Loading included data
Let’s load the demo data.
library(microbiomedataset)
library(tidyverse)
data("global_patterns")
global_patterns
#> --------------------
#> microbiomedataset version: 0.99.1
#> --------------------
#> 1.expression_data:[ 19216 x 26 data.frame]
#> 2.sample_info:[ 26 x 8 data.frame]
#> 3.variable_info:[ 19216 x 8 data.frame]
#> 4.sample_info_note:[ 8 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() 2022-07-10 10:56:13
Accessors
dim(global_patterns)
#> variables samples
#> 19216 26
nrow(global_patterns)
#> variables
#> 19216
ncol(global_patterns)
#> samples
#> 26
colnames(global_patterns)
#> [1] "CL3" "CC1" "SV1" "M31Fcsw" "M11Fcsw" "M31Plmr"
#> [7] "M11Plmr" "F21Plmr" "M31Tong" "M11Tong" "LMEpi24M" "SLEpi20M"
#> [13] "AQC1cm" "AQC4cm" "AQC7cm" "NP2" "NP3" "NP5"
#> [19] "TRRsed1" "TRRsed2" "TRRsed3" "TS28" "TS29" "Even1"
#> [25] "Even2" "Even3"
head(rownames(global_patterns))
#> [1] "549322" "522457" "951" "244423" "586076" "246140"
extract_sample_info(global_patterns) %>%
colnames()
#> [1] "sample_id" "Primer"
#> [3] "Final_Barcode" "Barcode_truncated_plus_T"
#> [5] "Barcode_full_length" "SampleType"
#> [7] "Description" "class"
extract_variable_info(global_patterns) %>%
colnames()
#> [1] "variable_id" "Kingdom" "Phylum" "Class" "Order"
#> [6] "Family" "Genus" "Species"
extract_expression_data(global_patterns) %>%
head()
#> CL3 CC1 SV1 M31Fcsw M11Fcsw M31Plmr M11Plmr F21Plmr M31Tong M11Tong
#> 549322 0 0 0 0 0 0 0 0 0 0
#> 522457 0 0 0 0 0 0 0 0 0 0
#> 951 0 0 0 0 0 0 1 0 0 0
#> 244423 0 0 0 0 0 0 0 0 0 0
#> 586076 0 0 0 0 0 0 0 0 0 0
#> 246140 0 0 0 0 0 0 0 0 0 0
#> LMEpi24M SLEpi20M AQC1cm AQC4cm AQC7cm NP2 NP3 NP5 TRRsed1 TRRsed2
#> 549322 0 1 27 100 130 1 0 0 0 0
#> 522457 0 0 0 2 6 0 0 0 0 0
#> 951 0 0 0 0 0 0 0 0 0 0
#> 244423 0 0 0 22 29 0 0 0 0 0
#> 586076 0 0 0 2 1 0 0 0 0 0
#> 246140 0 0 0 1 3 0 0 0 0 0
#> TRRsed3 TS28 TS29 Even1 Even2 Even3
#> 549322 0 0 0 0 0 0
#> 522457 0 0 0 0 0 0
#> 951 0 0 0 0 0 0
#> 244423 0 0 0 0 0 0
#> 586076 0 0 0 0 0 0
#> 246140 0 0 0 0 0 0
extract_sample_info(global_patterns) %>%
head()
#> sample_id Primer Final_Barcode Barcode_truncated_plus_T Barcode_full_length
#> 1 CL3 ILBC_01 AACGCA TGCGTT CTAGCGTGCGT
#> 2 CC1 ILBC_02 AACTCG CGAGTT CATCGACGAGT
#> 3 SV1 ILBC_03 AACTGT ACAGTT GTACGCACAGT
#> 4 M31Fcsw ILBC_04 AAGAGA TCTCTT TCGACATCTCT
#> 5 M11Fcsw ILBC_05 AAGCTG CAGCTT CGACTGCAGCT
#> 6 M31Plmr ILBC_07 AATCGT ACGATT CGAGTCACGAT
#> SampleType Description class
#> 1 Soil Calhoun South Carolina Pine soil, pH 4.9 Subject
