Package 'ScanCentricPeakCharacterization'

Description

takes a list from xmlToList for "run" and looks at whether all scans are positive, negative, or mixed

Usage

.get_scan_polarity(spectrum_list)

Arguments

Description

removes a list entry called ".attrs" from a list, and makes them first level partners

Usage

.remove_attrs(in_list)

Arguments

Description

transform to data frame

Usage

.to_data_frame(in_list)

Arguments

Description

add scan level info

Usage

add_scan_info(mzml_data)

Arguments

Details

returns a data.frame with:

• scanIndex: the indices of the scans

• scan: the number of the scan by number. This will be used to name scans.

• polarity: +1 or -1 depending on if the scan is positive or negative

• rtime: the retention time or injection time of the scan for for direct-injection data

• tic: the total intensity of the scan

• rtime_lag: how long between this scan and previous scan

• rtime_lead: how long between this scan and next scan

After running predict_frequency(), the following fields are added from the information returned from frequency conversion:

• mad: mean absolute deviation of residuals

• ⁠frequency model coefficients⁠: the coefficients from the fit frequency, named whatever you named them

• ⁠mz model coefficients⁠: similar, but for the m/z model

Value

data.frame, see Details

Description

given an output object, filename, and zip file, write the output object to the file, and then add to the zip file

Usage

add_to_zip(object, filename, zip_file)

Arguments

Details

a directory created by tempdir is used to hold the file, which is then added to the zip file.

Description

calculate the area based on summing the points

Usage

area_sum_points(peak_mz, peak_intensity, zero_value = 0)

Arguments

Value

numeric

Description

characterize peaks from points and picked peaks

Usage

characterize_peaks(peak_region, calculate_peak_area = FALSE)

Arguments

Value

list

Description

check r2

Usage

check_frequency_r2(mz_frequency_list)

Arguments

Description

Given M/Z point data in a data.frame, create IRanges based point "regions" of width 1, using the frequency_multiplier argument to convert from the floating point double to an integer.

Usage

check_ranges_convert_to_regions(frequency_list, frequency_multiplier = 400)

Arguments

Description

checks that the zip file has the basic contents it should have, and that files listed in the metadata actually exist.

Usage

check_zip_file(zip_dir)

Arguments

Description

default single model

Usage

choose_frequency_model_builtin(sc_mzml)

Arguments

Details

This is the default function to choose a single frequency and mz model. It takes the scan_info after filtering scans, and calculates the median of the square root terms, and chooses the one closest to the median value.

Please examine this function and write your own if needed. You can view the function definition using choose_frequency_model_builtin

Value

SCmzml

Description

Given a data.frame of m/z, generate frequency values for the data.

Usage

convert_mz_frequency(mz_data, keep_all = TRUE)

Arguments

Details

The M/Z values from FTMS data do not have constant spacing between them. This produces challenges in working with ranged intervals and windows. The solution for FTMS data then is to convert them to frequency space. This is done by:

• taking subsequent M/Z points

• averaging their M/Z

• taking the difference to get an offset value

• dividing averaged M/Z by offset to generate frequency

• taking subsequent differences of frequency points

• keep points with a difference in the supplied range as valid for modeling

After deciding on the valid points for modeling, each point gets an interpolated frequency value using the two averaged points to the left and right in M/Z.

Value

list

See Also

mz_scans_to_frequency

Description

Given a corrected SD, corrects the mean assuming that it is the result of a truncated normal distribution.

Usage

correct_mean(observed_mean, corrected_sd, fraction)

Arguments

Value

corrected mean

References

https://en.wikipedia.org/wiki/Truncated_normal_distribution

See Also

correct_peak() correct_variance()

Description

Assuming that an observed mean (intensity) and sd are from a truncated normal distribution that is truncated on one side only.

Usage

correct_peak(observed_mean, observed_sd, n_observed, n_should_observe)

Arguments

Value

data.frame, with corrected mean and sd

References

https://en.wikipedia.org/wiki/Truncated_normal_distribution

See Also

correct_mean() correct_variance()

Description

correct peak height and sd

Usage

correct_peak_sd_height(
  original_height,
  list_of_heights,
  n_observed,
  n_should_observe
)

Arguments

Value

data.frame

Description

Given a variance observed from a truncated normal distribution, correct it assuming that it should have had 100% observaionts

Usage

correct_variance(observed_variance, fraction)

Arguments

Value

corrected variance

References

https://en.wikipedia.org/wiki/Truncated_normal_distribution

See Also

correct_mean() correct_peak()

Description

Given a point-point spacing and a frequency range, create IRanges based regions of specified width. Overlapping sliding regions can be creating by specifying a region_size bigger than delta, adjacent tiled regions can be created by specifying a region_size == delta.

