Package index • musicatk

Importing and processing

Functions to import, extract, and process variants

extract_variants(): Extract variants from mutliple objects

extract_variants_from_maf(): Extract variants from a maf object

extract_variants_from_maf_file(): Extracts variants from a maf file

extract_variants_from_matrix(): Extract variants from matrix or data.frame like objects

extract_variants_from_vcf(): Extracts variants from a VariantAnnotation VCF object

extract_variants_from_vcf_file(): Extracts variants from a vcf file

musica objects

Functions to create and subset musica objects

select_genome(): Helper function to load common human or mouse genomes

create_musica_from_variants(): Creates a musica object from a variant table

create_musica_from_counts(): Creates a musica object from a mutation count table

subset_musica_by_annotation(): Creates a new musica object subsetted to only one value of a sample annotation

subset_musica_by_counts(): Creates a new musica subsetted to only samples with enough variants

subset_variant_by_type(): Subsets a variant table based on Variant Type

subset_variants_by_samples(): Return sample from musica_variant object

variants() `variants<-`(): Retrieve variants from a musica object

Creating mutation count tables

Functions to build and combine mutation count tables

build_standard_table(): Builds count tables using various mutation type schemas

build_custom_table(): Builds a custom table from specified user variants

add_flank_to_variants(): Uses a genome object to find context and add it to the variant table

annotate_replication_strand(): Add replication strand annotation to SBS variants based on bedgraph file

annotate_transcript_strand(): Add transcript strand annotation to SBS variants (defined in genes only)

annotate_variant_length(): Adds an annotation to the input musica's variant table with length of each variant

annotate_variant_type(): Annotate variants with variant type ("SBS", "INS", "DEl", "DBS")

combine_count_tables(): Combines tables into a single table that can be used for discovery/prediction

extract_count_tables(): Extract count tables list from a musica object

tables() `tables<-`(): Retrieve the list of count_tables from a musica object

get_count_table(): Retrieve count_table matrix from count_table object

Sample annotations

Functions to add and manipulate sample annotations

samp_annot() `samp_annot<-`(): Get or set sample annotations from a musica object

sample_names(): Retrieve sample names from a musica object

drop_annotation(): Drops a column from the variant table that the user no longer needs

K value help

Functions to help determine k value

compare_k_vals(): Compare k values

plot_k_comparison(): Plot k comparison

Discovery and prediction

Functions to identify signatures and exposures from mutation count tables

discover_signatures(): Discover mutational signatures

exposures() `exposures<-`(): Retrieve exposures from a result_model, result_collection, or musica object

signatures() `signatures<-`(): Retrieve signatures from a result_model, result_collection, or musica object

name_signatures(): Rename signatures for a model

auto_predict_grid(): Automatic filtering of signatures for exposure prediction gridded across specific annotation

combine_predict_grid(): Combine signatures and exposures of different models. Exposure values are zero for samples in an annotation where that signature was not predicted

generate_result_grid(): Generate result_grid from musica based on annotation and range of k

Accessing result data in musica objects

Functions to add, extract, and access data within the result_list of a musica object

add_result(): Load an external model into a musica object

get_model(): Retrieve model from a musica or result collection object

get_result_list_entry(): Retrieve result_list entry from a musica object

get_modality(): Retrieve a specific modality entry from a musica or result_collection object

result_list() `result_list<-`(): Retrieve result_list from a musica object

hyperparameter() `hyperparameter<-`(): Retrieve hyperparameter from a musica or result_collection object

parameter() `parameter<-`(): Retrieve parameter from a musica or result_collection object

signatures() `signatures<-`(): Retrieve signatures from a result_model, result_collection, or musica object

exposures() `exposures<-`(): Retrieve exposures from a result_model, result_collection, or musica object

num_signatures() `num_signatures<-`(): Retrieve num_signatures from a result_model, result_collection, or musica object

other_parameters() `other_parameters<-`(): Retrieve other_parameters from a result_model, result_collection, or musica object

credible_intervals() `credible_intervals<-`(): Retrieve credible_intervals from a result_model, result_collection, or musica object

metrics() `metrics<-`(): Retrieve metrics from a result_model, result_collection, or musica object

model_id() `model_id<-`(): Retrieve model_id from a result_model, result_collection, or musica object

modality() `modality<-`(): Retrieve modality from a result_model, result_collection, or musica object

Visualization

Functions to identify signatures and exposures from mutation count tables

plot_exposures(): Display sample exposures with bar, box, or violin plots

plot_signatures(): Plots the mutational signatures

plot_umap(): Plot a UMAP from a musica result

plot_cluster(): Visualize clustering results

plot_differential_analysis(): Compare exposures of annotated samples

plot_heatmap(): Plot heatmaps using the exposures matrix

plot_sample_counts(): Plot distribution of sample counts

plot_sample_reconstruction_error(): Plot reconstruction error for a sample

predict_exposure(): Prediction of exposures in new samples using pre-existing signatures

Downstream analysis

Functions to compare the signatures between two results objects

create_umap(): Create a UMAP from a model result

umap() `umap<-`(): Retrieve umap list from a result_model, result_collection, or musica object

cluster_exposure(): Perform clustering analysis from a musica result object

k_select(): Plots for helping decide number of clusters

Signature comparisons

Functions to compare the signatures between two results objects

compare_cosmic_v2(): Compare a result object to COSMIC V2 SBS Signatures (combination whole-exome and whole-genome)

compare_cosmic_v3(): Compare a result object to COSMIC V3 Signatures; Select exome or genome for SBS and only genome for DBS or Indel classes

compare_results(): Compare two result files to find similar signatures

exposure_differential_analysis(): Compare exposures of annotated samples

COSMIC signatures

Result objects that contain COSMIC signatures

cosmic_v2_sigs: COSMIC v2 SBS96 Signatures Result Object

cosmic_v3_dbs_sigs: COSMIC v3 DBS Genome Signatures Result Object

cosmic_v3_indel_sigs: COSMIC v3 Indel Genome Signatures Result Object

cosmic_v3_sbs_sigs: COSMIC v3 SBS96 Genome Signatures Result Object

cosmic_v3_sbs_sigs_exome: COSMIC v3 SBS96 Exome Signatures Result Object

Miscellaneous functions

cosmic_v2_subtype_map(): Input a cancer subtype to return a list of related COSMIC signatures

Data objects and classes

musica_sbs96: musica_sbs96

musica_sbs96_tiny: musica_sbs96_tiny

dbs_musica: dbs_musica

indel_musica: indel_musica

musica: musica

musica_annot: musica_annot

res: res

res_annot: res_annot

rep_range: Replication Timing Data as GRanges Object

musica-class: The primary object that contains variants, count_tables, and samples annotations

count_table-class: Object containing the count table matrices, their names and descriptions that we generated by provided and by user functions. These are used to discover and infer signatures and exposures.

result_collection-class: The Result Collection object that contains modality, input parameters, prior hyperparameters

result_model-class: Object that contains results for a single model

built_tables(): Retrieve the names of count_tables from a musica object

musicatk(): Starts the musicatk interactive Shiny app

rc(): Reverse complement of a string using biostrings

table_selected(): Retrieve table name used for plotting from a result_model object