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

    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:

    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.

    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.

    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.

    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

    head(LRinter(bsrinf.comp))

    ##           L     R         pw.id                             pw.name       pval
    ## 37  COL16A1 ITGB1  R-HSA-210991       REACTOME_BASIGIN_INTERACTIONS 0.13358086
    ## 371 COL16A1 ITGB1 R-HSA-3000170      REACTOME_SYNDECAN_INTERACTIONS 0.05247988
    ## 372 COL16A1 ITGB1 R-HSA-3000178          REACTOME_ECM_PROTEOGLYCANS 0.02353755
    ## 373 COL16A1 ITGB1  R-HSA-446728 REACTOME_CELL_JUNCTION_ORGANIZATION 0.12993374
    ## 39   COL5A2 ITGB1  R-HSA-210991       REACTOME_BASIGIN_INTERACTIONS 0.13869391
    ## 391  COL5A2 ITGB1 R-HSA-3000170      REACTOME_SYNDECAN_INTERACTIONS 0.05448865
    ##           qval  L.logFC  R.logFC   LR.pval LR.corr rank len rank.pval rank.corr
    ## 37  0.18443989 1.929043 1.942352 0.3877306       1    6  11 0.4412335         1
    ## 371 0.10998635 1.929043 1.942352 0.3877306       1    5  10 0.3804001         1
    ## 372 0.07399432 1.929043 1.942352 0.3877306       1   15  29 0.3602822         1
    ## 373 0.18443989 1.929043 1.942352 0.3877306       1   17  32 0.4473203         1
    ## 39  0.18663749 1.509200 1.942352 0.4025717       1    6  11 0.4412335         1
    ## 391 0.10998635 1.509200 1.942352 0.4025717       1    5  10 0.3804001         1
    ##      LR.score   L.expr   R.expr
    ## 37  0.1498170 5.941247 1.875136
    ## 371 0.1498170 5.941247 1.875136
    ## 372 0.1498170 5.941247 1.875136
    ## 373 0.1498170 5.941247 1.875136
    ## 39  0.1405483 4.304669 1.875136
    ## 391 0.1405483 4.304669 1.875136

## 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

    sessionInfo()

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
    ## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C
    ##  [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: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] BulkSignalR_1.2.2 rmarkdown_2.30
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] RColorBrewer_1.1-3          sys_3.4.3
    ##   [3] jsonlite_2.0.0              shape_1.4.6.1
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