Usage

create_frequency_regions(
  point_spacing = 0.5,
  frequency_range = NULL,
  n_point = 10,
  delta_point = 1,
  multiplier = 500
)

Arguments

Details

For Fourier-transform mass spec, points are equally spaced in frequency space, which will lead to unequal spacing in M/Z space. Therefore, we create regions using the point-point differences in frequency space.

What will be returned is an IRanges object, where the widths are constantly increasing over M/Z space.

Value

IRanges

Description

given a mantissa and exponent, returns the actual value as a numeric

Usage

create_value(mantissa, exponent)

Arguments

Value

numeric

Description

Given a MasterPeakList object and the MultiScansPeakList that generated it, correct the m/z values using offset predictions

Usage

default_correct_offset_function(
  master_peak_list,
  multi_scan_peaklist,
  min_scan = 0.1
)

Arguments

Value

list

Description

The offset predictor using loess

Usage

default_offset_predict_function(model, x)

Arguments

Value

numeric

Description

There may be good reasons to turn the logging off after it's been turned on. This basically tells the package that the logger isn't available.

Usage

disable_logging()

Description

Choose to enable logging, to a specific file if desired.

Usage

enable_logging(log_file = NULL, memory = FALSE)

Arguments

Details

Uses the logger package under the hood, which is suggested in the dependencies. Having logging enabled is nice to see when things are starting and stopping, and what exactly has been done, without needing to write messages to the console. It is especially useful if you are getting errors, but can't really see them, then you can add "memory" logging to see if you are running out of memory.

Default log file has the pattern:

YYYY.MM.DD.HH.MM.SS_ScanCentricPeakCharacterization_run.log

Description

Often we want to transform a number into it's exponential representation, having the number itself and the number of decimal places. This function provides that functionality

Usage

extract(x)

Arguments

Value

list

Description

Given a Thermo ".raw" file, attempts to extract the "method" definition from a translated hexdump of the file.

Usage

extract_raw_method(in_file, output_type = "data.frame")

Arguments

Value

string or data.frame

Description

built in filter scan function

Usage

filter_scans_builtin(sc_mzml)

Arguments

Details

This is the built in filtering and removing outliers function. It is based on the Moseley groups normal samples and experience. However, it does not reflect everyone's experience and needs. We expect that others have different use cases and needs, and therefore they should create their own function and use it appropriately.

Please examine this function and write your own as needed. It must take an SCMzml object, work on the scan_info slot, and then create a column with the name "keep" denoting which scans to keep. To view the current definition, you can do filter_scans_builtin

Value

SCmzml

Description

Given some regions and point_regions, find the regions that actually should contain real data. See details for an explanation of what is considered real.

Usage

find_signal_regions(
  regions,
  point_regions_list,
  region_percentile = 0.99,
  multiplier = 1.5,
  n_point_region = 2000
)

Arguments

Value

IRanges

Description

Given a set of frequency points in a data.frame, create IRanges based point "regions" of width 1, using the multiplier to convert from a floating point double to an integer

Usage

frequency_points_to_frequency_regions(
  frequency_data,
  frequency_variable = "frequency",
  multiplier = 400
)

Arguments

Description

given the peak, returns the location and intensity

Usage

get_fitted_peak_info(
  possible_peak,
  use_loc = "mz",
  w = NULL,
  addend = 1e-08,
  calculate_peak_area = FALSE
)

Arguments

Value

list

Description

extract mzML header

Usage

get_mzml_header(mzml_file)

Arguments

Description

get mzML metadata

Usage

get_mzml_metadata(mzml_file)

Arguments

Description

figures out which metadata function to run, and returns back the metadata generated by it.

Usage

get_raw_ms_metadata(in_file)

Arguments

Value

list

Description

import json from a file correctly given some things where things get written differently

Usage

import_json(json_file)

Arguments

Value

list

Description

function to import mzml mass spec data in a way that provides what we need to work with it. mzml_data should be the full path to the data.

Usage

import_sc_mzml(mzml_data, ms_level = 1)

Arguments

Value

MSnbase

Description

Given a directory of characterized samples, attempts to determine which peaks may be standards or contaminants that should be removed after assignment.

Usage

indicate_standards_contaminents(
  zip_dir,
  file_pattern = ".zip",
  blank_pattern = "^blank",
  save_dir = NULL,
  conversion_factor = 400,
  progress = TRUE
)

Arguments

Details

For each sample, the scan level frequencies are read in and converted to ranges, and then compared with tiled ranges over the whole frequency range. For those ranges that have 90 to 110% of scan level peaks in ALL blanks, and have 10 to 110% of scan level peaks in at least N-sample - 1, we consider a possible standard or contaminant. The peak is marked so that it can be removed by filtering out it's assignments later.

