infercnv (version 1.3.3)

inferCNVBayesNet: inferCNVBayesNet: Run Bayesian Network Mixture Model To Obtain Posterior Probabilities For HMM Predicted States

Description

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.

Usage

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)

Arguments

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.

Value

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.

Examples

# NOT RUN {
data(data)
data(annots)
data(genes)
data(HMM_states)

infercnv_obj <- infercnv::CreateInfercnvObject(raw_counts_matrix=data, 
                                               gene_order_file=genes,
                                               annotations_file=annots,
                                               ref_group_names=c("normal"))

out_dir = tempfile()
infercnv_obj <- infercnv::run(infercnv_obj,
                              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_obj,
                              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)
                              
# }