Envelope Search¶

The ms_deisotope.deconvolution module contains algorithms for deisotoping centroided mass spectra, computing the monoisotopic neutral mass and charge of each fitted envelope. There are two types of searches that can be used, envelope search can be targeted, using a list of compositions, or exhaustive using an average monomer isotopic model or averagine [Senko].

These strategies are implemented as large object hierarchies, but the high level function deconvolute_peaks() encapsulates the process for most use cases.

import ms_deisotope

deconvoluted_peaks, targeted = ms_deisotope.deconvolute_peaks(peaks, averagine=ms_deisotope.peptide,
                                                              scorer=ms_deisotope.MSDeconVFitter(10.))

Table of Contents

High Level API
- Parameter Recomendations and Commentary
Algorithms for Complex Spectra
Algorithms for Simple Spectra
Base Classes
Accounting For Lower Quality Data
Utilities and Implementation Details

This module defines a collection of isotopic envelope search strategies for deisotoping and charge state deconvolution. Each strategy is implemented through a subtype of DeconvoluterBase and provide a common set of methods for deconvolving a peak list.

High Level API ¶

ms_deisotope.deconvolution.deconvolute_peaks(peaklist, decon_config=None, charge_range=(1, 8), error_tolerance=2e-05, priority_list=None, left_search_limit=1, right_search_limit=0, left_search_limit_for_priorities=None, right_search_limit_for_priorities=None, verbose_priorities=False, verbose=False, charge_carrier=1.00727646677, truncate_after=0.95, iterations=10, deconvoluter_type=<class 'ms_deisotope.deconvolution.averagine_based.AveraginePeakDependenceGraphDeconvoluter'>, retention_strategy=None, use_quick_charge=False, **kwargs)[source]¶

Deconvolute a centroided mass spectrum.

This function constructs a deconvoluter object using the deconvoluter_type argument and deconvolutes the input peaklist by calling its deconvolute() method.

If priority_list is not None, it is expected to be an iterable of either tuples of (FittedPeak, (min charge, max charge)) pairs, or instances of PriorityTarget. These will be passed to targeted_deconvolution() of the deconvoluter.

Parameters

peaklist (PeakSet or list of Peak-like objects, see prepare_peaklist()) – The centroided mass spectrum to deconvolute.

decon_config (dict, optional) – Parameters to use to initialize the deconvoluter instance produced by deconvoluter_type

charge_range (tuple of integers, optional) – The range of charge states to consider. The range is inclusive.

error_tolerance (float, optional) – PPM error tolerance to use to match experimental to theoretical peaks

priority_list (list, optional) – The set of peaks to target for deconvolution to be able to enforce external constraints on, such as selected precursors for fragmentation.

left_search_limit (int, optional) – The maximum number of neutron shifts to search to the left (decrease) from each query peak

right_search_limit (int, optional) – The maximum number of neutron shifts to search to the right (increase) from each query peak

left_search_limit_for_priorities (int, optional) – The maximum number of neutron shifts to search to the left (decrease) from each query peak for priority targets

right_search_limit_for_priorities (int, optional) – The maximum number of neutron shifts to search to the right (increase) from each query peak for priority targets

verbose_priorities (bool, optional) – Whether to turn on verbose mode for priority targets

verbose (bool, optional) – Passed to the deconvoluter to enable verbose mode globally

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percentage of the isotopic pattern to include. Defaults to 0.95

deconvoluter_type (type or callable, optional) – A callable returning a deconvoluter. Defaults to AveraginePeakDependenceGraphDeconvoluter

retention_strategy (PeakRetentionStrategyBase or callable, optional) – A callable that may compute additional peaks to include in the output deconvoluted peaks.

use_quick_charge (bool) – Whether or not to use the QuickCharge algorithm to quickly filter theoretical charge states to consider for each peak.

**kwargs – Additional keywords included in decon_config

See also

ms_deisotope.peak_set.decharge()

Notes

If speed is an issue, consider setting use_quick_charge True. This will pre-screen charge states that are are missing peaks supporting the monoisotopic peak or the A+1 peak. Alternatively, you may set the charge range upper bound to something reasonable for your data, such as the precursor ion’s charge when considering a product ion spectrum.

When you do not expect a complete isotopic pattern for large ions, as is often the case for low abundance FT-MS/MS it may be useful to shrink the truncate_after parameter from the default (0.95) to a slightly smaller value.

Returns

Return type

DeconvolutionProcessResult

Parameter Recomendations and Commentary ¶

Because deconvolution can be a fussy problem to solve when dealing with disparate instrument types and signal qualities, here are some notes when tuning parameters.

Missing High Mass Ions? Understanding how and when to use truncate_after ¶

Note

Short Answer: Use 0.95 for MS1 and 0.8 for MSn spectra, or experiment with incremental_truncation.

