, 1, 0, 1, 0, 1). Default is NULL. If detection_call column is available in data, it will be used.
  9. size    #points size.
  10. font.label    #a vector of length 3 indicating respectively the size (e.g.: 14), the style (e.g.: "plain", "bold", "italic", "bold.italic") and the color (e.g.: "red") of point labels. For example font.label = c(14, "bold", "red").
  11. label.rectangle    #logical value. If TRUE, add rectangle underneath the text, making it easier to read.
  12. palette    #the color palette to be used for coloring or filling by groups. Allowed values include "grey" for grey color palettes; brewer palettes e.g. "RdBu", "Blues", ...; or custom color palette e.g. c("blue", "red"); and scientific journal palettes from ggsci R package, e.g.: "npg", "aaas", "lancet", "jco", "ucscgb", "uchicago", "simpsons" and "rickandmorty".
  13. top    #选定top基因进行展示 the number of top genes to be shown on the plot. Use top = 0 to hide to gene labels.
  14. select.top.method    #methods to be used for selecting top genes. Allowed values include "padj" and "fc" for selecting by adjusted p values or fold changes, respectively.
  15. main    #plot main title.
  16. xlab    #character vector specifying x axis labels. Use xlab = FALSE to hide xlab.
  17. ylab    #character vector specifying y axis labels. Use ylab = FALSE to hide ylab.
  18. ggtheme    #function, ggplot2 theme name. Default value is theme_pubr(). Allowed values include ggplot2 official themes: theme_gray(), theme_bw(), theme_minimal(), theme_classic(), theme_void()
  19. ...     #other arguments to be passed to ggpar.

使用示例

  1. # 加载数据集
  2. data(diff_express)
  3. head(diff_express)
  1. ##                     name     baseMean log2FoldChange         padj detection_call
  2. ## ENSG00000000003   TSPAN6    0.1184475      0.0000000           NA              0
  3. ## ENSG00000000419     DPM1 1654.4618144      0.6789538 5.280802e-02              1
  4. ## ENSG00000000457    SCYL3  681.0463277      1.5263838 3.915112e-07              1
  5. ## ENSG00000000460 C1orf112  389.7226640      3.8933573 1.180333e-14              1
  6. ## ENSG00000000938      FGR  364.7810090     -2.3554014 1.559228e-04              1
  7. ## ENSG00000000971      CFH    1.1346239      1.2932740 4.491812e-01              0
  1. p1 <- ggmaplot(diff_express, fdr = 0.05, fc = 2, size = 0.4,
  2.                palette = c("red", "green", "gray"))
  3. p1

unnamed-chunk-4-1.png

  1. p2 <- ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"),
  2.                fdr = 0.05, fc = 2, size = 0.4,
  3.                palette = c("#B31B21", "#1465AC", "darkgray"),
  4.                genenames = as.vector(diff_express$name),
  5.                xlab = "M", ylab = "A",
  6.                legend = "top", top = 20,
  7.                font.label = c(11, "blue", "red"),
  8.                font.legend = "bold", font.main = "bold",
  9.                ggtheme = ggplot2::theme_minimal())
  10. p2

unnamed-chunk-5-1.png

  1. # Add rectangle around labels
  2. p3 <- ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"),
  3.                fdr = 0.05, fc = 2, size = 0.4,
  4.                palette = c("#B31B21", "#1465AC", "darkgray"),
  5.                genenames = as.vector(diff_express$name),
  6.                legend = "top", top = 20, font.label = c("bold", 11),
  7.                label.rectangle = TRUE, font.legend = "bold", 
  8.                select.top.method = "padj", font.main = "bold",
  9.                ggtheme = ggplot2::theme_minimal())
  10. p3

unnamed-chunk-6-1.png

  1. p4 <- ggmaplot(diff_express, main = expression("Group 1" %->% "Group 2"),
  2.                fdr = 0.05, fc = 2, size = 0.5,
  3.                palette = c("#B31B21", "#1465AC", "darkgray"),
  4.                genenames = as.vector(diff_express$name),
  5.                legend = "top", top = 20, font.label = c("bold", 11),
  6.                label.rectangle = TRUE, font.legend = "bold", 
  7.                select.top.method = "fc", font.main = "bold",
  8.                ggtheme = ggplot2::theme_minimal())
  9. p4

unnamed-chunk-7-1.png

  1. sessionInfo()
  1. ## R version 4.0.2 (2020-06-22)
  2. ## Platform: x86_64-w64-mingw32/x64 (64-bit)
  3. ## Running under: Windows 10 x64 (build 17763)
  4. ## 
  5. ## Matrix products: default
  6. ## 
  7. ## locale:
  8. ## [1] LC_COLLATE=Chinese (Simplified)_China.936  LC_CTYPE=Chinese (Simplified)_China.936   
  9. ## [3] LC_MONETARY=Chinese (Simplified)_China.936 LC_NUMERIC=C                              
  10. ## [5] LC_TIME=Chinese (Simplified)_China.936    
  11. ## system code page: 65001
  12. ## 
  13. ## attached base packages:
  14. ## [1] stats     graphics  grDevices utils     datasets  methods   base     
  15. ## 
  16. ## other attached packages:
  17. ##  [1] ggpubr_0.4.0       nycflights13_1.0.1 forcats_0.5.0      stringr_1.4.0     
  18. ##  [5] dplyr_1.0.2        purrr_0.3.4        readr_1.3.1        tidyr_1.1.2       
  19. ##  [9] tibble_3.0.3       ggplot2_3.3.2      tidyverse_1.3.0   
  20. ## 
  21. ## loaded via a namespace (and not attached):
  22. ##  [1] ggrepel_0.8.2     Rcpp_1.0.5        lubridate_1.7.9   assertthat_0.2.1  digest_0.6.25    
  23. ##  [6] utf8_1.1.4        R6_2.4.1          cellranger_1.1.0  backports_1.1.9   reprex_0.3.0     
  24. ## [11] evaluate_0.14     httr_1.4.2        highr_0.8         pillar_1.4.6      rlang_0.4.7      
  25. ## [16] curl_4.3          readxl_1.3.1      rstudioapi_0.11   data.table_1.13.0 car_3.0-9        
  26. ## [21] blob_1.2.1        labeling_0.3      foreign_0.8-80    munsell_0.5.0     broom_0.7.0      
  27. ## [26] compiler_4.0.2    modelr_0.1.8      xfun_0.17         pkgconfig_2.0.3   tidyselect_1.1.0 
  28. ## [31] rio_0.5.16        fansi_0.4.1       crayon_1.3.4      dbplyr_1.4.4      withr_2.2.0      
  29. ## [36] grid_4.0.2        jsonlite_1.7.1    gtable_0.3.0      lifecycle_0.2.0   DBI_1.1.0        
  30. ## [41] magrittr_1.5      scales_1.1.1      zip_2.1.1         cli_2.0.2         stringi_1.5.3    
  31. ## [46] carData_3.0-4     farver_2.0.3      ggsignif_0.6.0    fs_1.5.0          xml2_1.3.2       
  32. ## [51] ellipsis_0.3.1    generics_0.0.2    vctrs_0.3.4       openxlsx_4.1.5    ggsci_2.9        
  33. ## [56] tools_4.0.2       glue_1.4.2        hms_0.5.3         abind_1.4-5       colorspace_1.4-1 
  34. ## [61] rstatix_0.6.0     rvest_0.3.6       knitr_1.29        haven_2.3.1