I find that subtype information can have a large amount of incompleteness. For example, in Genomic Classification of Cutaneous Melanoma, Cell, 2015
BRAF Subtype
The largest genomic subtype is defined by the presence of BRAF hot-spot mutations (n = 166).
RAS Subtype
The second major subtype is defined by the presence of RAS hot-spot mutations (n = 95), including known amino acid changes with functional consequences, in all three RAS family members (N-, K- and H-RAS).
NF1 Subtype
The third most frequently observed SMG in the MAPK pathway was NF1, which was mutated in 14% (n = 28) of samples.
Triple Wild-Type Subtype
We defined the Triple-WT subtype (n = 46) as a heterogeneous subgroup characterized by a lack of hot-spot BRAF, N/H/K-RAS, or NF1 mutations.
but the the R package is
> table(colData(cutaneousMelanoma)[, "MUTATIONSUBTYPES"])
BRAF_Hotspot_Mutants NF1_Any_Mutants RAS_Hotspot_Mutants Triple_WT
32 5 11 8The R package has 343 patients and the journal article has 331, so it is unclear why there are so few assigned to a subtype.
I find that subtype information can have a large amount of incompleteness. For example, in Genomic Classification of Cutaneous Melanoma, Cell, 2015
but the the R package is
The R package has 343 patients and the journal article has 331, so it is unclear why there are so few assigned to a subtype.