# BulkSignalR : Inference of ligand-receptor interactions from bulk data or spatial transcriptomics

#### Jean-Philippe Villemin

#### Jacques Colinge

#### 09/29/2025

Code

    library(BulkSignalR)

## Configure local cache and hidden environment variables

`BulkSignalR` donwloads resource files just once after the first installation.

A mecanism handled by `BioCFileCache` package informs the user if the local files are up to date. Several functions as `cacheVersion` and `cacheInfo` give informations about the content of the local cache and its synchronization state with its remote source.

Cache directory can be cleaned by `cacheClear` function and and forced to be created again using `createResources(onRequest=TRUE)`. Otherwise it will be automatically recreated when the package is next loaded.

The knowledge database (pathways - GO-BP / Reactome, Ligand & recepors - LRdb, and gene Network are handled inside a hidden environment variable called `.SignalR`)

The sources can be rewritten using `resetPathways`, `resetLRdb` and `resetNetwork`.

Several data versions can be access remotely from : here These datasets can be used to reset the sources locally.

Code

    # You can see hidden environment variable list as follows.
    ls(BulkSignalR:::.SignalR)

    # Setup Pathways for reactome (can be done for GO-BP also)

    subset <- c("REACTOME_BASIGIN_INTERACTIONS",
    "REACTOME_SYNDECAN_INTERACTIONS",
    "REACTOME_ECM_PROTEOGLYCANS",
    "REACTOME_CELL_JUNCTION_ORGANIZATION")

    reactSubset <- BulkSignalR:::.SignalR$BulkSignalR_Reactome[
    BulkSignalR:::.SignalR$BulkSignalR_Reactome$`Reactome name` %in% subset,]

    resetPathways(dataframe = reactSubset,
    resourceName = "Reactome")

    cacheInfo()
    cacheVersion()

    cacheClear()

    # Ask to download again
    createResources(onRequest=TRUE)

    # Set up LRdb
    df <- data.frame(
      ligand    = c("A2M", "AANAT", "AANAT", "ACE", "ACE"),
      receptor  = c("LRP1", "MTNR1A", "MTNR1B", "AGTR2", "BDKRB2")
    )
    resetLRdb(df,switch=TRUE)

    # Set up Network
    df <- data.frame(
      a.gn   = c("A2M", "AANAT", "AANAT", "ACE", "ACE"),
      type  = c("regulates-transcription-of",
       "regulates-transcription-of",
       "regulates-transcription-of",
       "regulates-transcription-of",
       "regulates-transcription-of"),
      b.gn  = c("LRP1", "MTNR1A", "MTNR1B", "AGTR2", "BDKRB2")
    )

    resetNetwork(df)

Thank you for reading this guide and for using `BulkSignalR`.

## Session Information

Code

    sessionInfo()

