The compareSpectriPy function allows to calculate spectral similarity
scores using the calculate_scores module of the python
matchms.similarity package
package.
Selection and configuration of the algorithm can be performed with one of the parameter objects:
CosineGreedyParam: calculate the cosine similarity score between
spectra. The score is calculated by finding 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 CosineHungarianParam algorithm). In practice this will rarely
affect similarity scores notably, in particular for smaller tolerances. The
algorithm can be configured with parameters tolerance, mzPower and
intensityPower (see parameter description for more details).
CosineHungarianParam: calculate the cosine similarity score as with
CosineGreedyParam, but using the Hungarian algorithm to find the best
matching peaks between the compared spectra. The algorithm can be
configured with parameters tolerance, mzPower and intensityPower
(see parameter description for more details).
ModifiedCosineParam: The modified cosine score aims at quantifying the
similarity between two mass spectra. The score is calculated by finding
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.
CosineGreedyParam(tolerance = 0.1, mzPower = 0, intensityPower = 1)
CosineHungarianParam(tolerance = 0.1, mzPower = 0, intensityPower = 1)
ModifiedCosineParam(tolerance = 0.1, mzPower = 0, intensityPower = 1)
# S4 method for Spectra,Spectra,CosineGreedyParam
compareSpectriPy(x, y, param, ...)
# S4 method for Spectra,missing,CosineGreedyParam
compareSpectriPy(x, y, param, ...)numeric(1): tolerated differences in peaks' m/z. Peaks
with m/z differences <= tolerance are considered matching.
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.
numeric(1): the power to raise intensity to in the
cosine function. The default is 1.
A Spectra() object.
A Spectra() object to compare against. If missing, spectra
similarities are calculated between all spectra in x.
one of parameter classes listed above (such as
CosineGreedyParam) defining the similarity scoring function in python
and its parameters.
ignored.
compareSpectriPy returns a numeric matrix with the scores,
number of rows being equal to length(x) and number of columns equal to
length(y).
compareSpectra() in the Spectra package for pure R
implementations of spectra similarity calculations.
library(Spectra)
#> Loading required package: S4Vectors
#> Loading required package: stats4
#> Loading required package: BiocGenerics
#>
#> Attaching package: ‘BiocGenerics’
#> The following objects are masked from ‘package:stats’:
#>
#> IQR, mad, sd, var, xtabs
#> The following objects are masked from ‘package:base’:
#>
#> Filter, Find, Map, Position, Reduce, anyDuplicated, append,
#> as.data.frame, basename, cbind, colnames, dirname, do.call,
#> duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
#> lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
#> pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
#> tapply, union, unique, unsplit, which.max, which.min
#>
#> Attaching package: ‘S4Vectors’
#> The following objects are masked from ‘package:base’:
#>
#> I, expand.grid, unname
#> Loading required package: BiocParallel
#> Loading required package: ProtGenerics
#>
#> Attaching package: ‘ProtGenerics’
#> The following object is masked from ‘package:stats’:
#>
#> smooth
#>
#> Attaching package: ‘Spectra’
#> The following object is masked from ‘package:ProtGenerics’:
#>
#> addProcessing
## 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 = CosineGreedyParam())
res
#> [,1] [,2] [,3]
#> [1,] 1.0000000 0.1948181 0
#> [2,] 0.1948181 1.0000000 0
#> [3,] 0.0000000 0.0000000 1
## Next we calculate similarities for all spectra against the first one
res <- compareSpectriPy(sps, sps[1], param = CosineGreedyParam())
## Calculate pairwise similarity of all spectra in sps with matchms'
## ModifiedCosine algorithm
res <- compareSpectriPy(sps, param = ModifiedCosineParam())
res
#> [,1] [,2] [,3]
#> [1,] 1.0000000 0.1948181 0.1384183
#> [2,] 0.1948181 1.0000000 0.8520549
#> [3,] 0.1384183 0.8520549 1.0000000
## 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 = ModifiedCosineParam())
#> [,1] [,2] [,3]
#> [1,] 1.0000000 0.1948181 0.1384183
#> [2,] 0.1948181 1.0000000 0.8520549
#> [3,] 0.1384183 0.8520549 1.0000000