Contents

1 Introduction

Here, we explain the way to generate CCI simulation data. scTensor has a function cellCellSimulate to generate the simulation data.

The simplest way to generate such data is cellCellSimulate with default parameters.

suppressPackageStartupMessages(library("scTensor"))
sim <- cellCellSimulate()
## Getting the values of params...
## Setting random seed...
## Generating simulation data...
## Done!

This function internally generate the parameter sets by newCCSParams, and the values of the parameter can be changed, and specified as the input of cellCellSimulate by users as follows.

# Default parameters
params <- newCCSParams()
str(params)
## Formal class 'CCSParams' [package "scTensor"] with 5 slots
##   ..@ nGene  : num 1000
##   ..@ nCell  : num [1:3] 50 50 50
##   ..@ cciInfo:List of 4
##   .. ..$ nPair: num 500
##   .. ..$ CCI1 :List of 4
##   .. .. ..$ LPattern: num [1:3] 1 0 0
##   .. .. ..$ RPattern: num [1:3] 0 1 0
##   .. .. ..$ nGene   : num 50
##   .. .. ..$ fc      : chr "E10"
##   .. ..$ CCI2 :List of 4
##   .. .. ..$ LPattern: num [1:3] 0 1 0
##   .. .. ..$ RPattern: num [1:3] 0 0 1
##   .. .. ..$ nGene   : num 50
##   .. .. ..$ fc      : chr "E10"
##   .. ..$ CCI3 :List of 4
##   .. .. ..$ LPattern: num [1:3] 0 0 1
##   .. .. ..$ RPattern: num [1:3] 1 0 0
##   .. .. ..$ nGene   : num 50
##   .. .. ..$ fc      : chr "E10"
##   ..@ lambda : num 1
##   ..@ seed   : num 1234
# Setting different parameters
# No. of genes : 1000
setParam(params, "nGene") <- 1000
# 3 cell types, 20 cells in each cell type
setParam(params, "nCell") <- c(20, 20, 20)
# Setting for Ligand-Receptor pair list
setParam(params, "cciInfo") <- list(
    nPair=500, # Total number of L-R pairs
    # 1st CCI
    CCI1=list(
        LPattern=c(1,0,0), # Only 1st cell type has this pattern
        RPattern=c(0,1,0), # Only 2nd cell type has this pattern
        nGene=50, # 50 pairs are generated as CCI1
        fc="E10"), # Degree of differential expression (Fold Change)
    # 2nd CCI
    CCI2=list(
        LPattern=c(0,1,0),
        RPattern=c(0,0,1),
        nGene=30,
        fc="E100")
    )
# Degree of Dropout
setParam(params, "lambda") <- 10
# Random number seed
setParam(params, "seed") <- 123

# Simulation data
sim <- cellCellSimulate(params)
## Getting the values of params...
## Setting random seed...
## Generating simulation data...
## Done!

The output object sim has some attributes as follows.

Firstly, sim$input contains a synthetic gene expression matrix. The size can be changed by nGene and nCell parameters described above.

dim(sim$input)
## [1] 1000   60
sim$input[1:2,1:3]
##       Cell1 Cell2 Cell3
## Gene1  9105     2     0
## Gene2     4    37   850

Next, sim$LR contains a ligand-receptor (L-R) pair list. The size can be changed by nPair parameter of cciInfo, and the differentially expressed (DE) L-R pairs are saved in the upper side of this matrix. Here, two DE L-R patterns are specified as cciInfo, and each number of pairs is 50 and 30, respectively.

