def mzAlign(table, mz_reference_table, tol=15 * MMU,
destination=None, minR2=0.95, maxTol=1 * MMU, minPoints=5,
interactive=False):
"""
performs affine linear mz-correction for a feature table.
and retention times of known metabolites. This table needs
columns ``mz_calc`` for the mz value calculated from the mass of the isotope,
``rtmin``, ``rtmax`` for the retention time window where the peak is expected
to elute from the column in order to restrict the match of the table against
the ``mz_reference_table`.
``destination`` is a directory which will be used for storing the
result and intermediate data. If you do not specify this value, a
dialog for choosing the destination directory will be opened.
The input table **is not mofied** in place, the function returns the
aligned table.
the parameter *tol* is related to find matching peaks, *maxTol* and
*minR2* determine stop criterion when removing outlier points in
non interactive mode.
"""
import os
import numpy as np
from _mzalign_helpers import (_findMzMatches,
_findParametersAutomatically,
_findParametersManually,
_plotAndSaveMatch,
_applyTransform)
if not interactive:
assert minR2 <= 1.0
assert minPoints > 1
sources = set(table.source.values)
assert len(sources) == 1
source = sources.pop()
polarities = set(table.polarity.values)
assert len(polarities) == 1, "multiple polarities in table"
polarity = polarities.pop()
hypot = mz_reference_table
hypot.requireColumn("mz_hypot")
hypot.requireColumn("rtmin")
hypot.requireColumn("rtmax")
hypot.requireColumn("name")
hypot.requireColumn("polarity")
polarity_hypot = hypot.getColumn("polarity").uniqueValue()
assert hypot.getColType("mz_hypot") == float, "col mz_hypot is not float"
assert hypot.getColType("rtmin") == float, "col rtmin is not float"
assert hypot.getColType("rtmax") == float, "col rtmax is not float"
assert polarity_hypot == polarity, "polarity of mz reference table (%s)"\
"does not correspond to polarity of sample tables %s" \
% (polarity_hypot, polarity)
# hypot = _buildHypotheseTable(polarity, univ.copy(), fullC13)
if destination is not None:
basename = os.path.basename(source)
fname, _ = os.path.splitext(basename)
hypot.store(os.path.join(destination, fname + "_hypot.table"), True)
real, tobe, matches = _findMzMatches(hypot, table, tol)
if len(real) <= 1:
print "NOT ENOUGH MATCHES FOR ALIGNMENT"
return
if interactive:
from .. import gui
gui.inspect(matches, offerAbortOption=True)
elif len(tobe) < minPoints:
raise Exception("could only match %d peaks" % len(tobe))
if not interactive:
transform, used = _findParametersAutomatically(tobe.copy(), real.copy(),
minR2, maxTol, minPoints)
else:
transform, used = _findParametersManually(tobe.copy(), real.copy())
if transform is None:
print "ABORTED"
return
if destination is not None:
matches.addColumn("error", np.linalg.norm(transform(real) - tobe), float,
"%.3e")
matches.store(os.path.join(destination, fname + "_mzalign.table"), True)
matches.storeCSV(os.path.join(destination, fname + "_mzalign.csv"))
path = os.path.join(destination, fname + "_mzalign.png")
_plotAndSaveMatch(tobe, real, used, transform, path)
transformedTable = _applyTransform(table, transform)
print "DONE"
return transformedTable
def mzAlign(table,
mz_reference_table,
tol=15 * MMU,
destination=None,
minR2=0.95,
maxTol=1 * MMU,
minPoints=5,
interactive=False):
"""
performs affine linear mz-correction for a feature table.
and retention times of known metabolites. This table needs
columns ``mz_calc`` for the mz value calculated from the mass of the isotope,
``rtmin``, ``rtmax`` for the retention time window where the peak is expected
to elute from the column in order to restrict the match of the table against
the ``mz_reference_table`.
``destination`` is a directory which will be used for storing the
result and intermediate data. If you do not specify this value, a
dialog for choosing the destination directory will be opened.
The input table **is not modified** in place, the function returns the
aligned table.
the parameter *tol* is related to find matching peaks, *maxTol* and
*minR2* determine stop criterion when removing outlier points in
non interactive mode.
