Introduction

mass_dataset can be easily used with ggplot2 package with ggplot_mass_dataset function. Now, ggplot() function also supports mass_dataset class.

Data preparation

library(massdataset)
library(tidyverse)

data("expression_data")
data("sample_info")
data("sample_info_note")
data("variable_info")
data("variable_info_note")

object =
  create_mass_dataset(
    expression_data = expression_data,
    sample_info = sample_info,
    variable_info = variable_info,
    sample_info_note = sample_info_note,
    variable_info_note = variable_info_note
  )

Sample wise

We need to replace ggplot with ggplot_mass_dataset, and then other functions are same with ggplot2 for graphics.

plot <- 
object %>%
  `+`(1) %>%
  log(10) %>%
  scale() %>%
ggplot_mass_dataset(direction = "sample",
                    sample_index = 2)
class(plot)
#> [1] "gg"     "ggplot"

The default y is value, here is the intensity of all the features in the second sample.

plot

head(plot$data)
#>     variable_id        mz        rt     value
#> 1 M136T55_2_POS 136.06140  54.97902        NA
#> 2    M79T35_POS  79.05394  35.36550        NA
#> 3  M307T548_POS 307.14035 547.56641        NA
#> 4  M183T224_POS 183.06209 224.32777        NA
#> 5   M349T47_POS 349.01584  47.00262        NA
#> 6  M182T828_POS 181.99775 828.35712 -1.778405
plot <- 
object %>%
  `+`(1) %>%
  log(10) %>%
  scale() %>%
ggplot_mass_dataset(direction = "sample",
                    sample_index = 2) +
  geom_boxplot(aes(x = 1)) +
  geom_jitter(aes(x = 1, color = mz)) +
  theme_bw()
plot

Variable wise

ggplot_mass_dataset(object, direction = "variable",
                    variable_index = 2) +
  geom_boxplot(aes(x = class, color = class)) +
  geom_jitter(aes(x = class, color = class)) +
  theme_bw()

object %>% 
  `+`(1) %>% 
  log(10) %>% 
  scale() %>% 
ggplot_mass_dataset(direction = "variable",
                    variable_index = 2) +
  geom_boxplot(aes(x = class, color = class)) +
  geom_jitter(aes(x = class, color = class)) +
  theme_bw() +
  labs(x = "", y = "Z-score")

ggplot() function

You need use activate_mass_dataset() to tell which slot you want to use for ggplot().

object %>% 
  `+`(1) %>% 
  log(10) %>% 
  scale() %>% 
  activate_mass_dataset(what = "variable_info") %>% 
  ggplot(aes(rt, mz)) +
  geom_point() +
  theme_bw() +
  labs(x = "mz", y = "RT (second)")

