When I download a study with cBioPortalData, the mutations assay is placed into a RaggedExperiment. Is there a way to reconstruct the underlying data.frame similar to if I had read data_mutations.txt directly? Ultimately, what I want to do is filter by type of mutation (e.g., remove silent mutations) and percentage of patients affected. Without using the API, I did this via a filter on Variant_Classification, followed by grouping by Hugo_Symbol and counting Tumor_Sample_Barcode (i.e., patient ID) then filter for Hugo_Symbol entries with enough patients affected. If I can reconstruct the data.frame that would be great, but there might be a better alternative to produce a data.frame of just data from the original columns Hugo_Symbol, Tumor_Sample_Barcode, Variant_Classification, and Protein_position.

Hi Ryan,

We use the RaggedExperiment class because it allows you to do much more than what a simple data.frame can do.

Have a look at the assay-functions example at:

https://code.bioconductor.org/browse/RaggedExperiment/blob/master/inst/scripts/assay-functions-Ex.R

To highlight some of the examples, you can filter by non-silent mutations and reduce to genic regions via qreduceAssay using gn as the reference:

  nonsilent <- function(scores, ranges, qranges)
        any(scores != "Silent")
mutations <- qreduceAssay(mre, gn, nonsilent, "Variant_Classification")

To summarize the percentages (e.g., about 13947 rows have 0 % mutations):

table(rowSums(!is.na(mutations)) / ncol(mutations))

                 0 0.0111111111111111 0.0222222222222222 0.0333333333333333 0.0444444444444444 0.0555555555555556 0.0666666666666667 0.0777777777777778 
             13947               5163               2034                838                356                166                123                 95 
0.0888888888888889                0.1  0.111111111111111  0.122222222222222  0.133333333333333  0.144444444444444  0.155555555555556  0.166666666666667 
                41                 43                 31                 27                 26                 10                 15                  9 
 0.177777777777778  0.188888888888889                0.2  0.211111111111111  0.222222222222222  0.233333333333333  0.244444444444444  0.255555555555556 
                15                  8                  7                 21                  7                  9                  5                  3 
 0.266666666666667  0.277777777777778  0.288888888888889                0.3  0.322222222222222  0.333333333333333  0.344444444444444  0.355555555555556 
                 4                  7                  4                  5                  1                  1                  1                  1 
 0.366666666666667  0.377777777777778  0.411111111111111  0.422222222222222  0.433333333333333  0.511111111111111  0.522222222222222 
                 1                  1                  3                  1                  1                  1                  2

You may be able to reconstruct the data.frame from the data but I am not aware of the specific data organization (i.e., shape, variables, etc.) that you are looking for. Perhaps mcols() provides what you are looking for.

Best regards,

Marcel