Merge branch 'master' into jpfeuffer-patch-6

docs/source/hyperscore.rst

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+Scoring spectra with HyperScore
+===============================
+
+In the chapter on spectrum alignment we showed how to determine matching peaks between theoretical and experimental spectra.
+For many use cases we might actually not be interested in obtaining the list of matched peaks but would like to have
+a simple, single score that indicates how "well" the two spectra matched.
+The HyperScore is a method to assign a spectrum match score to spectrum matches.
+
+
+
+Background
+**********
+
+HyperScore computes the (ln transformed) X!Tandem HyperScore of theoretical spectrum,
+
+calculated from a peptide/oligonucleotide sequence, with an experimental spectrum,
+loaded from an mzML file.
+
+    1. the dot product of peak intensities between matching peaks in experimental and theoretical spectrum is calculated
+    2. the HyperScore is calculated from the dot product by multiplying by factorials of matching b- and y-ions
+
+.. code-block:: python
+
+    from urllib.request import urlretrieve
+    from pyopenms import *
+
+    gh = "https://raw.githubusercontent.com/OpenMS/pyopenms-docs/master"
+    urlretrieve(gh + "/src/data/SimpleSearchEngine_1.mzML", "searchfile.mzML")
+
+
+Generate a theoretical spectrum
+*******************************
+
+
+We now use the TheoreticalSpectrumGenerator to generate a theoretical spectrum for the sequence we are interested in,
+``RPGADSDIGGFGGLFDLAQAGFR``, and compare the peaks to a spectra from our file.
+
+
+.. code-block:: python
+
+    tsg = TheoreticalSpectrumGenerator()
+    thspec = MSSpectrum()
+    p = Param()
+    p.setValue("add_metainfo", "true")
+    tsg.setParameters(p)
+    peptide = AASequence.fromString("RPGADSDIGGFGGLFDLAQAGFR")
+    tsg.getSpectrum(thspec, peptide, 1, 1)
+    # Iterate over annotated ions and their masses
+    for ion, peak in zip(thspec.getStringDataArrays()[0], thspec):
+        print(ion, peak.getMZ())
+
+    e = MSExperiment()
+    MzMLFile().load("searchfile.mzML", e)
+    spectrum_of_interest = e[2]
+    print("Spectrum native id", spectrum_of_interest.getNativeID())
+    mz, i = spectrum_of_interest.get_peaks()
+    peaks = [(mz, i) for mz, i in zip(mz, i) if i > 1500 and mz > 300]
+    for peak in peaks:
+        print(peak[0], "mz", peak[1], "int")
+
+Comparing the theoretical spectrum and the experimental spectrum for
+``RPGADSDIGGFGGLFDLAQAGFR`` we can easily see that the most abundant ions in the
+spectrum are y8 (877.452 m/z), b10 (926.432), y9 (1024.522 m/z) and b13
+(1187.544 m/z).
+
+Getting a score
+***************
+
+We now run HyperScore to compute the similarity of the theoretical spectrum
+and the experimental spectrum and print the result 
+
+
+.. code-block:: python
+
+    hscore = HyperScore()
+    fragment_mass_tolerance = 5.0
+    is_tol_in_ppm = True
+    result = hscore.compute(fragment_mass_tolerance, is_tol_in_ppm, spectrum_of_interest, thspec)
+    result
+
+If we didn't know ahead of time which spectrum was a match we can loop through all the spectra from our file,
+ calculate scores for all of them, and print the result:
+
+.. code-block:: python
+
+    for f in e:
+        score = hscore.compute(fragment_mass_tolerance, is_tol_in_ppm, f, thspec)
+        print(f.getNativeID() + ":" + str(score))
+