Return clusters assigned to each pathway

For the package to be usable, license specification is required. Currently, DESCRIPTION file contains mostly placeholder values, including:

License: What license is it under?

It would be nice to have a F/LOSS license for this package (i.e., BSD-3-Clause, MIT, LGPL-v3 just to name a few).

Thank you for providing such a good interactive plotting method to visualize the enrichment results. However, I would like to hide/remove the text on the graph nodes, how can I realize this? It seems not work when I set the fontSize=0.

Hi,
Thank you for creating such a useful package.

I have a straightforward question to ask you.
After running the function “enrichmentNetwork”, the text in the output PDF file exceeds the boundaries of the image and cannot be fully displayed. How can this issue be resolved?

I look forward to your reply.
Jeff

library(Spectrum)
enrichmentNetwork(enrich@result,
clustMethod = as.character("spectral"),
drawEllipses = T,
fontSize = 2.5,
repelLabels = T
)

Error in as.character(package) :
cannot coerce type 'closure' to vector of type 'character'

Thank you in advance. How to fix this problem？

R version 4.2.3 (2023-03-15)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Ventura 13.0.1

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
[1] Spectrum_1.1 plotly_4.10.1 forcats_1.0.0
[4] stringr_1.5.0 dplyr_1.1.0 purrr_1.0.1
[7] readr_2.1.3 tidyr_1.3.0 tibble_3.1.8
[10] ggplot2_3.4.2 tidyverse_1.3.2 DOSE_3.24.2
[13] org.Hs.eg.db_3.15.0 AnnotationDbi_1.60.0 IRanges_2.32.0
[16] S4Vectors_0.36.1 Biobase_2.58.0 BiocGenerics_0.44.0
[19] clusterProfiler_4.6.0 aPEAR_1.0

loaded via a namespace (and not attached):
[1] readxl_1.4.1 shadowtext_0.1.2 backports_1.4.1
[4] fastmatch_1.1-3 plyr_1.8.8 igraph_1.3.5
[7] lazyeval_0.2.2 splines_4.2.3 gmp_0.6-10
[10] crosstalk_1.2.0 BiocParallel_1.32.5 SnowballC_0.7.0
[13] MCL_1.0 GenomeInfoDb_1.34.9 digest_0.6.31
[16] foreach_1.5.2 yulab.utils_0.0.6 htmltools_0.5.4
[19] GOSemSim_2.24.0 viridis_0.6.2 GO.db_3.15.0
[22] arules_1.7-6 fansi_1.0.4 magrittr_2.0.3
[25] memoise_2.0.1 googlesheets4_1.0.1 tzdb_0.3.0
[28] Biostrings_2.66.0 graphlayouts_0.8.4 modelr_0.1.10
[31] timechange_0.2.0 enrichplot_1.18.3 colorspace_2.1-0
[34] rvest_1.0.3 blob_1.2.3 ggrepel_0.9.3
[37] haven_2.5.1 xfun_0.37 crayon_1.5.2
[40] RCurl_1.98-1.10 jsonlite_1.8.4 scatterpie_0.1.8
[43] iterators_1.0.14 ape_5.6-2 glue_1.6.2
[46] polyclip_1.10-4 gtable_0.3.1 gargle_1.3.0
[49] zlibbioc_1.44.0 XVector_0.38.0 RcppZiggurat_0.1.6
[52] scales_1.2.1 DBI_1.1.3 Rcpp_1.0.10
[55] viridisLite_0.4.1 gridGraphics_0.5-1 tidytree_0.4.2
[58] bit_4.0.5 htmlwidgets_1.6.1 httr_1.4.4
[61] fgsea_1.24.0 RColorBrewer_1.1-3 ellipsis_0.3.2
[64] ClusterR_1.3.1 pkgconfig_2.0.3 farver_2.1.1
[67] dbplyr_2.3.0 utf8_1.2.3 labeling_0.4.2
[70] ggplotify_0.1.0 tidyselect_1.2.0 rlang_1.1.1
[73] reshape2_1.4.4 munsell_0.5.0 cellranger_1.1.0
[76] tools_4.2.3 cachem_1.0.6 downloader_0.4
[79] cli_3.6.0 generics_0.1.3 RSQLite_2.2.20
[82] gson_0.0.9 bayesbio_1.0.0 broom_1.0.3
[85] evaluate_0.20 fastmap_1.1.0 yaml_2.3.7
[88] ggtree_3.6.2 knitr_1.42 bit64_4.0.5
[91] fs_1.6.0 tidygraph_1.2.3 KEGGREST_1.38.0
[94] ggraph_2.1.0 nlme_3.1-162 aplot_0.1.9
[97] xml2_1.3.3 compiler_4.2.3 rstudioapi_0.14
[100] png_0.1-8 reprex_2.0.2 treeio_1.22.0
[103] tweenr_2.0.2 stringi_1.7.12 lattice_0.20-45
[106] Matrix_1.5-3 vctrs_0.5.2 pillar_1.8.1
[109] lifecycle_1.0.3 data.table_1.14.6 cowplot_1.1.1
[112] bitops_1.0-7 patchwork_1.1.2 qvalue_2.30.0
[115] R6_2.5.1 gridExtra_2.3 lsa_0.73.3
[118] codetools_0.2-19 MASS_7.3-58.2 assertthat_0.2.1
[121] withr_2.5.0 GenomeInfoDbData_1.2.9 diptest_0.76-0
[124] expm_0.999-7 parallel_4.2.3 hms_1.1.2
[127] grid_4.2.3 ggfun_0.0.9 HDO.db_0.99.1
[130] Rfast_2.0.7 rmarkdown_2.20 googledrive_2.0.0
[133] ggforce_0.4.1 lubridate_1.9.1

• add an option to visualize only a subset of clusters (would that change clustering results?)
• pass clusters and pathways which need to be visualized
• make sure the methods work with a small number of pathways

Add size and color legends.

