mass_dataset and ggplot2
Xiaotao Shen
Created on 2021-12-04 and updated on 2026-03-02
Source:vignettes/ggplot_mass_dataset.Rmd
ggplot_mass_dataset.RmdIntroduction
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] "ggplot2::ggplot" "ggplot" "ggplot2::gg" "S7_object"
#> [5] "gg"The default y is value, here is the
intensity of all the features in the second sample.
{r,eval=TRUE,warning=FALSE, R.options="", message=FALSE, cache=FALSE, fig.alt="Distribution of scaled log-transformed feature intensities 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(){r,eval=TRUE,warning=FALSE, R.options="", message=FALSE, cache=FALSE, fig.alt="Boxplot and jitter plot of scaled log-transformed intensities in the second sample, colored by m/z."} plot
Variable wise
{r,eval=TRUE,warning=FALSE, R.options="", message=FALSE, cache=FALSE, fig.alt="Boxplot and jitter plot of one feature across sample classes."} 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()
{r,eval=TRUE,warning=FALSE, R.options="", message=FALSE, cache=FALSE, fig.alt="Boxplot and jitter plot of one feature across sample classes after log transformation and scaling."} 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().
{r,eval=TRUE,warning=FALSE, R.options="", message=FALSE, cache=FALSE, fig.alt="Scatter plot of retention time and m/z values from variable_info."} 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 4.5.2 (2025-10-31)
#> Platform: aarch64-apple-darwin20
#> Running under: macOS Tahoe 26.3
#>
#> Matrix products: default
#> BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.1
#>
#> locale:
#> [1] C.UTF-8/C.UTF-8/C.UTF-8/C/C.UTF-8/C.UTF-8
#>
#> time zone: Asia/Singapore
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] lubridate_1.9.4 forcats_1.0.0 stringr_1.5.1 purrr_1.1.0
#> [5] readr_2.1.5 tidyr_1.3.1 tibble_3.3.0 tidyverse_2.0.0
#> [9] magrittr_2.0.3 dplyr_1.1.4 ggplot2_4.0.2 massdataset_0.99.1
#>
#> loaded via a namespace (and not attached):
#> [1] tidyselect_1.2.1 farver_2.1.2
#> [3] S7_0.2.0 fastmap_1.2.0
#> [5] digest_0.6.37 timechange_0.3.0
#> [7] lifecycle_1.0.4 cluster_2.1.8.1
#> [9] compiler_4.5.2 rlang_1.1.6
#> [11] sass_0.4.10 tools_4.5.2
#> [13] yaml_2.3.10 knitr_1.50
#> [15] S4Arrays_1.8.1 htmlwidgets_1.6.4
#> [17] DelayedArray_0.34.1 RColorBrewer_1.1-3
#> [19] abind_1.4-8 withr_3.0.2
#> [21] BiocGenerics_0.54.0 desc_1.4.3
#> [23] grid_4.5.2 stats4_4.5.2
#> [25] colorspace_2.1-1 scales_1.4.0
#> [27] iterators_1.0.14 dichromat_2.0-0.1
#> [29] SummarizedExperiment_1.38.1 cli_3.6.5
#> [31] rmarkdown_2.29 crayon_1.5.3
#> [33] ragg_1.4.0 generics_0.1.4
#> [35] rstudioapi_0.17.1 httr_1.4.7
#> [37] tzdb_0.5.0 rjson_0.2.23
#> [39] cachem_1.1.0 parallel_4.5.2
#> [41] XVector_0.48.0 matrixStats_1.5.0
#> [43] vctrs_0.6.5 Matrix_1.7-4
#> [45] jsonlite_2.0.0 IRanges_2.42.0
#> [47] hms_1.1.3 GetoptLong_1.0.5
#> [49] S4Vectors_0.48.0 clue_0.3-66
#> [51] systemfonts_1.2.3 foreach_1.5.2
#> [53] jquerylib_0.1.4 glue_1.8.0
#> [55] pkgdown_2.1.3 codetools_0.2-20
#> [57] stringi_1.8.7 shape_1.4.6.1
#> [59] gtable_0.3.6 GenomeInfoDb_1.44.2
#> [61] GenomicRanges_1.60.0 UCSC.utils_1.4.0
#> [63] ComplexHeatmap_2.24.1 pillar_1.11.0
#> [65] htmltools_0.5.8.1 GenomeInfoDbData_1.2.14
#> [67] circlize_0.4.16 R6_2.6.1
#> [69] textshaping_1.0.1 doParallel_1.0.17
#> [71] evaluate_1.0.4 Biobase_2.68.0
#> [73] lattice_0.22-7 png_0.1-8
#> [75] openxlsx_4.2.8 bslib_0.9.0
#> [77] Rcpp_1.1.0 zip_2.3.3
#> [79] SparseArray_1.8.1 xfun_0.53
#> [81] fs_1.6.6 MatrixGenerics_1.20.0
#> [83] pkgconfig_2.0.3 GlobalOptions_0.1.2