Summary information
Xiaotao Shen (https://www.shenxt.info/)
Created on 2021-12-04 and updated on 2023-10-01
Source:vignettes/summary_info.Rmd
summary_info.RmdFor one mass_dataset class object, we can get the
summary information of it.
Data preparation
library(massdataset)
library(tidyverse)
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
)Summary information
Just type this object in the R session.
object
#> --------------------
#> massdataset version: 1.0.12
#> --------------------
#> 1.expression_data:[ 1000 x 8 data.frame]
#> 2.sample_info:[ 8 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())
#> 1 processings in total
#> create_mass_dataset ----------
#> Package Function.used Time
#> 1 massdataset create_mass_dataset() 2022-08-07 19:36:22We can just basic information of the object.
Use functions to get the summary information
##dim of object
dim(object)
#> variables samples
#> 1000 8
##row number
nrow(object)
#> variables
#> 1000
##column number
ncol(object)
#> samples
#> 8
##sample number
get_sample_number(object)
#> [1] 8
#variable number
get_variable_number(object)
#> [1] 1000
##sample id
colnames(object)
#> [1] "Blank_3" "Blank_4" "QC_1" "QC_2" "PS4P1" "PS4P2" "PS4P3"
#> [8] "PS4P4"
##variable id
rownames(object) %>%
head()
#> [1] "M136T55_2_POS" "M79T35_POS" "M307T548_POS" "M183T224_POS"
#> [5] "M349T47_POS" "M182T828_POS"
##sample id
get_sample_id(object)
#> [1] "Blank_3" "Blank_4" "QC_1" "QC_2" "PS4P1" "PS4P2" "PS4P3"
#> [8] "PS4P4"
##variable id
get_variable_id(object) %>%
head()
#> [1] "M136T55_2_POS" "M79T35_POS" "M307T548_POS" "M183T224_POS"
#> [5] "M349T47_POS" "M182T828_POS"Explore
###show mz rt plot
object %>%
show_mz_rt_plot()
###should log
object %>%
`+`(1) %>%
log(10) %>%
show_mz_rt_plot()
###use hex
object %>%
show_mz_rt_plot(hex = TRUE)
Missing values
##show missing values plot
show_missing_values(object)
show_missing_values(object[1:10,], cell_color = "white")
###only show subject samples
object %>%
activate_mass_dataset(what = "sample_info") %>%
filter(class == "Subject") %>%
show_missing_values()
###only show QC samples
object %>%
activate_mass_dataset(what = "expression_data") %>%
dplyr::select(contains("QC")) %>%
show_missing_values()
###only show features with mz < 100
object %>%
activate_mass_dataset(what = "variable_info") %>%
dplyr::filter(mz < 100) %>%
show_missing_values(cell_color = "white",
show_row_names = TRUE,
row_names_side = "left")
##show missing values plot
show_sample_missing_values(object)
show_sample_missing_values(object, color_by = "class")
show_sample_missing_values(object, color_by = "class", order_by = "na")
show_sample_missing_values(object, color_by = "class", order_by = "na",
desc = TRUE)
##show missing values plot
show_variable_missing_values(object)
show_variable_missing_values(object, color_by = "mz")
show_variable_missing_values(object, color_by = "rt") +
scale_color_gradient(low = "skyblue", high = "red") 
show_variable_missing_values(object, color_by = "mz",
order_by = "na")
show_variable_missing_values(object, color_by = "mz",
order_by = "na",
desc = TRUE, percentage = TRUE)
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] masstools_1.0.2 plyr_1.8.7 forcats_0.5.1.9000 stringr_1.4.0
#> [5] dplyr_1.0.9 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0
#> [9] tibble_3.1.7 ggplot2_3.3.6 tidyverse_1.3.1 magrittr_2.0.3
#> [13] tinytools_0.9.1 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] lazyeval_0.2.2 BiocParallel_1.30.3
#> [7] GenomeInfoDb_1.32.2 Rdisop_1.56.0
#> [9] digest_0.6.29 foreach_1.5.2
#> [11] yulab.utils_0.0.5 htmltools_0.5.2
#> [13] magick_2.7.3 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 hexbin_1.28.2
#> [31] crayon_1.5.1 RCurl_1.98-1.7
#> [33] jsonlite_1.8.0 impute_1.70.0
#> [35] iterators_1.0.14 glue_1.6.2
#> [37] gtable_0.3.0 zlibbioc_1.42.0
#> [39] XVector_0.36.0 GetoptLong_1.0.5
#> [41] DelayedArray_0.22.0 shape_1.4.6
#> [43] BiocGenerics_0.42.0 scales_1.2.0
#> [45] vsn_3.64.0 DBI_1.1.3
#> [47] Rcpp_1.0.8.3 mzR_2.30.0
#> [49] viridisLite_0.4.0 clue_0.3-61
#> [51] gridGraphics_0.5-1 preprocessCore_1.58.0
#> [53] stats4_4.2.1 MsCoreUtils_1.8.0
#> [55] htmlwidgets_1.5.4 httr_1.4.3
#> [57] RColorBrewer_1.1-3 ellipsis_0.3.2
#> [59] farver_2.1.1 pkgconfig_2.0.3
#> [61] XML_3.99-0.10 sass_0.4.1
#> [63] dbplyr_2.2.1 utf8_1.2.2
#> [65] labeling_0.4.2 ggplotify_0.1.0
#> [67] tidyselect_1.1.2 rlang_1.0.3
#> [69] munsell_0.5.0 cellranger_1.1.0
#> [71] tools_4.2.1 cachem_1.0.6
#> [73] cli_3.3.0 generics_0.1.3
#> [75] broom_1.0.0 evaluate_0.15
#> [77] fastmap_1.1.0 mzID_1.34.0
#> [79] yaml_2.3.5 ragg_1.2.2
#> [81] knitr_1.39 fs_1.5.2
#> [83] zip_2.2.0 ncdf4_1.19
#> [85] pbapply_1.5-0 xml2_1.3.3
#> [87] compiler_4.2.1 rstudioapi_0.13
#> [89] plotly_4.10.0 png_0.1-7
#> [91] affyio_1.66.0 reprex_2.0.1
#> [93] bslib_0.3.1 stringi_1.7.6
#> [95] highr_0.9 desc_1.4.1
#> [97] MSnbase_2.22.0 lattice_0.20-45
#> [99] ProtGenerics_1.28.0 Matrix_1.4-1
#> [101] ggsci_2.9 vctrs_0.4.1
#> [103] pillar_1.7.0 lifecycle_1.0.1
#> [105] BiocManager_1.30.18 jquerylib_0.1.4
#> [107] MALDIquant_1.21 GlobalOptions_0.1.2
#> [109] data.table_1.14.2 bitops_1.0-7
#> [111] GenomicRanges_1.48.0 R6_2.5.1
#> [113] pcaMethods_1.88.0 affy_1.74.0
#> [115] IRanges_2.30.0 codetools_0.2-18
#> [117] MASS_7.3-57 assertthat_0.2.1
#> [119] SummarizedExperiment_1.26.1 rprojroot_2.0.3
#> [121] rjson_0.2.21 withr_2.5.0
#> [123] S4Vectors_0.34.0 GenomeInfoDbData_1.2.8
#> [125] parallel_4.2.1 hms_1.1.1
#> [127] grid_4.2.1 rmarkdown_2.14
#> [129] MatrixGenerics_1.8.1 Cairo_1.6-0
#> [131] Biobase_2.56.0 lubridate_1.8.0