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 4.3.0 RC (2023-04-13 r84266)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Monterey 12.6.1
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib 
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## 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_1.14.0                          
##  [2] Homo.sapiens_1.3.1                     
##  [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
##  [4] org.Hs.eg.db_3.17.0                    
##  [5] GO.db_3.17.0                           
##  [6] OrganismDbi_1.42.0                     
##  [7] GenomicFeatures_1.52.0                 
##  [8] AnnotationDbi_1.62.1                   
##  [9] SingleCellExperiment_1.22.0            
## [10] SummarizedExperiment_1.30.1            
## [11] Biobase_2.60.0                         
## [12] GenomicRanges_1.52.0                   
## [13] GenomeInfoDb_1.36.0                    
## [14] IRanges_2.34.0                         
## [15] S4Vectors_0.38.1                       
## [16] MatrixGenerics_1.12.0                  
## [17] matrixStats_0.63.0                     
## [18] scTensor_2.10.0                        
## [19] RSQLite_2.3.1                          
## [20] LRBaseDbi_2.10.0                       
## [21] AnnotationHub_3.8.0                    
## [22] BiocFileCache_2.8.0                    
## [23] dbplyr_2.3.2                           
## [24] BiocGenerics_0.46.0                    
## [25] BiocStyle_2.28.0                       
## 
## loaded via a namespace (and not attached):
##   [1] rTensor_1.4.8                 GSEABase_1.62.0              
##   [3] progress_1.2.2                goftest_1.2-3                
##   [5] Biostrings_2.68.0             vctrs_0.6.1                  
##   [7] spatstat.random_3.1-4         digest_0.6.31                
##   [9] png_0.1-8                     registry_0.5-1               
##  [11] ggrepel_0.9.3                 deldir_1.0-6                 
##  [13] parallelly_1.35.0             magick_2.7.4                 
##  [15] MASS_7.3-58.4                 reshape2_1.4.4               
##  [17] httpuv_1.6.9                  foreach_1.5.2                
##  [19] qvalue_2.32.0                 withr_2.5.0                  
##  [21] xfun_0.38                     ggfun_0.0.9                  
##  [23] ellipsis_0.3.2                survival_3.5-5               
##  [25] memoise_2.0.1                 hexbin_1.28.3                
##  [27] gson_0.1.0                    tidytree_0.4.2               
##  [29] zoo_1.8-12                    pbapply_1.7-0                
##  [31] entropy_1.3.1                 prettyunits_1.1.1            
##  [33] KEGGREST_1.40.0               promises_1.2.0.1             
##  [35] httr_1.4.5                    restfulr_0.0.15              
##  [37] schex_1.14.0                  globals_0.16.2               
##  [39] fitdistrplus_1.1-8            miniUI_0.1.1.1               
##  [41] generics_0.1.3                DOSE_3.26.1                  
##  [43] reactome.db_1.84.0            babelgene_22.9               
##  [45] concaveman_1.1.0              curl_5.0.0                   
##  [47] fields_14.1                   zlibbioc_1.46.0              
##  [49] ggraph_2.1.0                  polyclip_1.10-4              
##  [51] ca_0.71.1                     GenomeInfoDbData_1.2.10      
##  [53] RBGL_1.76.0                   interactiveDisplayBase_1.38.0
##  [55] xtable_1.8-4                  stringr_1.5.0                
##  [57] evaluate_0.20                 S4Arrays_1.0.1               
##  [59] hms_1.1.3                     bookdown_0.33                
##  [61] irlba_2.3.5.1                 colorspace_2.1-0             
##  [63] filelock_1.0.2                visNetwork_2.1.2             
##  [65] ROCR_1.0-11                   reticulate_1.28              
##  [67] spatstat.data_3.0-1           magrittr_2.0.3               
##  [69] lmtest_0.9-40                 Rgraphviz_2.44.0             
##  [71] later_1.3.0                   viridis_0.6.2                
##  [73] ggtree_3.8.0                  lattice_0.21-8               
##  [75] misc3d_0.9-1                  spatstat.geom_3.1-0          
##  [77] future.apply_1.10.0           genefilter_1.82.1            
##  [79] plot3D_1.4                    scattermore_0.8              
##  [81] XML_3.99-0.14                 shadowtext_0.1.2             
##  [83] cowplot_1.1.1                 RcppAnnoy_0.0.20             
##  [85] pillar_1.9.0                  nlme_3.1-162                 
##  [87] iterators_1.0.14              compiler_4.3.0               
