extract_patient_id()
function to get IMPACT patient ID from sample IDfreq_cutoff
, freq_cutoff_by_gene
, and gene_subset
arguments in tbl_genomic()
. It is now recommended that users use subset_by_frequency()
instead before passing data to tbl_genomic()
.other_vars
argument to subset_by_frequency()
, subset_by_panel()
, summarize_by_gene()
and add_pathways()
to allow retention of other clinical vars when using functions within pipeline.count_pathways_by
argument of add_pathways()
function. Now, user must specify which specific alteration to count towards the pathway via the .mut
, .Amp
, .Del
, .fus
suffix (e.g. custom_pathways = c('TP53.mut', 'APC.Del)
). subset_by_panel
function allowing users to easily subset an alteration dataframe and include only genes in a specific panelcreate_gene_binary(specify_panel)
create_gene_binary(recode_aliases = "genie")
to check and recode aliases for genes in any of the GENIE BPC panels.add_pathways()
where custom_pathways
wasn't catching all types of alterations when GENE.all
was used due to paste0()
vectorization.rlang::arg_match()
) instead of partial matching (match.arg()
) (e.g. mut_type = "s"
doesn't work anymore and must be fully specified mut_type = "somatic_only"
).rename_columns()
output is now an attribute of the returned object. Now messages can reference the original names of data columns (ex: TumorAllele2
not tumor_allele_2
) to make it more intuitive to users (#302).subset_by_frequency()
to users to select hugo_symbols if they reach a threshold in any level of a variable (ex: high risk vs low risk) (#305)create_gene_binary()
summarize_by_gene()
function was changed to run faster (#259)subset_by_frequency()
added to allow users to filter for specific prevalence levels of mutations/alterations/fusions (#270)tbl_genomic()
and create_gene_binary()
. NEWS.md
file to track changes to the package.Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.