Theoretical isotopic patterns can simulate any number of isotopic peaks, though outside of native MS of large molecules, most of these isotopic peaks are trivially small and undetectable. ms_deisotope uses a truncation strategy to stop including isotopic peaks after truncate_after * 100% of the signal has been observed in an isotopic pattern. By default truncate_after is 0.95, which I’ve found works well for MS1 spectra on all instruments. This is trivial for most biomolecules below 800 Da, but as molecules grow larger, the theoretical pattern starts to exceed the instrument’s ability to detect all peaks within 95% of the isotopic pattern, especially on highly processed spectra like FTMS from Orbitraps. For MSn spectra, ions often appear on both sides of this line, making it especially important to account for. For these spectra, I have found truncate_after at 0.8 works well.

There is another option, which is to use the experimental incremental_truncation strategy which takes each isotopic pattern after truncate_after and considers variants incrementally dropping trailing peaks until incremental_truncation * 100% of the signal from the starting pattern remains. Starting with truncate_after set to 0.999 and incremental_truncation set to 0.8 will allow you to match both ends of the spectrum, but at the cost of doing a bit more work per peak.

quick_charge To Speed Things Up ¶

Use quick_charge = True when missing peaks around the monoisotopic peak are not an issue to drastically cut down on the number of theoretical pattern fits that need to be performed per peak.

Searching Left and Right ¶

When using one of the graph-based algorithms for complex spectra (the default), these parameters are superfluous. When using one of the much simpler algorithms suitable for deconvolution of limited complexity data or targeted deconvolution only, these parameters benefit from being set to a value between 1 and 3, depending upon the distance form the base peak of an isotopic pattern to the monoisotopic peak.

When To Use priority_list ¶

The priority_list parameter is really only useful when you need to tie a specific deconvolution result to a specific experimental peak, as in the case of extracting the deconvoluting a precursor ion while also deconvolving the entire spectrum. Unless you have these types of scenarios, this argument is not generally necessary. It does get used heavily by ms_deisotope.processor.ScanProcessor.

Algorithms for Complex Spectra ¶

The following algorithms are appropriate for deconvoluting complex, partially overlapping isotopic envelopes. The default algorithm used by all functions is AveraginePeakDependenceGraphDeconvoluter.

class ms_deisotope.deconvolution.AveraginePeakDependenceGraphDeconvoluter(peaklist, *args, **kwargs)[source]¶

A Deconvoluter which uses an averagine [1] model to generate theoretical isotopic patterns for each peak to consider, using a peak dependence graph to solve complex mass spectra.

Extends AveragineDeconvoluter to include features from PeakDependenceGraphDeconvoluterBase making it suitable for deconvoluting complex spectra where peak overlaps are common.

peaklist¶

The centroided mass spectrum to deconvolute

Type

PeakSet

scorer¶

The criterion for evaluating individual isotopic pattern fits

Type

IsotopicFitterBase

averagine¶

The averagine model and associated theoretical isotopic pattern cache to use to build theoretical isotopic patterns.

Type

AveragineCache

max_missed_peaks¶

The maximum number of missing peaks to tolerate in an isotopic fit

Type

int

peak_dependency_network¶

The peak dependence graph onto which isotopic fit dependences on peaks are constructed and solved.

Type

PeakDependenceGraph

merge_isobaric_peaks¶

If multiple passes produce peaks with identical mass values, should those peaks be summed

Type

bool

minimum_intensity¶

Experimental peaks whose intensity is below this level will be ignored by peak querying methods

Type

float

scale_method¶

The name of the method to use to scale theoretical isotopic pattern intensities to match the experimental isotopic pattern. For a description of options, see scale().

Type

str

use_subtraction¶

Whether or not to apply a subtraction procedure to experimental peaks after they have been fitted. This is only necessary if the same signal may be examined multiple times as in a multi-pass method or when peak dependence is not considered

Type

bool

verbose¶

Produce extra logging information

Type

bool

References

[1] Senko, M. W., Beu, S. C., & McLafferty, F. W. (1995). Determination of monoisotopic masses and ion populations
for large biomolecules from resolved isotopic distributions. Journal of the American Society for Mass Spectrometry, 6(4), 229–233. http://doi.org/10.1016/1044-0305(95)00017-8

charge_state_determination(peak, error_tolerance=2e-05, charge_range=(1, 8), left_search_limit=3, right_search_limit=3, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001)¶

Determine the optimal isotopic fit for peak, extracting it’s charge state and monoisotopic peak.

This method invokes _fit_all_charge_states(), and then uses scorer’s select method to choose the optimal solution.

Parameters

peak (FittedPeak) – The peak to start the search from

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 3

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 3

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

Returns

The best scoring isotopic fit

Return type

IsotopicFitRecord

deconvolute(error_tolerance=2e-05, charge_range=(1, 8), left_search_limit=1, right_search_limit=0, iterations=10, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001, convergence=0.001, **kwargs)¶

Completely deconvolute the spectrum.

For each iteration, clear peak_depencency_network, then invoke populate_graph() followed by select_best_disjoint_subgraphs() to populate the resulting DeconvolutedPeakSet

Parameters

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

order_chooser (callable, optional:) – A callable used as a key function for sorting peaks into the order they will be visited during deconvolution. Defaults to operator.attrgetter("index")

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 1

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 0

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

ignore_below (float, optional) – The minimum relative abundance to consider a peak in a theoretical isotopic pattern

convergence (float, optional) – The threshold of the below which after the (sum(intensity_before) - sum( intensity_after)) / sum(intensity_after)

Returns

Return type

DeconvolutedPeakSet

populate_graph(DeconvoluterBase self, error_tolerance=ERROR_TOLERANCE, charge_range=(1, 8), left_search_limit=1, right_search_limit=0, charge_carrier=PROTON, truncate_after=TRUNCATE_AFTER, ignore_below=IGNORE_BELOW)¶

Visit each experimental peak and execute _explore_local() on it with the provided parameters, populating the peak dependence graph with all viable candidates.

