inferCNVBayesNet: inferCNVBayesNet: Run Bayesian Network Mixture Model To...
In infercnv: Infer Copy Number Variation from Single-Cell RNA-Seq Data

Description Usage Arguments Value Examples

Uses Markov Chain Monte Carlo (MCMC) and Gibbs sampling to estimate the posterior probability of being in one of six Copy Number Variation states (states: 0, 0.5, 1, 1.5, 2, 3) for CNV's identified by inferCNV's HMM. Posterior probabilities are found for the entire CNV cluster and each individual cell line in the CNV.

inferCNVBayesNet(
  file_dir,
  infercnv_obj,
  HMM_states,
  out_dir,
  resume_file_token,
  model_file = NULL,
  CORES = 1,
  postMcmcMethod = NULL,
  plotingProbs = TRUE,
  quietly = TRUE,
  diagnostics = FALSE,
  HMM_type = HMM_type,
  k_obs_groups = k_obs_groups,
  cluster_by_groups = cluster_by_groups,
  reassignCNVs = TRUE,
  no_plot = no_plot
)

`file_dir`	Location of the directory of the inferCNV outputs.
`infercnv_obj`	InferCNV object.
`HMM_states`	InferCNV object with HMM states in expression data.
`out_dir`	(string) Path to where the output file should be saved to.
`resume_file_token`	(string) String token that contains some info on settings used to name files.
`model_file`	Path to the BUGS Model file.
`CORES`	Option to run parallel by specifying the number of cores to be used. (Default: 1)
`postMcmcMethod`	What actions to take after finishing the MCMC.
`plotingProbs`	Option for adding plots of Cell and CNV probabilities. (Default: TRUE)
`quietly`	Option to print descriptions along each step. (Default: TRUE)
`diagnostics`	Option to plot Diagnostic plots and tables. (Default: FALSE)
`HMM_type`	The type of HMM that was ra, either 'i3' or 'i6'. Determines how many state were predicted by the HMM.
`k_obs_groups`	Number of groups in which to break the observations. (default: 1)
`cluster_by_groups`	If observations are defined according to groups (ie. patients), each group of cells will be clustered separately. (default=FALSE, instead will use k_obs_groups setting)
`reassignCNVs`	(boolean) Given the CNV associated probability of belonging to each possible state, reassign the state assignments made by the HMM to the state that has the highest probability. (default: TRUE)
`no_plot`	(boolean) Option set by infercnv::run() for producing visualizations.

Returns a MCMC_inferCNV_obj and posterior probability of being in one of six Copy Number Variation states (states: 0, 0.5, 1, 1.5, 2, 3) for CNV's identified by inferCNV's HMM.

data(infercnv_data_example)
data(infercnv_annots_example)
data(infercnv_genes_example)
data(HMM_states)

infercnv_object_example <- infercnv::CreateInfercnvObject(raw_counts_matrix=infercnv_data_example, 
                                                          gene_order_file=infercnv_genes_example,
                                                          annotations_file=infercnv_annots_example,
                                                          ref_group_names=c("normal"))
          
out_dir = tempfile()
infercnv_object_example <- infercnv::run(infercnv_object_example,
                                         cutoff=1,
                                         out_dir=out_dir, 
                                         cluster_by_groups=TRUE, 
                                         denoise=TRUE,
                                         HMM=TRUE,
                                         num_threads=2,
                                         no_plot=TRUE)
mcmc_obj <- infercnv::inferCNVBayesNet(infercnv_obj      = infercnv_object_example,
                                       HMM_states        = HMM_states,
                                       file_dir          = out_dir,
                                       postMcmcMethod    = "removeCNV",
                                       out_dir           = out_dir,
                                       resume_file_token = "HMMi6.hmm_mode-samples",
                                       quietly           = TRUE,
                                       CORES             = 2,
                                       plotingProbs      = FALSE,
                                       diagnostics       = FALSE,
                                       HMM_type          = 'i6',
                                       k_obs_groups      = 1,
                                       cluster_by_groups = FALSE,
                                       reassignCNVs      = FALSE,
                                       no_plot           = TRUE)