Package index • SelectSim

All functions

GENIE_maf_schema: GENIE_maf_schema: GENIE_maf_schema: schema for TCGA maf file to process the mutations

TCGA_maf_schema: TCGA_maf_schema: schema for TCGA maf file to process the mutations

add(): Compute weight overlap stats

al.pairwise.alteration.stats(): Compute weight overlap stats

al.stats(): Create an alteration stats object

am.pairwise.alteration.coverage(): Compute weight overlap stats

am.pairwise.alteration.overlap(): Compute overlap stats

am.stats(): Create an alteration matrix stats object

am.weight.pairwise.alteration.overlap(): Compute weight overlap stats

binary.yule(): Compute yule coefficent

effectSize(): Compute weight overlap stats

estimateFDR2(): Compute FDR

estimate_p_val(): Compute pairwise p-value

estimate_pairwise_p(): Compute pairwise p-value

filter_maf_column(): Filter maf function

filter_maf_complex(): This function filters a MAF dataframe by retaining only the rows with a combination of values compatible with the values

filter_maf_gene.name(): This function filters a MAF dataframe by sample id

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(): This function filters a MAF dataframe by sample id

filter_maf_mutations(): This function filters a MAF dataframe by sample id

filter_maf_sample(): This function filters 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

generateS(): Generate S matrix

generateW_block(): Generating the weight matrix taking sample covariate

generateW_mean_tmb(): Generating the weight matrix

get.blocks(): Get sample/alteration blocks

interaction.table(): Compute weight overlap stats

luad_maf: Lung adenocarcinoma MAF from TCGA cohort

luad_result: Lung adenocarcinoma from TCGA cohort as selectX run results

luad_run_data: Lung adenocarcinoma from TCGA cohort as selectX run object

maf2gam(): Generate gam from the maf file

mutation_type: Mutation list object

new.AL.general(): Create an AL object

new.ALS(): Create an alteration landscape object

new.AMS(): Create an alteration matrix stats object

null_model_parallel(): Generating the null_simulation matrix

obs_exp_scatter(): Create an AL object

oncokb_genes: OncoKB v3.9 cancer genes

oncokb_truncating_genes: OncoKB v3.9 cancer genes consider for truncating mutations

overlap_pair_extract(): Extract the backgroun distribution

r.am.pairwise.alteration.overlap(): Compute weight overlap stats

r.effectSize(): Compute weight overlap stats

retrieveOutliers(): Removing the Outliers

ridge_plot_ed(): Generate a pairs background plot

ridge_plot_ed_compare(): Generate a pairs background plot for two dataset comaprision

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

stat_maf_column(): Summary functions for MAF file

stat_maf_gene(): Summary functions for MAF file

stat_maf_sample(): Summary functions for MAF file

templeate.obj.gen(): Generating the template matrix

theme_Publication(): A nice theme for making good plots

variant_catalogue: OncoKB v3.9 cancer genes

w.r.am.pairwise.alteration.overlap(): Compute weight overlap stats