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hammerlab
Utilities for analyzing mutations and neoepitopes in patient cohorts
From this comment: https://github.com/hammerlab/cohorts/pull/111#issuecomment-238894095 https://github.com/hammerlab/cohorts/pull/111/commits/6eed798fafa8233268b04d866f22bf2e49113f40#r74260647 This should be fixed to be more clear.
See https://github.com/iskandr/sercol
Via implementing the re-scaling as in `tximport`
To make it more clear what's going on! Inspired by https://github.com/hammerlab/cohorts/issues/96
From @arahuja: > We definitely need a way to not filter which no_fitler solves, but for some reason, I'd rather None did that? Does it make sense for filter_fn to...
For example: ``` {'dataframe_hash': -312166828261663650, 'provenance_file_summary': {'cached-effects': {u'cohorts': u'0+untagged.260.g71e0082', u'isovar': u'0.0.6', u'mhctools': u'0.2.3', u'numpy': u'1.11.0', u'pandas': u'0.18.1', u'pyensembl': u'0.9.3', u'scipy': u'0.17.0', u'topiary': u'0.0.21', u'varcode': u'0.4.14+6.g4bb441f'}, 'cached-expressed-neoantigens': {u'cohorts': u'0+untagged.188.g6260531.dirty', u'isovar': u'0.0.6',...
See https://github.com/hammerlab/cohorts/pull/86#issuecomment-234575671 One solution that we currently have is manually disabling summary printing.
e.g. `Cohort(read_only=True)` would allow use of a shared cache without fear of overwriting anything in it.
By adding something like this, albeit less hacky, to `plot_boolean`: ``` if type(boolean_col) == FunctionType: cols, df = self.as_dataframe([on, boolean_col], **kwargs) boolean_col = cols[1] df = filter_not_null(df, boolean_col) ```
@tavinathanson: > i think this is where you'll need to change something for lung, somehow making this configurable to not load the ensembl_coverage loader https://github.com/hammerlab/cohorts/blob/4718cb08ccb9f0c08af159b30e11dcaac09b5cfb/cohorts/functions.py#L91 > maybe e.g. if 'MB'...