dim(sim$LR)
## [1] 500   2
sim$LR[1:10,]
##    GENEID_L GENEID_R
## 1     Gene1   Gene81
## 2     Gene2   Gene82
## 3     Gene3   Gene83
## 4     Gene4   Gene84
## 5     Gene5   Gene85
## 6     Gene6   Gene86
## 7     Gene7   Gene87
## 8     Gene8   Gene88
## 9     Gene9   Gene89
## 10   Gene10   Gene90
sim$LR[46:55,]
##    GENEID_L GENEID_R
## 46   Gene46  Gene126
## 47   Gene47  Gene127
## 48   Gene48  Gene128
## 49   Gene49  Gene129
## 50   Gene50  Gene130
## 51   Gene51  Gene131
## 52   Gene52  Gene132
## 53   Gene53  Gene133
## 54   Gene54  Gene134
## 55   Gene55  Gene135
sim$LR[491:500,]
##     GENEID_L GENEID_R
## 491  Gene571  Gene991
## 492  Gene572  Gene992
## 493  Gene573  Gene993
## 494  Gene574  Gene994
## 495  Gene575  Gene995
## 496  Gene576  Gene996
## 497  Gene577  Gene997
## 498  Gene578  Gene998
## 499  Gene579  Gene999
## 500  Gene580 Gene1000

Finally, sim$celltypes contains a cell type vector. Since nCell is specified as “c(20, 20, 20)” described above, three cell types are generated.

length(sim$celltypes)
## [1] 60
head(sim$celltypes)
## Celltype1 Celltype1 Celltype1 Celltype1 Celltype1 Celltype1 
##   "Cell1"   "Cell2"   "Cell3"   "Cell4"   "Cell5"   "Cell6"
table(names(sim$celltypes))
## 
## Celltype1 Celltype2 Celltype3 
##        20        20        20