#> 2 Soil Cedar Creek Minnesota, grassland, pH 6.1 Subject
#> 3 Soil Sevilleta new Mexico, desert scrub, pH 8.3 Subject
#> 4 Feces M3, Day 1, fecal swab, whole body study Subject
#> 5 Feces M1, Day 1, fecal swab, whole body study Subject
#> 6 Skin M3, Day 1, right palm, whole body study Subject
extract_variable_info(global_patterns) %>%
head()
#> variable_id Kingdom Phylum Class Order Family
#> 1 549322 Archaea Crenarchaeota Thermoprotei <NA> <NA>
#> 2 522457 Archaea Crenarchaeota Thermoprotei <NA> <NA>
#> 3 951 Archaea Crenarchaeota Thermoprotei Sulfolobales Sulfolobaceae
#> 4 244423 Archaea Crenarchaeota Sd-NA <NA> <NA>
#> 5 586076 Archaea Crenarchaeota Sd-NA <NA> <NA>
#> 6 246140 Archaea Crenarchaeota Sd-NA <NA> <NA>
#> Genus Species
#> 1 <NA> <NA>
#> 2 <NA> <NA>
#> 3 Sulfolobus Sulfolobusacidocaldarius
#> 4 <NA> <NA>
#> 5 <NA> <NA>
#> 6 <NA> <NA>
Preprocessing
The microbiomedataset package also includes functions for filtering, subsetting, and merging abundance data.
In the following example, the global_patterns data is first transformed to relative abundance, creating the new global_patterns2 object, which is then filtered such that only OTUs with a mean greater than 10^-5 are kept.
global_patterns2 <-
global_patterns %>%
transform2relative_intensity() %>%
mutate2variable(what = "mean_intensity") %>%
activate_microbiome_dataset(what = "variable_info") %>%
filter(mean_intensity > 10 ^ (-5))
This results in a highly-subsetted object, global_patterns2, containing just 4624 of the original ~19216 OTUs.
Next, only remain the variables that phylum Chlamydiae.
global_patterns_chl <-
global_patterns %>%
activate_microbiome_dataset(what = "variable_info") %>%
dplyr::filter(Phylum == "Chlamydiae")
Next, only remain the samples with total intensity > 20.
global_patterns_chl <-
global_patterns_chl %>%
mutate2sample(what = "sum_intensity") %>%
activate_microbiome_dataset(what = "sample_info") %>%
filter(sum_intensity > 20)
Session information
sessionInfo()
#> R version 4.3.0 (2023-04-21)
#> Platform: x86_64-apple-darwin20 (64-bit)
#> Running under: macOS Ventura 13.5.2
#>
#> 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
#> [3] stringr_1.5.0 dplyr_1.1.2
#> [5] purrr_1.0.1 readr_2.1.4
#> [7] tidyr_1.3.0 tibble_3.2.1
#> [9] ggplot2_3.4.2 tidyverse_2.0.0
#> [11] microbiomedataset_0.99.10
#>
#> loaded via a namespace (and not attached):
#> [1] RColorBrewer_1.1-3 rstudioapi_0.14
#> [3] jsonlite_1.8.5 shape_1.4.6
#> [5] magrittr_2.0.3 farver_2.1.1
#> [7] MALDIquant_1.22.1 rmarkdown_2.22
#> [9] GlobalOptions_0.1.2 zlibbioc_1.46.0
