Find features using piek

Uses the piek algorithm to find features.

findFeaturesPiek(
  analysisInfo,
  genEICParams = getPiekEICParams(),
  peakParams = getDefPeakParams("chrom", "piek"),
  IMS = FALSE,
  suspects = NULL,
  adduct = NULL,
  assignMethod = "basepeak",
  assignRTWindow = defaultLim("retention", "very_narrow"),
  rtWindowDup = defaultLim("retention", "narrow"),
  mzWindowDup = defaultLim("mz", "medium"),
  mobWindowDup = defaultLim("mobility", "medium"),
  minPeakOverlapDup = 0.25,
  minIntensityIMS = 25,
  EICBatchSize = Inf,
  keepDups = FALSE,
  verbose = TRUE
)

getPiekEICParams(..., IMS = getLimIMS())

Arguments

analysisInfo

A data.frame (or data.table) with Analysis information.

genEICParams

A list of parameters for the EIC generation. See the EIC generation parameters section below. The getPiekEICParams function is used to generate the parameter list.

peakParams

A list of parameters for the peak detection. See getDefPeakParams for details.

IMS

A character that specifies for which IMS instrument defaults are returned. Should be "bruker" or "agilent". Defaults to what is specified in limits.

suspects

The suspect list to be used for suspect pre-filtering of EIC bins. See suspect screening for details on the suspect list format and EIC generation parameters to enable suspect filtering.

NOTE: Suspect matching can only be performed by mobilities and not CCS values. The assignMobilities method should be used to convert any CCS data in advance.

adduct

An adduct object (or something that can be converted to it with as.adduct). Examples: "[M-H]-", "[M+Na]+". Only needs to be specified if suspects is set.

assignMethod

Should be "basepeak" or "weighted.mean". This parameter sets how measured m/z or mobilities across the EIC datapoints are handled for feature assignment. If assignMethod="basepeak", then the value of the base peak (=highest intensity peak) from each EIC datapoint is taken. If assignMethod="weighted.mean" then the intensity weighted mean is calculated of the values that fall within the EIC bin.

assignRTWindow

The retention time window (+/- seconds) used for aggregating EIC datapoints to assign feature m/z and mobility data, using an intensity weighted mean. The maximum window is always bound by the feature retention time range. Increasing this number may improve accuracy by averaging more points. However, decreasing the window may reduce inaccuracies due to inclusion of data from closely eluting features (with similar m/z and mobility) or noisy data from the chromatographic peak extremes. If assignRTWindow=0 then only the EIC datapoint at the feature retention time is used.

The assignment window is automatically adjusted for the values set for sumWindowMZ and sumWindowMob (see EIC generation parameters).

rtWindowDup, mzWindowDup, mobWindowDup

The retention time (seconds), m/z and mobility windows used to identify duplicate (redundant) features detected in multiple EIC bins. These values default to defaultLim("retention", "very_narrow"), defaultLim("mz", "medium") and defaultLim("mobility", "medium"), respectively (see limits).

minPeakOverlapDup

The minimum overlap (fraction between 0 and 1) in retention time between two features to be considered a duplicate.

minIntensityIMS

(IMS workflow) Raw intensity threshold for IMS data. This is primarily intended to speed up raw data processing.

EICBatchSize

The number of EICs to be processed in a single batch. Decreasing this number will reduce memory usage, at the cost of speed. Set to Inf to process all EICs in a single batch.

keepDups

Set to TRUE to keep duplicate features and features with non-centered m/z or mobility values. This is primarily intended for debugging, but can be useful to investigate why features are missing or optimize tolerance windows for duplicate feature detection.

verbose

If set to FALSE then no text output is shown.

...

Any additional parameters to be set in the returned parameter list. These will override the defaults. See the EIC generation parameters section for details.

Value

getPiekEICParams returns a list of parameters for the EIC generation, which is used to set the genEICParams argument to findFeaturesPiek.

Details

This function uses piek to automatically find features. This function is called when calling findFeatures with algorithm="piek".

