cellCellSimulate functionscTensor 2.16.0
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 60sim$input[1:2,1:3]## Cell1 Cell2 Cell3
## Gene1 9105 2 0
## Gene2 4 37 850Next, sim$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$LR)## [1] 500 2sim$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 Gene90sim$LR[46:55,]## GENEID_L GENEID_R
## 46 Gene46 Gene126
## 47 Gene47 Gene127
## 48 Gene48 Gene128
## 49 Gene49 Gene129
## 50 Gene50 Gene130
## 51 Gene51 Gene131
## 52 Gene52 Gene132
## 53 Gene53 Gene133
## 54 Gene54 Gene134
## 55 Gene55 Gene135sim$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 Gene1000Finally, 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] 60head(sim$celltypes)## Celltype1 Celltype1 Celltype1 Celltype1 Celltype1 Celltype1
## "Cell1" "Cell2" "Cell3" "Cell4" "Cell5" "Cell6"table(names(sim$celltypes))##
## Celltype1 Celltype2 Celltype3
## 20 20 20## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
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## time zone: America/New_York
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] scTGIF_1.20.0
## [2] Homo.sapiens_1.3.1
## [3] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
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## [16] MatrixGenerics_1.18.0
## [17] matrixStats_1.4.1
## [18] scTensor_2.16.0
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