dim_red
UMAP_samples
Analyzer info
## Datasets:
## - Dataset: humet_data with 833 samples and 2672 metabolites
## - Dataset: humet_subset_data with 833 samples and 312 metabolites
## Results:
## 1.UMAP_samples :
## - dimensionality reduction method: UMAP for samples data of humet_subset_data
## 2.tSNE_samples :
## - dimensionality reduction method: tSNE for samples data of humet_subset_data object %>%
(which_data = 'humet_subset_data') %>%
(which_data = 'humet_subset_data',type = 'row_data',mutations = 'Factor.Value.Challenge.Day.=factor, Factor.Value.Challenge.Day.=Factor.Value.Challenge.Day.') %>%
(which_data = 'humet_subset_data',type = 'UMAP',cols_for_meta = c('Factor.Value.Challenge.', 'Factor.Value.Challenge.Day.'))
(which_data = 'humet_subset_data',type = 'row_data',mutations = 'Factor.Value.Challenge.Day.=factor, Factor.Value.Challenge.Day.=Factor.Value.Challenge.Day.') %>%
(which_data = 'humet_subset_data',type = 'UMAP',cols_for_meta = c('Factor.Value.Challenge.', 'Factor.Value.Challenge.Day.'))
Plots
umap_samples
dimensionality reduction method: UMAP for samples data of humet_subset_data
tSNE_samples
Analyzer info
## Datasets:
## - Dataset: humet_data with 833 samples and 2672 metabolites
## - Dataset: humet_subset_data with 833 samples and 312 metabolites
## Results:
## 1.UMAP_samples :
## - dimensionality reduction method: UMAP for samples data of humet_subset_data
## 2.tSNE_samples :
## - dimensionality reduction method: tSNE for samples data of humet_subset_data object %>%
(which_data = 'humet_subset_data',type = 'tSNE',cols_for_meta = c('Factor.Value.Challenge.', 'Factor.Value.Challenge.Day.'))
Plots
tsne_samples
dimensionality reduction method: tSNE for samples data of humet_subset_data
Session info
## R version 4.3.2 (2023-10-31 ucrt)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19045)
##
## Matrix products: default
##
##
## locale:
## [1] LC_COLLATE=English_India.utf8 LC_CTYPE=English_India.utf8 LC_MONETARY=English_India.utf8
## [4] LC_NUMERIC=C LC_TIME=English_India.utf8
##
## time zone: Europe/Berlin
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] MeTime_1.0.0 lmerTest_3.2-0 lme4_1.1-38 Matrix_1.6-5
## [5] MultiAssayExperiment_1.28.0 maplet_1.1.2 SummarizedExperiment_1.32.0 Biobase_2.62.0
## [9] GenomicRanges_1.54.1 GenomeInfoDb_1.38.2 IRanges_2.36.0 S4Vectors_0.40.2
## [13] BiocGenerics_0.48.1 MatrixGenerics_1.14.0 matrixStats_1.5.0 magrittr_2.0.3
## [17] lubridate_1.9.4 forcats_1.0.1 stringr_1.6.0 dplyr_1.1.4
## [21] purrr_1.1.0 readr_2.1.6 tidyr_1.3.2 tibble_3.3.1
## [25] ggplot2_4.0.1 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.6 bitops_1.0-7 devtools_2.4.5 httr_1.4.7
## [5] RColorBrewer_1.1-3 doParallel_1.0.17 Rgraphviz_2.46.0 numDeriv_2016.8-1.1
## [9] dynamicTreeCut_1.63-1 profvis_0.3.8 tools_4.3.2 doRNG_1.8.6.2
## [13] backports_1.5.0 R6_2.6.1 DT_0.34.0 