vignettes/base_function.Rmd
base_function.Rmdmass_dataset object support many R base functions.
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
)For example, you can get the information of your object.
dim(object)
#> variables samples
#> 1000 8
nrow(object)
#> variables
#> 1000
ncol(object)
#> samples
#> 8
dimnames(object)This means that object has 1000 variables and 8
samples.
apply(object, 2, mean)You can also get the sample ids and variables.
colnames(object)
#> [1] "Blank_3" "Blank_4" "QC_1" "QC_2" "PS4P1" "PS4P2" "PS4P3"
#> [8] "PS4P4"
head(rownames(object))
#> [1] "M136T55_2_POS" "M79T35_POS" "M307T548_POS" "M183T224_POS"
#> [5] "M349T47_POS" "M182T828_POS"Use [ to select variables and samples from object.
##only remain first 5 variables
object[1:5,]
#> --------------------
#> massdataset version: 1.0.12
#> --------------------
#> 1.expression_data:[ 5 x 8 data.frame]
#> 2.sample_info:[ 8 x 4 data.frame]
#> 3.variable_info:[ 5 x 3 data.frame]
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:21:11
#> subset ----------
#> Package Function.used Time
#> 1 massdataset [ 2022-08-07 19:21:12
##only remain first 5 samples
object[,1:5]
#> --------------------
#> massdataset version: 1.0.12
#> --------------------
#> 1.expression_data:[ 1000 x 5 data.frame]
#> 2.sample_info:[ 5 x 4 data.frame]
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:21:11
#> subset ----------
#> Package Function.used Time
#> 1 massdataset [ 2022-08-07 19:21:12
##only remain first 5 samples and 5 variables
object[1:5,1:5]
#> --------------------
#> massdataset version: 1.0.12
#> --------------------
#> 1.expression_data:[ 5 x 5 data.frame]
#> 2.sample_info:[ 5 x 4 data.frame]
#> 3.variable_info:[ 5 x 3 data.frame]
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:21:11
#> subset ----------
#> Package Function.used Time
#> 1 massdataset [ 2022-08-07 19:21:12If you know the variables or sample names you want to select, you can also use the samples ids or variables ids.
colnames(object)
#> [1] "Blank_3" "Blank_4" "QC_1" "QC_2" "PS4P1" "PS4P2" "PS4P3"
#> [8] "PS4P4"
object[,c("Blank_3", "Blank_4")]
#> --------------------
#> massdataset version: 1.0.12
#> --------------------
#> 1.expression_data:[ 1000 x 2 data.frame]
#> 2.sample_info:[ 2 x 4 data.frame]
#> 3.variable_info:[ 1000 x 3 data.frame]
#> 4.sample_info_note:[ 4 x 2 data.frame]
#> 5.variable_info_note:[ 3 x 2 data.frame]
#> 6.ms2_data:[ 0 variables x 0 MS2 spectra]
#> --------------------
#> Processing information (extract_process_info())
#> 2 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:21:11
#> subset ----------
#> Package Function.used Time
#> 1 massdataset [ 2022-08-07 19:21:12
###log
object2 =
log(object + 1, 10)
unlist(object[1,,drop = TRUE])
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2 PS4P3 PS4P4
#> NA NA 1857925 1037764 1494436 3496912 1959179 1005419
unlist(object2[1,,drop = TRUE])
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2 PS4P3 PS4P4
#> NA NA 6.269028 6.016099 6.174478 6.543685 6.292074 6.002347
###scale
object2 =
scale(object, center = TRUE, scale = TRUE)
unlist(object[1,,drop = TRUE])
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2 PS4P3 PS4P4
#> NA NA 1857925 1037764 1494436 3496912 1959179 1005419
unlist(object2[1,,drop = TRUE])
#> Blank_3 Blank_4 QC_1 QC_2 PS4P1 PS4P2
#> NA NA 0.05372526 -0.83970979 -0.34223794 1.83914160
#> PS4P3 PS4P4
#> 0.16402547 -0.87494460
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