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) (2024-03-06 r86056)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so 
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              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: America/New_York
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] scTGIF_1.17.0                          
##  [2] Homo.sapiens_1.3.1                     
##  [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
##  [4] org.Hs.eg.db_3.18.0                    
##  [5] GO.db_3.18.0                           
##  [6] OrganismDbi_1.45.0                     
##  [7] GenomicFeatures_1.55.4                 
##  [8] AnnotationDbi_1.65.2                   
##  [9] SingleCellExperiment_1.25.0            
## [10] SummarizedExperiment_1.33.3            
## [11] Biobase_2.63.0                         
## [12] GenomicRanges_1.55.3                   
## [13] GenomeInfoDb_1.39.9                    
## [14] IRanges_2.37.1                         
## [15] S4Vectors_0.41.4                       
## [16] MatrixGenerics_1.15.0                  
## [17] matrixStats_1.2.0                      
## [18] scTensor_2.13.1                        
## [19] RSQLite_2.3.5                          
## [20] LRBaseDbi_2.13.1                       
## [21] AnnotationHub_3.11.1                   
## [22] BiocFileCache_2.11.1                   
## [23] dbplyr_2.4.0                           
## [24] BiocGenerics_0.49.1                    
## [25] BiocStyle_2.31.0                       
## 
## loaded via a namespace (and not attached):
##   [1] fs_1.6.3                 bitops_1.0-7             enrichplot_1.23.1       
##   [4] HDO.db_0.99.1            httr_1.4.7               webshot_0.5.5           
##   [7] RColorBrewer_1.1-3       Rgraphviz_2.47.0         tools_4.4.0             
##  [10] backports_1.4.1          utf8_1.2.4               R6_2.5.1                
##  [13] lazyeval_0.2.2           withr_3.0.0              graphite_1.49.0         
##  [16] gridExtra_2.3            schex_1.17.0             fdrtool_1.2.17          
##  [19] cli_3.6.2                TSP_1.2-4                scatterpie_0.2.1        
##  [22] entropy_1.3.1            sass_0.4.8               genefilter_1.85.1       
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##  [31] AnnotationForge_1.45.0   nnTensor_1.2.0           plotrix_3.8-4           
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##  [37] gridGraphics_0.5-1       GOstats_2.69.0           BiocIO_1.13.0           
##  [40] dplyr_1.1.4              dendextend_1.17.1        Matrix_1.6-5            
##  [43] fansi_1.0.6              abind_1.4-5              lifecycle_1.0.4         
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##  [85] fields_15.2              nlme_3.1-164             Category_2.69.0         
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##  [91] bslib_0.6.1              colorspace_2.1-0         DBI_1.2.2               
##  [94] tidyselect_1.2.1         bit_4.0.5                compiler_4.4.0          
##  [97] curl_5.2.1               graph_1.81.0             DelayedArray_0.29.9     
## [100] plotly_4.10.4            bookdown_0.38            shadowtext_0.1.3        
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## [106] hexbin_1.28.3            RBGL_1.79.0              plot3D_1.4.1            
## [109] rappdirs_0.3.3           stringr_1.5.1            digest_0.6.35           
## [112] rmarkdown_2.26           ca_0.71.1                XVector_0.43.1          
## [115] htmltools_0.5.7          pkgconfig_2.0.3          highr_0.10              
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## [121] farver_2.1.1             jquerylib_0.1.4          jsonlite_1.8.8          
## [124] BiocParallel_1.37.1      GOSemSim_2.29.1          RCurl_1.98-1.14         
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## [139] ggraph_2.2.1             zlibbioc_1.49.3          MASS_7.3-60.2           
## [142] plyr_1.8.9               parallel_4.4.0           ggrepel_0.9.5           
## [145] Biostrings_2.71.4        graphlayouts_1.1.1       splines_4.4.0           
## [148] igraph_2.0.3             reshape2_1.4.4           BiocVersion_3.19.1      
## [151] XML_3.99-0.16.1          evaluate_0.23            BiocManager_1.30.22     
## [154] foreach_1.5.2            tweenr_2.0.3             tidyr_1.3.1             
## [157] purrr_1.0.2              polyclip_1.10-6          heatmaply_1.5.0         
## [160] ggplot2_3.5.0            ReactomePA_1.47.0        ggforce_0.4.2           
## [163] xtable_1.8-4             restfulr_0.0.15          reactome.db_1.86.2      
## [166] tidytree_0.4.6           viridisLite_0.4.2        tibble_3.2.1            
## [169] aplot_0.2.2              ccTensor_1.0.2           memoise_2.0.1           
## [172] registry_0.5-1           GenomicAlignments_1.39.4 cluster_2.1.6           
## [175] concaveman_1.1.0         GSEABase_1.65.1