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 Under development (unstable) (2024-10-26 r87273 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
## 
## Matrix products: default
## 
## 
## locale:
## [1] LC_COLLATE=C                          
## [2] LC_CTYPE=English_United States.utf8   
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.utf8    
## 
## time zone: America/New_York
## tzcode source: internal
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] scTGIF_1.21.0                          
##  [2] Homo.sapiens_1.3.1                     
##  [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
##  [4] org.Hs.eg.db_3.20.0                    
##  [5] GO.db_3.20.0                           
##  [6] OrganismDbi_1.49.0                     
##  [7] GenomicFeatures_1.59.0                 
##  [8] AnnotationDbi_1.69.0                   
##  [9] SingleCellExperiment_1.29.0            
## [10] SummarizedExperiment_1.37.0            
## [11] Biobase_2.67.0                         
## [12] GenomicRanges_1.59.0                   
## [13] GenomeInfoDb_1.43.0                    
## [14] IRanges_2.41.0                         
## [15] S4Vectors_0.45.0                       
## [16] MatrixGenerics_1.19.0                  
## [17] matrixStats_1.4.1                      
## [18] scTensor_2.17.0                        
## [19] RSQLite_2.3.7                          
## [20] LRBaseDbi_2.17.0                       
## [21] AnnotationHub_3.15.0                   
## [22] BiocFileCache_2.15.0                   
## [23] dbplyr_2.5.0                           
## [24] BiocGenerics_0.53.1                    
## [25] generics_0.1.3                         
## [26] BiocStyle_2.35.0                       
## 
## loaded via a namespace (and not attached):
##   [1] fs_1.6.5                 bitops_1.0-9             enrichplot_1.27.1       
##   [4] httr_1.4.7               webshot_0.5.5            RColorBrewer_1.1-3      
##   [7] Rgraphviz_2.51.0         tools_4.5.0              backports_1.5.0         
##  [10] utf8_1.2.4               R6_2.5.1                 lazyeval_0.2.2          
##  [13] withr_3.0.2              prettyunits_1.2.0        graphite_1.53.0         
##  [16] gridExtra_2.3            schex_1.21.0             fdrtool_1.2.18          
##  [19] cli_3.6.3                TSP_1.2-4                entropy_1.3.1           
##  [22] sass_0.4.9               genefilter_1.89.0        meshr_2.13.0            
##  [25] Rsamtools_2.23.0         yulab.utils_0.1.7        txdbmaker_1.3.0         
##  [28] gson_0.1.0               DOSE_4.1.0               R.utils_2.12.3          
##  [31] MeSHDbi_1.43.0           AnnotationForge_1.49.0   nnTensor_1.3.0          
##  [34] plotrix_3.8-4            maps_3.4.2               visNetwork_2.1.2        
##  [37] gridGraphics_0.5-1       GOstats_2.73.0           BiocIO_1.17.0           
##  [40] dplyr_1.1.4              dendextend_1.18.1        Matrix_1.7-1            
##  [43] fansi_1.0.6              abind_1.4-8              R.methodsS3_1.8.2       
##  [46] lifecycle_1.0.4          yaml_2.3.10              qvalue_2.39.0           
##  [49] SparseArray_1.7.0        grid_4.5.0               blob_1.2.4              
##  [52] misc3d_0.9-1             crayon_1.5.3             ggtangle_0.0.4          
##  [55] lattice_0.22-6           msigdbr_7.5.1            cowplot_1.1.3           
##  [58] annotate_1.85.0          KEGGREST_1.47.0          magick_2.8.5            
##  [61] pillar_1.9.0             knitr_1.48               fgsea_1.33.0            
##  [64] tcltk_4.5.0              rjson_0.2.23             codetools_0.2-20        
##  [67] fastmatch_1.1-4          glue_1.8.0               outliers_0.15           
##  [70] ggfun_0.1.7              data.table_1.16.2        vctrs_0.6.5             
##  [73] png_0.1-8                treeio_1.31.0            spam_2.11-0             
##  [76] rTensor_1.4.8            gtable_0.3.6             assertthat_0.2.1        
##  [79] cachem_1.1.0             xfun_0.49                S4Arrays_1.7.1          
##  [82] mime_0.12                tidygraph_1.3.1          survival_3.7-0          
##  [85] seriation_1.5.6          iterators_1.0.14         tinytex_0.54            
##  [88] fields_16.3              nlme_3.1-166             Category_2.73.0         
##  [91] ggtree_3.15.0            bit64_4.5.2              progress_1.2.3          
##  [94] filelock_1.0.3           bslib_0.8.0              colorspace_2.1-1        
##  [97] DBI_1.2.3                tidyselect_1.2.1         bit_4.5.0               
## [100] compiler_4.5.0           curl_5.2.3               httr2_1.0.6             
## [103] graph_1.85.0             xml2_1.3.6               DelayedArray_0.33.1     
## [106] plotly_4.10.4            bookdown_0.41            rtracklayer_1.67.0      
## [109] checkmate_2.3.2          scales_1.3.0             hexbin_1.28.4           
## [112] RBGL_1.83.0              plot3D_1.4.1             rappdirs_0.3.3          
## [115] stringr_1.5.1            digest_0.6.37            rmarkdown_2.29          
## [118] ca_0.71.1                XVector_0.47.0           htmltools_0.5.8.1       
## [121] pkgconfig_2.0.3          highr_0.11               fastmap_1.2.0           
## [124] rlang_1.1.4              htmlwidgets_1.6.4        UCSC.utils_1.3.0        
## [127] farver_2.1.2             jquerylib_0.1.4          jsonlite_1.8.9          
## [130] BiocParallel_1.41.0      GOSemSim_2.33.0          R.oo_1.27.0             
## [133] RCurl_1.98-1.16          magrittr_2.0.3           GenomeInfoDbData_1.2.13 
## [136] ggplotify_0.1.2          dotCall64_1.2            patchwork_1.3.0         
## [139] munsell_0.5.1            Rcpp_1.0.13-1            babelgene_22.9          
## [142] ape_5.8                  viridis_0.6.5            stringi_1.8.4           
## [145] tagcloud_0.6             ggraph_2.2.1             zlibbioc_1.53.0         
## [148] MASS_7.3-61              plyr_1.8.9               parallel_4.5.0          
## [151] ggrepel_0.9.6            Biostrings_2.75.0        graphlayouts_1.2.0      
## [154] splines_4.5.0            hms_1.1.3                igraph_2.1.1            
## [157] biomaRt_2.63.0           reshape2_1.4.4           BiocVersion_3.21.1      
## [160] XML_3.99-0.17            evaluate_1.0.1           BiocManager_1.30.25     
## [163] foreach_1.5.2            tweenr_2.0.3             tidyr_1.3.1             
## [166] purrr_1.0.2              polyclip_1.10-7          heatmaply_1.5.0         
## [169] ggplot2_3.5.1            ReactomePA_1.51.0        ggforce_0.4.2           
## [172] xtable_1.8-4             restfulr_0.0.15          reactome.db_1.89.0      
## [175] tidytree_0.4.6           viridisLite_0.4.2        tibble_3.2.1            
## [178] aplot_0.2.3              ccTensor_1.0.2           GenomicAlignments_1.43.0
## [181] memoise_2.0.1            registry_0.5-1           cluster_2.1.6           
## [184] concaveman_1.1.0         GSEABase_1.69.0