CDSeq: Complete deconvolution using sequencing data.
In kkang7/CDSeq_R_Package: A Complete Deconvolution Method using Sequencing Data

Description Usage Arguments Value Examples

CDSeq takes bulk RNA-seq data as input and simultaneously returns estimates of both cell-type-specific gene expression profiles and sample-specific cell-type proportions.

CDSeq(
  bulk_data,
  beta = 0.5,
  alpha = 5,
  cell_type_number = NULL,
  mcmc_iterations = 700,
  dilution_factor = 1,
  gene_subset_size = NULL,
  block_number = 1,
  cpu_number = NULL,
  gene_length = NULL,
  reference_gep = NULL,
  verbose = FALSE,
  print_progress_msg_to_file = 0
)

`bulk_data`	RNA-Seq read counts matrix. Columns represent samples and rows represent genes.
`beta`	beta is a scalar or a vector of length G where G is the number of genes; default value for beta is 0.5; When beta=Null, CDSeq uses reference_gep to estimate beta.
`alpha`	alpha is a scalar or a vector of length cell_type_number where cell_type_number is the number of cell type; default value for alpha is 5.
`cell_type_number`	number of cell types. cell_type_number can be an integer or a vector of different integers. To estimate the number of cell types, please provide a vector for cell_type_number, e.g. cell_type_number <- 2:30, then CDSeq will estimate the number of cell types.
`mcmc_iterations`	number of iterations for the Gibbs sampler; default value is 700.
`dilution_factor`	a scalar to dilute the read counts for speeding up; default value is 1. CDSeq will use bulk_data/dilution_factor.
`gene_subset_size`	number of genes randomly sampled for each block. Default is NULL.
`block_number`	number of blocks. Each block contains gene_subset_size genes. Default is 1.
`cpu_number`	number of cpu cores that can be used for parallel computing; Default is NULL and CDSeq will detect the available number of cores on the device and use number of all cores - 1 for parallel computing.
`gene_length`	a vector of the effective length (gene length - read length + 1) of each gene; Default is NULL.
`reference_gep`	a reference gene expression profile can be used to determine the cell type and/or estimate beta; Default is NULL.
`verbose`	if TRUE, then print progress message to the console. Default is FALSE.
`print_progress_msg_to_file`	print progress message to a text file. Set 1 if need to print progress msg to a file and set 0 if no printing. Default is 0;

CDSeq returns estimates of both cell-type-specific gene expression profiles and sample-specific cell-type proportions. CDSeq will also return estimated number of cell types. and the log posterior values for different number of cell types.

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result1<-CDSeq(bulk_data =  mixtureGEP, cell_type_number = 6, mcmc_iterations = 5, 
       dilution_factor = 50, block_number = 1, gene_length = as.vector(gene_length), 
       reference_gep = refGEP, cpu_number=1, print_progress_msg_to_file=0)