The piek algorithm extends and improves on the simple and fast feature detection algorithm introduced by Dietrich C, Wick A, Ternes TA (2021). “Open‐source feature detection for non‐target LC–MS analytics.” Rapid Communications in Mass Spectrometry, 36(2). ISSN 1097-0231, doi:10.1002/rcm.9206 , http://dx.doi.org/10.1002/rcm.9206. . This algorithm first forms extracted ion chromatograms (EICs) and subsequently performs automatic peak detection to generate features. The piek algorithm introduces the following improvements and changes:

Support for IMS-HRMS workflows.
The msdata interface is used to efficiently form EICs from the raw data. All the file formats and types can be used that are supported by msdata. This includes IMS data, even if not used for feature detection, which allows the use of IMS data directly in non-IMS or post mobility assignment workflows.
The EIC binning approach can be extended with the mobility dimension to support direct mobility assignment workflows.
The EIC bins can be filtered with suspect or MS2 data to speed up feature detection.
Several filters are available to eliminate EICs with are likely devoid of any signal of interest.
The original peak detection algorithm was further optimized or can be be exchanged with others: see getDefPeakParams for details.
Several filters are available to improve the data and reduce redundancy:
- The original redundancy detection, which performs a second feature detection with EIC bins that are shifted by 50% width and eliminates features with m/z values outside the center of any bin, was extended for IMS support.
- Redundant features across bins are eliminated if with close retention time, m/z, mobility and chromatographic overlap. The most intense feature is kept.
- Data from suspects or MS2 precursors that was used to pre-filter EICs, can also be used to filter the final feature list.
Various small bug fixes and improvements for the original code.
The output feature tables contain raw intensities/areas and those subtracted by the estimated noise level (intensity, intensitySub, area and areaSub columns, respectively) and the estimated signal to noise (signalToNoise column).

IMS workflows

If IMS data is used to resolve features (IMS=TRUE), a 'pre-check' is performed to avoid excessive numbers of two-dimensional bins for EIC formation and peak detection. These EICs are formed by only considering the m/z dimension, and subsequently filtered by the parameters described in the EIC generation parameters section. The final EICs for feature detection are then only formed if they have m/z data that was not removed during the pre-check.

The m/z and mobility data from IMS-HRMS data is typically not or partially centroided. The feature m/z and mobility values are derived from m/z or mobility versus intensity profiles. The profiles are generated for each EIC timepoint, and the value at the maximum intensity or intensity weighted mean of the profile is used to derive the intermediate values (configured by assignMethod). Several parameters exist to improve the profile data (see next section).

EIC generation parameters

The genEICParams argument to findFeaturesPiek configures the generation of EICs. The getPiekEICParams function should be used to generate the parameter list.

The following general parameters exist:

filter Controls the pre-filtering of EIC bins with m/z data. Should be "none" (no filtering), "suspects" (filter with suspect data) or "ms2" (filter with data from precursors detected in a data-dependent MS/MS experiment).
mzRange,mzStep Configures the formation of m/z bins. mzRange is a numeric vector of length two that specifies the min/max m/z range. mzStep specifies the bin widths.
retRange A numeric vector of length two that specifies the retention time range for the EICs. Data outside this range is excluded. Set to NULL to use the full range.
gapFactor A numeric that configures gap filling for EICs. See getDefEICParams for further details.
minEICIntensity The minimum intensity of the highest data point in the EIC. Used to filter EICs.
minEICAdjTime,minEICAdjPoints,minEICAdjIntensity The EIC should have at least a continuous signal of minEICAdjTime seconds and minEICAdjPoints data points, where the continuity is defined by data points with an intensity of at least minEICAdjIntensity high. Set minEICAdjTime or minEICAdjPoints to zero to disable continuity checks for time or data points, respectively. Set minEICAdjIntensity to zero to completely disable continuity checks.
topMostEICMZ Only keep this number of top-most intense EICs. The intensity is derived from the data point with the highest intensity in the EIC. Set to zero to always select all EICs.

For IMS workflows, this parameter is only used to limit the number of EICs resulting from the 'pre-check' in the m/z dimension.

The following parameters are specifically used for IMS workflows:

filterIMS Similar to the filter parameter, but controls how mobility data is used for pre-filtering of EIC bins.