Parameters

peak (FittedPeak) –

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 1

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 0

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

targeted_deconvolution(peak, error_tolerance=2e-05, charge_range=(1, 8), left_search_limit=3, right_search_limit=3, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001)¶

Express the intent that this peak’s deconvolution solution will be retrieved at a later point in the process and that it should be deconvoluted, and return a handle to retrieve the results with.

As the operation does not immediately result in a deconvoluted peak but just adds the resulting fits to peak_dependency_network, this method constructs an instance of NetworkedTargetedDeconvolutionResult which holds all the required information for recovering the best fit containing peak.

Parameters

peak (FittedPeak) – The peak to start the search from

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 3

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 3

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

Returns

Return type

NetworkedTargetedDeconvolutionResult

class ms_deisotope.deconvolution.CompositionListPeakDependenceGraphDeconvoluter(peaklist, composition_list, scorer, use_subtraction=False, scale_method='sum', verbose=False, use_quick_charge=False, **kwargs)[source]¶

Fit exact isotopic patterns from a list of compositions.

Fits are added to a peak dependence graph, and the best fit is chosen after all fits are calculated at each iteration.

composition_list¶

A series of objects which represent elemental compositions and support the Mapping interface to access their individual elements.

Type

list of Mapping

peaklist¶

The collection of ms_peak_picker.FittedPeak instances and possible associated data to deconvolute.

Type

PeakSet

scorer¶

The criterion for evaluating individual isotopic pattern fits

Type

IsotopicFitterBase

max_missed_peaks¶

The maximum number of missing peaks to tolerate in an isotopic fit

Type

int

peak_dependency_network¶

The peak dependence graph onto which isotopic fit dependences on peaks are constructed and solved.

Type

PeakDependenceGraph

merge_isobaric_peaks¶

If multiple passes produce peaks with identical mass values, should those peaks be summed

Type

bool

minimum_intensity¶

Experimental peaks whose intensity is below this level will be ignored by peak querying methods

Type

float

scale_method¶

The name of the method to use to scale theoretical isotopic pattern intensities to match the experimental isotopic pattern. For a description of options, see scale().

Type

str

use_subtraction¶

Whether or not to apply a subtraction procedure to experimental peaks after they have been fitted. This is only necessary if the same signal may be examined multiple times as in a multi-pass method or when peak dependence is not considered

Type

bool

verbose¶

Produce extra logging information

Type

bool

deconvolute(error_tolerance=2e-05, charge_range=(1, 8), iterations=10, truncate_after=0.95, charge_carrier=1.00727646677, ignore_below=0.001, mass_shift=None, convergence=0.001, **kwargs)[source]¶

Deconvolute the spectrum, extracting isotopic patterns from the composition list.

Parameters

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

mass_shift (float, optional) – An optional mass shift to apply to each composition

convergence (float, optional) – The threshold of the below which after the (sum(intensity_before) - sum( intensity_after)) / sum(intensity_after)

Returns

Return type

DeconvolutedPeakSet

deconvolute_composition(composition, error_tolerance=2e-05, charge_range=(1, 8), charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001, mass_shift=None)[source]¶

For each charge state under consideration, fit the theoretical isotopic pattern for this composition, and if the fit is satisfactory, add it to the results set.

Parameters

composition (Mapping) – An object representing an elemental composition

error_tolerance (float) – The mass accuracy required to for peak matches

charge_range (tuple) – The charge state range to generate the isotopic patterns for

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

mass_shift (float, optional) – An arbitrary mass shift to apply to the generated theoretical isotopic pattern, moving all peaks forward by that mass charge ratio transformed mass.

charge_carrier (float, optional) – The mass of the charge carrier, or more specifically, the moiety which is added for each incremental change in charge state. Defaults to 1.0072

populate_graph(error_tolerance=2e-05, charge_range=(1, 8), truncate_after=0.95, charge_carrier=1.00727646677, ignore_below=0.001, mass_shift=None)[source]¶

For each composition, for each charge state under consideration, fit the theoretical isotopic pattern for this composition, and if the fit is satisfactory, add it to the peak dependence graph for later selecting the optimal solution.

Parameters

error_tolerance (float) – The mass accuracy required to for peak matches

charge_range (tuple) – The charge state range to generate the isotopic patterns for

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

charge_carrier (float, optional) – The mass of the charge carrier, or more specifically, the moiety which is added for each incremental change in charge state. Defaults to 1.0072

mass_shift (float, optional) – An arbitrary mass shift to apply to the generated theoretical isotopic pattern, moving all peaks forward by that mass charge ratio transformed mass.

If your data do not conform to a single averagine, MultipleAveraginePeakDependenceGraphDeconvoluter can take a list of Averagine objects, selecting the best averagine for each experimental isotopic pattern.

