References

Stuart T, Butler A, Hoffman P, Hafemeister C, Papalexi E, Mauck WM 3^rd, Hao Y, Stoeckius M, Smibert P, Satija R. Comprehensive Integration of Single-Cell Data. Cell. 2019 Jun 13;177(7):1888-1902.e21. doi: 10.1016/j.cell.2019.05.031. Epub 2019 Jun 6. PMID: 31178118; PMCID: PMC6687398.

Neo Christopher Chung, John D. Storey, Statistical significance of variables driving systematic variation in high-dimensional data, Bioinformatics, Volume 31, Issue 4, 15 February 2015, Pages 545–554, https://doi.org/10.1093/bioinformatics/btu674

Aravind Subramanian, Pablo Tamayo, Vamsi K. Mootha, +7, Sayan Mukherjee, Benjamin L. Ebert, Michael A. Gillette, Amanda Paulovich, Scott L. Pomeroy, Todd R. Golub, Eric S. Lander, and Jill P. Mesirov. Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles. PNAS, Volume 102, Number 43, Pages 15545-15550, (2005) https://doi.org/10.1073/pnas.0506580102

Mootha, V., Lindgren, C., Eriksson, KF. et al. PGC-1α-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34, 267–273 (2003). https://doi.org/10.1038/ng1180

Arthur Liberzon, Aravind Subramanian, Reid Pinchback, Helga Thorvaldsdóttir, Pablo Tamayo, Jill P. Mesirov, Molecular signatures database (MSigDB) 3.0, Bioinformatics, Volume 27, Issue 12, 15 June 2011, Pages 1739–1740, https://doi.org/10.1093/bioinformatics/btr260

The Gene Ontology Consortium, The Gene Ontology resource: enriching a GOld mine, Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D325–D334, https://doi.org/10.1093/nar/gkaa1113

Ashburner, M., Ball, C., Blake, J. et al. Gene Ontology: tool for the unification of biology. Nat Genet 25, 25–29 (2000). https://doi.org/10.1038/75556

Minoru Kanehisa, Susumu Goto, KEGG: Kyoto Encyclopedia of Genes and Genomes, Nucleic Acids Research, Volume 28, Issue 1, 1 January 2000, Pages 27–30, https://doi.org/10.1093/nar/28.1.27

Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Science. 2019; 28: 1947– 1951. https://doi.org/10.1002/pro.3715

Minoru Kanehisa, Miho Furumichi, Yoko Sato, Mari Ishiguro-Watanabe, Mao Tanabe, KEGG: integrating viruses and cellular organisms, Nucleic Acids Research, Volume 49, Issue D1, 8 January 2021, Pages D545–D551, https://doi.org/10.1093/nar/gkaa970

Xinxin Zhang, Yujia Lan, Jinyuan Xu, Fei Quan, Erjie Zhao, Chunyu Deng, Tao Luo, Liwen Xu, Gaoming Liao, Min Yan, Yanyan Ping, Feng Li, Aiai Shi, Jing Bai, Tingting Zhao, Xia Li, Yun Xiao, CellMarker: a manually curated resource of cell markers in human and mouse, Nucleic Acids Research, Volume 47, Issue D1, 08 January 2019, Pages D721–D728, https://doi.org/10.1093/nar/gky900

sessionInfo()

