BiTSC2

Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data

Software dependencies

BiTSC2 is written with R and C++. Before implementing our software, please install the following packages in R:

tidyr, reshape, dplyr, coda, Rcpp, RcppArmadillo, ggplot2, igraph, mclust, gtools, vegan

Usage

To use BiTSC2, please set R working directory to BiTSC2-master after downloading this repository. Please make sure you have installed all the dependencies correctly, and then open source code BiTSC2_app.R to execute the commands line by line as following.

• In Model Input section, first set random seed myseed, and then specify the folder foldername to save output files (a new folder will be created if it does not exist). For example:

  #################################################################
  ###################### MODEL INPUT ##############################
  #################################################################
  
  myseed <-  1               # set random seed
  foldername <-  "temp_out"          # set output foldername
  dir.create(foldername)  # folder where outputs are saved
  

  Then input the total reads matrix and the mutant reads matrix D and X and squencing depth psi and segment information. For the given example data:

  scdata <- readRDS('example_data.RDS')
  D <- scdata$obs.reads$D_drop # total reads, M * N matrix, where row represents locus, column represents cell
  X <- scdata$obs.reads$X_drop # variant reads, M * N matrix. where row represents locus, column represents cell
  #segments <- NULL
  segments <- scdata$segment
  psi <- rep(3,dim(D)[2]) # squencing depth of each cell
  

  If there is genome segment information, it can be used as input information to improve the accuracy of the estimation. If not, assign variable segment as NULL, that is, use locus specific segments (each gene/ SNV locus as a segment) to update the CNA genotype matrix L;

• Next, assign Bayesian sampling parameters in specify_pars.R. For most of the parameters, BiTSC2 works just fine with default values. Some of the parameters you can change are:

  #### maximum number of copy
  Params$max_CN <- 4
  
  #### maximum number of mutant copies
  Params$max_mut <- 1
  
  MCMC_par$burnin <- 500  # burnin sample size
  MCMC_par$Nsamp <- 500   # number of samples for inference
  MCMC_par$Ntune <- 500  # number of samples used for adaptive parameter tuning
  
  Nclone <- c(3:10)  # candidate subclone numbers K
  

• Then execute the remaining sections step by step:

  • sampler.R to perform MCMC sampling, then the samples used for inference are stored in the .Rdata files;

  • Model_select.R to make model selection. The corresponding K and calculated BIC values are stored in the BIC_model_selection.Rdata, and the visual graphics of K and BIC are displayed in selection.pdf;

  • Visualization.R to visualize model sampling results: under different K, visualize the estimated subclonal evolutionary tree T, CNA genotype matrix L and SNV genotype matrix Z, and the results are shown in _fit.pdf files;

  • point_estimate.R to get the final estimated results from a given posterior sample .Rdata file, which are stored in the variable point_est.

Citation

Please cite the BiTSC2 in your publications if it helps your research.

@article{chen2022bitsc,
  title={BiTSC 2: Bayesian inference of Tumor clonal Tree by joint analysis of Single-Cell SNV and CNA data},
  author={Chen, Ziwei and Gong, Fuzhou and Wan, Lin and Ma, Liang},
  journal={Briefings in Bioinformatics},
  volume={23},
  number={3},
  pages={bbac092},
  year={2022},
  publisher={Oxford University Press}
}