    ## R version 4.5.1 Patched (2025-06-14 r88325)
    ## Platform: aarch64-apple-darwin20
    ## Running under: macOS Ventura 13.7.5
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.5-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.1
    ##
    ## locale:
    ## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
    ##
    ## time zone: America/New_York
    ## tzcode source: internal
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] BulkSignalR_1.0.5
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] RColorBrewer_1.1-3          jsonlite_2.0.0
    ##   [3] shape_1.4.6.1               magrittr_2.0.4
    ##   [5] magick_2.9.0                farver_2.1.2
    ##   [7] rmarkdown_2.30              GlobalOptions_0.1.2
    ##   [9] fs_1.6.6                    vctrs_0.6.5
    ##  [11] multtest_2.64.0             memoise_2.0.1
    ##  [13] RCurl_1.98-1.17             ggtree_3.16.3
    ##  [15] rstatix_0.7.2               htmltools_0.5.8.1
    ##  [17] S4Arrays_1.8.1              curl_7.0.0
    ##  [19] broom_1.0.10                SparseArray_1.8.1
    ##  [21] Formula_1.2-5               gridGraphics_0.5-1
    ##  [23] sass_0.4.10                 bslib_0.9.0
    ##  [25] htmlwidgets_1.6.4           plotly_4.11.0
    ##  [27] cachem_1.1.0                igraph_2.1.4
    ##  [29] lifecycle_1.0.4             iterators_1.0.14
    ##  [31] pkgconfig_2.0.3             Matrix_1.7-4
    ##  [33] R6_2.6.1                    fastmap_1.2.0
    ##  [35] GenomeInfoDbData_1.2.14     MatrixGenerics_1.20.0
    ##  [37] clue_0.3-66                 digest_0.6.37
    ##  [39] aplot_0.2.9                 colorspace_2.1-2
    ##  [41] patchwork_1.3.2             S4Vectors_0.46.0
    ##  [43] grr_0.9.5                   GenomicRanges_1.60.0
    ##  [45] RSQLite_2.4.3               ggpubr_0.6.1
    ##  [47] filelock_1.0.3              httr_1.4.7
    ##  [49] abind_1.4-8                 compiler_4.5.1
    ##  [51] withr_3.0.2                 bit64_4.6.0-1
    ##  [53] doParallel_1.0.17           S7_0.2.0
    ##  [55] backports_1.5.0             orthogene_1.14.01
    ##  [57] carData_3.0-5               DBI_1.2.3
    ##  [59] homologene_1.4.68.19.3.27   ggsignif_0.6.4
    ##  [61] MASS_7.3-65                 rappdirs_0.3.3
    ##  [63] DelayedArray_0.34.1         rjson_0.2.23
    ##  [65] tools_4.5.1                 ape_5.8-1
    ##  [67] glue_1.8.0                  stabledist_0.7-2
    ##  [69] nlme_3.1-168                grid_4.5.1
    ##  [71] Rtsne_0.17                  cluster_2.1.8.1
    ##  [73] generics_0.1.4              gtable_0.3.6
    ##  [75] tidyr_1.3.1                 data.table_1.17.8
    ##  [77] car_3.1-3                   XVector_0.48.0
    ##  [79] BiocGenerics_0.54.0         ggrepel_0.9.6
    ##  [81] RANN_2.6.2                  foreach_1.5.2
    ##  [83] pillar_1.11.1               yulab.utils_0.2.1
    ##  [85] babelgene_22.9              circlize_0.4.16
    ##  [87] splines_4.5.1               dplyr_1.1.4
    ##  [89] BiocFileCache_2.16.2        treeio_1.32.0
    ##  [91] lattice_0.22-7              survival_3.8-3
    ##  [93] bit_4.6.0                   tidyselect_1.2.1
    ##  [95] ComplexHeatmap_2.24.1       SingleCellExperiment_1.30.1
    ##  [97] knitr_1.50                  gridExtra_2.3
    ##  [99] IRanges_2.42.0              SummarizedExperiment_1.38.1
    ## [101] stats4_4.5.1                xfun_0.53
    ## [103] Biobase_2.68.0              matrixStats_1.5.0
    ## [105] UCSC.utils_1.4.0            lazyeval_0.2.2
    ## [107] ggfun_0.2.0                 yaml_2.3.10
    ## [109] evaluate_1.0.5              codetools_0.2-20
    ## [111] tibble_3.3.0                ggplotify_0.1.3
    ## [113] cli_3.6.5                   jquerylib_0.1.4
    ## [115] dichromat_2.0-0.1           Rcpp_1.1.0
    ## [117] GenomeInfoDb_1.44.3         dbplyr_2.5.1
    ## [119] gprofiler2_0.2.3            png_0.1-8
    ## [121] parallel_4.5.1              ggplot2_4.0.0
    ## [123] blob_1.2.4                  ggalluvial_0.12.5
    ## [125] bitops_1.0-9                glmnet_4.1-10
    ## [127] SpatialExperiment_1.18.1    viridisLite_0.4.2
    ## [129] tidytree_0.4.6              scales_1.4.0
    ## [131] purrr_1.1.0                 crayon_1.5.3
    ## [133] GetoptLong_1.0.5            rlang_1.1.6