Different values for filter and filterIMS can be specified:
- filter="none" and filterIMS="none"
- filter="suspects" and filterIMS="suspects"
- filter="suspects" and filterIMS="none" (only use m/z filtering)
- filter="ms2" and filterIMS="ms2"
- filter="ms2" and filterIMS="none"
Currently only Bruker DDA-PASEF experiments provide the data needed for "ms2" filtering.
mobRange,mobStep Equivalent to mzRange and mzStep, but for ion mobility binning.
sumWindowMZ,sumWindowMob The retention time window (+/- s) used to sum adjacent datapoints for the determination of intermediate EIC m/z and mobility values. This data is aggregated to determine the final feature values (see also the assignRTWindow argument). Set to 0 to not sum any adjacent timepoints. Larger values can generally improve accuracy for noisy data (e.g. from TIMS), but care must be taken to stay below the expected minimum chromatographic peak width to avoid inclusion of data from other features. Defaults to defaultLim("retention", "very_narrow") (see limits).
smoothWindowMZ,smoothWindowMob The window size used to perform centered moving average smoothing on intensity data of the m/z and mobility profiles used to determine intermediate EIC values. Smoothing of noisy data (e.g. TIMS) is highly recommended to improve accuracy and consistency. Set to 0 to disable smoothing.
smoothExtMZ,smoothExtMob The m/z or mobility window to extend the smoothing at the edges of the EIC bin. This is recommended to improve smoothing, e.g. when the peak profile is only partially captured in the bin. Defaults to the bin width, i.e. data from an adjacent bin on each side is additionally included for smoothing. The final smoothed data is only taken from the actual EIC bin. Set to 0 to disable extension.
saveMZProfiles,saveEIMs Set to TRUE to save the m/z and mobility profiles for each feature. Only the profiles at the feature retention time is saved. This can be useful for debugging or parameter optimization, but will increase memory usage and processing times.
topMostEICMob Equivalent to topMostEICMZ, used to reduce the final two-dimensional EIC bins with m/z and mobility information.
minEICsIMSPreCheck Only perform the m/z pre-check if the number of two-dimensional EIC bins is at least minEICsIMSPreCheck.

The following parameters are specifically for when suspect data is used to pre-filter EIC bins:

rtWindow,mzwindow,mobWindow: The retention time, m/z and mobility tolerance windows for suspect data. These are used for:
1. Pre-filtering of EIC bins with suspect data, i.e. larger tolerances will lead to more EIC bins being kept. (only applicable for mzWindow and mobWindow).
2. Matching the final features to suspect data. rtWindow=Inf can be used to disable retention time matching.
Defaults to defaultLim("retention", "medium"), defaultLim("mz", "medium") and defaultLim("mobility", "medium"), see limits.
skipInvalid,prefCalcChemProps,neutralChemProps Controls preparing the suspect list data. See screenSuspects.

The following parameters are specifically for when MS2 data is used to pre-filter EICs:

rtWindow Eliminates any features without an MS/MS spectrum within this retention time window. Set rtWindow=Inf to disable this filter. Defaults to defaultLim("retention", "very_narrow") (see limits).
mzIsoWindow The maximum m/z window considered for MS/MS precursors that were isolated by DDA. These m/z isolation windows are used to pre-filter EICs and match the final features. Setting mzIsoWindow to a value lower than typical instrument isolation windows will make feature detection more specific, as features need to be more close to the triggered DDA precursor m/z values. In contrast, larger values for mzIsoWindow allows to include features that were not specifically targeted by DDA, but may still have MS/MS data as their m/z could still fall within the MS/MS isolation window. The effective window used will never exceed the instrumental isolation window. Setting mzIsoWindow=Inf will always use instrumental windows.
mobWindow The mobility tolerance window to match DDA MS/MS precursors in IMS workflows. Used for pre-filtering EICs and the final features. To match DDA precursor data, the measured mobility range of the corresponding MS/MS data is used as the mobility window. This window is then adjusted to be at least +/- mobWindow. Defaults to defaultLim("mobility", "medium") (see limits)
minTIC The minimum total ion current (TIC) signal for an MS/MS spectrum to be considered. Can be increased to eliminate features with low intensity MS/MS data.

Use of raw HRMS data

The raw data interface of patRoon is used by findFeaturesPiek to process HRMS (or IMS-HRMS) data. Please see its documentation for more information on the supported formats and available configuration options.

The use of profile m/z HRMS data (not IMS-HRMS) is currently not supported.

References

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