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 4.1.1 (2021-08-10)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
## 
## 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       
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] scTGIF_1.7.0                           
##  [2] Homo.sapiens_1.3.1                     
##  [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
##  [4] org.Hs.eg.db_3.13.0                    
##  [5] GO.db_3.13.0                           
##  [6] OrganismDbi_1.35.0                     
##  [7] GenomicFeatures_1.45.2                 
##  [8] AnnotationDbi_1.55.1                   
##  [9] SingleCellExperiment_1.15.2            
## [10] SummarizedExperiment_1.23.4            
## [11] Biobase_2.53.0                         
## [12] GenomicRanges_1.45.0                   
## [13] GenomeInfoDb_1.29.8                    
## [14] IRanges_2.27.2                         
## [15] S4Vectors_0.31.3                       
## [16] MatrixGenerics_1.5.4                   
## [17] matrixStats_0.60.1                     
## [18] scTensor_2.3.3                         
## [19] RSQLite_2.2.8                          
## [20] LRBaseDbi_2.3.1                        
## [21] AnnotationHub_3.1.5                    
## [22] BiocFileCache_2.1.1                    
## [23] dbplyr_2.1.1                           
## [24] BiocGenerics_0.39.2                    
## [25] BiocStyle_2.21.3                       
## 
## loaded via a namespace (and not attached):
##   [1] ica_1.0-2                     Rsamtools_2.9.1              
##   [3] foreach_1.5.1                 lmtest_0.9-38                
##   [5] crayon_1.4.1                  spatstat.core_2.3-0          
##   [7] MASS_7.3-54                   nlme_3.1-152                 
##   [9] backports_1.2.1               GOSemSim_2.19.1              
##  [11] MeSHDbi_1.29.2                rlang_0.4.11                 
##  [13] XVector_0.33.0                ROCR_1.0-11                  
##  [15] irlba_2.3.3                   nnTensor_1.1.5               
##  [17] filelock_1.0.2                GOstats_2.59.1               
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## [101] ccTensor_1.0.2                concaveman_1.1.0             
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## [121] survival_3.2-13               yaml_2.2.1                   
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## [125] memoise_2.0.0                 rTensor_1.4.8                
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## [137] registry_0.5-1                spam_2.7-0                   
## [139] yulab.utils_0.0.2             future.apply_1.8.1           
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## [187] tensor_1.5                    httr_1.4.2                   
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## [215] pkgconfig_2.0.3               knitr_1.33.5