Examples
When the number of cell types is a scalar
The following code runs CDSeq using synthetic mixtures comes with the R package. The mixtureGEP consists of 40 samples with 500 genes. These in silico mixtures are generated using six pure cell lines and random generated mixing proportions. CDSeq takes mixtureGEP as input and outputs the estimates of cell-type-specific gene expressions and sample-specific cell-type proportions. When refGEP is provided, CDSeq will assign the identities of those estimated cell types by comparing the GEP of estimated cell types with the GEP of refGEP. If refGEP is not provided, the estimated cell types will remain anonymous. The other way to annotate CDSeq-estimated cell types is to use single cell RNAseq data.
result1<-CDSeq(bulk_data = mixtureGEP,
cell_type_number = 6,
mcmc_iterations = 2000,
gene_length = as.vector(gene_length),
reference_gep = refGEP, # gene expression profile of pure cell lines
cpu_number = 1)The output result1 is a list containing multiple entities as listed in Output. When cell_type_number is a scalar, CDSeq will only return the following values.
ls(result1)
#> [1] "cellTypeAssignSplit" "cell_type_assignment" "estGEP"
#> [4] "estProp" "gibbsRunningTime" "parameters"
#> [7] "processIDs"We can compare the CDSeq-estimated cell-type-specific GEPs with the ground truth.
trueGEP <- true_GEP_rpkm[,result1$cell_type_assignment]
.pardefault <- par()
par(mar=c(4,3,1,1), mfrow=c(2,3),mai = c(.1, .01, 0.1, 0.1), pty="s")
for (i in 1:6) {
max_v <- max(c(result1$estGEP[,i], trueGEP[,i]))
plot(result1$estGEP[,i],
trueGEP[,i],
xlab = "CDSeq-estimated GEPs",
ylab = "True GEPs",
pch = 21,
bg = 'green',
cex=2,
xlim = c(0, max_v),
ylim = c(0,max_v),
main = paste0("cell type ",i))
lines(c(0,max_v),c(0,max_v), type = "l")
}
We then compare the CDSeq-estimated cell type proportions with group truth.
trueProp <- true_prop_cell[result1$cell_type_assignment,]
.pardefault <- par()
par(mar=c(4,3,1,1), mfrow=c(2,3),mai = c(.1, .01, 0.1, 0.1), pty="s")
for (i in 1:6) {
plot(result1$estProp[i,],
trueProp[i,],
pch = 21,
cex = 3,
bg='red',
xlab = "CDSeq-estimated proportions",
ylab = "True proportions",
xlim = c(0,1),
ylim = c(0,1),
main = paste0("cell type ",i))
lines(c(0,1),c(0,1), type = "l")
}
For this dataset and given parameter settings, CDSeq took this much amount of time to finish:
When the number of cell types is a vector
The cell_type_number can also be a vector which contains different integer values. CDSeq will perform estimation for each integer in the vector and estimate the number of cell types in the mixtures. For example, one can set cell_type_number = 2:10 as follows, and CDSeq will estimate the most likely number of cell types from 2 to 10.
result2<-CDSeq(bulk_data = mixtureGEP,
cell_type_number = 2:10,
mcmc_iterations = 2000,
dilution_factor = 1,
block_number = 1,
gene_length = as.vector(gene_length),
reference_gep = refGEP, # gene expression profile of pure cell lines
cpu_number = 9, # use multiple cores to save time. Set the cpu_number = length(cell_type_number) if there is enough cores.
print_progress_msg_to_file = 0)CDSeq will automatically estimate the number of cell types from the cell_type_number vector (2:10 in our example) and return the corresponding estimate in estGEP and estProp and it will keep all the estimations for the different values in cell_type_number in est_all. Check out output for details.
trueGEP <- true_GEP_gene[,result2$cell_type_assignment]
.pardefault <- par()
par(mar=c(4,3,1,1), mfrow=c(2,3),mai = c(.1, .01, 0.1, 0.1), pty="s")
for (i in 1:6) {
max_v <- max(c(result2$estGEP[,i], trueGEP[,i]))
plot(result2$estGEP[,i],
trueGEP[,i],
xlab = "CDSeq-estimated GEPs",
ylab = "True GEPs",
pch = 21,
bg = 'green',
cex=2,
xlim = c(0, max_v),
ylim = c(0,max_v),
main = paste0("cell type ",i))
lines(c(0,max_v),c(0,max_v), type = "l")
}
Similarly, we can compare the CDSeq-estimated cell type proportions with ground truth.
