What is it for?

SingleCellSignalR is a tool that enables the inference of L-R interactions from single-cell data.

See also BulkSignalR vignette for a more complete description of all functionalities.

Main worfklow

The following code snippet give an example of the main commands to use in order to process your dataset with SingleCellSignalR package.

data(example_dataset,package='SingleCellSignalR')
mat <- log1p(data.matrix(example_dataset[,-1]))/log(2)
rownames(mat) <- example_dataset[[1]]
rme <- rowMeans(mat)
mmat <- mat[rme>0.05,]
d <- dist(t(mmat))
h <- hclust(d, method="ward.D")
clusters <- paste0("pop_", cutree(h, 5))

# SCSRNoNet -> LRscore based approach

scsrnn <- SCSRNoNet(mat,normalize=FALSE,method="log-only",
    min.count=1,prop=0.001,
    log.transformed=TRUE,populations=clusters)

scsrnn <- performInferences(scsrnn,verbose=TRUE,
    min.logFC=1e-10,max.pval=1,min.LR.score=0.5)

## Computing diffential expression tables:

##  pop_1

##  pop_2

##  pop_3

##  pop_4

##  pop_5

## Computing autocrine naive (network-free) interactions

## Computing paracrine naive (network-free) interactions

# SCSRNet -> DifferentialMode based approach

scsrcn <- SCSRNet(mat,normalize=FALSE,method="log-only",
    min.count=1,prop=0.001,
    log.transformed=TRUE,populations=clusters)

if(FALSE){
scsrcn <- performInferences(scsrcn,
    selected.populations = c("pop_1","pop_2","pop_3"),
    verbose=TRUE,rank.p=0.8,
    min.logFC=log2(1.01),max.pval=0.05)

print("getAutocrines")
inter1 <- getAutocrines(scsrcn, "pop_1")
head(inter1)

print("getParacrines")
inter2 <- getParacrines(scsrcn, "pop_1","pop_2")
head(inter2)

# Visualisation

cellNetBubblePlot(scsrcn)
}

Acknowledgements

We thank Morgan Maillard for his help with the LRdb database and Pierre Giroux for the work with proteomics.

Thank you for reading this guide and for using SingleCellSignalR.

Session Information

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] SingleCellSignalR_1.99.1 BulkSignalR_1.1.5       
## 
## 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_2.65.0             matrixTests_0.2.3          
##  [13] memoise_2.0.1               RCurl_1.98-1.17            
##  [15] ggtree_3.99.0               rstatix_0.7.2              
##  [17] htmltools_0.5.8.1           S4Arrays_1.9.1             
##  [19] curl_7.0.0                  broom_1.0.10               
##  [21] SparseArray_1.9.1           Formula_1.2-5              
##  [23] gridGraphics_0.5-1          sass_0.4.10                
##  [25] bslib_0.9.0                 htmlwidgets_1.6.4          
##  [27] httr2_1.2.1                 plotly_4.11.0              
##  [29] cachem_1.1.0                uuid_1.2-1                 
##  [31] igraph_2.1.4                lifecycle_1.0.4            
##  [33] iterators_1.0.14            pkgconfig_2.0.3            
##  [35] Matrix_1.7-4                R6_2.6.1                   
##  [37] fastmap_1.2.0               MatrixGenerics_1.21.0      
##  [39] clue_0.3-66                 digest_0.6.37              
##  [41] aplot_0.2.9                 colorspace_2.1-2           
##  [43] patchwork_1.3.2             S4Vectors_0.47.4           
##  [45] grr_0.9.5                   GenomicRanges_1.61.5       
##  [47] RSQLite_2.4.3               ggpubr_0.6.1               
##  [49] filelock_1.0.3              httr_1.4.7                 
##  [51] abind_1.4-8                 compiler_4.5.1             
##  [53] withr_3.0.2                 bit64_4.6.0-1              
##  [55] doParallel_1.0.17           S7_0.2.0                   
##  [57] backports_1.5.0             orthogene_1.15.02          
##  [59] carData_3.0-5               DBI_1.2.3                  
##  [61] homologene_1.4.68.19.3.27   ggsignif_0.6.4             
##  [63] MASS_7.3-65                 rappdirs_0.3.3             
##  [65] DelayedArray_0.35.3         rjson_0.2.23               
##  [67] tools_4.5.1                 ape_5.8-1                  
##  [69] glue_1.8.0                  stabledist_0.7-2           
##  [71] nlme_3.1-168                grid_4.5.1                 
##  [73] Rtsne_0.17                  cluster_2.1.8.1            
##  [75] generics_0.1.4              gtable_0.3.6               
##  [77] tidyr_1.3.1                 data.table_1.17.8          
##  [79] car_3.1-3                   XVector_0.49.1             
##  [81] BiocGenerics_0.55.1         ggrepel_0.9.6              
##  [83] RANN_2.6.2                  foreach_1.5.2              
##  [85] pillar_1.11.1               yulab.utils_0.2.1          
##  [87] babelgene_22.9              circlize_0.4.16            
##  [89] splines_4.5.1               dplyr_1.1.4                
##  [91] BiocFileCache_2.99.6        treeio_1.33.0              
##  [93] lattice_0.22-7              survival_3.8-3             
##  [95] bit_4.6.0                   tidyselect_1.2.1           
##  [97] ComplexHeatmap_2.25.2       SingleCellExperiment_1.31.1
##  [99] knitr_1.50                  gridExtra_2.3              
## [101] IRanges_2.43.5              Seqinfo_0.99.2             
## [103] SummarizedExperiment_1.39.2 stats4_4.5.1               
## [105] xfun_0.53                   Biobase_2.69.1             
## [107] matrixStats_1.5.0           lazyeval_0.2.2             
## [109] ggfun_0.2.0                 yaml_2.3.10                
## [111] evaluate_1.0.5              codetools_0.2-20           
## [113] tibble_3.3.0                ggplotify_0.1.3            
## [115] cli_3.6.5                   systemfonts_1.3.1          
## [117] jquerylib_0.1.4             dichromat_2.0-0.1          
## [119] Rcpp_1.1.0                  dbplyr_2.5.1               
## [121] gprofiler2_0.2.3            png_0.1-8                  
## [123] parallel_4.5.1              ggplot2_4.0.0              
## [125] blob_1.2.4                  ggalluvial_0.12.5          
## [127] bitops_1.0-9                glmnet_4.1-10              
## [129] SpatialExperiment_1.19.1    viridisLite_0.4.2          
## [131] tidytree_0.4.6              ggiraph_0.9.1              
## [133] scales_1.4.0                purrr_1.1.0                
## [135] crayon_1.5.3                GetoptLong_1.0.5           
## [137] rlang_1.1.6