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It offers a method to determine pairs across two consensus maps. The corresponding consensus features must be aligned, but may have small position deviations.

The distance measure is implemented in class @ref FeatureDistance - see there for details.

<B> Additional criteria for pairing </B>

Depending on parameter @p use_identifications, peptide identifications annotated to the features may have to be compatible (i.e. no annotation or the same annotation) for a pairing to occur.

Stability criterion: The distance to the nearest neighbor must be smaller than the distance to the second-nearest neighbor by a certain factor, see parameter @p second_nearest_gap. There is a non-trivial relation between this parameter and the maximum allowed difference (in RT or m/z) of the distance measure: If @p second_nearest_gap is greater than one, lowering @p max_difference may in fact lead to more - rather than fewer - pairings, because it increases the distance difference between the nearest and the second-nearest neighbor, so that the constraint imposed by @p second_nearest_gap may be fulfilled more often.

<B> Quality calculation </B>

The quality of a pairing is computed from the distance between the paired elements (nearest neighbors) and the distances to the second-nearest neighbors of both elements, according to the formula:

@f[

q_{i,j} = \big( 1 - d_{i,j} \big) \cdot

\big( 1 - \frac{g \cdot d_{i,j}}{d_{2,i}} \big) \cdot

\big( 1 - \frac{g \cdot d_{i,j}}{d_{2,j}} \big) \cdot

@f]

@f$ q_{i,j} @f$ is the quality of the pairing of elements @em i and @em j, @f$ d_{i,j} @f$ is the distance between the two, @f$ d_{2,i} @f$ and @f$d_{2,j} @f$ are the distances to the second-nearest neighbors of @em i and @em j, respectively, and @em g is the factor defined by parameter @p second_nearest_gap.

Note that by the definition of the distance measure, @f$ 0 \leq d_{i,j} \leq 1 @f$ if @em i and @em j are to form a pair. The criteria for pairing further require that @f$ g \cdot d_{i,j} \leq d_{2,i} @f$ and @f$ g \cdot d_{i,j} \leq d_{2,j} @f$. This ensures that the resulting quality is always between one (best) and zero (worst).

For the final quality @em q of the consensus feature produced by merging two paired elements (@em i and @em j), the existing quality values of the two elements are taken into account. The final quality is a weighted average of the existing qualities (@f$ q_i @f$ and @f$ q_j @f$) and the quality of the pairing (@f$ q_{i,j} @f$, see above):

@f[

q = \frac{q_{i,j} + (s_i - 1) \cdot q_i + (s_j - 1) \cdot q_j}{s_i + s_j - 1}

@f]

The weighting factors @f$ s_i @f$ and @f$ s_j @f$ are the sizes (i.e. numbers of subelements) of the two consensus features @em i and @em j. That way, it is possible to link several feature maps to a growing consensus map in a stepwise fashion (as done by @ref FeatureGroupingAlgorithmUnlabeled), and in the end obtain quality values that incorporate the qualities of all pairings that occurred during the generation of a consensus feature. Note that "missing" elements (if a consensus feature does not contain sub-features from all input maps) are not punished in this definition of quality.

@htmlinclude OpenMS_StablePairFinder.parameters

@ingroup FeatureGrouping

class OPENMS_DLLAPI StablePairFinder : public BaseGroupFinder

{

public:

///Base class

typedef BaseGroupFinder Base;

/// Constructor

StablePairFinder();

/// Destructor

virtual

~StablePairFinder()

{

}

/// Returns an instance of this class

static BaseGroupFinder*

create()

{

return new StablePairFinder();

}

/// Returns the name of this module

static const String

getProductName()

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{

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return "stable";

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}

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/**

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@brief Run the algorithm

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@note Exactly two @em input maps must be provided.

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@exception Exception::IllegalArgument is thrown if the input data is not valid.

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void run(const std::vector<ConsensusMap>& input_maps,

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ConsensusMap &result_map );

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protected:

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///@name Internal helper classes and enums

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//@{

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enum

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{

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RT = Peak2D::RT,

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MZ = Peak2D::MZ

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};

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//@}

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//docu in base class

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virtual void

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updateMembers_();

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/**

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@brief Checks if the peptide IDs of two features are compatible.

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A feature without identification is always compatible. Otherwise, two features are compatible if the best peptide hits of their identifications have the same sequences.

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bool compatibleIDs_(const ConsensusFeature& feat1,

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const ConsensusFeature& feat2) const;

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/// The distance to the second nearest neighbors must be by this factor larger than the distance to the matched element itself.

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DoubleReal second_nearest_gap_;

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/// Only match if peptide IDs are compatible?

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bool use_IDs_;

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};

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} // namespace OpenMS

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#endif // OPENMS_ANALYSIS_MAPMATCHING_STABLEPAIRFINDER_H

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gnuplot history - how the plot was created - please do not delete this receipt

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f(x,intercept,exponent)=1/(1+(abs(x)*intercept)**exponent)

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set terminal postscript enhanced color

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set output "choosingstablepairfinderparams.ps"

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set size ratio .3

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plot [-3:3] [0:1] f(x,1,1), f(x,2,1), f(x,1,2), f(x,2,2)

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