vignettes/microbiomedataset.Rmd
microbiomedataset.RmdFor one mass_dataset class object, we can get the
summary information of it.
library(microbiomedataset)
expression_data <-
as.data.frame(matrix(
sample(1:100, 100, replace = TRUE),
nrow = 10,
ncol = 10
))
rownames(expression_data) <- paste0("OTU", 1:nrow(expression_data))
colnames(expression_data) <-
paste0("Sample", 1:ncol(expression_data))
expression_data
#> Sample1 Sample2 Sample3 Sample4 Sample5 Sample6 Sample7 Sample8 Sample9
#> OTU1 8 19 98 44 40 72 85 95 69
#> OTU2 41 81 65 25 22 72 72 24 62
#> OTU3 48 42 68 39 35 30 24 89 42
#> OTU4 22 87 29 30 12 78 13 27 65
#> OTU5 50 45 32 41 80 46 49 63 1
#> OTU6 39 31 87 24 75 53 27 14 6
#> OTU7 43 67 31 10 32 69 57 47 25
#> OTU8 99 26 41 81 41 50 7 14 98
#> OTU9 47 78 42 13 33 85 9 9 81
#> OTU10 43 64 66 11 20 65 48 56 82
#> Sample10
#> OTU1 60
#> OTU2 5
#> OTU3 53
#> OTU4 1
#> OTU5 42
#> OTU6 93
#> OTU7 11
#> OTU8 91
#> OTU9 100
#> OTU10 100
variable_info <-
as.data.frame(matrix(
sample(letters, 70, replace = TRUE),
nrow = nrow(expression_data),
ncol = 7
))
rownames(variable_info) <- rownames(expression_data)
colnames(variable_info) <-
c("Domain",
"Phylum",
"Class",
"Order",
"Family",
"Genus",
"Species")
variable_info$variable_id <-
rownames(expression_data)
sample_info <-
data.frame(sample_id = colnames(expression_data),
class = "Subject")
object <-
create_microbiome_dataset(
expression_data = expression_data,
sample_info = sample_info,
variable_info = variable_info
)
object
#> --------------------
#> microbiomedataset version: 0.99.1
#> --------------------
#> 1.expression_data:[ 10 x 10 data.frame]
#> 2.sample_info:[ 10 x 2 data.frame]
#> 3.variable_info:[ 10 x 8 data.frame]
#> 4.sample_info_note:[ 2 x 2 data.frame]
#> 5.variable_info_note:[ 8 x 2 data.frame]
#> --------------------
#> Processing information (extract_process_info())
#> create_microbiome_dataset ----------
#> Package Function.used Time
#> 1 microbiomedataset create_microbiome_dataset() 2022-07-09 21:49:52
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] dplyr_1.0.9 microbiomedataset_0.99.2
#>
#> loaded via a namespace (and not attached):
#> [1] readxl_1.4.0 circlize_0.4.15
#> [3] systemfonts_1.0.4 igraph_1.3.2
#> [5] plyr_1.8.7 lazyeval_0.2.2
#> [7] splines_4.2.1 BiocParallel_1.30.3
#> [9] GenomeInfoDb_1.32.2 ggplot2_3.3.6
#> [11] Rdisop_1.56.0 digest_0.6.29
#> [13] foreach_1.5.2 yulab.utils_0.0.5
#> [15] htmltools_0.5.2 massdataset_1.0.5
#> [17] fansi_1.0.3 magrittr_2.0.3
#> [19] memoise_2.0.1 cluster_2.1.3
#> [21] doParallel_1.0.17 tzdb_0.3.0
#> [23] openxlsx_4.2.5 limma_3.52.2
#> [25] ComplexHeatmap_2.12.0 Biostrings_2.64.0
#> [27] readr_2.1.2 matrixStats_0.62.0
#> [29] pkgdown_2.0.5 colorspace_2.0-3
#> [31] textshaping_0.3.6 xfun_0.31
#> [33] crayon_1.5.1 RCurl_1.98-1.7
#> [35] jsonlite_1.8.0 impute_1.70.0
#> [37] survival_3.3-1 iterators_1.0.14
#> [39] ape_5.6-2 glue_1.6.2
#> [41] gtable_0.3.0 zlibbioc_1.42.0
#> [43] XVector_0.36.0 GetoptLong_1.0.5
#> [45] DelayedArray_0.22.0 phyloseq_1.40.0
#> [47] Rhdf5lib_1.18.2 shape_1.4.6
#> [49] BiocGenerics_0.42.0 scales_1.2.0
#> [51] vsn_3.64.0 DBI_1.1.3
#> [53] Rcpp_1.0.8.3 mzR_2.30.0
#> [55] viridisLite_0.4.0 clue_0.3-61
#> [57] tidytree_0.3.9 gridGraphics_0.5-1
#> [59] preprocessCore_1.58.0 stats4_4.2.1
#> [61] MsCoreUtils_1.8.0 htmlwidgets_1.5.4
#> [63] httr_1.4.3 RColorBrewer_1.1-3
#> [65] ellipsis_0.3.2 pkgconfig_2.0.3
#> [67] XML_3.99-0.10 sass_0.4.1
#> [69] utf8_1.2.2 ggplotify_0.1.0
#> [71] tidyselect_1.1.2 rlang_1.0.3
#> [73] reshape2_1.4.4 munsell_0.5.0
#> [75] cellranger_1.1.0 tools_4.2.1
#> [77] cachem_1.0.6 cli_3.3.0
#> [79] generics_0.1.3 ade4_1.7-19
#> [81] evaluate_0.15 biomformat_1.24.0
#> [83] stringr_1.4.0 fastmap_1.1.0
#> [85] mzID_1.34.0 yaml_2.3.5
#> [87] ragg_1.2.2 knitr_1.39
#> [89] fs_1.5.2 zip_2.2.0
#> [91] purrr_0.3.4 ncdf4_1.19
#> [93] pbapply_1.5-0 nlme_3.1-158
#> [95] compiler_4.2.1 rstudioapi_0.13
#> [97] plotly_4.10.0 png_0.1-7
#> [99] affyio_1.66.0 tibble_3.1.7
#> [101] bslib_0.3.1 stringi_1.7.6
#> [103] desc_1.4.1 MSnbase_2.22.0
#> [105] lattice_0.20-45 ProtGenerics_1.28.0
#> [107] Matrix_1.4-1 permute_0.9-7
#> [109] vegan_2.6-2 multtest_2.52.0
#> [111] ggsci_2.9 vctrs_0.4.1
#> [113] rhdf5filters_1.8.0 pillar_1.7.0
#> [115] lifecycle_1.0.1 BiocManager_1.30.18
#> [117] jquerylib_0.1.4 MALDIquant_1.21
#> [119] GlobalOptions_0.1.2 data.table_1.14.2
#> [121] bitops_1.0-7 GenomicRanges_1.48.0
#> [123] R6_2.5.1 pcaMethods_1.88.0
#> [125] affy_1.74.0 IRanges_2.30.0
#> [127] codetools_0.2-18 MASS_7.3-57
#> [129] assertthat_0.2.1 rhdf5_2.40.0
#> [131] SummarizedExperiment_1.26.1 rprojroot_2.0.3
#> [133] rjson_0.2.21 S4Vectors_0.34.0
#> [135] GenomeInfoDbData_1.2.8 mgcv_1.8-40
#> [137] parallel_4.2.1 hms_1.1.1
#> [139] grid_4.2.1 tidyr_1.2.0
#> [141] rmarkdown_2.14 MatrixGenerics_1.8.1
#> [143] masstools_0.99.13 Biobase_2.56.0