Session information

## 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-3.21-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] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] scTGIF_1.22.0                          
##  [2] Homo.sapiens_1.3.1                     
##  [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
##  [4] org.Hs.eg.db_3.21.0                    
##  [5] GO.db_3.21.0                           
##  [6] OrganismDbi_1.50.0                     
##  [7] GenomicFeatures_1.60.0                 
##  [8] AnnotationDbi_1.70.0                   
##  [9] SingleCellExperiment_1.30.1            
## [10] SummarizedExperiment_1.38.1            
## [11] Biobase_2.68.0                         
## [12] GenomicRanges_1.60.0                   
## [13] GenomeInfoDb_1.44.0                    
## [14] IRanges_2.42.0                         
## [15] S4Vectors_0.46.0                       
## [16] MatrixGenerics_1.20.0                  
## [17] matrixStats_1.5.0                      
## [18] scTensor_2.18.2                        
## [19] RSQLite_2.4.1                          
## [20] LRBaseDbi_2.18.1                       
## [21] AnnotationHub_3.16.0                   
## [22] BiocFileCache_2.16.0                   
## [23] dbplyr_2.5.0                           
## [24] BiocGenerics_0.54.0                    
## [25] generics_0.1.4                         
## [26] BiocStyle_2.36.0                       
## 
## loaded via a namespace (and not attached):
##   [1] fs_1.6.6                 bitops_1.0-9             enrichplot_1.28.2       
##   [4] httr_1.4.7               webshot_0.5.5            RColorBrewer_1.1-3      
##   [7] Rgraphviz_2.52.0         tools_4.5.1              backports_1.5.0         
##  [10] R6_2.6.1                 lazyeval_0.2.2           withr_3.0.2             
##  [13] prettyunits_1.2.0        graphite_1.54.0          gridExtra_2.3           
##  [16] schex_1.22.0             fdrtool_1.2.18           cli_3.6.5               
##  [19] TSP_1.2-5                entropy_1.3.2            sass_0.4.10             
##  [22] genefilter_1.90.0        meshr_2.14.0             Rsamtools_2.24.0        
##  [25] yulab.utils_0.2.0        gson_0.1.0               txdbmaker_1.4.1         
##  [28] DOSE_4.2.0               R.utils_2.13.0           MeSHDbi_1.44.0          
##  [31] AnnotationForge_1.50.0   dichromat_2.0-0.1        nnTensor_1.3.0          
##  [34] plotrix_3.8-4            maps_3.4.3               visNetwork_2.1.2        
##  [37] gridGraphics_0.5-1       GOstats_2.74.0           BiocIO_1.18.0           
##  [40] dplyr_1.1.4              dendextend_1.19.0        Matrix_1.7-3            
##  [43] abind_1.4-8              R.methodsS3_1.8.2        lifecycle_1.0.4         
##  [46] yaml_2.3.10              qvalue_2.40.0            SparseArray_1.8.0       
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##  [61] knitr_1.50               fgsea_1.34.0             tcltk_4.5.1             
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##  [70] data.table_1.17.6        vctrs_0.6.5              png_0.1-8               
##  [73] treeio_1.32.0            spam_2.11-1              rTensor_1.4.8           
##  [76] gtable_0.3.6             assertthat_0.2.1         cachem_1.1.0            
##  [79] xfun_0.52                S4Arrays_1.8.1           mime_0.13               
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##  [88] nlme_3.1-168             Category_2.74.0          ggtree_3.16.0           
##  [91] bit64_4.6.0-1            progress_1.2.3           filelock_1.0.3          
##  [94] bslib_0.9.0              DBI_1.2.3                tidyselect_1.2.1        
##  [97] bit_4.6.0                compiler_4.5.1           curl_6.4.0              
## [100] httr2_1.1.2              graph_1.86.0             xml2_1.3.8              
## [103] DelayedArray_0.34.1      plotly_4.11.0            bookdown_0.43           
## [106] rtracklayer_1.68.0       checkmate_2.3.2          scales_1.4.0            
## [109] hexbin_1.28.5            RBGL_1.84.0              plot3D_1.4.1            
## [112] rappdirs_0.3.3           stringr_1.5.1            digest_0.6.37           
## [115] rmarkdown_2.29           ca_0.71.1                XVector_0.48.0          
## [118] htmltools_0.5.8.1        pkgconfig_2.0.3          fastmap_1.2.0           
## [121] rlang_1.1.6              htmlwidgets_1.6.4        UCSC.utils_1.4.0        
## [124] farver_2.1.2             jquerylib_0.1.4          jsonlite_2.0.0          
## [127] BiocParallel_1.42.1      GOSemSim_2.34.0          R.oo_1.27.1             
## [130] RCurl_1.98-1.17          magrittr_2.0.3           GenomeInfoDbData_1.2.14 
## [133] ggplotify_0.1.2          dotCall64_1.2            patchwork_1.3.1         
## [136] Rcpp_1.0.14              babelgene_22.9           ape_5.8-1               
## [139] viridis_0.6.5            stringi_1.8.7            tagcloud_0.6            
## [142] ggraph_2.2.1             MASS_7.3-65              plyr_1.8.9              
## [145] parallel_4.5.1           ggrepel_0.9.6            Biostrings_2.76.0       
## [148] graphlayouts_1.2.2       splines_4.5.1            hms_1.1.3               
## [151] igraph_2.1.4             reshape2_1.4.4           biomaRt_2.64.0          
## [154] BiocVersion_3.21.1       XML_3.99-0.18            evaluate_1.0.4          
## [157] BiocManager_1.30.26      foreach_1.5.2            tweenr_2.0.3            
## [160] tidyr_1.3.1              purrr_1.0.4              polyclip_1.10-7         
## [163] heatmaply_1.5.0          ggplot2_3.5.2            ReactomePA_1.52.0       
## [166] ggforce_0.5.0            xtable_1.8-4             restfulr_0.0.15         
## [169] reactome.db_1.92.0       tidytree_0.4.6           viridisLite_0.4.2       
## [172] tibble_3.3.0             aplot_0.2.7              ccTensor_1.0.2          
## [175] memoise_2.0.1            registry_0.5-1           GenomicAlignments_1.44.0
## [178] cluster_2.1.8.1          concaveman_1.1.0         GSEABase_1.70.0