SCPeakRegionFinder: R6 Peak Region Finder
In MoseleyBioinformaticsLab/ScanCentricPeakCharacterization: Functionality for Characterizing Peaks in Mass Spectrometry in a Scan-Centric Manner
SCPeakRegionFinder R Documentation
R6 Peak Region Finder
Description
R6 Peak Region Finder
R6 Peak Region Finder
Details
Think of it like managing all the stuff that needs to happen to find the peaks
in the regions.
Public fields
run_timehow long did the process take
start_timewhen did we start
stop_timewhen did we start
peak_regionsSCPeakRegions object
sliding_region_sizehow big are the sliding regions in data points
sliding_region_deltahow much space between sliding region starts
quantile_multiplierhow much to multiply quantile based cutoff by
n_point_regionhow many points are there in the big tiled regions for quantile based cutoff
tiled_region_sizehow wide are the tiled regions in data points
tiled_region_deltahow far in between each tiled region
region_percentile??
peak_methodwhat method to extract peak center, height, area, etc
min_pointshow many points wide does a peak have to be to get characterized
sample_idwhat sample are we processing
n_zero_tileshow many zero count tiled regions split up a region into multiple peaks?
zero_normalizationdo we want to pretend to do normalization
calculate_peak_areashould peak area be calculated as well?
Methods
Public methods
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Method add_regions()
Add the sliding and tiled regions
Usage
SCPeakRegionFinder$add_regions()
Method reduce_sliding_regions()
Find the regions most likely to contain real signal
Usage
SCPeakRegionFinder$reduce_sliding_regions()
Method split_peak_regions()
Split up signal regions by peaks found
Usage
SCPeakRegionFinder$split_peak_regions(
use_regions = NULL,
stop_after_initial_detection = FALSE
)
Arguments
use_regionsan index of the regions we want to split up
stop_after_initial_detectionshould it do full characterization or stop
Method remove_double_peaks_in_scans()
Check for the presence of two peaks with the same scan number in each
region and remove them. Any regions with zero peaks left, remove the region.
Usage
SCPeakRegionFinder$remove_double_peaks_in_scans()
Method normalize_data()
Normalize the intensity data
Usage
SCPeakRegionFinder$normalize_data(which_data = "both")
Arguments
which_dataraw, characterized, or both (default)
Method find_peaks_in_regions()
Find the peaks in the regions.
Usage
SCPeakRegionFinder$find_peaks_in_regions()
Method model_mzsd()
Model the m/z standard deviation.
Usage
SCPeakRegionFinder$model_mzsd()
Method model_heightsd()
Model the intensity height standard deviation.
Usage
SCPeakRegionFinder$model_heightsd()
Method indicate_high_frequency_sd()
Look for peaks with higher than expected frequency standard deviation.
Usage
SCPeakRegionFinder$indicate_high_frequency_sd()
Method add_data()
Add the data from an SCMzml object to the underlying SCPeakRegions object.
Usage
SCPeakRegionFinder$add_data(sc_mzml)
Arguments
sc_mzmlthe SCMzml object being passed in
Method summarize_peaks()
Summarize the peaks to go into JSON form.
Usage
SCPeakRegionFinder$summarize_peaks()
Method add_offset()
Add an offset based on width in frequency space to m/z to describe how wide the peak is.
Usage
SCPeakRegionFinder$add_offset()
Method sort_ascending_mz()
Sort the data in m/z order, as the default is frequency order
Usage
SCPeakRegionFinder$sort_ascending_mz()
Method characterize_peaks()
Run the overall peak characterization from start to finish.
Usage
SCPeakRegionFinder$characterize_peaks(stop_after_initial_detection = FALSE)
Arguments
stop_after_initial_detectiondo we stop the whole process after finding initial peaks in each scan?
Method summarize()
Summarize everything for output to the zip file after completion.
Usage
SCPeakRegionFinder$summarize(
package_used = "package:ScanCentricPeakCharacterization"
)
Arguments
package_usedwhich package is being used for this work.
Method peak_meta()
Generate the meta data that goes into the accompanying JSON file.
Usage
SCPeakRegionFinder$peak_meta()
Method new()
Make a new SCPeakRegionFinder object.
Usage
SCPeakRegionFinder$new(
sc_mzml = NULL,
sliding_region_size = 10,
sliding_region_delta = 1,
tiled_region_size = 1,
tiled_region_delta = 1,
region_percentile = 0.99,
offset_multiplier = 1,
frequency_multiplier = 400,
quantile_multiplier = 1.5,
n_point_region = 2000,
peak_method = "lm_weighted",
min_points = 4,
n_zero_tiles = 1,
zero_normalization = FALSE,
calculate_peak_area = FALSE
)
Arguments
sc_mzmlthe SCMzml object to use (can be missing)
sliding_region_sizehow wide to make the sliding regions in data points
sliding_region_deltahow far apart are the starting locations of the sliding regions
tiled_region_sizehow wide are the tiled regions
tiled_region_deltahow far apart are the tiled reigons
region_percentilecumulative percentile cutoff to use
offset_multiplierwhat offset multiplier should be used
frequency_multiplierhow much to multiply frequency points to interval ranges
quantile_multiplierhow much to adjust the quantile cutoff by
n_point_regionhow many points in the large tiled regions
peak_methodthe peak characterization method to use (lm_weighted)
min_pointshow many points to say there is a peak (4)
n_zero_tileshow many tiles in a row do there need to be to split things up? (1)
zero_normalizationdon't actually do normalization (FALSE)
calculate_peak_areashould peak area as well as peak height be returned? (FALSE)
Method clone()
The objects of this class are cloneable with this method.