Algorithms for Simple Spectra ¶

These algorithms do not take into account the optimal peak assignment across fits and should be used only for spot-checks or for simple spectra where best fit resolution across multiple fits is not required.

class ms_deisotope.deconvolution.AveragineDeconvoluter(peaklist, averagine=None, scorer=PenalizedMSDeconVFitter((MaximizeFitSelector(minimum_score=10.0), 1.0, 0.02)), use_subtraction=True, scale_method='sum', verbose=False, **kwargs)[source]¶

A Deconvoluter which uses an averagine [1] model to generate theoretical isotopic patterns for each peak to consider. Combines AveragineDeconvoluterBase and ExhaustivePeakSearchDeconvoluterBase to create a working Deconvoluter type.

averagine¶

The averagine model and associated theoretical isotopic pattern cache to use to build theoretical isotopic patterns.

Type

AveragineCache

peaklist¶

The collection of ms_peak_picker.FittedPeak instances and possible associated data to deconvolute.

Type

PeakSet

scorer¶

The criterion for evaluating individual isotopic pattern fits

Type

IsotopicFitterBase

verbose¶

How much diagnostic information to provide

Type

bool

References

[1] Senko, M. W., Beu, S. C., & McLafferty, F. W. (1995). Determination of monoisotopic masses and ion populations
for large biomolecules from resolved isotopic distributions. Journal of the American Society for Mass Spectrometry, 6(4), 229–233. http://doi.org/10.1016/1044-0305(95)00017-8

charge_state_determination(peak, error_tolerance=2e-05, charge_range=(1, 8), left_search_limit=3, right_search_limit=3, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001)¶

Determine the optimal isotopic fit for peak, extracting it’s charge state and monoisotopic peak.

This method invokes _fit_all_charge_states(), and then uses scorer’s select method to choose the optimal solution.

Parameters

peak (FittedPeak) – The peak to start the search from

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 3

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 3

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

Returns

The best scoring isotopic fit

Return type

IsotopicFitRecord

deconvolute(error_tolerance=2e-05, charge_range=(1, 8), order_chooser=operator.attrgetter('index'), left_search_limit=3, right_search_limit=3, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001, **kwargs)¶

Completely deconvolute the spectrum.

Visit each peak in the order chosen by order_chooser, and call deconvolute_peak() on it with the provided arguments. This assumes all overlaps in isotopic pattern are captured by the search limits. This is usually not the case. For an alternative see PeakDependenceGraphDeconvoluterBase

Parameters

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

order_chooser (callable, optional:) – A callable used as a key function for sorting peaks into the order they will be visited during deconvolution. Defaults to operator.attrgetter("index")

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 3

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 3

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

Returns

Return type

DeconvolutedPeakSet

targeted_deconvolution(peak, error_tolerance=2e-05, charge_range=(1, 8), left_search_limit=3, right_search_limit=3, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001)¶

Express the intent that this peak’s deconvolution solution will be retrieved at a later point in the process and that it should be deconvoluted, and return a handle to retrieve the results with.

This algorithm’s implementation is simple enough that this is equivalent to just performing the deconvolution now and storing the result in a TrivialTargetedDeconvolutionResult instance.

Otherwise identical to deconvolute_peak().

Parameters

peak (FittedPeak) – The peak to start the search from

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 3

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 3

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

Returns

Return type

TrivialTargetedDeconvolutionResult

class ms_deisotope.deconvolution.CompositionListDeconvoluter(peaklist, composition_list, scorer, use_subtraction=False, scale_method='sum', verbose=False, use_quick_charge=False, **kwargs)[source]¶

Fit exact isotopic patterns from a list of compositions.

Fits are accepted as they are made, making this algorithm unsuitable for complex spectra where isotopic patterns will share peaks.

composition_list¶

A series of objects which represent elemental compositions and support the Mapping interface to access their individual elements.

Type

list of Mapping

peaklist¶

The collection of FittedPeak instances and possible associated data to deconvolute.

Type

PeakSet

scorer¶

The criterion for evaluating individual isotopic pattern fits

Type

IsotopicFitterBase

merge_isobaric_peaks¶

If multiple passes produce peaks with identical mass values, should those peaks be summed

Type

bool

minimum_intensity¶

Experimental peaks whose intensity is below this level will be ignored by peak querying methods

Type

float

scale_method¶

The name of the method to use to scale theoretical isotopic pattern intensities to match the experimental isotopic pattern. For a description of options, see scale().

Type

str

use_subtraction¶

Whether or not to apply a subtraction procedure to experimental peaks after they have been fitted. This is only necessary if the same signal may be examined multiple times as in a multi-pass method or when peak dependence is not considered

Type

bool

verbose¶

Produce extra logging information

Type

bool

deconvolute(error_tolerance=2e-05, charge_range=(1, 8), charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001, mass_shift=None, **kwargs)[source]¶

Deconvolute the spectrum, extracting isotopic patterns from the composition list.

