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 4.0.3 (2020-10-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.12-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.12-bioc/R/lib/libRlapack.so
## 
## 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       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] AnnotationHub_2.22.0                   
##  [2] BiocFileCache_1.14.0                   
##  [3] dbplyr_1.4.4                           
##  [4] scTGIF_1.4.0                           
##  [5] Homo.sapiens_1.3.1                     
##  [6] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
##  [7] org.Hs.eg.db_3.12.0                    
##  [8] GO.db_3.12.0                           
##  [9] OrganismDbi_1.32.0                     
## [10] GenomicFeatures_1.42.0                 
## [11] AnnotationDbi_1.52.0                   
## [12] LRBase.Mmu.eg.db_1.2.0                 
## [13] SingleCellExperiment_1.12.0            
## [14] SummarizedExperiment_1.20.0            
## [15] Biobase_2.50.0                         
## [16] GenomicRanges_1.42.0                   
## [17] GenomeInfoDb_1.26.0                    
## [18] IRanges_2.24.0                         
## [19] S4Vectors_0.28.0                       
## [20] BiocGenerics_0.36.0                    
## [21] MatrixGenerics_1.2.0                   
## [22] matrixStats_0.57.0                     
## [23] scTensor_2.0.0                         
## [24] RSQLite_2.2.1                          
## [25] LRBase.Hsa.eg.db_1.2.0                 
## [26] LRBaseDbi_2.0.0                        
## [27] BiocStyle_2.18.0                       
## 
## loaded via a namespace (and not attached):
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##   [3] ica_1.0-2                     Rsamtools_2.6.0              
##   [5] foreach_1.5.1                 lmtest_0.9-38                
##   [7] crayon_1.3.4                  MASS_7.3-53                  
##   [9] nlme_3.1-150                  backports_1.1.10             
##  [11] GOSemSim_2.16.0               MeSHDbi_1.26.0               
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## [121] survival_3.2-7                yaml_2.2.1                   
## [123] plotrix_3.7-8                 htmltools_0.5.0              
## [125] memoise_1.1.0                 VariantAnnotation_1.36.0     
## [127] rTensor_1.4.1                 Seurat_3.2.2                 
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## [165] MeSH.AOR.db_1.13.0            meshr_1.26.0                 
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## [187] gtable_0.3.0                  DBI_1.1.0                    
## [189] tensor_1.5                    httr_1.4.2                   
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## [211] jpeg_0.1-8.1                  spatstat.data_1.4-3          
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## [215] MeSH.Hsa.eg.db_1.13.0         knitr_1.30