CDSeq is a complete deconvolution method for dissecting bulk RNA-Seq data. The input of CDSeq is, ideally, bulk RNA-Seq read counts (similar to the input format required by DESeq2), and CDSeq will estimate, simultaneously, the cell-type-specific gene expression profiles and the sample-specific cell-type proportions, no reference of pure cell line GEPs or scRNAseq reference is needed for running CDSeq.
For example, if you have a bulk RNA-Seq data, a G by M matrix A, which is a G by M matrix. G denotes the number of genes and M is the sample size, then CDSeq will output B (a G by T matrix) and C (a T by M matrix), where T is the number of cell types, B is the estimate of cell-type-specific GEPs and C is the estimate of sample-specific cell-type proportions.
Importantly, you can ask CDSeq to estimate the number of cell types, i.e. T, by providing a vector of possible integer values for T. For example, if the user input for T is a vector, i.e. (T={2,3,4,5,6}), then CDSeq will estimate the most likely number for T.
You can install the released version of CDSeq from CRAN with:
install.packages("CDSeq")And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("kkang7/CDSeq_R_Package")build the vignette with
# install.packages("devtools")
devtools::install_github("kkang7/CDSeq_R_Package", build_vignettes = TRUE)It is possible for Mac users to run into some errors when install from source due to problems of Rcpp compiler tools. Follow the instruction here may help: https://thecoatlessprofessor.com/programming/cpp/r-compiler-tools-for-rcpp-on-macos/
Load package
library(CDSeq)## basic example code
result1<-CDSeq(bulk_data = mixtureGEP,
cell_type_number = 6,
mcmc_iterations = 5, # increase the mcmc_iterations to 700 or above
gene_length = as.vector(gene_length),
reference_gep = refGEP, # gene expression profile of pure cell lines
cpu_number = 1)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 = 5,
dilution_factor = 1,
block_number = 1,
gene_length = as.vector(gene_length),
reference_gep = refGEP, # gene expression profile of pure cell lines
cpu_number = 1, # 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.result <- CDSeq::CDSeq(bulk_data = pbmc_mix,
cell_type_number = seq(3,12,3),
beta = 0.5,
alpha = 5,
mcmc_iterations = 700,
cpu_number = 4,
dilution_factor = 10)
cdseq.result.celltypeassign <- cellTypeAssignSCRNA(cdseq_gep = cdseq.result$estGEP, # CDSeq-estimated cell-type-specific GEPs
cdseq_prop = cdseq.result$estProp, # CDSeq-estimated cell type proportions
sc_gep = sc_gep, # PBMC single cell data
sc_annotation = sc_annotation,# PBMC single data annotations
sc_pt_size = 3,
cdseq_pt_size = 6,
seurat_nfeatures = 100,
seurat_npcs = 50,
seurat_dims=1:5,
plot_umap = 1,
plot_tsne = 0)We provide recommendations for parameter settings. Note that these recommendations are merely emperical and there is no theoretical justifications yet. User can tune the parameters based on specific applications and domain knowledges.
| Parameters | Recommended setting |
|---|---|
| beta | 0.5 |
| alpha | 5 |
| mcmc_iteration | 700-2000 |
| dilution_factor | 2-10 |
| gene_subset_size | 200-500 |
| block_number | >5 |
Check vignette for more details and examples: browseVignettes(“CDSeq”).
email: [email protected]
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