Parameters

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

mass_shift (float, optional) – An optional mass shift to apply to each composition

Returns

Return type

DeconvolutedPeakSet

deconvolute_composition(composition, error_tolerance=2e-05, charge_range=(1, 8), charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001, mass_shift=None)¶

For each charge state under consideration, fit the theoretical isotopic pattern for this composition, and if the fit is satisfactory, add it to the results set.

Parameters

composition (Mapping) – An object representing an elemental composition

error_tolerance (float) – The mass accuracy required to for peak matches

charge_range (tuple) – The charge state range to generate the isotopic patterns for

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

mass_shift (float, optional) – An arbitrary mass shift to apply to the generated theoretical isotopic pattern, moving all peaks forward by that mass charge ratio transformed mass.

charge_carrier (float, optional) – The mass of the charge carrier, or more specifically, the moiety which is added for each incremental change in charge state. Defaults to 1.0072

Base Classes ¶

class ms_deisotope.deconvolution.DeconvoluterBase(use_subtraction=False, scale_method='sum', merge_isobaric_peaks=True, minimum_intensity=5., *args, **kwargs)¶

Base class for all Deconvoluter types. Provides basic configuration for common operations, regardless of implementation. Because these methods form the backbone of all deconvolution algorithms, this class has a C-extension implementation as well.

peaklist¶

The centroided mass spectrum to deconvolute

Type

ms_peak_picker.PeakSet

scorer¶

The criterion for evaluating individual isotopic pattern fits

Type

IsotopicFitterBase

merge_isobaric_peaks¶

If multiple passes produce peaks with identical mass values, should those peaks be summed

Type

bool

minimum_intensity¶

Experimental peaks whose intensity is below this level will be ignored by peak querying methods

Type

float

scale_method¶

The name of the method to use to scale theoretical isotopic pattern intensities to match the experimental isotopic pattern. For a description of options, see scale().

Type

str

use_subtraction¶

Whether or not to apply a subtraction procedure to experimental peaks after they have been fitted. This is only necessary if the same signal may be examined multiple times as in a multi-pass method or when peak dependence is not considered

Type

bool

verbose¶

Produce extra logging information

Type

bool

between(self, double m1, double m2) → PeakSet¶

fit_incremental_truncation(self, IsotopicFitRecord seed_fit, double lower_bound) → list¶

Fit incrementally truncated versions of the seed fit to check to see if a narrower theoretical fit matches the data better.

Parameters

seed_fit (IsotopicFitRecord) – The original fit to explore truncations of

lower_bound (float) – The percentage of total signal remaining to stop truncating at.

Returns

Return type

list of IsotopicFitRecord

has_peak(self, double mz, double error_tolerance) → FittedPeak¶

match_theoretical_isotopic_distribution(self, list theoretical_distribution, double error_tolerance=2e-5) → list¶

merge_isobaric_peaks¶

‘bool’

Type

merge_isobaric_peaks

minimum_intensity¶

‘double’

Type

minimum_intensity

peaklist¶

ms_peak_picker._c.peak_set.PeakSet

Type

peaklist

scale_method¶

str

Type

scale_method

scale_theoretical_distribution(self, TheoreticalIsotopicPattern theoretical_distribution, list experimental_distribution)¶

scorer¶

ms_deisotope._c.scoring.IsotopicFitterBase

Type

scorer

subtraction(self, TheoreticalIsotopicPattern isotopic_cluster, double error_tolerance=2e-5)¶

use_subtraction¶

‘bool’

Type

use_subtraction

verbose¶

‘bool’

Type

verbose

class ms_deisotope.deconvolution.AveragineDeconvoluterBase(bool use_subtraction=False, str scale_method='sum', bool merge_isobaric_peaks=True, double minimum_intensity=5., *args, **kwargs)¶

A base class derived from DeconvoluterBase which provides some common methods for fitting isotopic patterns using an Averagine model.

averagine¶

ms_deisotope._c.averagine.AveragineCache

Type

averagine

fit_theoretical_distribution(self, FittedPeak peak, double error_tolerance, int charge, double charge_carrier=PROTON, double truncate_after=0.95, double ignore_below=0) → IsotopicFitRecord¶

class ms_deisotope.deconvolution.CompositionListDeconvoluterBase(composition_list, *args, **kwargs)[source]¶

A mixin class to provide common features for deconvoluters which process spectra using a list of targeted compositions.

composition_list¶

A series of objects which represent elemental compositions and support the Mapping interface to access their individual elements.

Type

list of Mapping

deconvolute_composition(composition, error_tolerance=2e-05, charge_range=(1, 8), charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001, mass_shift=None)[source]¶

For each charge state under consideration, fit the theoretical isotopic pattern for this composition, and if the fit is satisfactory, add it to the results set.

Parameters

composition (Mapping) – An object representing an elemental composition

error_tolerance (float) – The mass accuracy required to for peak matches

charge_range (tuple) – The charge state range to generate the isotopic patterns for

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

mass_shift (float, optional) – An arbitrary mass shift to apply to the generated theoretical isotopic pattern, moving all peaks forward by that mass charge ratio transformed mass.

charge_carrier (float, optional) – The mass of the charge carrier, or more specifically, the moiety which is added for each incremental change in charge state. Defaults to 1.0072

fit_composition_at_charge(composition, charge, error_tolerance=2e-05, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001, mass_shift=None)[source]¶

Produce an isotopic fit for composition at charge against the experimental peak set.