"""
import os
import numpy as np
from _mzalign_helpers import (_findMzMatches, _findParametersAutomatically,
_findParametersManually, _plotAndSaveMatch,
_applyTransform)
if not interactive:
assert minR2 <= 1.0
assert minPoints > 1
sources = set(table.source.values)
assert len(sources) == 1
source = sources.pop()
polarities = set(table.polarity.values)
assert len(polarities) == 1, "multiple polarities in table"
polarity = polarities.pop()
hypot = mz_reference_table
hypot.requireColumn("mz_hypot")
hypot.requireColumn("rtmin")
hypot.requireColumn("rtmax")
hypot.requireColumn("name")
hypot.requireColumn("polarity")
polarity_hypot = hypot.getColumn("polarity").uniqueValue()
assert hypot.getColType("mz_hypot") == float, "col mz_hypot is not float"
assert hypot.getColType("rtmin") == float, "col rtmin is not float"
assert hypot.getColType("rtmax") == float, "col rtmax is not float"
assert polarity_hypot == polarity, "polarity of mz reference table (%s)"\
"does not correspond to polarity of sample tables %s" \
% (polarity_hypot, polarity)
# hypot = _buildHypotheseTable(polarity, univ.copy(), fullC13)
if destination is not None:
basename = os.path.basename(source)
fname, _ = os.path.splitext(basename)
hypot.store(os.path.join(destination, fname + "_hypot.table"), True)
real, tobe, matches = _findMzMatches(hypot, table, tol)
if len(real) <= 1:
print "NOT ENOUGH MATCHES FOR ALIGNMENT"
return
if interactive:
from .. import gui
gui.inspect(matches, offerAbortOption=True)
elif len(tobe) < minPoints:
raise Exception("could only match %d peaks" % len(tobe))
if not interactive:
transform, used = _findParametersAutomatically(tobe.copy(),
real.copy(), minR2,
maxTol, minPoints)
else:
transform, used = _findParametersManually(tobe.copy(), real.copy())
if transform is None:
print "ABORTED"
return
if destination is not None:
matches.addColumn("error", np.linalg.norm(transform(real) - tobe),
float, "%.3e")
matches.store(os.path.join(destination, fname + "_mzalign.table"),
True)
matches.storeCSV(os.path.join(destination, fname + "_mzalign.csv"))
path = os.path.join(destination, fname + "_mzalign.png")
_plotAndSaveMatch(tobe, real, used, transform, path)
transformedTable = _applyTransform(table, transform)
print "DONE"
return transformedTable
def mzAlign(table,
fullC13=False,
tol=15 * MMU,
mz_reference_table=None,
destination=None,
minR2=0.95,
minPoints=5,
interactive=False):
"""
performs affine linear mz-correction for a feature table.
you need a ``mz_reference_table`` with the theoretical ideal masses
and retention times of some universal metabolites. This table needs
columns ``m0`` for the neutral mass, ``rtmin``, ``rtmax`` for the
retention time window for restricting the match of the table against
the ``mz_reference_table`` and the molecular formula ``mf``.
If you specify ``fullC13=True`` the ``mf`` is used to correct ``m0``.
``destination`` is a directory which will be used for storing the
result and intermediate data. If you do not specify this value, a
dialog for choosing the destination directory will be opened.
The input table **is not mofied** in place, the function returns the
aligned table.
"""
import ms
import tab
import os
import numpy as np
from _mzalign_helpers import (_buildHypotheseTable, _findMzMatches,
_findParametersAutomatically,
_findParametersManually, _plotAndSaveMatch,
_applyTransform)
if not interactive:
assert minR2 <= 1.0
assert minPoints > 1
sources = set(table.source.values)
assert len(sources) == 1
source = sources.pop()
if mz_reference_table is None:
mz_reference_table = tab.universal_metabolites
univ = mz_reference_table
univ.requireColumn("m0")
univ.requireColumn("rtmin")
univ.requireColumn("rtmax")
univ.requireColumn("mf")
assert univ.get(univ.colTypes, "m0") == float,\
"col m0 is not float"
assert univ.get(univ.colTypes, "rtmin") == float,\
"col rtmin is not float"
assert univ.get(univ.colTypes, "rtmax") == float,\
"col rtmax is not float"
polarities = set(table.polarity.values)
assert len(polarities) == 1, "multiple polarities in table"
polarity = polarities.pop()
hypot = _buildHypotheseTable(polarity, univ.copy(), fullC13)
if destination is not None:
basename = os.path.basename(source)
fname, _ = os.path.splitext(basename)
hypot.store(os.path.join(destination, fname + "_hypot.table"), True)
real, tobe, matches = _findMzMatches(hypot, table, tol)
if len(real) <= 1:
print "NOT ENOUGH MATCHES"
return
if interactive:
ms.inspect(matches, offerAbortOption=True)
elif len(tobe) < minPoints:
raise Exception("could only match %d peaks" % len(tobe))
if not interactive:
transform, used = _findParametersAutomatically(tobe.copy(), real.copy(),\
minR2, minPoints)
else:
transform, used = _findParametersManually(tobe.copy(), real.copy())
if transform is None:
print "ABORTED"
return
if destination is not None:
matches.addColumn("error", np.linalg.norm(transform(real)-tobe), float,\
"%.3e")
matches.store(os.path.join(destination, fname + "_mzalign.table"),
True)
matches.storeCSV(os.path.join(destination, fname + "_mzalign.csv"))
path = os.path.join(destination, fname + "_mzalign.png")
_plotAndSaveMatch(tobe, real, used, transform, path)
transformedTable = _applyTransform(table, transform)
print "DONE"
return transformedTable