##  [89] stringi_1.7.12                Category_2.66.0              
##  [91] TSP_1.2-4                     tensor_1.5                   
##  [93] dendextend_1.17.1             GenomicAlignments_1.36.0     
##  [95] plyr_1.8.8                    msigdbr_7.5.1                
##  [97] BiocIO_1.10.0                 crayon_1.5.2                 
##  [99] abind_1.4-5                   gridGraphics_0.5-1           
## [101] sp_1.6-0                      graphlayouts_0.8.4           
## [103] bit_4.0.5                     dplyr_1.1.1                  
## [105] fastmatch_1.1-3               tagcloud_0.6                 
## [107] codetools_0.2-19              bslib_0.4.2                  
## [109] plotly_4.10.1                 mime_0.12                    
## [111] splines_4.3.0                 Rcpp_1.0.10                  
## [113] HDO.db_0.99.1                 knitr_1.42                   
## [115] blob_1.2.4                    utf8_1.2.3                   
## [117] BiocVersion_3.17.1            listenv_0.9.0                
## [119] checkmate_2.1.0               ggplotify_0.1.0              
## [121] tibble_3.2.1                  Matrix_1.5-4                 
## [123] tweenr_2.0.2                  pkgconfig_2.0.3              
## [125] tools_4.3.0                   cachem_1.0.7                 
## [127] viridisLite_0.4.1             DBI_1.1.3                    
## [129] graphite_1.46.0               fastmap_1.1.1                
## [131] rmarkdown_2.21                scales_1.2.1                 
## [133] grid_4.3.0                    outliers_0.15                
## [135] ica_1.0-3                     Seurat_4.3.0                 
## [137] Rsamtools_2.16.0              sass_0.4.5                   
## [139] patchwork_1.1.2               BiocManager_1.30.20          
## [141] dotCall64_1.0-2               graph_1.78.0                 
## [143] RANN_2.6.1                    farver_2.1.1                 
## [145] tidygraph_1.2.3               scatterpie_0.1.8             
## [147] yaml_2.3.7                    AnnotationForge_1.42.0       
## [149] rtracklayer_1.60.0            cli_3.6.1                    
## [151] purrr_1.0.1                   webshot_0.5.4                
## [153] leiden_0.4.3                  lifecycle_1.0.3              
## [155] uwot_0.1.14                   backports_1.4.1              
## [157] BiocParallel_1.34.1           annotate_1.78.0              
## [159] MeSHDbi_1.36.0                rjson_0.2.21                 
## [161] gtable_0.3.3                  ggridges_0.5.4               
## [163] progressr_0.13.0              parallel_4.3.0               
## [165] ape_5.7-1                     jsonlite_1.8.4               
## [167] seriation_1.4.2               bitops_1.0-7                 
## [169] ggplot2_3.4.2                 bit64_4.0.5                  
## [171] assertthat_0.2.1              Rtsne_0.16                   
## [173] yulab.utils_0.0.6             ReactomePA_1.44.0            
## [175] spatstat.utils_3.0-2          SeuratObject_4.1.3           
## [177] heatmaply_1.4.2               jquerylib_0.1.4              
## [179] highr_0.10                    nnTensor_1.1.13              
## [181] GOSemSim_2.26.0               ccTensor_1.0.2               
## [183] lazyeval_0.2.2                shiny_1.7.4                  
## [185] htmltools_0.5.5               enrichplot_1.20.0            
## [187] sctransform_0.3.5             rappdirs_0.3.3               
## [189] glue_1.6.2                    tcltk_4.3.0                  
## [191] spam_2.9-1                    XVector_0.40.0               
## [193] RCurl_1.98-1.12               treeio_1.24.0                
## [195] gridExtra_2.3                 igraph_1.4.2                 
## [197] R6_2.5.1                      tidyr_1.3.0                  
## [199] fdrtool_1.2.17                cluster_2.1.4                
## [201] aplot_0.1.10                  DelayedArray_0.26.2          
## [203] tidyselect_1.2.0              plotrix_3.8-2                
## [205] GOstats_2.66.0                maps_3.4.1                   
## [207] xml2_1.3.3                    ggforce_0.4.1                
## [209] future_1.32.0                 munsell_0.5.0                
## [211] KernSmooth_2.23-20            data.table_1.14.8            
## [213] htmlwidgets_1.6.2             fgsea_1.26.0                 
## [215] RColorBrewer_1.1-3            biomaRt_2.56.0               
## [217] rlang_1.1.0                   spatstat.sparse_3.0-1        
## [219] meshr_2.6.0                   spatstat.explore_3.1-0       
## [221] fansi_1.0.4