mass_data
class objectvignettes/process_info.Rmd
process_info.RmdIn mass_dataset class object, it contains all the
processing information in it. We can trace the analysis and parameters
so we can do the reproducible analysis.
library(massdataset)
data("expression_data")
data("sample_info")
data("variable_info")
object =
create_mass_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info
)
library(tidyverse)
object =
object %>%
activate_mass_dataset(what = "expression_data") %>%
filter(!is.na(QC_1))
object =
object %>%
activate_mass_dataset(what = "expression_data") %>%
filter(!is.na(QC_2))
object =
object %>%
mutate_mean_intensity()
object =
object %>%
mutate_median_intensity() %>%
mutate_rsd()
process_info = extract_process_info(object)
process_info
#> $create_mass_dataset
#> --------------------
#> pacakge_name: massdataset
#> function_name: create_mass_dataset()
#> time: 2022-08-07 19:36:01
#> parameters:
#> no : no
#>
#> $filter
#> $filter[[1]]
#> --------------------
#> pacakge_name: massdataset
#> function_name: filter()
#> time: 2022-08-07 19:36:01
#> parameters:
#> parameter : `~!is.na(QC_1)`
#>
#> $filter[[2]]
#> --------------------
#> pacakge_name: massdataset
#> function_name: filter()
#> time: 2022-08-07 19:36:01
#> parameters:
#> parameter : `~!is.na(QC_2)`
#>
#>
#> $mutate_mean_intensity
#> --------------------
#> pacakge_name: massdataset
#> function_name: mutate_mean_intensity()
#> time: 2022-08-07 19:36:01
#> parameters:
#> according_to_samples : c("Blank_3", "Blank_4", "QC_1", "QC_2", "PS4P1", "PS4P2", "PS4P3", "PS4P4")
#>
#> $mutate_median_intensity
#> --------------------
#> pacakge_name: massdataset
#> function_name: mutate_median_intensity()
#> time: 2022-08-07 19:36:01
#> parameters:
#> according_to_samples : c("Blank_3", "Blank_4", "QC_1", "QC_2", "PS4P1", "PS4P2", "PS4P3", "PS4P4")
#>
#> $mutate_rsd
#> --------------------
#> pacakge_name: massdataset
#> function_name: mutate_rsd()
#> time: 2022-08-07 19:36:01
#> parameters:
#> according_to_samples : c("Blank_3", "Blank_4", "QC_1", "QC_2", "PS4P1", "PS4P2", "PS4P3", "PS4P4")The process_info contains all the steps which are ordered by time.
process_info$mutate_median_intensity
#> --------------------
#> pacakge_name: massdataset
#> function_name: mutate_median_intensity()
#> time: 2022-08-07 19:36:01
#> parameters:
#> according_to_samples : c("Blank_3", "Blank_4", "QC_1", "QC_2", "PS4P1", "PS4P2", "PS4P3", "PS4P4")
process_info$mutate_median_intensity@parameter
#> $according_to_samples
#> [1] "Blank_3" "Blank_4" "QC_1" "QC_2" "PS4P1" "PS4P2" "PS4P3"
#> [8] "PS4P4"
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] masstools_1.0.2 forcats_0.5.1.9000 stringr_1.4.0 dplyr_1.0.9
#> [5] purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.7
#> [9] ggplot2_3.3.6 tidyverse_1.3.1 magrittr_2.0.3 tinytools_0.9.1
#> [13] 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 pkgconfig_2.0.3
#> [59] XML_3.99-0.10 sass_0.4.1
#> [61] dbplyr_2.2.1 utf8_1.2.2
#> [63] ggplotify_0.1.0 tidyselect_1.1.2
#> [65] rlang_1.0.3 munsell_0.5.0
#> [67] cellranger_1.1.0 tools_4.2.1
#> [69] cachem_1.0.6 cli_3.3.0
#> [71] generics_0.1.3 broom_1.0.0
#> [73] evaluate_0.15 fastmap_1.1.0
#> [75] mzID_1.34.0 yaml_2.3.5
#> [77] ragg_1.2.2 knitr_1.39
#> [79] fs_1.5.2 zip_2.2.0
#> [81] ncdf4_1.19 pbapply_1.5-0
#> [83] xml2_1.3.3 compiler_4.2.1
#> [85] rstudioapi_0.13 plotly_4.10.0
#> [87] png_0.1-7 affyio_1.66.0
#> [89] reprex_2.0.1 bslib_0.3.1
#> [91] stringi_1.7.6 desc_1.4.1
#> [93] MSnbase_2.22.0 lattice_0.20-45
#> [95] ProtGenerics_1.28.0 Matrix_1.4-1
#> [97] ggsci_2.9 vctrs_0.4.1
#> [99] pillar_1.7.0 lifecycle_1.0.1
#> [101] BiocManager_1.30.18 jquerylib_0.1.4
#> [103] MALDIquant_1.21 GlobalOptions_0.1.2
#> [105] data.table_1.14.2 bitops_1.0-7
#> [107] GenomicRanges_1.48.0 R6_2.5.1
#> [109] pcaMethods_1.88.0 affy_1.74.0
#> [111] IRanges_2.30.0 codetools_0.2-18
#> [113] MASS_7.3-57 assertthat_0.2.1
#> [115] SummarizedExperiment_1.26.1 rprojroot_2.0.3
#> [117] rjson_0.2.21 withr_2.5.0
#> [119] S4Vectors_0.34.0 GenomeInfoDbData_1.2.8
#> [121] parallel_4.2.1 hms_1.1.1
#> [123] grid_4.2.1 rmarkdown_2.14
#> [125] MatrixGenerics_1.8.1 Biobase_2.56.0
#> [127] lubridate_1.8.0