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

#### Jean-Philippe Villemin

#### Jacques Colinge

#### 05/18/2026

Code

    library(BulkSignalR)

## Cluster-based differential analysis

By default, `BulkSignalR` inference of L-R interactions relies on correlation analysis across all the samples. That is, a putative L-R interaction is assessed for coherent expression of the ligand, the receptor, and target genes in a pathway below receptor across all the samples.

Alternatively, it is possible to define sample clusters based on an independent analysis (outside of `BulkSignalR`) to group similar samples. Based on cluster definitions, it is for instance possible to assess whether L-R interactions are significantly stronger in a cluster compared to all the samples, to all the samples but the chosen cluster, or in comparison with another cluster. In practice, the user must simply provide a table of differential gene or protein expression analysis that is relevant for the selected comparison.

In this mode, L-R interactions are inferred based on gene or protein regulation-associated P-values instead of P-values resulting from the correlation analysis in the default mode. As already stated, differential P-value estimations are left to the user to compute. With bulk data, commonly used libraries such as `DESeq2`, `EdgeR`, or `limma` constitute obvious options.

In the next chunk of code, we illustrate the differential mode using Salivary Duct Carcinoma (SDC) samples where we compare two clusters of patients.

We first create a BSRDataModelComp object as follows:

Code

    data(sdc, package = "BulkSignalR")
    normal <- grep("^N", names(sdc))
    bsrdm.comp <- BSRDataModelComp(sdc[, -normal])

To make a very small example we generate random values, but user should provide his own logFC and associated pvalues from DGE ouputs.

Code

    colA <- as.integer(1:3)
    colB <- as.integer(12:15)

    n <- nrow(ncounts(bsrdm.comp))
    stats <- data.frame(pval = runif(n),
    logFC = rnorm(n, 0, 2),
    expr = runif(n, 0, 10))
    rownames(stats) <- rownames(ncounts(bsrdm.comp))

We define the cluster comparison and add it.

Code

    bsrcc <- BSRClusterComp(bsrdm.comp, colA, colB, stats)
    bsrdm.comp <- addClusterComp(bsrdm.comp, bsrcc, "random.example")

Finally we infer ligand-receptor interactions from the comparison. We use a subset of the reference to speed up inference in the context of the vignette.

Code

    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")

    bsrinf.comp <- BSRInferenceComp(bsrdm.comp,
    reference="REACTOME",
    max.pval = 1,
    "random.example")

    ## Warning: executing %dopar% sequentially: no parallel backend registered

Code

    head(LRinter(bsrinf.comp))

    ##          L     R         pw.id                             pw.name       pval
    ## 2    ADAM2 ITGA9 R-HSA-3000178          REACTOME_ECM_PROTEOGLYCANS 0.06796082
    ## 11    ASIP  F11R  R-HSA-446728 REACTOME_CELL_JUNCTION_ORGANIZATION 0.01197886
    ## 54  COL4A2 ITGB5 R-HSA-3000170      REACTOME_SYNDECAN_INTERACTIONS 0.13596421
    ## 541 COL4A2 ITGB5 R-HSA-3000178          REACTOME_ECM_PROTEOGLYCANS 0.06837346
    ## 55  COL4A3 ITGA3  R-HSA-210991       REACTOME_BASIGIN_INTERACTIONS 0.17827411
    ## 60  CXCL10  SDC4 R-HSA-3000170      REACTOME_SYNDECAN_INTERACTIONS 0.58135941
    ##           qval  L.logFC  R.logFC   LR.pval LR.corr rank len rank.pval rank.corr
    ## 2   0.14566521 1.573022 1.011078 0.1698568       1    9  17 0.4697862         1
    ## 11  0.09782733 2.083179 2.753485 0.0553769       1    2   5 0.5220781         1
    ## 54  0.19594842 1.848077 2.491462 0.4540991       1    4   8 0.4697862         1
    ## 541 0.14566521 1.848077 2.491462 0.4540991       1    8  16 0.4043164         1
    ## 55  0.23609274 1.553252 4.991324 0.5412893       1    4   9 0.5358882         1
    ## 60  0.58135941 3.178845 3.358091 0.6028182       1    4   9 0.8478262         1
    ##       LR.score   L.expr     R.expr
    ## 2   0.18955673 9.013990 3.78069491
    ## 11  0.14126124 3.261024 2.42164897
    ## 54  0.04490531 6.653779 0.07419511
    ## 541 0.04490531 6.653779 0.07419511
    ## 55  0.17332739 4.638940 4.33731827
    ## 60  0.19391543 8.647296 4.37824189

## Technical notes

The three basic `BulkSignalR` S4 classes BSRDataModel, BSRInference, and BSRSignature were derived into new classes to add the functionalities required by the differential mode:

- BSRDataModelComp, denoted here as `bsrdm.comp` is a daughter class of BSRDataModel, previously denoted `bsrdm`
- BSRInferenceComp, denoted here as `bsrinf.comp` is a daughter class of BSRInference, previously denoted `bsrinf`
- BSRSignatureComp, denoted here as `bsrsig.comp` is a daughter class of BSRSignature, previously denoted `bsrsig`

The new S4 class BSRClusterComp is meant to represent the comparison between two clusters of samples. It primarily contains the results of the user-provided differential analysis in a table containing gene/protein names, P-values, and log-fold-changes (logFC).

Usually, we recommend instantiating a `BSRDataModel` object to contain the expression matrix underlying the differential analysis. Then, it is promoted into a `BSRDataModelComp` object such that cluster definitions and comparisons can be added to it. This can be for instance done with `as(bsrdm, "BSRDataModelComp")`. The `addClusterComp` method of a `BSRDataModelComp` object allows adding one or several comparisons between clusters, each added comparison being defined in a `BSRClusterComp` object.

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

## Session Information

Code

    sessionInfo()

    ## R version 4.6.0 Patched (2026-05-01 r89994)
    ## Platform: aarch64-apple-darwin23
    ## Running under: macOS Tahoe 26.3.1
    ##
    ## Matrix products: default
    ## BLAS:   /Library/Frameworks/R.framework/Versions/4.6/Resources/lib/libRblas.0.dylib
    ## LAPACK: /Library/Frameworks/R.framework/Versions/4.6/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_1.5.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] RColorBrewer_1.1-3          jsonlite_2.0.0
    ##   [3] shape_1.4.6.1               magrittr_2.0.5
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    ##   [7] rmarkdown_2.31              GlobalOptions_0.1.4
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