This method requires that the instance also possess a method named match_theoretical_isotopic_distribution such as the one implemented in DeconvoluterBase.

Parameters

composition (Mapping) – An object representing an elemental composition

charge (int) – The charge state to generate the isotopic pattern for

error_tolerance (float) – The mass accuracy required to for peak matches

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

mass_shift (float, optional) – An arbitrary mass shift to apply to the generated theoretical isotopic pattern, moving all peaks forward by that mass charge ratio transformed mass.

charge_carrier (float, optional) – The mass of the charge carrier, or more specifically, the moiety which is added for each incremental change in charge state. Defaults to 1.0072

Returns

Return type

IsotopicFitRecord

generate_theoretical_isotopic_cluster(composition, charge, truncate_after=0.95, mass_shift=None, charge_carrier=1.00727646677, ignore_below=0.001)[source]¶

Generate a theoretical isotopic pattern for composition

Parameters

composition (Mapping) – An object representing an elemental composition

charge (int) – The charge state to generate the isotopic pattern for

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

mass_shift (float, optional) – An arbitrary mass shift to apply to the generated theoretical isotopic pattern, moving all peaks forward by that mass charge ratio transformed mass.

charge_carrier (float, optional) – The mass of the charge carrier, or more specifically, the moiety which is added for each incremental change in charge state. Defaults to 1.0072

Returns

The theoretical isotopic pattern generated

Return type

TheoreticalIsotopicPattern

recalibrate_theoretical_mz(theoretical_distribution, experimental_mz)[source]¶

Recalibrate the m/z of the theoretical isotopic pattern to start from the peak matching the experimental monoisotopic m/z

Parameters

theoretical_distribution (TheoreticalIsotopicPattern) – The theoretical isotopic pattern to adjust

experimental_mz (float) – The experimental monoisotopic peak m/z

Returns

Return type

TheoreticalIsotopicPattern

class ms_deisotope.deconvolution.ExhaustivePeakSearchDeconvoluterBase(peaklist, *args, **kwargs)[source]¶

Provides common methods for algorithms which attempt to find a deconvolution for every peak in a spectrum. This assumes no dependence between different peaks, instead it relies on subtraction, breadth of search, and order of encounter to avoid artefactual fits. This is usually not reasonable, so instead please use this class’s extension, PeakDependenceGraphDeconvoluterBase which can express dependence of fits on common resources.

This class is not meant to be instantiated, but instead used as a mixin for classes that also inherit from DeconvoluterBase and provide methods fit_theoretical_distribution and _fit_peaks_at_charges

use_quick_charge¶

Whether or not to use the QuickCharge algorithm when generating putative charge states.

Type

bool

incremental_truncation¶

If not None, the isotopic pattern truncation lower bound to contract each fit to incrementally, generating new fits for each dropped peak using fit_incremental_truncation()

Type

float or None

charge_state_determination(peak, error_tolerance=2e-05, charge_range=(1, 8), left_search_limit=3, right_search_limit=3, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001)[source]¶

Determine the optimal isotopic fit for peak, extracting it’s charge state and monoisotopic peak.

This method invokes _fit_all_charge_states(), and then uses scorer’s select method to choose the optimal solution.

Parameters

peak (FittedPeak) – The peak to start the search from

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 3

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 3

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

Returns

The best scoring isotopic fit

Return type

IsotopicFitRecord

deconvolute(error_tolerance=2e-05, charge_range=(1, 8), order_chooser=operator.attrgetter('index'), left_search_limit=3, right_search_limit=3, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001, **kwargs)[source]¶

Completely deconvolute the spectrum.

Visit each peak in the order chosen by order_chooser, and call deconvolute_peak() on it with the provided arguments. This assumes all overlaps in isotopic pattern are captured by the search limits. This is usually not the case. For an alternative see PeakDependenceGraphDeconvoluterBase

Parameters

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

order_chooser (callable, optional:) – A callable used as a key function for sorting peaks into the order they will be visited during deconvolution. Defaults to operator.attrgetter("index")

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 3

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 3

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

Returns

Return type

DeconvolutedPeakSet

deconvolute_peak(peak, error_tolerance=2e-05, charge_range=(1, 8), left_search_limit=3, right_search_limit=3, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001)[source]¶

Perform a deconvolution for peak, generating a new ms_deisotope.peak_set.DeconvolutedPeak instance corresponding to the optimal solution.

This new peak has an m/z matching the monoisotopic peak of the pattern containing peak, and its intensity is the sum of all the matched peaks in its isotopic pattern. Its charge, isotopic fit, and other qualities are derived from the ms_deisotope.scoring.IsotopicFitRecord instance corresponding to its best solution.

Parameters

peak (FittedPeak) – The peak to start the search from

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 3

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 3

charge_carrier (float, optional) – The mass of the charge carrier, or more specifically, the moiety which is added for each incremental change in charge state. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

Returns

Return type

DeconvolutedPeak

targeted_deconvolution(peak, error_tolerance=2e-05, charge_range=(1, 8), left_search_limit=3, right_search_limit=3, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001)[source]¶

Express the intent that this peak’s deconvolution solution will be retrieved at a later point in the process and that it should be deconvoluted, and return a handle to retrieve the results with.

