mass_dataset and ggplot2
Xiaotao Shen (https://www.shenxt.info/)
Created on 2021-12-04 and updated on 2023-10-01
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] "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
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#> [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
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