If you use this work in published research, please cite:
Using clusterProfiler to characterise Multi-Omics Data
This repo contains source code and data to produce Figures of the above paper.
The IBD_2_subtypes_example, Phyllostachys_heterocyla_example and
single_cell_example contain the data, scripts and results of the three
examples in the above article. Each sub directory contains input_data,
result, script.
- input_data: contains all the data sets that used to generate the figures.
- result: contains the results.
- script: contains the source code to produce the figures.
More details information can be found from here.
Here is the output of sessionInfo() of the system was compiled:
## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /mnt/d/UbuntuApps/R/4.3.0/lib/R/lib/libRblas.so
## LAPACK: /mnt/d/UbuntuApps/R/4.3.0/lib/R/lib/libRlapack.so; LAPACK version 3.11.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Asia/Shanghai
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] gridExtra_2.3 patchwork_1.2.0
## [3] ggsc_1.1.2.002 ggrepel_0.9.3
## [5] CelliD_1.8.1 SingleCellExperiment_1.22.0
## [7] SeuratObject_4.1.3 Seurat_4.3.0
## [9] ggfun_0.1.3 DESeq2_1.40.1
## [11] SummarizedExperiment_1.30.1 Biobase_2.60.0
## [13] MatrixGenerics_1.12.0 matrixStats_0.63.0
## [15] GenomicRanges_1.52.0 GenomeInfoDb_1.36.0
## [17] IRanges_2.36.0 S4Vectors_0.40.2
## [19] BiocGenerics_0.48.1 aplot_0.2.2
## [21] dplyr_1.1.2 enrichplot_1.21.2
## [23] ggplot2_3.5.0 clusterProfiler_4.8.1
## [25] MicrobiotaProcess_1.15.0 tictoc_1.2.1
##
## loaded via a namespace (and not attached):
## [1] fs_1.6.2 spatstat.sparse_3.0-1
## [3] bitops_1.0-7 HDO.db_0.99.1
## [5] httr_1.4.5 RColorBrewer_1.1-3
## [7] tools_4.3.0 sctransform_0.3.5
## [9] utf8_1.2.3 R6_2.5.1
## [11] vegan_2.6-4 lazyeval_0.2.2
## [13] uwot_0.1.14 mgcv_1.8-42
## [15] permute_0.9-7 withr_2.5.0
## [17] sp_1.6-0 progressr_0.13.0
## [19] cli_3.6.1 spatstat.explore_3.1-0
## [21] scatterpie_0.2.2 sandwich_3.0-2
## [23] mvtnorm_1.1-3 spatstat.data_3.0-1
## [25] askpass_1.1 ggridges_0.5.4
## [27] pbapply_1.7-0 yulab.utils_0.1.4
## [29] gson_0.1.0 DOSE_3.26.1
## [31] scater_1.28.0 parallelly_1.35.0
## [33] RSQLite_2.3.1 generics_0.1.3
## [35] gridGraphics_0.5-1 ica_1.0-3
## [37] spatstat.random_3.1-5 GO.db_3.17.0
## [39] Matrix_1.5-4 ggbeeswarm_0.7.2
## [41] fansi_1.0.4 abind_1.4-5
## [43] lifecycle_1.0.3 multcomp_1.4-25
## [45] yaml_2.3.7 qvalue_2.32.0
## [47] SparseArray_1.2.4 Rtsne_0.16
## [49] grid_4.3.0 blob_1.2.4
## [51] promises_1.2.0.1 