Usage
SCPeakRegionFinder$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
MoseleyBioinformaticsLab/ScanCentricPeakCharacterization documentation built on March 29, 2024, 11:32 p.m.
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| SCPeakRegionFinder | R Documentation |
R6 Peak Region Finder
Description
R6 Peak Region Finder
R6 Peak Region Finder
Details
Think of it like managing all the stuff that needs to happen to find the peaks in the regions.
Public fields
run_timehow long did the process take
start_timewhen did we start
stop_timewhen did we start
peak_regionsSCPeakRegions object
sliding_region_sizehow big are the sliding regions in data points
sliding_region_deltahow much space between sliding region starts
quantile_multiplierhow much to multiply quantile based cutoff by
n_point_regionhow many points are there in the big tiled regions for quantile based cutoff
tiled_region_sizehow wide are the tiled regions in data points
tiled_region_deltahow far in between each tiled region
region_percentile??
peak_methodwhat method to extract peak center, height, area, etc
min_pointshow many points wide does a peak have to be to get characterized
sample_idwhat sample are we processing
n_zero_tileshow many zero count tiled regions split up a region into multiple peaks?
zero_normalizationdo we want to pretend to do normalization
calculate_peak_areashould peak area be calculated as well?
Methods
Public methods
Method add_regions()
Add the sliding and tiled regions
Usage
SCPeakRegionFinder$add_regions()
Method reduce_sliding_regions()
Find the regions most likely to contain real signal
Usage
SCPeakRegionFinder$reduce_sliding_regions()
Method split_peak_regions()
Split up signal regions by peaks found
Usage
SCPeakRegionFinder$split_peak_regions( use_regions = NULL, stop_after_initial_detection = FALSE )
Arguments
use_regionsan index of the regions we want to split up
stop_after_initial_detectionshould it do full characterization or stop
Method remove_double_peaks_in_scans()
Check for the presence of two peaks with the same scan number in each region and remove them. Any regions with zero peaks left, remove the region.
Usage
SCPeakRegionFinder$remove_double_peaks_in_scans()
Method normalize_data()
Normalize the intensity data
Usage
SCPeakRegionFinder$normalize_data(which_data = "both")
Arguments
which_dataraw, characterized, or both (default)
Method find_peaks_in_regions()
Find the peaks in the regions.
Usage
SCPeakRegionFinder$find_peaks_in_regions()
Method model_mzsd()
Model the m/z standard deviation.
Usage
SCPeakRegionFinder$model_mzsd()
Method model_heightsd()
Model the intensity height standard deviation.
Usage
SCPeakRegionFinder$model_heightsd()
Method indicate_high_frequency_sd()
Look for peaks with higher than expected frequency standard deviation.
Usage
SCPeakRegionFinder$indicate_high_frequency_sd()
Method add_data()
Add the data from an SCMzml object to the underlying SCPeakRegions object.
Usage
SCPeakRegionFinder$add_data(sc_mzml)
Arguments
sc_mzmlthe SCMzml object being passed in
Method summarize_peaks()
Summarize the peaks to go into JSON form.
Usage
SCPeakRegionFinder$summarize_peaks()
Method add_offset()
Add an offset based on width in frequency space to m/z to describe how wide the peak is.
Usage
SCPeakRegionFinder$add_offset()
Method sort_ascending_mz()
Sort the data in m/z order, as the default is frequency order
Usage
SCPeakRegionFinder$sort_ascending_mz()
Method characterize_peaks()
Run the overall peak characterization from start to finish.
Usage
SCPeakRegionFinder$characterize_peaks(stop_after_initial_detection = FALSE)
Arguments
stop_after_initial_detectiondo we stop the whole process after finding initial peaks in each scan?
Method summarize()
Summarize everything for output to the zip file after completion.
Usage
SCPeakRegionFinder$summarize( package_used = "package:ScanCentricPeakCharacterization" )
Arguments
package_usedwhich package is being used for this work.
Method peak_meta()
Generate the meta data that goes into the accompanying JSON file.
Usage
SCPeakRegionFinder$peak_meta()
Method new()
Make a new SCPeakRegionFinder object.
Usage
SCPeakRegionFinder$new( sc_mzml = NULL, sliding_region_size = 10, sliding_region_delta = 1, tiled_region_size = 1, tiled_region_delta = 1, region_percentile = 0.99, offset_multiplier = 1, frequency_multiplier = 400, quantile_multiplier = 1.5, n_point_region = 2000, peak_method = "lm_weighted", min_points = 4, n_zero_tiles = 1, zero_normalization = FALSE, calculate_peak_area = FALSE )
Arguments
sc_mzmlthe SCMzml object to use (can be missing)
sliding_region_sizehow wide to make the sliding regions in data points
sliding_region_deltahow far apart are the starting locations of the sliding regions
tiled_region_sizehow wide are the tiled regions
tiled_region_deltahow far apart are the tiled reigons
region_percentilecumulative percentile cutoff to use
offset_multiplierwhat offset multiplier should be used
frequency_multiplierhow much to multiply frequency points to interval ranges
quantile_multiplierhow much to adjust the quantile cutoff by
n_point_regionhow many points in the large tiled regions
peak_methodthe peak characterization method to use (lm_weighted)
min_pointshow many points to say there is a peak (4)
n_zero_tileshow many tiles in a row do there need to be to split things up? (1)
zero_normalizationdon't actually do normalization (FALSE)
calculate_peak_areashould peak area as well as peak height be returned? (FALSE)
Method clone()
The objects of this class are cloneable with this method.
Usage
SCPeakRegionFinder$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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