## R version 4.3.1 (2023-06-16)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS/LAPACK: /opt/rstudio-server_conda/conda/envs/rstudio-server_4.3.1/lib/libopenblasp-r0.3.24.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: Europe/Paris
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] presto_1.0.0           data.table_1.15.4      Rcpp_1.0.12           
##  [4] vroom_1.6.5            msigdbr_7.5.1          rmarkdown_2.26        
##  [7] knitr_1.46             enrichplot_1.22.0      org.Hs.eg.db_3.18.0   
## [10] AnnotationDbi_1.64.1   IRanges_2.36.0         S4Vectors_0.40.2      
## [13] Biobase_2.62.0         BiocGenerics_0.48.1    clusterProfiler_4.10.0
## [16] clustree_0.5.1         magrittr_2.0.3         dplyr_1.1.4           
## [19] plyr_1.8.9             biomaRt_2.58.0         RColorBrewer_1.1-3    
## [22] gridExtra_2.3          ggraph_2.1.0           ggplot2_3.5.1         
## [25] Seurat_5.0.3           SeuratObject_5.0.1     sp_2.1-3              
## 
## loaded via a namespace (and not attached):
##   [1] fs_1.6.4                matrixStats_1.3.0       spatstat.sparse_3.0-3  
##   [4] bitops_1.0-7            HDO.db_0.99.1           httr_1.4.7             
##   [7] backports_1.4.1         tools_4.3.1             sctransform_0.4.1      
##  [10] utf8_1.2.4              R6_2.5.1                lazyeval_0.2.2         
##  [13] uwot_0.2.2              withr_3.0.0             prettyunits_1.2.0      
##  [16] progressr_0.14.0        cli_3.6.2               spatstat.explore_3.2-7 
##  [19] fastDummies_1.7.3       scatterpie_0.2.2        labeling_0.4.3         
##  [22] spatstat.data_3.0-4     ggridges_0.5.6          pbapply_1.7-2          
##  [25] yulab.utils_0.1.4       gson_0.1.0              DOSE_3.28.2            
##  [28] R.utils_2.12.3          parallelly_1.37.1       limma_3.56.2           
##  [31] rstudioapi_0.16.0       RSQLite_2.3.6           generics_0.1.3         
##  [34] gridGraphics_0.5-1      ica_1.0-3               spatstat.random_3.2-3  
##  [37] GO.db_3.18.0            Matrix_1.6-5            ggbeeswarm_0.7.2       
##  [40] fansi_1.0.6             abind_1.4-5             R.methodsS3_1.8.2      
##  [43] lifecycle_1.0.4         yaml_2.3.8              qvalue_2.34.0          
##  [46] BiocFileCache_2.10.1    Rtsne_0.17              grid_4.3.1             
##  [49] blob_1.2.4              promises_1.3.0          crayon_1.5.2           
##  [52] miniUI_0.1.1.1          lattice_0.22-6          cowplot_1.1.3          
##  [55] KEGGREST_1.42.0         pillar_1.9.0            fgsea_1.28.0           
##  [58] future.apply_1.11.2     codetools_0.2-20        fastmatch_1.1-4        
##  [61] leiden_0.4.3.1          glue_1.7.0              ggfun_0.1.4            
##  [64] vctrs_0.6.5             png_0.1-8               treeio_1.26.0          
##  [67] spam_2.10-0             gtable_0.3.5            cachem_1.0.8           
##  [70] xfun_0.43               mime_0.12               tidygraph_1.2.3        
##  [73] survival_3.5-8          fitdistrplus_1.1-11     ROCR_1.0-11            
##  [76] nlme_3.1-164            ggtree_3.10.0           bit64_4.0.5            
##  [79] progress_1.2.3          filelock_1.0.3          RcppAnnoy_0.0.22       
##  [82] GenomeInfoDb_1.38.5     irlba_2.3.5.1           vipor_0.4.7            
##  [85] KernSmooth_2.23-22      colorspace_2.1-0        DBI_1.2.2              
##  [88] ggrastr_1.0.2           tidyselect_1.2.1        bit_4.0.5              
##  [91] compiler_4.3.1          curl_5.0.2              xml2_1.3.6             
##  [94] plotly_4.10.4           shadowtext_0.1.3        checkmate_2.3.0        
##  [97] scales_1.3.0            lmtest_0.9-40           rappdirs_0.3.3         
## [100] stringr_1.5.1           digest_0.6.35           goftest_1.2-3          
## [103] spatstat.utils_3.0-4    XVector_0.42.0          htmltools_0.5.8.1      
## [106] pkgconfig_2.0.3         highr_0.10              dbplyr_2.5.0           
## [109] fastmap_1.1.1           rlang_1.1.3             htmlwidgets_1.6.4      
## [112] shiny_1.8.1.1           farver_2.1.1            zoo_1.8-12             
## [115] jsonlite_1.8.8          BiocParallel_1.36.0     GOSemSim_2.28.1        
## [118] R.oo_1.26.0             RCurl_1.98-1.14         GenomeInfoDbData_1.2.11
## [121] ggplotify_0.1.2         dotCall64_1.1-1         patchwork_1.2.0        
## [124] munsell_0.5.1           ape_5.8                 babelgene_22.9         
## [127] viridis_0.6.5           reticulate_1.35.0       stringi_1.8.3          
## [130] zlibbioc_1.48.0         MASS_7.3-60.0.1         parallel_4.3.1         
## [133] listenv_0.9.1           ggrepel_0.9.5           deldir_2.0-4           
## [136] Biostrings_2.70.1       graphlayouts_1.0.1      splines_4.3.1          
## [139] tensor_1.5              hms_1.1.3               igraph_2.0.3           
## [142] spatstat.geom_3.2-9     RcppHNSW_0.6.0          reshape2_1.4.4         
## [145] XML_3.99-0.16.1         evaluate_0.23           tzdb_0.4.0             
## [148] tweenr_2.0.2            httpuv_1.6.15           RANN_2.6.1             
## [151] tidyr_1.3.1             purrr_1.0.2             polyclip_1.10-6        
## [154] future_1.33.2           scattermore_1.2         ggforce_0.4.1          
## [157] xtable_1.8-4            RSpectra_0.16-1         tidytree_0.4.6         
## [160] later_1.3.2             viridisLite_0.4.2       tibble_3.2.1           
## [163] aplot_0.2.2             memoise_2.0.1           beeswarm_0.4.0         
## [166] cluster_2.1.6           globals_0.16.3