How to perform CCI simulation by cellCellSimulate function

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 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.3 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] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] scTGIF_1.25.0                           
##  [2] Homo.sapiens_1.3.1                      
##  [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.22.1
##  [4] org.Hs.eg.db_3.22.0                     
##  [5] GO.db_3.22.0                            
##  [6] OrganismDbi_1.53.2                      
##  [7] GenomicFeatures_1.63.1                  
##  [8] AnnotationDbi_1.73.0                    
##  [9] SingleCellExperiment_1.33.0             
## [10] SummarizedExperiment_1.41.0             
## [11] Biobase_2.71.0                          
## [12] GenomicRanges_1.63.1                    
## [13] Seqinfo_1.1.0                           
## [14] IRanges_2.45.0                          
## [15] S4Vectors_0.49.0                        
## [16] MatrixGenerics_1.23.0                   
## [17] matrixStats_1.5.0                       
## [18] scTensor_2.21.0                         
## [19] RSQLite_2.4.5                           
## [20] LRBaseDbi_2.21.0                        
## [21] AnnotationHub_4.1.0                     
## [22] BiocFileCache_3.1.0                     
## [23] dbplyr_2.5.1                            
## [24] BiocGenerics_0.57.0                     
## [25] generics_0.1.4                          
## [26] BiocStyle_2.39.0                        
## 
## loaded via a namespace (and not attached):
##   [1] fs_1.6.6                 bitops_1.0-9             enrichplot_1.31.3       
##   [4] httr_1.4.7               webshot_0.5.5            RColorBrewer_1.1-3      
##   [7] Rgraphviz_2.55.0         tools_4.5.2              backports_1.5.0         
##  [10] R6_2.6.1                 lazyeval_0.2.2           withr_3.0.2             
##  [13] graphite_1.57.0          gridExtra_2.3            schex_1.25.0            
##  [16] fdrtool_1.2.18           cli_3.6.5                TSP_1.2.6               
##  [19] scatterpie_0.2.6         entropy_1.3.2            sass_0.4.10             
##  [22] S7_0.2.1                 genefilter_1.93.0        meshr_2.17.0            
##  [25] Rsamtools_2.27.0         systemfonts_1.3.1        yulab.utils_0.2.3       
##  [28] gson_0.1.0               DOSE_4.5.1               MeSHDbi_1.47.0          
##  [31] AnnotationForge_1.53.0   nnTensor_1.3.0           plotrix_3.8-13          
##  [34] maps_3.4.3               visNetwork_2.1.4         gridGraphics_0.5-1      
##  [37] GOstats_2.77.0           BiocIO_1.21.0            dplyr_1.1.4             
##  [40] dendextend_1.19.1        Matrix_1.7-4             abind_1.4-8             
##  [43] lifecycle_1.0.4          yaml_2.3.12              SparseArray_1.11.10     
##  [46] grid_4.5.2               blob_1.2.4               misc3d_0.9-1            
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##  [55] KEGGREST_1.51.1          sys_3.4.3                maketools_1.3.2         
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##  [94] bit_4.6.0                compiler_4.5.2           curl_7.0.0              
##  [97] httr2_1.2.2              graph_1.89.1             fontBitstreamVera_0.1.1 
## [100] DelayedArray_0.37.0      plotly_4.11.0            rtracklayer_1.71.3      
## [103] checkmate_2.3.3          scales_1.4.0             hexbin_1.28.5           
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## [109] stringr_1.6.0            digest_0.6.39            rmarkdown_2.30          
## [112] ca_0.71.1                XVector_0.51.0           htmltools_0.5.9         
## [115] pkgconfig_2.0.3          fastmap_1.2.0            rlang_1.1.6             
## [118] htmlwidgets_1.6.4        farver_2.1.2             jquerylib_0.1.4         
## [121] jsonlite_2.0.0           BiocParallel_1.45.0      GOSemSim_2.37.1         
## [124] RCurl_1.98-1.17          magrittr_2.0.4           ggplotify_0.1.3         
## [127] dotCall64_1.2            patchwork_1.3.2          Rcpp_1.1.0.8.1          
## [130] babelgene_22.9           ape_5.8-1                ggnewscale_0.5.2        
## [133] viridis_0.6.5            gdtools_0.4.4            stringi_1.8.7           
## [136] tagcloud_0.7.0           ggraph_2.2.2             MASS_7.3-65             
## [139] plyr_1.8.9               parallel_4.5.2           ggrepel_0.9.6           
## [142] Biostrings_2.79.3        graphlayouts_1.2.2       splines_4.5.2           
## [145] igraph_2.2.1             enrichit_0.0.8           buildtools_1.0.0        
## [148] reshape2_1.4.5           BiocVersion_3.23.1       XML_3.99-0.20           
## [151] evaluate_1.0.5           BiocManager_1.30.27      foreach_1.5.2           
## [154] tweenr_2.0.3             tidyr_1.3.2              purrr_1.2.0             
## [157] polyclip_1.10-7          heatmaply_1.6.0          ggplot2_4.0.1           
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## [163] restfulr_0.0.16          reactome.db_1.94.0       tidytree_0.4.6          
## [166] tidydr_0.0.6             viridisLite_0.4.2        tibble_3.3.0            
## [169] aplot_0.2.9              ccTensor_1.0.3           memoise_2.0.1           
## [172] registry_0.5-1           GenomicAlignments_1.47.0 cluster_2.1.8.1         
## [175] concaveman_1.2.0         GSEABase_1.73.0