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 4.2.1 Patched (2022-07-09 r82577)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] C/en_US.UTF-8/en_US.UTF-8/C/en_GB/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] scTGIF_1.12.0                          
##  [2] Homo.sapiens_1.3.1                     
##  [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
##  [4] org.Hs.eg.db_3.16.0                    
##  [5] GO.db_3.16.0                           
##  [6] OrganismDbi_1.40.0                     
##  [7] GenomicFeatures_1.50.0                 
##  [8] AnnotationDbi_1.60.0                   
##  [9] SingleCellExperiment_1.20.0            
## [10] SummarizedExperiment_1.28.0            
## [11] Biobase_2.58.0                         
## [12] GenomicRanges_1.50.0                   
## [13] GenomeInfoDb_1.34.0                    
## [14] IRanges_2.32.0                         
## [15] S4Vectors_0.36.0                       
## [16] MatrixGenerics_1.10.0                  
## [17] matrixStats_0.62.0                     
## [18] scTensor_2.8.0                         
## [19] RSQLite_2.2.18                         
## [20] LRBaseDbi_2.8.0                        
## [21] AnnotationHub_3.6.0                    
## [22] BiocFileCache_2.6.0                    
## [23] dbplyr_2.2.1                           
## [24] BiocGenerics_0.44.0                    
## [25] BiocStyle_2.26.0                       
## 
## loaded via a namespace (and not attached):
##   [1] ica_1.0-3                     Rsamtools_2.14.0             
##   [3] foreach_1.5.2                 lmtest_0.9-40                
##   [5] crayon_1.5.2                  spatstat.core_2.4-4          
##   [7] MASS_7.3-58.1                 nlme_3.1-160                 
##   [9] backports_1.4.1               GOSemSim_2.24.0              
##  [11] MeSHDbi_1.34.0                rlang_1.0.6                  
##  [13] XVector_0.38.0                HDO.db_0.99.1                
##  [15] ROCR_1.0-11                   irlba_2.3.5.1                
##  [17] nnTensor_1.1.8                ca_0.71.1                    
##  [19] filelock_1.0.2                GOstats_2.64.0               
##  [21] rjson_0.2.21                  BiocParallel_1.32.0          
##  [23] tagcloud_0.6                  bit64_4.0.5                  
##  [25] glue_1.6.2                    sctransform_0.3.5            
##  [27] parallel_4.2.1                spatstat.sparse_3.0-0        
##  [29] dotCall64_1.0-2               tcltk_4.2.1                  
##  [31] DOSE_3.24.0                   spatstat.geom_3.0-3          
##  [33] tidyselect_1.2.0              SeuratObject_4.1.2           
##  [35] fitdistrplus_1.1-8            XML_3.99-0.12                
##  [37] tidyr_1.2.1                   zoo_1.8-11                   
##  [39] GenomicAlignments_1.34.0      xtable_1.8-4                 
##  [41] magrittr_2.0.3                evaluate_0.17                
##  [43] ggplot2_3.3.6                 cli_3.4.1                    
##  [45] zlibbioc_1.44.0               miniUI_0.1.1.1               
##  [47] sp_1.5-0                      bslib_0.4.0                  
##  [49] rpart_4.1.19                  fastmatch_1.1-3              
##  [51] treeio_1.22.0                 maps_3.4.1                   
##  [53] fields_14.1                   shiny_1.7.3                  
##  [55] xfun_0.34                     gson_0.0.9                   
##  [57] cluster_2.1.4                 tidygraph_1.2.2              
##  [59] TSP_1.2-1                     KEGGREST_1.38.0              
##  [61] tibble_3.1.8                  interactiveDisplayBase_1.36.0
##  [63] ggrepel_0.9.1                 ape_5.6-2                    
##  [65] listenv_0.8.0                 dendextend_1.16.0            
##  [67] Biostrings_2.66.0             png_0.1-7                    
##  [69] future_1.28.0                 withr_2.5.0                  
##  [71] bitops_1.0-7                  ggforce_0.4.1                
##  [73] RBGL_1.74.0                   plyr_1.8.7                   
##  [75] GSEABase_1.60.0               pillar_1.8.1                 
##  [77] cachem_1.0.6                  graphite_1.44.0              
##  [79] vctrs_0.5.0                   ellipsis_0.3.2               
##  [81] generics_0.1.3                plot3D_1.4                   
##  [83] rgdal_1.5-32                  outliers_0.15                
##  [85] tools_4.2.1                   entropy_1.3.1                
##  [87] munsell_0.5.0                 tweenr_2.0.2                 
##  [89] fgsea_1.24.0                  DelayedArray_0.24.0          
##  [91] rtracklayer_1.58.0            fastmap_1.1.0                
##  [93] compiler_4.2.1                abind_1.4-5                  
##  [95] httpuv_1.6.6                  plotly_4.10.0                
##  [97] rgeos_0.5-9                   GenomeInfoDbData_1.2.9       
##  [99] gridExtra_2.3                 lattice_0.20-45              
## [101] deldir_1.0-6                  visNetwork_2.1.2             
## [103] AnnotationForge_1.40.0        utf8_1.2.2                   
## [105] later_1.3.0                   dplyr_1.0.10                 
## [107] jsonlite_1.8.3                ccTensor_1.0.2               
## [109] concaveman_1.1.0              scales_1.2.1                 
## [111] graph_1.76.0                  tidytree_0.4.1               
## [113] pbapply_1.5-0                 genefilter_1.80.0            
## [115] lazyeval_0.2.2                promises_1.2.0.1             
## [117] goftest_1.2-3                 spatstat.utils_3.0-1         
## [119] reticulate_1.26               checkmate_2.1.0              
## [121] rmarkdown_2.17                cowplot_1.1.1                
## [123] schex_1.12.0                  webshot_0.5.4                
## [125] Rtsne_0.16                    uwot_0.1.14                  
## [127] igraph_1.3.5                  survival_3.4-0               
## [129] yaml_2.3.6                    plotrix_3.8-2                
## [131] htmltools_0.5.3               memoise_2.0.1                
## [133] rTensor_1.4.8                 BiocIO_1.8.0                 
## [135] Seurat_4.2.0                  seriation_1.4.0              
## [137] graphlayouts_0.8.3            viridisLite_0.4.1            
## [139] digest_0.6.30                 assertthat_0.2.1             
## [141] ReactomePA_1.42.0             mime_0.12                    
## [143] rappdirs_0.3.3                registry_0.5-1               
## [145] spam_2.9-1                    yulab.utils_0.0.5            
## [147] future.apply_1.9.1            misc3d_0.9-1                 
## [149] data.table_1.14.4             blob_1.2.3                   
## [151] splines_4.2.1                 RCurl_1.98-1.9               
## [153] hms_1.1.2                     colorspace_2.0-3             
## [155] BiocManager_1.30.19           aplot_0.1.8                  
## [157] sass_0.4.2                    Rcpp_1.0.9                   
## [159] bookdown_0.29                 RANN_2.6.1                   
## [161] enrichplot_1.18.0             fansi_1.0.3                  
## [163] parallelly_1.32.1             R6_2.5.1                     
## [165] grid_4.2.1                    ggridges_0.5.4               
## [167] lifecycle_1.0.3               curl_4.3.3                   
## [169] leiden_0.4.3                  meshr_2.4.0                  
## [171] jquerylib_0.1.4               Matrix_1.5-1                 
## [173] qvalue_2.30.0                 RcppAnnoy_0.0.20             
## [175] RColorBrewer_1.1-3            iterators_1.0.14             
## [177] stringr_1.4.1                 htmlwidgets_1.5.4            
## [179] polyclip_1.10-4               biomaRt_2.54.0               
## [181] purrr_0.3.5                   shadowtext_0.1.2             
## [183] gridGraphics_0.5-1            reactome.db_1.82.0           
## [185] mgcv_1.8-41                   globals_0.16.1               
## [187] patchwork_1.1.2               spatstat.random_3.0-0        
## [189] progressr_0.11.0              codetools_0.2-18             
## [191] prettyunits_1.1.1             gtable_0.3.1                 
## [193] DBI_1.1.3                     ggfun_0.0.7                  
## [195] tensor_1.5                    httr_1.4.4                   
## [197] highr_0.9                     KernSmooth_2.23-20           
## [199] stringi_1.7.8                 progress_1.2.2               
## [201] msigdbr_7.5.1                 reshape2_1.4.4               
## [203] farver_2.1.1                  heatmaply_1.4.0              
## [205] annotate_1.76.0               viridis_0.6.2                
## [207] hexbin_1.28.2                 fdrtool_1.2.17               
## [209] Rgraphviz_2.42.0              magick_2.7.3                 
## [211] ggtree_3.6.0                  xml2_1.3.3                   
## [213] restfulr_0.0.15               ggplotify_0.1.0              
## [215] Category_2.64.0               scattermore_0.8              
## [217] BiocVersion_3.16.0            bit_4.0.4                    
## [219] scatterpie_0.1.8              spatstat.data_3.0-0          
## [221] ggraph_2.1.0                  babelgene_22.9               
## [223] pkgconfig_2.0.3               knitr_1.40