BulkSignalR-Differential.knit

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")

    head(LRinter(bsrinf.comp))

    ##         L     R         pw.id                             pw.name        pval
    ## 3  ADAM12 ITGB1  R-HSA-210991       REACTOME_BASIGIN_INTERACTIONS 0.067023343
    ## 31 ADAM12 ITGB1 R-HSA-3000170      REACTOME_SYNDECAN_INTERACTIONS 0.051901679
    ## 32 ADAM12 ITGB1 R-HSA-3000178          REACTOME_ECM_PROTEOGLYCANS 0.127311024
    ## 33 ADAM12 ITGB1  R-HSA-446728 REACTOME_CELL_JUNCTION_ORGANIZATION 0.027158835
    ## 5  ADAM17 ITGB1  R-HSA-210991       REACTOME_BASIGIN_INTERACTIONS 0.003591322
    ## 51 ADAM17 ITGB1 R-HSA-3000170      REACTOME_SYNDECAN_INTERACTIONS 0.002781056
    ##          qval  L.logFC R.logFC    LR.pval LR.corr rank len rank.pval rank.corr
    ## 3  0.11246184 3.302479 2.17398 0.15704712       1    6  11 0.4728156         1
    ## 31 0.09952965 3.302479 2.17398 0.15704712       1    6  11 0.4356370         1
    ## 32 0.16194711 3.302479 2.17398 0.15704712       1   17  32 0.5614918         1
    ## 33 0.08032886 3.302479 2.17398 0.15704712       1   17  32 0.4027523         1
    ## 5  0.06239251 1.580187 2.17398 0.00841508       1    6  11 0.4728156         1
    ## 51 0.06239251 1.580187 2.17398 0.00841508       1    6  11 0.4356370         1
    ##     LR.score   L.expr   R.expr
    ## 3  0.2090322 7.743552 7.351342
    ## 31 0.2090322 7.743552 7.351342
    ## 32 0.2090322 7.743552 7.351342
    ## 33 0.2090322 7.743552 7.351342
    ## 5  0.2132808 8.794301 7.351342
    ## 51 0.2132808 8.794301 7.351342

## 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 4.5.1 Patched (2025-08-23 r88802)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.22-bioc/R/lib/libRblas.so
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_GB              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: America/New_York
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats     graphics  grDevices utils     datasets  methods   base
    ##
    ## other attached packages:
    ## [1] BulkSignalR_1.2.0
    ##
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    ##   [1] RColorBrewer_1.1-3          jsonlite_2.0.0
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