I am using aPEAR in Rstudio, with the version aPEAR_1.0. It worked perfectly in a cloud computing env. However, in Rstudio, when I tried to use findPathClusters, I got the error telling me the function could not be found. I wonder if you could advise in this regard.

versions is as follows:

R version 4.3.2 (2023-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 11 x64 (build 22631)

Matrix products: default

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

time zone: America/New_York
tzcode source: internal

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

other attached packages:
[1] org.Hs.eg.db_3.18.0 AnnotationDbi_1.64.1 IRanges_2.36.0 S4Vectors_0.40.2 Biobase_2.62.0 BiocGenerics_0.48.1 DOSE_3.28.2
[8] clusterProfiler_4.10.0 stringr_1.5.1 ggplot2_3.4.4 data.table_1.14.10 aPEAR_1.0 dplyr_1.1.4 Seurat_5.0.1
[15] SeuratObject_5.0.1 sp_2.1-2 devtools_2.4.5 usethis_2.2.2

loaded via a namespace (and not attached):
[1] RcppAnnoy_0.0.21 splines_4.3.2 later_1.3.2 ggplotify_0.1.2 bitops_1.0-7 tibble_3.2.1 polyclip_1.10-6
[8] fastDummies_1.7.3 lifecycle_1.0.4 globals_0.16.2 lattice_0.21-9 MASS_7.3-60 SnowballC_0.7.1 magrittr_2.0.3
[15] plotly_4.10.3 remotes_2.4.2.1 httpuv_1.6.13 sctransform_0.4.1 spam_2.10-0 sessioninfo_1.2.2 pkgbuild_1.4.3
[22] spatstat.sparse_3.0-3 reticulate_1.34.0 cowplot_1.1.2 pbapply_1.7-2 DBI_1.2.0 RColorBrewer_1.1-3 abind_1.4-5
[29] pkgload_1.3.3 zlibbioc_1.48.0 expm_0.999-8 Rtsne_0.17 purrr_1.0.2 ggraph_2.1.0 RCurl_1.98-1.13
[36] yulab.utils_0.1.2 tweenr_2.0.2 GenomeInfoDbData_1.2.11 enrichplot_1.22.0 arules_1.7-7 ggrepel_0.9.4 irlba_2.3.5.1
[43] listenv_0.9.0 spatstat.utils_3.0-4 tidytree_0.4.6 goftest_1.2-3 RSpectra_0.16-1 spatstat.random_3.2-2 fitdistrplus_1.1-11
[50] parallelly_1.36.0 leiden_0.4.3.1 codetools_0.2-19 ggforce_0.4.1 tidyselect_1.2.0 aplot_0.2.2 farver_2.1.1
[57] viridis_0.6.4 matrixStats_1.2.0 spatstat.explore_3.2-5 jsonlite_1.8.8 tidygraph_1.3.0 ellipsis_0.3.2 progressr_0.14.0
[64] ggridges_0.5.5 survival_3.5-7 iterators_1.0.14 foreach_1.5.2 tools_4.3.2 treeio_1.26.0 ica_1.0-3
[71] Rcpp_1.0.11 glue_1.6.2 gridExtra_2.3 qvalue_2.34.0 GenomeInfoDb_1.38.2 withr_2.5.2 BiocManager_1.30.22
[78] fastmap_1.1.1 fansi_1.0.6 digest_0.6.33 gridGraphics_0.5-1 R6_2.5.1 mime_0.12 colorspace_2.1-0
[85] GO.db_3.18.0 scattermore_1.2 tensor_1.5 spatstat.data_3.0-3 RSQLite_2.3.4 utf8_1.2.4 tidyr_1.3.0
[92] generics_0.1.3 graphlayouts_1.0.2 httr_1.4.7 htmlwidgets_1.6.4 scatterpie_0.2.1 uwot_0.1.16 MCL_1.0
[99] pkgconfig_2.0.3 gtable_0.3.4 blob_1.2.4 bayesbio_1.0.0 lmtest_0.9-40 XVector_0.42.0 shadowtext_0.1.2
[106] htmltools_0.5.7 fgsea_1.28.0 profvis_0.3.8 dotCall64_1.1-1 scales_1.3.0 png_0.1-8 ggfun_0.1.3
[113] rstudioapi_0.15.0 reshape2_1.4.4 nlme_3.1-163 cachem_1.0.8 zoo_1.8-12 KernSmooth_2.23-22 HDO.db_0.99.1
[120] parallel_4.3.2 miniUI_0.1.1.1 pillar_1.9.0 grid_4.3.2 vctrs_0.6.5 RANN_2.6.1 urlchecker_1.0.1
[127] lsa_0.73.3 promises_1.2.1 xtable_1.8-4 cluster_2.1.4 cli_3.6.2 compiler_4.3.2 rlang_1.1.2
[134] crayon_1.5.2 future.apply_1.11.1 plyr_1.8.9 fs_1.6.3 stringi_1.8.3 BiocParallel_1.36.0 viridisLite_0.4.2
[141] deldir_2.0-2 munsell_0.5.0 Biostrings_2.70.1 lazyeval_0.2.2 spatstat.geom_3.2-7 GOSemSim_2.28.0 Matrix_1.6-4
[148] RcppHNSW_0.5.0 patchwork_1.1.3 bit64_4.0.5 future_1.33.1 KEGGREST_1.42.0 shiny_1.8.0 ROCR_1.0-11
[155] igraph_1.6.0 memoise_2.0.1 ggtree_3.10.0 fastmatch_1.1-4 bit_4.0.5 gson_0.1.0 ape_5.7-1