Hierarchical Modeling to Identify and Correct Genomic Biases in Hi-C

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Documentation for package ‘HiCPotts’ version 1.3.1

• DESCRIPTION file
• User guides, package vignettes and other documentation
• Package NEWS
• Package README

Help Pages

HiCPotts-package	HiCPotts: Hierarchical Modeling to Identify and Correct Genomic Biases in Hi-C
allocation_diagnostics	Monte-Carlo reliability of the per-cell latent-state probabilities
classify_hicpotts	Officially classify interactions from sampled HiCPotts latent states
combine_hicpotts_blocks	Combine independently fitted HiCPotts matrix blocks
compute_HMRFHiC_probabilities	Compute HiCPotts Probabilities of Assigning an Interaction to Each Component
diagnose_hicpotts_fit	Diagnose HiCPotts parameter estimation
get_data	Extract Hi-C Bin Interactions with GC, Accessibility, and TE Counts
HiCPotts	HiCPotts: Hierarchical Modeling to Identify and Correct Genomic Biases in Hi-C
plot_hicpotts_mcmc_by_component	Plot HiCPotts MCMC traces by component
plot_upper_prob_lower_count	Plot a dual-triangle Hi-C heatmap
posterior_predictive_hicpotts	Conditional posterior-predictive diagnostics for HiCPotts
process_data	CSV file Data Processing for Hi-C Interaction Matrices and Covariates
relabel_hicpotts	Relabel HiCPotts output
run_chain_betas	Fit one or more Hi-C datasets with HiCPotts
summarise_hicpotts_parameters	Report HiCPotts parameter summaries and diagnostic criteria
summarise_hicpotts_probabilities	Summarise HiCPotts posterior probabilities