compareSpectriPy: Spectra similarity calculations using Python's matchms...

In rformassspectrometry/SpectriPy: Integrating Spectra with Python's matchms

Spectra similarity calculations using Python's matchms library

Description

The compareSpectriPy() function allows to calculate spectral similarity scores using the calculate_scores() function of the Python matchms.similarity. module.

Selection and configuration of the algorithm can be performed with one of the parameter objects/functions:

• CosineGreedy(): calculate the cosine similarity score between spectra. The score is calculated by finding the best possible matches between peaks of two spectra. Two peaks are considered a potential match if their m/z ratios lie within the given tolerance. The underlying peak assignment problem is here solved in a greedy way. This can perform notably faster, but does occasionally deviate slightly from a fully correct solution (as with the CosineHungarian algorithm). In practice this will rarely affect similarity scores notably, in particular for smaller tolerances. The algorithm can be configured with parameters tolerance, mz_power and intensity_power (see parameter description for more details). See also matchms CosineGreedy for more information.

• CosineHungarian(): calculate the cosine similarity score as with CosineGreedy, but using the Hungarian algorithm to find the best matching peaks between the compared spectra. The algorithm can be configured with parameters tolerance, mz_power and intensity_power (see parameter description for more details). See also matchms CosingHungarian for more information.

• ModifiedCosine(): The modified cosine score aims at quantifying the similarity between two mass spectra. The score is calculated by finding the best possible matches between peaks of two spectra. Two peaks are considered a potential match if their m/z ratios lie within the given tolerance, or if their m/z ratios lie within the tolerance once a mass shift is applied. The mass shift is simply the difference in precursor-m/z between the two spectra. See also matchms ModifiedCosine for more information.

• NeutralLossesCosine(): The neutral losses cosine score aims at quantifying the similarity between two mass spectra. The score is calculated by finding the best possible matches between peaks of two spectra. Two peaks are considered a potential match if their m/z ratios lie within the given tolerance once a mass shift is applied. The mass shift is the difference in precursor-m/z between the two spectra. See also matchms NeutralLossesCosine for more information.

• FingerprintSimilarity(): Calculate similarity between molecules based on their fingerprints. For this similarity measure to work, fingerprints are expected to be derived by running add_fingerprint(). See also matchms FingerprintSimilarity for more information.

Usage

CosineGreedy(tolerance = 0.1, mz_power = 0, intensity_power = 1)

CosineHungarian(tolerance = 0.1, mz_power = 0, intensity_power = 1)

ModifiedCosine(tolerance = 0.1, mz_power = 0, intensity_power = 1)

NeutralLossesCosine(
  tolerance = 0.1,
  mz_power = 0,
  intensity_power = 1,
  ignore_peaks_above_precursor = TRUE
)

## S4 method for signature 'Spectra,Spectra,CosineGreedy'
compareSpectriPy(x, y, param, ...)

## S4 method for signature 'Spectra,missing,CosineGreedy'
compareSpectriPy(x, y, param, ...)

Arguments

tolerance	numeric(1): tolerated differences in the peaks' m/z. Peaks with m/z differences ⁠<= tolerance⁠ are considered matching.
mz_power	numeric(1): the power to raise m/z to in the cosine function. The default is 0, in which case the peak intensity products will not depend on the m/z ratios.
intensity_power	numeric(1): the power to raise intensity to in the cosine function. The default is 1.
ignore_peaks_above_precursor	For NeutralLossesCosine(): logical(1): if TRUE (the default), peaks with m/z values larger than the precursor m/z are ignored.
x	A Spectra::Spectra() object.
y	A Spectra::Spectra() object to compare against. If missing, spectra similarities are calculated between all spectra in x.
param	One of the parameter classes listed above (such as CosineGreedy) defining the similarity scoring function in Python and its parameters.
...	ignored.

Value

compareSpectriPy() Returns a numeric matrix with the scores, with the number of rows equal to length(x) and the number of columns equal to length(y).

Note

Parameters and algorithms are named as originally defined in the matchms library (i.e. all parameters in snake_case while CamelCase is used for the algorithms.

Author(s)

Carolin Huber, Michael Witting, Johannes Rainer, Helge Hecht, Marilyn De Graeve

See Also

Spectra::compareSpectra() in the Spectra package for pure R implementations of spectra similarity calculations.

Examples

library(Spectra)
## Create some example Spectra.
DF <- DataFrame(
    msLevel = c(2L, 2L, 2L),
    name = c("Caffeine", "Caffeine", "1-Methylhistidine"),
    precursorMz = c(195.0877, 195.0877, 170.0924)
)
DF$intensity <- list(
    c(340.0, 416, 2580, 412),
    c(388.0, 3270, 85, 54, 10111),
    c(3.407, 47.494, 3.094, 100.0, 13.240)
)
DF$mz <- list(
    c(135.0432, 138.0632, 163.0375, 195.0880),
    c(110.0710, 138.0655, 138.1057, 138.1742, 195.0864),
    c(109.2, 124.2, 124.5, 170.16, 170.52)
)
sps <- Spectra(DF)

## Calculate pairwise similarity beween all spectra within sps with
## matchms' CosineGreedy algorithm
## Note: the first compareSpectriPy will take longer because the Python
## environment needs to be set up.
res <- compareSpectriPy(sps, param = CosineGreedy())
res

## Next we calculate similarities for all spectra against the first one
res <- compareSpectriPy(sps, sps[1], param = CosineGreedy())

## Calculate pairwise similarity of all spectra in sps with matchms'
## ModifiedCosine algorithm
res <- compareSpectriPy(sps, param = ModifiedCosine())
res

## Note that the ModifiedCosine method requires the precursor m/z to be
## known for all input spectra. Thus, it is advisable to remove spectra
## without precursor m/z before using this algorithm.
sps <- sps[!is.na(precursorMz(sps))]
compareSpectriPy(sps, param = ModifiedCosine())

rformassspectrometry/SpectriPy documentation built on March 1, 2025, 12:30 p.m.