# 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_1.24.0
    ##  [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_1.52.0
    ##  [7] GenomicFeatures_1.62.0
    ##  [8] AnnotationDbi_1.72.0
    ##  [9] SingleCellExperiment_1.32.0
    ## [10] SummarizedExperiment_1.40.0
    ## [11] Biobase_2.70.0
    ## [12] GenomicRanges_1.62.0
    ## [13] Seqinfo_1.0.0
    ## [14] IRanges_2.44.0
    ## [15] S4Vectors_0.48.0
    ## [16] MatrixGenerics_1.22.0
    ## [17] matrixStats_1.5.0
    ## [18] scTensor_2.20.0
    ## [19] RSQLite_2.4.4
    ## [20] LRBaseDbi_2.20.0
    ## [21] AnnotationHub_4.0.0
    ## [22] BiocFileCache_3.0.0
    ## [23] dbplyr_2.5.1
    ## [24] BiocGenerics_0.56.0
    ## [25] generics_0.1.4
    ## [26] BiocStyle_2.38.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] fs_1.6.6                 bitops_1.0-9             enrichplot_1.30.2
    ##   [4] httr_1.4.7               webshot_0.5.5            RColorBrewer_1.1-3
    ##   [7] Rgraphviz_2.54.0         tools_4.5.2              backports_1.5.0
    ##  [10] R6_2.6.1                 lazyeval_0.2.2           withr_3.0.2
    ##  [13] graphite_1.56.0          gridExtra_2.3            schex_1.24.0
    ##  [16] fdrtool_1.2.18           cli_3.6.5                TSP_1.2-5
    ##  [19] entropy_1.3.2            sass_0.4.10              S7_0.2.0
    ##  [22] genefilter_1.92.0        meshr_2.16.0             Rsamtools_2.26.0
    ##  [25] systemfonts_1.3.1        yulab.utils_0.2.1        gson_0.1.0
    ##  [28] DOSE_4.4.0               R.utils_2.13.0           MeSHDbi_1.46.0
    ##  [31] AnnotationForge_1.52.0   nnTensor_1.3.0           plotrix_3.8-4
    ##  [34] maps_3.4.3               visNetwork_2.1.4         gridGraphics_0.5-1
    ##  [37] GOstats_2.76.0           BiocIO_1.20.0            dplyr_1.1.4
    ##  [40] dendextend_1.19.1        Matrix_1.7-4             abind_1.4-8
    ##  [43] R.methodsS3_1.8.2        lifecycle_1.0.4          yaml_2.3.10
    ##  [46] qvalue_2.42.0            SparseArray_1.10.1       grid_4.5.2
    ##  [49] blob_1.2.4               misc3d_0.9-1             crayon_1.5.3
    ##  [52] ggtangle_0.0.8           lattice_0.22-7           msigdbr_25.1.1
    ##  [55] cowplot_1.2.0            cigarillo_1.0.0          annotate_1.88.0
    ##  [58] KEGGREST_1.50.0          sys_3.4.3                maketools_1.3.2
    ##  [61] pillar_1.11.1            knitr_1.50               fgsea_1.36.0
    ##  [64] tcltk_4.5.2              rjson_0.2.23             codetools_0.2-20
    ##  [67] fastmatch_1.1-6          glue_1.8.0               ggiraph_0.9.2
    ##  [70] outliers_0.15            ggfun_0.2.0              fontLiberation_0.1.0
    ##  [73] data.table_1.17.8        vctrs_0.6.5              png_0.1-8
    ##  [76] treeio_1.34.0            spam_2.11-1              rTensor_1.4.9
    ##  [79] gtable_0.3.6             assertthat_0.2.1         cachem_1.1.0
    ##  [82] xfun_0.54                S4Arrays_1.10.0          tidygraph_1.3.1
    ##  [85] survival_3.8-3           seriation_1.5.8          iterators_1.0.14
    ##  [88] fields_17.1              nlme_3.1-168             Category_2.76.0
    ##  [91] ggtree_4.0.1             bit64_4.6.0-1            fontquiver_0.2.1
    ##  [94] filelock_1.0.3           bslib_0.9.0              DBI_1.2.3
    ##  [97] tidyselect_1.2.1         bit_4.6.0                compiler_4.5.2
    ## [100] curl_7.0.0               httr2_1.2.1              graph_1.88.0
    ## [103] fontBitstreamVera_0.1.1  DelayedArray_0.36.0      plotly_4.11.0
    ## [106] rtracklayer_1.70.0       checkmate_2.3.3          scales_1.4.0
    ## [109] hexbin_1.28.5            RBGL_1.86.0              plot3D_1.4.2
    ## [112] rappdirs_0.3.3           stringr_1.6.0            digest_0.6.37
    ## [115] rmarkdown_2.30           ca_0.71.1                XVector_0.50.0
    ## [118] htmltools_0.5.8.1        pkgconfig_2.0.3          fastmap_1.2.0
    ## [121] rlang_1.1.6              htmlwidgets_1.6.4        farver_2.1.2
    ## [124] jquerylib_0.1.4          jsonlite_2.0.0           BiocParallel_1.44.0
    ## [127] GOSemSim_2.36.0          R.oo_1.27.1              RCurl_1.98-1.17
    ## [130] magrittr_2.0.4           ggplotify_0.1.3          dotCall64_1.2
    ## [133] patchwork_1.3.2          Rcpp_1.1.0               babelgene_22.9
    ## [136] ape_5.8-1                viridis_0.6.5            gdtools_0.4.4
    ## [139] stringi_1.8.7            tagcloud_0.7.0           ggraph_2.2.2
    ## [142] MASS_7.3-65              plyr_1.8.9               parallel_4.5.2
    ## [145] ggrepel_0.9.6            Biostrings_2.78.0        graphlayouts_1.2.2
    ## [148] splines_4.5.2            igraph_2.2.1             buildtools_1.0.0
    ## [151] reshape2_1.4.4           BiocVersion_3.23.1       XML_3.99-0.20
    ## [154] evaluate_1.0.5           BiocManager_1.30.26      foreach_1.5.2
    ## [157] tweenr_2.0.3             tidyr_1.3.1              purrr_1.2.0
    ## [160] polyclip_1.10-7          heatmaply_1.6.0          ggplot2_4.0.0
    ## [163] ReactomePA_1.54.0        ggforce_0.5.0            xtable_1.8-4
    ## [166] restfulr_0.0.16          reactome.db_1.94.0       tidytree_0.4.6
    ## [169] viridisLite_0.4.2        tibble_3.3.0             aplot_0.2.9
    ## [172] ccTensor_1.0.3           GenomicAlignments_1.46.0 memoise_2.0.1
    ## [175] registry_0.5-1           cluster_2.1.8.1          concaveman_1.2.0
    ## [178] GSEABase_1.72.0