Data preparation
In 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
process_info = extract_process_info(object)
process_info
#> $create_mass_dataset
#> --------------------
#> pacakge_name: massdataset
#> function_name: create_mass_dataset()
#> time: 2023-09-03 10:46:22.299476
#> parameters:
#> no : no
#>
#> $filter
#> $filter[[1]]
#> --------------------
#> pacakge_name: massdataset
#> function_name: filter()
#> time: 2023-09-03 10:46:22.712919
#> parameters:
#> parameter : `~!is.na(QC_1)`
#>
#> $filter[[2]]
#> --------------------
#> pacakge_name: massdataset
#> function_name: filter()
#> time: 2023-09-03 10:46:22.716696
#> parameters:
#> parameter : `~!is.na(QC_2)`
#>
#>
#> $mutate_mean_intensity
#> --------------------
#> pacakge_name: massdataset
#> function_name: mutate_mean_intensity()
#> time: 2023-09-03 10:46:22.724256
#> 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: 2023-09-03 10:46:22.747706
#> parameters:
#> according_to_samples : c("Blank_3", "Blank_4", "QC_1", "QC_2", "PS4P1", "PS4P2", "PS4P3", "PS4P4")
#>
#> $mutate_rsd
#> --------------------
#> pacakge_name: massdataset
#> function_name: mutate_rsd()
#> time: 2023-09-03 10:46:22.760049
#> parameters:
#> according_to_samples : c("Blank_3", "Blank_4", "QC_1", "QC_2", "PS4P1", "PS4P2", "PS4P3", "PS4P4")
The process_info contains all the steps which are ordered by time.
process_info$mutate_median_intensity
#> --------------------
#> pacakge_name: massdataset
#> function_name: mutate_median_intensity()
#> time: 2023-09-03 10:46:22.747706
#> 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"
Output html processing information
We can output the process_info into a html format file, so we can know what processing steps have been made to this object and the accurate parameters.
Then we can use report_parameters() to output this into a html file.
report_parameters(object = object,
path = "data_cleaning")
A html file named as parameter_report.html will be generated and saved in data_cleaning folder.
Session information
sessionInfo()
#> R version 4.3.0 (2023-04-21)
#> Platform: x86_64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.5.1
#>
#> Matrix products: default
#> BLAS: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRblas.0.dylib
#> LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
#>
#> locale:
#> [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#>
#> time zone: America/Los_Angeles
#> tzcode source: internal
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] lubridate_1.9.2 forcats_1.0.0 stringr_1.5.0 purrr_1.0.1
#> [5] readr_2.1.4 tidyr_1.3.0 tibble_3.2.1 tidyverse_2.0.0
#> [9] ggplot2_3.4.2 dplyr_1.1.2 magrittr_2.0.3 masstools_1.0.10
#> [13] massdataset_1.0.25
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-7 pbapply_1.7-0
#> [3] remotes_2.4.2 rlang_1.1.1
#> [5] clue_0.3-64 GetoptLong_1.0.5
#> [7] matrixStats_1.0.0 compiler_4.3.0
#> [9] png_0.1-8 vctrs_0.6.2
#> [11] ProtGenerics_1.32.0 pkgconfig_2.0.3
#> [13] shape_1.4.6 crayon_1.5.2
#> [15] fastmap_1.1.1 XVector_0.40.0
#> [17] utf8_1.2.3 rmarkdown_2.22
#> [19] tzdb_0.4.0 preprocessCore_1.62.1
#> [21] xfun_0.39 zlibbioc_1.46.0
#> [23] cachem_1.0.8 GenomeInfoDb_1.36.0
#> [25] jsonlite_1.8.5 DelayedArray_0.26.3
#> [27] BiocParallel_1.34.2 parallel_4.3.0
#> [29] cluster_2.1.4 R6_2.5.1
#> [31] stringi_1.7.12 bslib_0.5.0
#> [33] RColorBrewer_1.1-3 limma_3.56.2
#> [35] GenomicRanges_1.52.0 jquerylib_0.1.4
#> [37] Rcpp_1.0.10 bookdown_0.34
#> [39] SummarizedExperiment_1.30.2 iterators_1.0.14
#> [41] knitr_1.43 IRanges_2.34.0
#> [43] timechange_0.2.0 Matrix_1.5-4
#> [45] tidyselect_1.2.0 rstudioapi_0.14
#> [47] yaml_2.3.7 doParallel_1.0.17
#> [49] codetools_0.2-19 affy_1.78.0
#> [51] blogdown_1.18.1 lattice_0.21-8
#> [53] plyr_1.8.8 withr_2.5.0
#> [55] Biobase_2.60.0 evaluate_0.21
#> [57] zip_2.3.0 circlize_0.4.15
#> [59] pillar_1.9.0 affyio_1.70.0
#> [61] BiocManager_1.30.21 MatrixGenerics_1.12.2
#> [63] foreach_1.5.2 stats4_4.3.0
#> [65] MSnbase_2.26.0 MALDIquant_1.22.1
#> [67] ncdf4_1.21 generics_0.1.3
#> [69] rprojroot_2.0.3 RCurl_1.98-1.12
#> [71] hms_1.1.3 S4Vectors_0.38.1
#> [73] munsell_0.5.0 scales_1.2.1
#> [75] glue_1.6.2 tools_4.3.0
#> [77] mzID_1.38.0 vsn_3.68.0
#> [79] mzR_2.34.0 openxlsx_4.2.5.2
#> [81] XML_3.99-0.14 grid_4.3.0
#> [83] impute_1.74.1 MsCoreUtils_1.12.0
#> [85] colorspace_2.1-0 GenomeInfoDbData_1.2.10
#> [87] cli_3.6.1 fansi_1.0.4
#> [89] S4Arrays_1.0.4 ComplexHeatmap_2.16.0
#> [91] pcaMethods_1.92.0 gtable_0.3.3
#> [93] sass_0.4.6 digest_0.6.31
#> [95] BiocGenerics_0.46.0 rjson_0.2.21
#> [97] htmltools_0.5.5 lifecycle_1.0.3
#> [99] here_1.0.1 GlobalOptions_0.1.2
#> [101] Rdisop_1.60.0 MASS_7.3-58.4