Analyze cohort

Added `analyze_cohort.py` which summarizes a directory of MAF/VCF files by associating each filename with the number of immunogenic mutations. Also renamed cancer\_pipeline to mutation_report and deleted unused files. 
Sadly, I can't use `and` on collections, they get interpreted by Python as
being either `True` or `False`. That's why NumPy & Pandas have to overload
the bitwise operators.


On Tue, Jul 29, 2014 at 6:27 PM, Tavi Nathanson <notifications@github.com>
wrote:

> In immuno/analyze_cohort.py:
>
> > +		# and either why it failed or what protein mutation resulted
> > +		if not args.quiet:
> > +		    print_mutation_report(path, variant_report, raw_genomic_mutation_df, transcripts_df)
> > +
> > +		mhc = PanBindingPredictor(hla_
> allele_names)
> > +		imm = ImmunogenicityPredictor(alleles = hla_allele_names)
> > +
> > +		scored_epitopes = mhc.predict(transcripts_df)
> > +		scored_epitopes = imm.predict(scored_epitopes)
> > +
> > +		grouped = scored_epitopes.groupby(["Gene", "GeneMutationInfo"])
> > +		n_coding_mutations = len(grouped)
> > +		n_ligand_mutations = 0
> > +		n_immunogenic_mutations = 0
> > +		for (gene, mut), group in grouped:
> > +			start_mask = group.EpitopeStart <= group.MutationEnd
>
> Doh. I thought you were trying to do some bitwise stuff. Better to use and
> vs. &?
>
> —
> Reply to this email directly or view it on GitHub
> <https://github.com/hammerlab/immuno/pull/115/files#r15556940>.
>