Package 'CTSV'

Detection of cell-type-specific spatially variable genes

Description

Detection of cell-type-specific spatially variable genes

Usage

CTSV(spe, W, num_core = 1, BPPARAM = NULL)

Arguments

spe	A SpatialExperiment class.
W	A n by K cell-type proportion matrix, where K is the number of cell types. The column names of W are cell type names.
num_core	Number of cores if using paralleling. The default is one.
BPPARAM	Optional additional argument for parallelization. The default is NULL, in which case num_core will be used. If provided, this should be an instance of BiocParallelParam. For most users, the recommended option is to use the num_core argument.

Value

A list with a G by 2K matrix of p-values and a G by 2K matrix of q-values.

pval	A G by 2K matrix of p-values. The first K columns correspond to the first coordinate, and the last K columns to the second coordinate.
qval	A G by 2K matrix of q-values. The first K columns correspond to the first coordinate, and the last K columns to the second coordinate.

Examples

library(CTSV)
#read example data
data(CTSVexample_data)
spe <- CTSVexample_data[[1]]
W <- CTSVexample_data[[2]]
gamma_true <- CTSVexample_data[[3]]
# gene number
G <- nrow(spe)
# spot number
n <- ncol(spe)
# cell type number
K <- ncol(W)
G
n
K
# SV genes in each cell type:
rownames(W)[which(gamma_true[,1] == 1)]
rownames(W)[which(gamma_true[,2] == 1)]
# Number of SV genes at the aggregated level:
sum(rowSums(gamma_true)>0)
#--- Run CTSV ----
result <- CTSV(spe,W,num_core = 8)
# View on q-value matrix
head(result$qval)
# detect SV genes
re <- svGene(result$qval,0.05)
#SV genes in each cell type:
re$SVGene

---

A simulated data set

Description

A simulated data set for demonstrating how to use the ctsv function.

gamma_true

A 20 by 2 0-1 matrix, indicator of SV genes.

spe

A SpatialExperiment class.

W

A 100 by 2 cell-type proportion matrix.

Format

A list containing 3 elements.

Examples

library(SpatialExperiment)
rDirichlet <- function(n,alpha){
    l <- length(alpha)
    x <- matrix(rgamma(l * n, alpha), ncol = l, byrow = TRUE)
    sm <- x 
    return(x/as.vector(sm))
    
}
seed <- 20210509
set.seed(seed)
#  gene numbers
G <- 20
# cell type numbers
K <- 2
# spot numbers
n <- 100
# number of DE genes
DE_num <- 10
# drop out probability
pai <- 0.5
# parameter of NB distribution
size = 100

# coordinates of spots
loc <- NULL
for(i in 1:10){
    for(j in 1:10){
        loc <- rbind(loc,c(i,j))
    }
}
rownames(loc) <- paste0("spot",1:n)
colnames(loc) <- c("x","y")
NDE_scrna <- rnorm(G, mean=2, sd=0.2)
scrna_1 <- NDE_scrna
scrna_2 <- NDE_scrna
scrna_2[sample(1:G,DE_num,replace = FALSE)] <- rnorm(DE_num, mean=3, sd=0.2)
eta <- cbind(scrna_1,scrna_2)

gamma_true <- matrix(0, G, K)
gamma_true[11:13,1] <- 1
gamma_true[14:16,2] <- 1
beta1 <- matrix(0, G, K)
beta2 <- matrix(0, G, K)

# cell type proportion
W <- rDirichlet(n, c(1,2))
W <- t(W)

S <- t(loc) - colMeans(loc)
S <- t(S / apply(S, 1, sd))

h1 <- S[,1]
h2 <- S[,2]
beta1[gamma_true == 1] <- 1
beta2[gamma_true == 1] <- 0.5

log_lambda <- eta 
W <- t(W)
Y <- matrix(rnbinom(G*n,size = size, mu = exp(c(log_lambda))), G, n)
set.seed(5)
r_unif <- matrix(runif(G*n),G,n)
Y[r_unif <= pai] <- 0
colnames(Y) = rownames(loc)
rownames(W) = rownames(loc)
rownames(Y) <- paste0("gene",1:G)
spe <- SpatialExperiment(
    assay = list(counts = Y),
    colData = loc,
    spatialCoordsNames = c("x","y")
)
CTSVexample_data <- list(spe,W,gamma_true)

---

Report spatially variable genes

Description

Report spatially variable genes

Usage

svGene(Q_val, thre.alpha = 0.05)

Arguments

Q_val	A G by 2K q-value matrix, where G is the number of genes and K is the number of cell types.
thre.alpha	numeric, a q-value threshold to control FDR less than thre.alpha.

Value

A list with a G by 2K 0-1 matrix and a list with SV gene names in each cell type. The first K columns of the 0-1 matrix correspond to the coordinate of $S_1$, and the last K columns to the coordinate of $S_2$.

SV	A G by 2K 0-1 matrix. The first K columns correspond to the coordinate of $S_1$, the last K columns to the coordinate of $S_2$.
SVGene	A list with SV gene names in each cell type.

Examples

library(CTSV)
# Simulate a Q value matrix
K <- 2 # cell-type number
G <- 10 # gene number
set.seed(1)
Q_val <-matrix(runif(G*K,0,0.1),G,K)
rownames(Q_val) <- paste0("gene",seq_len(G))
# detect SV genes
re <- svGene(Q_val,0.05)
#SV genes in each cell type:
re$SVGene

---