In rikenbit/scTensor: Detection of cell-cell interaction from single-cell RNA-seq dataset by tensor decomposition

Introduction

Here, we explain the way to generate CCI simulation data. r Biocpkg("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()

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)

# 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)

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$input)
sim$input[1:2,1:3]

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$LR)
sim$LR[1:10,]
sim$LR[46:55,]
sim$LR[491:500,]

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$celltypes)
head(sim$celltypes)
table(names(sim$celltypes))

Session information {.unnumbered}

sessionInfo()

rikenbit/scTensor documentation built on Jan. 28, 2023, noon