This algorithm’s implementation is simple enough that this is equivalent to just performing the deconvolution now and storing the result in a TrivialTargetedDeconvolutionResult instance.

Otherwise identical to deconvolute_peak().

Parameters

peak (FittedPeak) – The peak to start the search from

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 3

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 3

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

Returns

Return type

TrivialTargetedDeconvolutionResult

class ms_deisotope.deconvolution.PeakDependenceGraphDeconvoluterBase(peaklist, *args, **kwargs)[source]¶

Extends the concept of ExhaustivePeakSearchDeconvoluterBase to include a way to handle conflicting solutions which claim the same experimental peak.

This lets the Deconvoluter assign a single peak only once, and to the “best” solution to use it. To do this, the Deconvoluter constructs a graph where peaks are nodes, and isotopic fits are hyperedges connecting multiple nodes. Rather than deconvoluting the spectrum step by step, assigning signal as it explores the spectrum, the Deconvoluter instead inserts each considered isotopic fit into the graph. After completely traversing the spectrum, the Deconvoluter solves the dependence graph attempting to maximize some criterion and produce a set of disjoint isotopic fits. These fits are then assigned signal and added to the deconvoluted spectrum as normal.

The criterion used is currently a greedy maximization (or minimization) of each connected component of the peak dependence graph.

max_missed_peaks¶

The maximum number of missing peaks to tolerate in an isotopic fit

Type

int

peak_dependency_network¶

The peak dependence graph onto which isotopic fit dependences on peaks are constructed and solved.

Type

PeakDependenceGraph

deconvolute(error_tolerance=2e-05, charge_range=(1, 8), left_search_limit=1, right_search_limit=0, iterations=10, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001, convergence=0.001, **kwargs)[source]¶

Completely deconvolute the spectrum.

For each iteration, clear peak_depencency_network, then invoke populate_graph() followed by select_best_disjoint_subgraphs() to populate the resulting DeconvolutedPeakSet

Parameters

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

order_chooser (callable, optional:) – A callable used as a key function for sorting peaks into the order they will be visited during deconvolution. Defaults to operator.attrgetter("index")

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 1

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 0

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

ignore_below (float, optional) – The minimum relative abundance to consider a peak in a theoretical isotopic pattern

convergence (float, optional) – The threshold of the below which after the (sum(intensity_before) - sum( intensity_after)) / sum(intensity_after)

Returns

Return type

DeconvolutedPeakSet

property max_missed_peaks¶

The maximum number of missed peaks per isotopic fit record permitted.

This property directly mirrors PeakDependenceGraph.max_missed_peaks

Returns

Return type

int

populate_graph(DeconvoluterBase self, error_tolerance=ERROR_TOLERANCE, charge_range=(1, 8), left_search_limit=1, right_search_limit=0, charge_carrier=PROTON, truncate_after=TRUNCATE_AFTER, ignore_below=IGNORE_BELOW)¶

Visit each experimental peak and execute _explore_local() on it with the provided parameters, populating the peak dependence graph with all viable candidates.

Parameters

peak (FittedPeak) –

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 1

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 0

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

truncate_after (float, optional) – The percent of intensity to ensure is included in a theoretical isotopic pattern starting from the monoisotopic peak. This will cause theoretical isotopic patterns to be truncated, excluding trailing peaks which do not contribute substantially to the overall shape of the isotopic pattern.

postprocess_fits(error_tolerance=2e-05, charge_range=(1, 8), charge_carrier=1.00727646677, *args, **kwargs)[source]¶

Postprocesses fits before solving the peak dependence graph.

Currently a no-op.

Parameters

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

select_best_disjoint_subgraphs(error_tolerance=2e-05, charge_carrier=1.00727646677)[source]¶

Construct connected envelope graphs from peak_dependency_network and extract the best disjoint isotopic pattern fits in each envelope graph. This in turn produces one or more DeconvolutedPeak instances from each disjoint fit, which are processed and added to the results set.

Parameters

error_tolerance (float, optional) – The error tolerance to use when performing subtraction, if subtraction is being performed.

charge_carrier (float, optional) – The mass of the charge carrier as used for the deconvolution. Required to back-out the neutral mass of the deconvoluted result

targeted_deconvolution(peak, error_tolerance=2e-05, charge_range=(1, 8), left_search_limit=3, right_search_limit=3, charge_carrier=1.00727646677, truncate_after=0.95, ignore_below=0.001)[source]¶

Express the intent that this peak’s deconvolution solution will be retrieved at a later point in the process and that it should be deconvoluted, and return a handle to retrieve the results with.

As the operation does not immediately result in a deconvoluted peak but just adds the resulting fits to peak_dependency_network, this method constructs an instance of NetworkedTargetedDeconvolutionResult which holds all the required information for recovering the best fit containing peak.

