filterSpectriPy: Filter Spectra using Python's matchms library

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

Filter Spectra using Python's matchms library

Description

The filterSpectriPy() function allows to filter/process a Spectra object using the select_by_intensity(), select_by_mz(), remove_peaks_around_precursor_mz(), and normalize_intensities() functions of the Python matchms.filtering module.

Selection and configuration of the algorithm can be performed with one of the parameter objects (equivalent to matchms' function names):

• select_by_intensity(): Keeps only the peaks within defined intensity range (keep if intensity_from >= intensity >= intensity_to). See also the respective documentation in matchms.

• select_by_mz(): Keeps only the peaks between mz_from and mz_to (keep if mz_from >= m/z >= mz_to). See also the respective documentation in matchms.

• remove_peaks_around_precursor_mz(): Removes the peaks that are within mz_tolerance (in Da) of the precursor mz, excluding the precursor peak.

• normalize_intensities(): Normalizes the intensities of peaks (and losses) to unit height.

Usage

select_by_intensity(intensity_from = 10, intensity_to = 200)

select_by_mz(mz_from = 0, mz_to = 1000)

remove_peaks_around_precursor_mz(mz_tolerance = 17)

normalize_intensities()

## S4 method for signature 'Spectra,filter_param'
filterSpectriPy(object, param, mapping = spectraVariableMapping(), ...)

Arguments

intensity_from	numeric(1): Set lower threshold for peak intensity. Default is 10.
intensity_to	numeric(1): Set upper threshold for peak intensity. Default is 200.
mz_from	numeric(1): Set lower threshold for m/z peak positions. Default is 0.
mz_to	numeric(1): Set upper threshold for m/z peak positions. Default is 1000.
mz_tolerance	numeric(1): Tolerance of m/z values that are not allowed to lie within the precursor mz. Default is 17 Da.
object	A Spectra::Spectra() object.
param	one of parameter classes listed above (such as select_by_intensity()) defining the filter/processing function in Python and its parameters.
mapping	named character() defining which spectra variables/metadata should be converted between R and Python and how they should be renamed. Defaults to spectraVariableMapping(). See setSpectraVariableMapping() for more information.
...	ignored.

Value

filterSpectriPy() returns a Spectra object on which the filtering/processing function has been applied

Note

The first call to the filterSpectriPy() function can take longer because the Python environment needs to be first set up.

filterSpectriPy() first translates the Spectra to Python, applies the filter functions from the matchms Python libraries and then translates the filtered data back to a Spectra object. Thus, any spectra variables other than those that are translated between R and Python will be lost during the processing. Use setSpectraVariableMapping() to define which spectra variables should be transferred/converted between R and Python. See also examples below for more information.

The Spectra::Spectra() object returned by filterSpectriPy() will always use an in-memory backend (i.e. the Spectra::MsBackendMemory()), independently of the backend used by the backend used by the input Spectra.

Author(s)

Thomas Naake

See Also

• Spectra::filterIntensity(), Spectra::filterMzRange(), Spectra::scalePeaks() in the Spectra package for pure R implementations of filtering/processing calculations.

• rspec_to_pyspec() or pyspec_to_rspec() for the functions used to translated the MS data between R and Python.

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)

## Filter: select_by_intensity
res <- filterSpectriPy(
    sps, select_by_intensity(intensity_from = 15, intensity_to = 300))
## Only mass peaks with intensities between the specified limits are
## retained
intensity(res)
## Compared to the original intensities
intensity(sps)

## Note that the spectra variable `"name"` was lost during conversion of
## the MS data between R and Python:
sps$name
any(spectraVariables(res) == "name")

## Only spectra variables defined by `spectraVariableMapping()` are
## converted and thus retained:
spectraVariableMapping()

## We can also pass a custom *spectra variable mapping* with the `mapping`
## parameter to the `filterSpectriPy()` function. Below we create such
## a mapping by adding the translation of a spectra variable `"name"` to
## a metadata name `"compound_name"` to the default spectra variable
## mapping `defaultSpectraVariableMapping()`.
map <- c(defaultSpectraVariableMapping(), name = "compound_name")
map

## Repeat the filtering operation passing this mapping information:
res <- filterSpectriPy(
    sps, select_by_intensity(intensity_from = 15, intensity_to = 300),
    mapping = map)
res$name

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