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:
To make a very small example we generate random values, but user should provide his own logFC and associated pvalues from DGE ouputs.
We define the cluster comparison and add it.
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")
head(LRinter(bsrinf.comp))## L R pw.id pw.name pval
## 2 ADAM15 ITGA9 R-HSA-3000178 REACTOME_ECM_PROTEOGLYCANS 1.641519e-03
## 3 ADAM15 ITGB3 R-HSA-3000170 REACTOME_SYNDECAN_INTERACTIONS 1.636647e-04
## 31 ADAM15 ITGB3 R-HSA-3000178 REACTOME_ECM_PROTEOGLYCANS 5.177718e-05
## 28 BST1 CAV1 R-HSA-210991 REACTOME_BASIGIN_INTERACTIONS 8.393723e-02
## 38 CALR ITGA2B R-HSA-3000178 REACTOME_ECM_PROTEOGLYCANS 3.053505e-03
## 66 COL2A1 ITGA2B R-HSA-3000178 REACTOME_ECM_PROTEOGLYCANS 5.653611e-04
## qval L.logFC R.logFC LR.pval LR.corr rank len rank.pval
## 2 0.004705688 1.487408 2.138180 0.0136388864 1 8 16 0.3877769
## 3 0.003518790 1.487408 1.276765 0.0001644444 1 4 8 0.9015719
## 31 0.002226419 1.487408 1.276765 0.0001644444 1 11 21 0.4481597
## 28 0.094981598 1.828147 3.101976 0.1701775928 1 4 8 0.5569583
## 38 0.006565035 2.305529 2.107446 0.2660845568 1 8 15 0.2338296
## 66 0.003579419 2.154383 2.107446 0.0492659637 1 8 15 0.2338296
## rank.corr LR.score L.expr R.expr
## 2 1 0.05350349 0.8718653 0.399133
## 3 1 0.09745895 0.8718653 2.406051
## 31 1 0.09745895 0.8718653 2.406051
## 28 1 0.10180364 2.2882128 1.036318
## 38 1 0.15029061 1.6587165 7.232899
## 66 1 0.21484988 9.3865761 7.232899
The three basic BulkSignalR S4 classes BSRDataModel, BSRInference, and BSRSignature were derived into new classes to add the functionalities required by the differential mode:
bsrdm.comp is a daughter class of BSRDataModel, previously denoted bsrdmbsrinf.comp is a daughter class of BSRInference, previously denoted bsrinfbsrsig.comp is a daughter class of BSRSignature, previously denoted bsrsigThe 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.
## R version 4.5.1 (2025-06-13)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.2 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_1.1.3
##
## loaded via a namespace (and not attached):
## [1] DBI_1.2.3 bitops_1.0-9
## [3] grr_0.9.5 gridExtra_2.3
## [5] httr2_1.2.1 rlang_1.1.6
## [7] magrittr_2.0.3 clue_0.3-66
## [9] GetoptLong_1.0.5 matrixStats_1.5.0
## [11] compiler_4.5.1 RSQLite_2.4.2
## [13] gprofiler2_0.2.3 png_0.1-8
## [15] vctrs_0.6.5 ggalluvial_0.12.5
## [17] pkgconfig_2.0.3 SpatialExperiment_1.19.1
## [19] shape_1.4.6.1 crayon_1.5.3
## [21] fastmap_1.2.0 backports_1.5.0
## [23] dbplyr_2.5.0 magick_2.8.7
## [25] XVector_0.49.0 rmarkdown_2.29
## [27] purrr_1.1.0 bit_4.6.0
## [29] xfun_0.52 glmnet_4.1-10
## [31] cachem_1.1.0 aplot_0.2.8
## [33] jsonlite_2.0.0 blob_1.2.4
## [35] DelayedArray_0.35.2 broom_1.0.9
## [37] parallel_4.5.1 cluster_2.1.8.1
## [39] R6_2.6.1 bslib_0.9.0
## [41] RColorBrewer_1.1-3 car_3.1-3
## [43] GenomicRanges_1.61.1 jquerylib_0.1.4
## [45] Rcpp_1.1.0 Seqinfo_0.99.2
## [47] SummarizedExperiment_1.39.1 iterators_1.0.14
## [49] knitr_1.50 IRanges_2.43.0
## [51] Matrix_1.7-3 splines_4.5.1
## [53] igraph_2.1.4 tidyselect_1.2.1
## [55] dichromat_2.0-0.1 abind_1.4-8
## [57] yaml_2.3.10 doParallel_1.0.17
## [59] codetools_0.2-20 curl_6.4.0
## [61] lattice_0.22-7 tibble_3.3.0
## [63] withr_3.0.2 treeio_1.33.0
## [65] Biobase_2.69.0 evaluate_1.0.4
## [67] Rtsne_0.17 gridGraphics_0.5-1
## [69] survival_3.8-3 BiocFileCache_2.99.5
## [71] circlize_0.4.16 ggtree_3.17.1
## [73] pillar_1.11.0 ggpubr_0.6.1
## [75] filelock_1.0.3 carData_3.0-5
## [77] MatrixGenerics_1.21.0 foreach_1.5.2
## [79] stats4_4.5.1 plotly_4.11.0
## [81] ggfun_0.2.0 generics_0.1.4
## [83] RCurl_1.98-1.17 S4Vectors_0.47.0
## [85] ggplot2_3.5.2 tidytree_0.4.6
## [87] scales_1.4.0 glue_1.8.0
## [89] orthogene_1.15.0 lazyeval_0.2.2
## [91] tools_4.5.1 data.table_1.17.8
## [93] ggsignif_0.6.4 babelgene_22.9
## [95] RANN_2.6.2 fs_1.6.6
## [97] grid_4.5.1 ape_5.8-1
## [99] tidyr_1.3.1 colorspace_2.1-1
## [101] SingleCellExperiment_1.31.1 patchwork_1.3.1
## [103] nlme_3.1-168 homologene_1.4.68.19.3.27
## [105] Formula_1.2-5 cli_3.6.5
## [107] rappdirs_0.3.3 viridisLite_0.4.2
## [109] S4Arrays_1.9.1 ComplexHeatmap_2.25.2
## [111] dplyr_1.1.4 gtable_0.3.6
## [113] stabledist_0.7-2 yulab.utils_0.2.0
## [115] rstatix_0.7.2 sass_0.4.10
## [117] digest_0.6.37 BiocGenerics_0.55.1
## [119] ggplotify_0.1.2 SparseArray_1.9.1
## [121] ggrepel_0.9.6 htmlwidgets_1.6.4
## [123] rjson_0.2.23 farver_2.1.2
## [125] memoise_2.0.1 htmltools_0.5.8.1
## [127] multtest_2.65.0 lifecycle_1.0.4
## [129] httr_1.4.7 GlobalOptions_0.1.2
## [131] bit64_4.6.0-1 MASS_7.3-65