Parameters

peak (FittedPeak) – The peak to start the search from

error_tolerance (float, optional) – The parts-per-million error tolerance in m/z to search with. Defaults to 2e-5

charge_range (tuple, optional) – The range of charge states to consider. Defaults to (1, 8)

left_search_limit (int, optional) – The number of steps to search to the left of peak. Defaults to 3

right_search_limit (int, optional) – The number of steps to search to the right of peak. Defaults to 3

charge_carrier (float, optional) – The mass of the charge carrier. Defaults to 1.0072

Returns

Return type

NetworkedTargetedDeconvolutionResult

Accounting For Lower Quality Data ¶

A set of strategies for retaining peaks following deconvolution to recover from low quality spectra. These methods are not recommended for use on spectra where most ions are multiply charged.

These objects are meant to be provided to deconvolut_peaks()’s retention_strategy argument, which will take care of calling them with the appropriate arguments.

class ms_deisotope.deconvolution.peak_retention_strategy.PeakRetentionStrategyBase[source]¶

A strategy for retaining peaks left behind by the deconvolution procedure.

Deconvolution requires that the isotopic peak structure is present in the peak list, which may not be consistent for all types of data. This type of method is appropriate when the caller expects real peaks may have been left behind, as is the case with smaller peptides and lower quality tandem mass spectra.

__call__(peaklist, original_peaklist=None, charge_range=None, solutions=None)[source]¶

A wrapper for retain_peaks(). Given a list of FittedPeak objects left over after deconvolution, produce a list of DeconvolutedPeak objects from them according to some criteria.

Parameters

peaklist (PeakSet) – The list of peaks that remain after deconvolution
original_peaklist (PeakSet, optional) – The list of peaks that were initially presented for deconvolution, which may be used to infer relative thresholds from. If not provided, these thresholds may be learned from the left over peaks, but the parameters may not be as good.
charge_range (tuple, optional) – The range of charge states considered during deconvolution. Used to infer the minimum charge state to assign to the peaks this method creates. If not provided, the default charge state is :const`1`.
solutions (DeconvolutedPeakSet, optional) – The accepted solutions which might be used to infer/reject additional peaks.

Returns

The list of DeconvolutedPeak objects.

Return type

list

Utilities and Implementation Details ¶

ms_deisotope.deconvolution.utils.prepare_peaklist(peaks)[source]¶

Ensure peaks is a PeakSet object, converting from other compatible types as needed. Additionally, make a deep copy of the peaks as signal subtraction methods will modify peaks in place.

This function ensures that any of the following common input types are coerced to the appropriate type:

ms_peak_picker.PeakSet will be copied and indexed
ms_peak_picker.PeakIndex will have its peaks extracted and copied
Any other sequence of PeakLike objects (objects having an mz and intensity attribute) will be converted into a ms_peak_picker.PeakSet
A pair of parallel sequences or NumPy arrays corresponding to m/z and intensity values for a series of peaks.
Any sequence of tuple or list having at least two entries will be converted into a ms_peak_picker.PeakSet with the m/z value of each peak being the the p[0] of each entry and the intensity p[1]. Any other entries will be ignored.

Parameters: peaks (Sequence) – Any sequence of FittedPeak objects, objects with mz and intensity attributes, or list / tuple objects containing paired values for mz and intensity
Returns
Return type: PeakSet

class ms_deisotope.utils.DeconvolutionProcessResult(deconvoluter, peak_set, priorities, errors=None)[source]¶

Hold information from a multi-stage deconvolution process.

Emulates an interface matching a tuple of (peak_set, priorities)

deconvoluter¶

The deconvoluter used.

Type: DeconvoluterBase

errors¶

A list of Exception instances that may have been thrown during any stage of deconvolution.

Type: list, optional

peak_set¶

The resulting deconvoluted peaks

Type: DeconvolutedPeakSet

priorities¶

The extracted results from the targeted deconvolution list

Type: list

class ms_deisotope.utils.TargetedDeconvolutionResultBase(deconvoluter, *args, **kwargs)[source]¶

Base class to store all of the necessary information to retrieve the optimal solution for a single peak.

deconvoluter¶

The deconvoluter to use to look up the result

Type: DeconvoluterBase

get()[source]¶

Fetch the optimal solution after the computation has finished.

Returns
Return type: DeconvolutedPeak

class ms_deisotope.peak_dependency_network.NetworkedTargetedDeconvolutionResult(deconvoluter, peak, *args, **kwargs)[source]¶

Stores the necessary information to retrieve the local optimal solution for a single peak for a deconvolution algorithm from the optimal fit containing query_peak in the set of disjoint best fits for the enclosing connected component

query_peak¶

The peak queried with

Type: FittedPeak

solution_peak¶

The optimal solution peak

Type: DeconvolutedPeak

get()[source]¶

Fetch the optimal solution after the computation has finished.

Returns
Return type: DeconvolutedPeak

class ms_deisotope.peak_dependency_network.PeakDependenceGraph(peaklist, nodes=None, dependencies=None, max_missed_peaks=1, use_monoisotopic_superceded_filtering=True, maximize=True)[source]¶

Senko: Senko, M. W., Beu, S. C., & McLafferty, F. W. (1995). Determination of monoisotopic masses and ion populations for large biomolecules from resolved isotopic distributions. Journal of the American Society for Mass Spectrometry, 6(4), 229–233. http://doi.org/10.1016/1044-0305(95)00017-8