lazyeval_0.2.2
## [17] uwot_0.1.16 mgcv_1.9-1 urlchecker_1.0.1 withr_3.0.2
## [21] gridExtra_2.3 tictoc_1.2.1 preprocessCore_1.64.0 fdrtool_1.2.18
## [25] textshaping_1.0.4 WGCNA_1.73 cli_3.6.5 logging_0.10-111
## [29] network_1.19.0 labeling_0.4.3 sass_0.4.10 KEGGgraph_1.62.0
## [33] S7_0.2.1 randomForest_4.7-1.2 systemfonts_1.3.1 bsplus_0.1.5
## [37] GeneNet_1.2.17 foreign_0.8-86 longitudinal_1.1.13 sessioninfo_1.2.2
## [41] limma_3.58.1 itertools_0.1-3 readxl_1.4.5 rstudioapi_0.18.0
## [45] impute_1.76.0 RSQLite_2.3.4 FNN_1.1.4.1 visNetwork_2.1.4
## [49] generics_0.1.4 shape_1.4.6.1 crosstalk_1.2.2 car_3.1-3
## [53] zip_2.3.0 GO.db_3.18.0 abind_1.4-8 pathview_1.47.1
## [57] lifecycle_1.0.5 yaml_2.3.12 carData_3.0-5 SparseArray_1.2.4
## [61] Rtsne_0.17 grid_4.3.2 blob_1.3.0 promises_1.2.1
## [65] crayon_1.5.3 miniUI_0.1.1.1 lattice_0.22-5 KEGGREST_1.42.0
## [69] sna_2.8 pillar_1.11.1 knitr_1.51 boot_1.3-28.1
## [73] corpcor_1.6.10 codetools_0.2-19 glue_1.8.0 data.table_1.18.2.1
## [77] remotes_2.4.2.1 vctrs_0.6.5 png_0.1-8 rmdformats_1.0.4
## [81] Rdpack_2.6.5 cellranger_1.1.0 gtable_0.3.6 cachem_1.0.8
## [85] xfun_0.56 openxlsx_4.2.5.2 rbibutils_2.4.1 S4Arrays_1.2.1
## [89] mime_0.12 coda_0.19-4.1 reformulas_0.4.3.1 rsconnect_1.6.1
## [93] survival_3.8-3 ggmosaic_0.4.0 pheatmap_1.0.12 iterators_1.0.14
## [97] tinytex_0.60 statmod_1.5.0 ellipsis_0.3.2 nlme_3.1-164
## [101] usethis_2.2.2 bit64_4.6.0-1 bslib_0.10.0 irlba_2.3.5.1
## [105] otel_0.2.0 rpart_4.1.23 colorspace_2.1-2 DBI_1.2.3
## [109] Hmisc_5.2-5 nnet_7.3-19 processx_3.8.6 mnormt_2.1.1
## [113] tidyselect_1.2.1 bit_4.6.0 compiler_4.3.2 MatrixEQTL_2.3
## [117] glmnet_4.1-10 graph_1.80.0 htmlTable_2.4.3 desc_1.4.3
## [121] DelayedArray_0.28.0 plotly_4.12.0 bookdown_0.46 checkmate_2.3.3
## [125] scales_1.4.0 psych_2.3.12 callr_3.7.6 digest_0.6.34
## [129] minqa_1.2.8 rmarkdown_2.30 XVector_0.42.0 htmltools_0.5.8.1
## [133] pkgconfig_2.0.3 base64enc_0.1-3 fastmap_1.1.1 rlang_1.1.6
## [137] htmlwidgets_1.6.4 shiny_1.8.0 farver_2.1.2 jquerylib_0.1.4
## [141] jsonlite_2.0.0 statnet.common_4.12.0 RCurl_1.98-1.14 Formula_1.2-5
## [145] GenomeInfoDbData_1.2.11 ggnetwork_0.5.15 Rcpp_1.0.12 stringi_1.8.3
## [149] zlibbioc_1.48.0 MASS_7.3-60.0.1 plyr_1.8.9 org.Hs.eg.db_3.18.0
## [153] pkgbuild_1.4.3 parallel_4.3.2 ggrepel_0.9.5 Biostrings_2.70.1
## [157] splines_4.3.2 hms_1.1.4 ps_1.9.1 Boruta_9.0.0
## [161] uuid_1.2-2 igraph_2.2.1 ggpubr_0.6.0 ranger_0.18.0
## [165] fastcluster_1.3.0 ggsignif_0.6.4 rngtools_1.5.2 reshape2_1.4.4
## [169] pkgload_1.4.1 XML_3.99-0.19 evaluate_1.0.5 BiocManager_1.30.22
## [173] nloptr_2.2.1 tzdb_0.5.0 foreach_1.5.2 missForest_1.6.1
## [177] httpuv_1.6.13 productplots_0.1.2 broom_1.0.12 xtable_1.8-4
## [181] rstatix_0.7.3 later_1.3.2 ragg_1.5.0 viridisLite_0.4.2
## [185] memoise_2.0.1 AnnotationDbi_1.64.1 cluster_2.1.6 timechange_0.3.0