Function reference • scDNA

All functions

BuildMDP(): The main function builds an adjacency list of the theoretical order of mutations. The same MDP mask can be reused different mutations

add_cell_annotation(): Title

annotate_variants(): Annotate variants of interest

attach_weights(): Assigns observed counts as weights to the Markov Decision Process

clonograph(): Plotting clonographs

compare_VAFs(): Title

compute_clone_statistics(): Title

enumerate_clones(): Enumerate clones

extract_droplet_size(): Extract protein library size, dna library size, and amplicon size for all droplets.

fcs_export(): Title

gemerate_txdb(): Title

get_own_path(): A way to navigate the MDP for any given starting root node to leaf node.

impute_cluster(): Title

loom_to_sce(): Title

match_clonal_graph(): Title

mdp_Q_learning_with_linklist(): This file run Reinforcement Learning (model-free Q-learning) to evaluate the most likely mutation paths from the data

normalize_protein_data(): Normalize Protein Data

optimize_matrix(): Title

quality_output(): Produce long form quality metrics for each cell-variant pair for read depth, allele frequency, and genotype quality

readDNA_CN_H5(): This function generates the Copy Number by determining the ploidy of each mutation

select_clones(): Select clones of interest on the basis QC metrics

tabulate_mutations(): Title

tapestri_h5_to_sce(): Import Tapestri H5 data and extract genotype matrix

trajectory_analysis(): Run Trajectory Analysis after extraction from SingleCellExperiment object

variant_ID(): Variant identification and frequency tallies

visualize_WT_dominant_clone(): Title

visualize_all_WT_dominant_clone(): Title

visualize_any_optimal_path(): Title

visualize_full_network(): Title