crayon_1.5.2
## [53] miniUI_0.1.1.1 lattice_0.21-8
## [55] beachmat_2.19.1 cowplot_1.1.1
## [57] KEGGREST_1.40.0 pillar_1.9.0
## [59] knitr_1.43 fgsea_1.26.0
## [61] future.apply_1.10.0 codetools_0.2-19
## [63] fastmatch_1.1-3 leiden_0.4.3
## [65] glue_1.6.2 RcppArmadillo_0.12.2.0.0
## [67] downloader_0.4 data.table_1.14.8
## [69] vctrs_0.6.3 png_0.1-8
## [71] treeio_1.27.0 gtable_0.3.3
## [73] cachem_1.0.8 xfun_0.39
## [75] S4Arrays_1.3.3 mime_0.12
## [77] libcoin_1.0-9 tidygraph_1.2.3
## [79] survival_3.5-5 iterators_1.0.14
## [81] ellipsis_0.3.2 fitdistrplus_1.1-11
## [83] TH.data_1.1-2 ROCR_1.0-11
## [85] nlme_3.1-162 ggtree_3.9.1
## [87] bit64_4.0.5 RcppAnnoy_0.0.20
## [89] irlba_2.3.5.1 vipor_0.4.5
## [91] KernSmooth_2.23-22 colorspace_2.1-0
## [93] DBI_1.1.3 tidyselect_1.2.0
## [95] bit_4.0.5 compiler_4.3.0
## [97] BiocNeighbors_1.18.0 DelayedArray_0.29.4
## [99] plotly_4.10.1 shadowtext_0.1.2
## [101] scales_1.3.0 lmtest_0.9-40
## [103] stringr_1.5.0 digest_0.6.33
## [105] goftest_1.2-3 spatstat.utils_3.0-3
## [107] rmarkdown_2.22 XVector_0.40.0
## [109] htmltools_0.5.5 pkgconfig_2.0.3
## [111] umap_0.2.10.0 sparseMatrixStats_1.12.0
## [113] fastmap_1.1.1 rlang_1.1.1
## [115] htmlwidgets_1.6.2 DelayedMatrixStats_1.22.0
## [117] shiny_1.7.4 farver_2.1.1
## [119] zoo_1.8-12 jsonlite_1.8.7
## [121] BiocParallel_1.34.2 GOSemSim_2.27.2
## [123] BiocSingular_1.16.0 RCurl_1.98-1.12
## [125] magrittr_2.0.3 modeltools_0.2-23
## [127] scuttle_1.10.1 GenomeInfoDbData_1.2.10
## [129] ggplotify_0.1.0 munsell_0.5.0
## [131] Rcpp_1.0.10 ape_5.7-1
## [133] ggnewscale_0.4.9 viridis_0.6.2
## [135] reticulate_1.28 stringi_1.7.12
## [137] ggstar_1.0.4.001 ggraph_2.1.0
## [139] zlibbioc_1.46.0 MASS_7.3-59
## [141] plyr_1.8.8 parallel_4.3.0
## [143] listenv_0.9.0 deldir_1.0-6
## [145] Biostrings_2.68.1 graphlayouts_1.0.0
## [147] splines_4.3.0 tensor_1.5
## [149] locfit_1.5-9.7 igraph_1.4.2
## [151] spatstat.geom_3.2-1 ggtreeExtra_1.11.0
## [153] ggsignif_0.6.4 ScaledMatrix_1.8.1
## [155] reshape2_1.4.4 evaluate_0.21
## [157] RcppParallel_5.1.7 foreach_1.5.2
## [159] tweenr_2.0.2 httpuv_1.6.11
## [161] openssl_2.0.6 RANN_2.6.1
## [163] tidyr_1.3.0 purrr_1.0.1
## [165] polyclip_1.10-4 future_1.32.0
## [167] scattermore_0.8 ggforce_0.4.1
## [169] rsvd_1.0.5 coin_1.4-2
## [171] xtable_1.8-4 RSpectra_0.16-1
## [173] tidytree_0.4.5 tidydr_0.0.5
## [175] later_1.3.1 viridisLite_0.4.2
## [177] tibble_3.2.1 beeswarm_0.4.0
## [179] memoise_2.0.1 AnnotationDbi_1.62.1
## [181] cluster_2.1.4 globals_0.16.2