Package index • SelectSim

Main interface

End-to-end analysis: run the full SelectSim pipeline in one call.

selectX(): SelectX main function from SelectSim to create alteration object with background model

Step-by-step building blocks called internally by selectX. Expose these directly for custom pipelines or debugging.

Overlap computation, effect sizes, FDR estimation, and results table.

new.ALS(): Initialize an Alteration Landscape Stats (ALS) container
new.AMS(): Initialize an Alteration Matrix Stats (AMS) container
al.stats(): Compute alteration landscape statistics
am.stats(): Compute summary statistics for a binary alteration matrix
al.pairwise.alteration.stats(): Compute pairwise alteration statistics for an alteration landscape
am.pairwise.alteration.overlap(): Compute pairwise alteration co-occurrence counts
am.pairwise.alteration.coverage(): Compute pairwise alteration coverage statistics
am.weight.pairwise.alteration.overlap(): Compute TMB-weighted pairwise alteration overlap
r.am.pairwise.alteration.overlap(): Compute null overlap matrix
w.r.am.pairwise.alteration.overlap(): Compute null weighted overlap matrix
binary.yule(): Compute Yule Q coefficient for all gene pairs
effectSize(): Compute effect size between observed and expected overlap
r.effectSize(): Compute effect sizes for null model permutations
add(): Sum a list of matrices element-wise
estimateFDR2(): Estimate FDR by scanning observed vs null effect sizes
estimate_p_val(): Compute empirical two-sided p-value for a gene pair
estimate_pairwise_p(): Compute p-values for all gene pairs in a results table
interaction.table(): Build the full interaction results table from selectX outputs

MAF file filtering utilities and gene alteration matrix (GAM) construction.

maf2gam(): Generate gam from the maf file
filter_maf_column(): Filter maf function
filter_maf_complex(): Filter a MAF dataframe by a combination of column values
filter_maf_gene.name(): Filter a MAF dataframe by gene name
filter_maf_ignore(): This function filters a MAF dataframe by retaining (or discarding) ignore mutations
filter_maf_missense(): This function filters a MAF dataframe by retaining (or discarding) missense mutations
filter_maf_mutation.type(): Filter a MAF dataframe by mutation type
filter_maf_mutations(): Filter a MAF dataframe by specific gene-mutation combinations
filter_maf_sample(): Filter a MAF dataframe by sample ID
filter_maf_schema(): This function filters a MAF dataframe by sample id
filter_maf_truncating(): This function filters a MAF dataframe by retaining (or discarding) truncating mutations
stat_maf_column(): Summary functions for MAF file
stat_maf_gene(): Count mutations per gene in a MAF file
stat_maf_sample(): Count mutations per sample in a MAF file
mutation_type: Mutation list object
TCGA_maf_schema: TCGA_maf_schema: schema for TCGA maf file to process the mutations
GENIE_maf_schema: GENIE_maf_schema: schema for GENIE maf file to process the mutations

Plot helpers for inspecting results and null model distributions.

obs_exp_scatter(): Scatter plot of observed vs expected weighted co-mutation
ridge_plot_ed(): Ridge plot of null-model background distribution for significant gene pairs
ridge_plot_ed_compare(): Ridge plot comparing null-model distributions for two datasets
overlap_pair_extract(): Extract null-model weighted overlap distribution for a gene pair
theme_Publication(): A clean ggplot2 theme for publication-quality plots

Example datasets bundled with the package.

luad_run_data: Lung adenocarcinoma from TCGA cohort as SelectSim run object
luad_result: Lung adenocarcinoma from TCGA cohort as SelectSim run results
luad_maf: Lung adenocarcinoma MAF from TCGA cohort
oncokb_genes: OncoKB v3.9 cancer genes
oncokb_truncating_genes: OncoKB v3.9 cancer genes consider for truncating mutations
variant_catalogue: OncoKB v3.9 cancer genes