cutoffs: Cutoffs for a supervised analysis of mutational signatures.
In YAPSA: Yet Another Package for Signature Analysis

Description Usage Author(s)

Series of data frames with signature-specific cutoffs. All values represent optimal cutoffs. The optimal cutoffs were determined for different choices of parameters in the cost function of the optimization. The row index is equivalent to the ratio between costs for false negative attribution and false positive attribution. The columns correspond to the different signatures. To be used with LCD_complex_cutoff. There are two different sets of cutoffs one for the signatures described by Alexandrov et al.(Natue 2013) and one for the signatures dokumented in Alexandriv et al. (biorxiv 2018). The calculation of the PCAWG signature specific cutoffs was perfomed in a single-sample resolution which are both valid for whole genome and whole exome sequencing data analysis.

cutoffCosmicValid_rel_df: Optimal cutoffs for AlexCosmicValid_sig_df, i.e. COSMIC signatures, only validated, trained on relative exposures.

cutoffCosmicArtif_rel_df: Optimal cutoffs for AlexCosmicArtif_sig_df, i.e. COSMIC signatures, including artifact signatures, trained on relative exposures.

cutoffCosmicValid_abs_df: Optimal cutoffs for AlexCosmicValid_sig_df, i.e. COSMIC signatures, only validated, trained on absolute exposures.

cutoffCosmicArtif_abs_df: Optimal cutoffs for AlexCosmicArtif_sig_df, i.e. COSMIC signatures, including artifact signatures, trained on absolute exposures.

cutoffInitialValid_rel_df: Optimal cutoffs for AlexInitialValid_sig_df, i.e. initially published signatures, only validated signatures, trained on relative exposures.

cutoffInitialArtif_rel_df: Optimal cutoffs for AlexInitialArtif_sig_df, i.e. initially published signatures, including artifact signatures, trained on relative exposures.

cutoffInitialValid_abs_df: Optimal cutoffs for AlexInitialValid_sig_df, i.e. initially published signatures, only validated signatures, trained on absolute exposures.

cutoffInitialArtif_abs_df: Optimal cutoffs for AlexInitialArtif_sig_df, i.e. initially published signatures, including artifact signatures, trained on absolute exposures.