Session information

sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-apple-darwin17.0 (64-bit)
#> Running under: macOS Big Sur ... 10.16
#> 
#> Matrix products: default
#> BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
#> 
#> 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] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#>  [1] forcats_0.5.1.9000 stringr_1.4.0      dplyr_1.0.9        purrr_0.3.4       
#>  [5] readr_2.1.2        tidyr_1.2.0        tibble_3.1.7       tidyverse_1.3.1   
#>  [9] ggplot2_3.3.6      magrittr_2.0.3     masstools_1.0.2    massdataset_1.0.12
#> 
#> loaded via a namespace (and not attached):
#>   [1] readxl_1.4.0                backports_1.4.1            
#>   [3] circlize_0.4.15             systemfonts_1.0.4          
#>   [5] plyr_1.8.7                  lazyeval_0.2.2             
#>   [7] BiocParallel_1.30.3         GenomeInfoDb_1.32.2        
#>   [9] Rdisop_1.56.0               digest_0.6.29              
#>  [11] foreach_1.5.2               yulab.utils_0.0.5          
#>  [13] htmltools_0.5.2             fansi_1.0.3                
#>  [15] memoise_2.0.1               cluster_2.1.3              
#>  [17] doParallel_1.0.17           tzdb_0.3.0                 
#>  [19] openxlsx_4.2.5              limma_3.52.2               
#>  [21] ComplexHeatmap_2.12.0       modelr_0.1.8               
#>  [23] matrixStats_0.62.0          pkgdown_2.0.5              
#>  [25] colorspace_2.0-3            rvest_1.0.2                
#>  [27] textshaping_0.3.6           haven_2.5.0                
#>  [29] xfun_0.31                   crayon_1.5.1               
#>  [31] RCurl_1.98-1.7              jsonlite_1.8.0             
#>  [33] impute_1.70.0               iterators_1.0.14           
#>  [35] glue_1.6.2                  gtable_0.3.0               
#>  [37] zlibbioc_1.42.0             XVector_0.36.0             
#>  [39] GetoptLong_1.0.5            DelayedArray_0.22.0        
#>  [41] shape_1.4.6                 BiocGenerics_0.42.0        
#>  [43] scales_1.2.0                vsn_3.64.0                 
#>  [45] DBI_1.1.3                   Rcpp_1.0.8.3               
#>  [47] mzR_2.30.0                  viridisLite_0.4.0          
#>  [49] clue_0.3-61                 gridGraphics_0.5-1         
#>  [51] preprocessCore_1.58.0       stats4_4.2.1               
#>  [53] MsCoreUtils_1.8.0           htmlwidgets_1.5.4          
#>  [55] httr_1.4.3                  RColorBrewer_1.1-3         
#>  [57] ellipsis_0.3.2              farver_2.1.1               
#>  [59] pkgconfig_2.0.3             XML_3.99-0.10              
#>  [61] sass_0.4.1                  dbplyr_2.2.1               
#>  [63] utf8_1.2.2                  labeling_0.4.2             
#>  [65] ggplotify_0.1.0             tidyselect_1.1.2           
#>  [67] rlang_1.0.3                 munsell_0.5.0              
#>  [69] cellranger_1.1.0            tools_4.2.1                
#>  [71] cachem_1.0.6                cli_3.3.0                  
#>  [73] generics_0.1.3              broom_1.0.0                
#>  [75] evaluate_0.15               fastmap_1.1.0              
#>  [77] mzID_1.34.0                 yaml_2.3.5                 
#>  [79] ragg_1.2.2                  knitr_1.39                 
#>  [81] fs_1.5.2                    zip_2.2.0                  
#>  [83] ncdf4_1.19                  pbapply_1.5-0              
#>  [85] xml2_1.3.3                  compiler_4.2.1             
#>  [87] rstudioapi_0.13             plotly_4.10.0              
#>  [89] png_0.1-7                   affyio_1.66.0              
#>  [91] reprex_2.0.1                bslib_0.3.1                
#>  [93] stringi_1.7.6               highr_0.9                  
#>  [95] desc_1.4.1                  MSnbase_2.22.0             
#>  [97] lattice_0.20-45             ProtGenerics_1.28.0        
#>  [99] Matrix_1.4-1                ggsci_2.9                  
#> [101] vctrs_0.4.1                 pillar_1.7.0               
#> [103] lifecycle_1.0.1             BiocManager_1.30.18        
#> [105] jquerylib_0.1.4             MALDIquant_1.21            
#> [107] GlobalOptions_0.1.2         data.table_1.14.2          
#> [109] bitops_1.0-7                GenomicRanges_1.48.0       
#> [111] R6_2.5.1                    pcaMethods_1.88.0          
#> [113] affy_1.74.0                 IRanges_2.30.0             
#> [115] codetools_0.2-18            MASS_7.3-57                
#> [117] assertthat_0.2.1            SummarizedExperiment_1.26.1
#> [119] rprojroot_2.0.3             rjson_0.2.21               
#> [121] withr_2.5.0                 S4Vectors_0.34.0           
#> [123] GenomeInfoDbData_1.2.8      parallel_4.2.1             
#> [125] hms_1.1.1                   grid_4.2.1                 
#> [127] rmarkdown_2.14              MatrixGenerics_1.8.1       
#> [129] Biobase_2.56.0              lubridate_1.8.0