trueProp <- true_prop_cell[result2$cell_type_assignment,]
.pardefault <- par()
par(mar=c(4,3,1,1), mfrow=c(2,3),mai = c(.1, .01, 0.1, 0.1), pty="s")
for (i in 1:6) {
plot(result2$estProp[i,],
trueProp[i,],
pch = 21,
cex = 3,
bg='red',
xlab = "CDSeq-estimated proportions",
ylab = "True proportions",
xlim = c(0,1),
ylim = c(0,1),
main = paste0("cell type ",i))
lines(c(0,1),c(0,1), type = "l")
}
CDSeq estimates the number of cell types in the bulk samples using log posterior values. You can plot the log posterior values as follows. In this example, the max log posterior value happens at 6, therefore, CDSeq will guess that the number of different cell types in the bulk samples is 6 (which is exactly the number of distinct cell types we used for generating this in silico mixtures).
lgpst <- rep(0,9)
for (i in 1:9) {
lgpst[i] <- result2$est_all[[i]]$lgpst
}
plot(2:10,lgpst,xlab = "number of cell typess", ylab = "log posterior")
points(6,lgpst[5],pch=16,col="red")
lines(2:10, lgpst)
lines(c(6,6),c(0,lgpst[5]),lty=2)
Use dilution factor to speed up CDSeq
To speed up CDSeq, you can use dilution_factor. Set dilution_factor equal to N, CDSeq will require N times less computation time. The cost is that using dilution factor will lose information in the bulk samples. In practice, one can first use a large value for dilution_factor and estimate how much time will be required for running CDSeq. For example, if setting dilution_factor=1000 takes CDSeq 1 minute to finish, then setting dilution_factor=1 will roughly take CDSeq 1000 minutes to finish. Depending on the available computational power and time, one can adjust the value of dilution_factor to the affordably miminum value.
Reduce-Recover strategy for speeding up
We provide another option to speed up CDSeq.The idea of Reduce-Recover is to randomly sample multiple subsets of genes and perform deconvolution on these subsets of genes in parallel and combine the estimations from these subsets and take the average as the final estimates.
To use this feature, you need to set values for block_number and gene_subset_size.
result3<-CDSeq(bulk_data = mixtureGEP,
cell_type_number = 6,
mcmc_iterations = 2000,
dilution_factor = 1,
block_number = 10,
gene_subset_size = 100,
gene_length = as.vector(gene_length),
reference_gep = refGEP,
cpu_number = 10,
print_progress_msg_to_file = 0)
trueGEP <- true_GEP_rpkm[,result3$cell_type_assignment]
.pardefault <- par()
par(mar=c(4,3,1,1), mfrow=c(2,3),mai = c(.1, .01, 0.1, 0.1), pty="s")
for (i in 1:6) {
max_v <- max(c(result3$estGEP[,i], trueGEP[,i]))
plot(result3$estGEP[,i],
trueGEP[,i],
xlab = "CDSeq-estimated GEPs",
ylab = "True GEPs",
pch = 21,
bg = 'green',
cex=2,
xlim = c(0, max_v),
ylim = c(0,max_v),
main = paste0("cell type ",i))
lines(c(0,max_v),c(0,max_v), type = "l")
}
trueProp <- true_prop_cell[result3$cell_type_assignment,]
.pardefault <- par()
par(mar=c(4,3,1,1), mfrow=c(2,3),mai = c(.1, .01, 0.1, 0.1), pty="s")
for (i in 1:6) {
plot(result3$estProp[i,],
trueProp[i,],
pch = 21,
cex = 3,
bg='red',
xlab = "CDSeq-estimated proportions",
ylab = "True proportions",
xlim = c(0,1),
ylim = c(0,1),
main = paste0("cell type ",i))
lines(c(0,1),c(0,1), type = "l")
}