Value

NULL nothing is returned, files are overwritten

Description

initialize metadata from mzML

Usage

initialize_metadata_from_mzml(zip_dir, mzml_file)

Arguments

Description

initialize metadata

Usage

initialize_zip_metadata(zip_dir)

Arguments

Description

provides the area integration for the peak that fits the parabolic model

Usage

integrate_model(model_mz, model_coeff, n_point = 100, log_transform = "log")

Arguments

Value

numeric

Description

provides ability to calculate the area on the sides of a peak that are not caught by the parabolic model assuming a triangle to each side of the parabola

Usage

integrate_sides(peak_mz, peak_int, full_peak_loc, model_peak_loc)

Arguments

Value

numeric

Description

gives the area of the peak based on integrating the model bits and the sides

Usage

integration_based_area(
  mz_data,
  int_data,
  full_peak_loc,
  model_peak_loc,
  model_coeff,
  n_point = 100,
  log_transform = "log"
)

Arguments

Description

takes json representing a PeakList object, and generates the data.frame version

Usage

json_2_peak_list(json_string, in_var = "Peaks")

Arguments

Value

tbl_df

Description

Given a json file or list of lists, return a data.frame with the most important bits of the data.

Usage

json_mzML_2_df(in_file)

Arguments

Value

data.frame

Description

lists_2_json

Usage

lists_2_json(
  lists_to_save,
  zip_file = NULL,
  digits = 8,
  temp_dir = tempfile(pattern = "json")
)

Arguments

Value

character

Description

given a zip and a metadata file, load it and return it

Usage

load_metadata(zip_dir, metadata_file)

Arguments

Value

list

Description

Given a loess model, creates a data.frame suitable for plotting via ggplot2

Usage

loess_to_df(loess_model)

Arguments

Value

data.frame

Description

Logs the amount of memory being used to a log file if it is available, and generating warnings if the amount of RAM hits zero.

Usage

log_memory()

Description

If a log_appender is available, logs the given message at the info level.

Usage

log_message(message_string)

Arguments

Description

performs a log-transform while adding a small value to the data based on finding the smallest non-zero value in the data

Usage

log_with_min(data, min_value = NULL, order_mag = 3, log_fun = log)

Arguments

Value

matrix

Description

export the list metadata to a json string

Usage

meta_export_json(meta_list)

Arguments

Description

use the derivative of the parabolic equation to find the peak center, and then put the center into the equation to find the intensity at that point.

Usage

model_peak_center_intensity(x, coefficients)

Arguments

Details

The coefficients are generated using the linear model:

$y = a + bx + cx^2$

.

The derivative of this is:

$y = b + 2cx$

The peak of a parabola is defined where y is zero for the derivative.

$x = -b / 2c$

We can use this to derive where the center of the peak is, and then put the center value back into the equation to get the intensity.

Value

numeric

Description

Given a query, and either two values of M/Z and two values of frequency or a previously generated model, return a data.frame with the predicted value, and the slope and the intercept so the model can be re-used later for other points when needed.

Usage

mz_frequency_interpolation(
  mz_query,
  mz_values = NULL,
  frequency_values = NULL,
  model = NULL
)

Arguments

Value

data.frame with predicted_value, intercept, and slope

Description

Given a multi-scan data.frame of m/z, generate frequency values for the data.

Usage

mz_scans_to_frequency(
  mz_df_list,
  frequency_fit_description,
  mz_fit_description,
  ...
)

Arguments

Value

list

See Also

convert_mz_frequency

Description

given an mzML file, create the initial zip file containing the zipped mzML, metadata.json, and mzml_metadata.json. This zip file is what will be operated on by anything that accesses files, so that our interface is consistent.

Usage

mzml_to_zip(mzml_file, out_file)

Arguments

Description

calculates the coefficients of a parabolic fit (y = x + x^2) of x to y

Usage

parabolic_fit(x, y, w = NULL)

Arguments

Value

list

Description

takes a PeakList object, and generates a json version

Usage

peak_list_2_json(peak_list)

Arguments

Value

json_string

Description

calculate r2

Usage

predicted_frequency_r2(mz_frequency_df)

Arguments

Description

When raw files are copied, we also generated metadata about their original locations and new locations, and some other useful info. We would like to capture it, and keep it along with the metadata from the mzml file. So, given a list of mzml files, and a location for the raw files, this function creates metadata json files for the mzml files.

Usage

raw_metadata_mzml(mzml_files, raw_file_loc, recursive = TRUE)

Arguments

Description

Given a previously generated zip file of characterized peaks, now we've realized that the offsets on each peak should be somehow different. This function takes a zip file, adjusts the offsets, and writes the file back out.

