Hello, I want to access the enriched pathways through the enrichGO function. I thought that I need to use ego@result to get the enriched pathways and it gives me a lot of IDs and pathways. But after searching I realized enriched pathways can be shown easily just by head(ego). First, I want to know what does ego@result show? Second, is there any way to know the scores of the enriched pathway? Third, are the enriched pathways ordered by their enrichment score? Thanks.

ego <- enrichGO(gene = sigOE_genes, universe = allOE_genes, keyType = "ENSEMBL", OrgDb = org.Mm.eg.db, ont = ont_type, pAdjustMethod = "BH", qvalueCutoff = 0.05, readable = TRUE)

sessionInfo() R version 4.2.0 (2022-04-22 ucrt) Platform: x86_64-w64-mingw32/x64 (64-bit) Running under: Windows 10 x64 (build 19044)

Matrix products: default

locale: [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8 LC_MONETARY=English_United States.utf8 [4] LC_NUMERIC=C LC_TIME=English_United States.utf8

attached base packages: [1] grid stats4 stats graphics grDevices utils datasets methods base

other attached packages: [1] Rgraphviz_2.41.1 topGO_2.49.0 SparseM_1.81 GO.db_3.15.0 graph_1.75.0
[6] GOSemSim_2.23.0 enrichplot_1.17.0 ggnewscale_0.4.7 forcats_0.5.1 stringr_1.4.0
[11] dplyr_1.0.9 purrr_0.3.4 readr_2.1.2 tidyr_1.2.0 tibble_3.1.7
[16] ggplot2_3.3.6 tidyverse_1.3.1 ensembldb_2.21.2 AnnotationFilter_1.21.0 GenomicFeatures_1.49.5 [21] GenomicRanges_1.49.0 GenomeInfoDb_1.33.3 AnnotationHub_3.5.0 BiocFileCache_2.5.0 dbplyr_2.2.1
[26] clusterProfiler_4.5.1 pathviewr_1.0.1 DOSE_3.23.2 org.Mm.eg.db_3.15.0 AnnotationDbi_1.59.1
[31] IRanges_2.31.0 S4Vectors_0.35.1 Biobase_2.57.1 BiocGenerics_0.43.0

loaded via a namespace (and not attached): [1] utf8_1.2.2 tidyselect_1.1.2 RSQLite_2.2.14 BiocParallel_1.31.9
[5] scatterpie_0.1.7 munsell_0.5.0 codetools_0.2-18 withr_2.5.0
[9] colorspace_2.0-3 filelock_1.0.2 rstudioapi_0.13 labeling_0.4.2
[13] MatrixGenerics_1.9.1 GenomeInfoDbData_1.2.8 polyclip_1.10-0 bit64_4.0.5
[17] farver_2.1.0 downloader_0.4 vctrs_0.4.1 treeio_1.21.0
[21] generics_0.1.3 gson_0.0.6 R6_2.5.1 graphlayouts_0.8.0
[25] locfit_1.5-9.5 bitops_1.0-7 cachem_1.0.6 fgsea_1.23.0
[29] gridGraphics_0.5-1 DelayedArray_0.23.0 assertthat_0.2.1 promises_1.2.0.1
[33] BiocIO_1.7.1 scales_1.2.0 ggraph_2.0.5 gtable_0.3.0
[37] tidygraph_1.2.1 rlang_1.0.3 genefilter_1.79.0 splines_4.2.0
[41] rtracklayer_1.57.0 lazyeval_0.2.2 broom_1.0.0 BiocManager_1.30.18
[45] yaml_2.3.5 reshape2_1.4.4 modelr_0.1.8 backports_1.4.1
[49] httpuv_1.6.5 qvalue_2.29.0 tools_4.2.0 ggplotify_0.1.0
[53] ellipsis_0.3.2 RColorBrewer_1.1-3 Rcpp_1.0.8.3 plyr_1.8.7
[57] progress_1.2.2 zlibbioc_1.43.0 RCurl_1.98-1.7 prettyunits_1.1.1
[61] viridis_0.6.2 cowplot_1.1.1 SummarizedExperiment_1.27.1 haven_2.5.0
[65] ggrepel_0.9.1 fs_1.5.2 magrittr_2.0.3 data.table_1.14.2
[69] DO.db_2.9 reprex_2.0.1 ProtGenerics_1.29.0 matrixStats_0.62.0
[73] hms_1.1.1 patchwork_1.1.1 mime_0.12 xtable_1.8-4
[77] XML_3.99-0.10 readxl_1.4.0 gridExtra_2.3 compiler_4.2.0
[81] biomaRt_2.53.2 crayon_1.5.1 shadowtext_0.1.2 htmltools_0.5.2
[85] ggfun_0.0.6 later_1.3.0 tzdb_0.3.0 geneplotter_1.75.0
[89] aplot_0.1.6 lubridate_1.8.0 DBI_1.1.3 tweenr_1.0.2
[93] MASS_7.3-57 rappdirs_0.3.3 Matrix_1.4-1 cli_3.3.0
[97] parallel_4.2.0 igraph_1.3.2 pkgconfig_2.0.3 GenomicAlignments_1.33.0
[101] xml2_1.3.3 ggtree_3.5.1 annotate_1.75.0 XVector_0.37.0
[105] rvest_1.0.2 yulab.utils_0.0.5 digest_0.6.29 Biostrings_2.65.1
[109] cellranger_1.1.0 fastmatch_1.1-3 tidytree_0.3.9 restfulr_0.0.15
[113] curl_4.3.2 shiny_1.7.1 Rsamtools_2.13.3 rjson_0.2.21
[117] lifecycle_1.0.1 nlme_3.1-158 jsonlite_1.8.0 viridisLite_0.4.0
[121] fansi_1.0.3 pillar_1.7.0 lattice_0.20-45 KEGGREST_1.37.2
[125] fastmap_1.1.0 httr_1.4.3 survival_3.3-1 interactiveDisplayBase_1.35.0 [129] glue_1.6.2 png_0.1-7 BiocVersion_3.16.0 bit_4.0.4
[133] ggforce_0.3.3 stringi_1.7.6 blob_1.2.3 DESeq2_1.37.4
[137] memoise_2.0.1 ape_5.6-2