# 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 24.04.4 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_1.33.1
    ## [10] SummarizedExperiment_1.41.1
    ## [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_2.4.6
    ## [20] LRBaseDbi_2.21.0
    ## [21] AnnotationHub_4.1.0
    ## [22] BiocFileCache_3.1.0
    ## [23] dbplyr_2.5.2
    ## [24] BiocGenerics_0.57.0
    ## [25] generics_0.1.4
    ## [26] BiocStyle_2.39.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] fs_1.6.7                 bitops_1.0-9             enrichplot_1.31.4
    ##   [4] httr_1.4.8               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
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    ##  [28] gson_0.1.0               DOSE_4.5.1               MeSHDbi_1.47.0
    ##  [31] AnnotationForge_1.53.0   nnTensor_1.3.0           plotrix_3.8-14
    ##  [34] maps_3.4.3               visNetwork_2.1.4         gridGraphics_0.5-1
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    ## [139] plyr_1.8.9               parallel_4.5.2           ggrepel_0.9.7
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    ## [145] igraph_2.2.2             enrichit_0.1.3           buildtools_1.0.0
    ## [148] reshape2_1.4.5           BiocVersion_3.23.1       XML_3.99-0.22
    ## [151] evaluate_1.0.5           BiocManager_1.30.27      foreach_1.5.2
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