#> [11] vctrs_0.6.2 multtest_2.56.0
#> [13] RCurl_1.98-1.12 blogdown_1.18.1
#> [15] htmltools_0.5.5 S4Arrays_1.0.4
#> [17] Rhdf5lib_1.22.0 rhdf5_2.44.0
#> [19] gridGraphics_0.5-1 mzID_1.38.0
#> [21] sass_0.4.6 bslib_0.5.0
#> [23] htmlwidgets_1.6.2 plyr_1.8.8
#> [25] zoo_1.8-12 plotly_4.10.2
#> [27] impute_1.74.1 cachem_1.0.8
#> [29] igraph_1.4.3 lifecycle_1.0.3
#> [31] iterators_1.0.14 pkgconfig_2.0.3
#> [33] Matrix_1.5-4 R6_2.5.1
#> [35] fastmap_1.1.1 GenomeInfoDbData_1.2.10
#> [37] MatrixGenerics_1.12.2 clue_0.3-64
#> [39] digest_0.6.31 pcaMethods_1.92.0
#> [41] colorspace_2.1-0 masstools_1.0.10
#> [43] S4Vectors_0.38.1 rprojroot_2.0.3
#> [45] GenomicRanges_1.52.0 vegan_2.6-4
#> [47] timechange_0.2.0 fansi_1.0.4
#> [49] httr_1.4.6 mgcv_1.8-42
#> [51] polyclip_1.10-4 compiler_4.3.0
#> [53] here_1.0.1 remotes_2.4.2
#> [55] withr_2.5.0 doParallel_1.0.17
#> [57] BiocParallel_1.34.2 viridis_0.6.3
#> [59] ggforce_0.4.1 MASS_7.3-58.4
#> [61] DelayedArray_0.26.3 biomformat_1.28.0
#> [63] rjson_0.2.21 permute_0.9-7
#> [65] ggsci_3.0.0 mzR_2.34.0
#> [67] tools_4.3.0 ape_5.7-1
#> [69] zip_2.3.0 glue_1.6.2
#> [71] rhdf5filters_1.12.1 nlme_3.1-162
#> [73] grid_4.3.0 cluster_2.1.4
#> [75] reshape2_1.4.4 ade4_1.7-22
#> [77] generics_0.1.3 gtable_0.3.3
#> [79] tzdb_0.4.0 preprocessCore_1.62.1
#> [81] data.table_1.14.8 hms_1.1.3
#> [83] tidygraph_1.2.3 utf8_1.2.3
#> [85] XVector_0.40.0 BiocGenerics_0.46.0
#> [87] ggrepel_0.9.3 foreach_1.5.2
#> [89] pillar_1.9.0 yulab.utils_0.0.6
#> [91] limma_3.56.2 splines_4.3.0
#> [93] circlize_0.4.15 tweenr_2.0.2
#> [95] lattice_0.21-8 survival_3.5-5
#> [97] tidyselect_1.2.0 ComplexHeatmap_2.16.0
#> [99] Biostrings_2.68.1 pbapply_1.7-0
#> [101] knitr_1.43 gridExtra_2.3
#> [103] bookdown_0.34 phyloseq_1.44.0
#> [105] IRanges_2.34.0 ProtGenerics_1.32.0
#> [107] SummarizedExperiment_1.30.2 Rdisop_1.60.0
#> [109] stats4_4.3.0 xfun_0.39
#> [111] graphlayouts_1.0.0 Biobase_2.60.0
#> [113] MSnbase_2.26.0 matrixStats_1.0.0
#> [115] stringi_1.7.12 lazyeval_0.2.2
#> [117] yaml_2.3.7 evaluate_0.21
#> [119] codetools_0.2-19 ggraph_2.1.0
#> [121] MsCoreUtils_1.12.0 BiocManager_1.30.21
#> [123] ggplotify_0.1.0 cli_3.6.1
#> [125] affyio_1.70.0 munsell_0.5.0
#> [127] jquerylib_0.1.4 Rcpp_1.0.10
#> [129] GenomeInfoDb_1.36.0 png_0.1-8
#> [131] XML_3.99-0.14 parallel_4.3.0
#> [133] bitops_1.0-7 tidytree_0.4.2
#> [135] viridisLite_0.4.2 scales_1.2.1
#> [137] affy_1.78.0 openxlsx_4.2.5.2
#> [139] ncdf4_1.21 crayon_1.5.2
#> [141] GetoptLong_1.0.5 rlang_1.1.1
#> [143] massdataset_1.0.25 vsn_3.68.0