Usage

recalculate_offsets(in_zip, offset = 2, out_file = in_zip)

Arguments

Description

Given a data.frame or character vector of files to run characterization on, processes them in sequence, to a particular saved location.

Usage

run_mzml_list(
  mzml_files,
  json_files = NULL,
  progress = TRUE,
  save_loc = ".",
  ...
)

Arguments

Value

list

Description

determine sample run time

Usage

sample_run_time(zip, units = "m")

Arguments

Value

data.frame with sample, start and end time

Description

make a new SCZip

Usage

sc_zip(
  in_file,
  mzml_meta_file = NULL,
  out_file = NULL,
  load_raw = TRUE,
  load_peak_list = TRUE
)

Arguments

Value

SCZip

Description

The ScanCentricPeakCharacterization package provides several classes and functions for working with direct injection, high-resolution mass spectrometry data.

Description

Peak characterization control

Details

Peak characterization associates data with the SCZip, SCPeakRegionFinder, and controls their execution.

Public fields

Methods

Public methods

• SCCharacterizePeaks$load_file()

• SCCharacterizePeaks$filter_scans()

• SCCharacterizePeaks$choose_frequency_model()

• SCCharacterizePeaks$prepare_mzml_data()

• SCCharacterizePeaks$set_frequency_fit_description()

• SCCharacterizePeaks$set_mz_fit_description()

• SCCharacterizePeaks$generate_filter_scan_function()

• SCCharacterizePeaks$generate_choose_frequency_model_function()

• SCCharacterizePeaks$predict_frequency()

• SCCharacterizePeaks$check_frequency_model()

• SCCharacterizePeaks$get_frequency_data()

• SCCharacterizePeaks$scan_info()

• SCCharacterizePeaks$find_peaks()

• SCCharacterizePeaks$summarize()

• SCCharacterizePeaks$save_peaks()

• SCCharacterizePeaks$write_zip()

• SCCharacterizePeaks$run_all()

• SCCharacterizePeaks$prep_data()

• SCCharacterizePeaks$add_regions()

• SCCharacterizePeaks$run_splitting()

• SCCharacterizePeaks$new()

• SCCharacterizePeaks$clone()

Method load_file()

Loads the mzml data into the SCZip

Usage

SCCharacterizePeaks$load_file()

Method filter_scans()

Filter the scans in data.

Usage

SCCharacterizePeaks$filter_scans()

Method choose_frequency_model()

Choose the single frequency model.

Usage

SCCharacterizePeaks$choose_frequency_model()

Method prepare_mzml_data()

Prepare the mzml data.

Usage

SCCharacterizePeaks$prepare_mzml_data()

Method set_frequency_fit_description()

Set the frequency fit description

Usage

SCCharacterizePeaks$set_frequency_fit_description(frequency_fit_description)

Arguments

Method set_mz_fit_description()

Set the mz fit description

Usage

SCCharacterizePeaks$set_mz_fit_description(mz_fit_description)

Arguments

Method generate_filter_scan_function()

Sets the scan filtering and check for outlier function.

Usage

SCCharacterizePeaks$generate_filter_scan_function(
  rtime = NA,
  y.freq = NA,
  f_function = NULL
)

Arguments

Method generate_choose_frequency_model_function()

Sets the function for choosing a single frequency model

Usage

SCCharacterizePeaks$generate_choose_frequency_model_function(f_function = NULL)

Arguments

Method predict_frequency()

Run frequency prediction

Usage

SCCharacterizePeaks$predict_frequency()

Method check_frequency_model()

Check the frequency model

Usage

SCCharacterizePeaks$check_frequency_model()

Method get_frequency_data()

Get the frequency data from the SCMzml bits

Usage

SCCharacterizePeaks$get_frequency_data()

Method scan_info()

Get the SCMzml$scan_info out

Usage

SCCharacterizePeaks$scan_info()

Method find_peaks()

Do the peak characterization without saving

Usage

SCCharacterizePeaks$find_peaks(stop_after_initial_detection = FALSE)

Arguments

Method summarize()

Generates the JSON output summary.

Usage

SCCharacterizePeaks$summarize()

Method save_peaks()

Saves the peaks and JSON to the temp file

Usage

SCCharacterizePeaks$save_peaks()

Method write_zip()

Write the zip file

Usage

SCCharacterizePeaks$write_zip()

Method run_all()

Runs all of the pieces for peak characterization in order

Usage

SCCharacterizePeaks$run_all(
  filter_scan_function = NULL,
  choose_frequency_model_function = NULL
)

Arguments

Method prep_data()

Loads and preps the data for characterization

Usage

SCCharacterizePeaks$prep_data()

Method add_regions()

Adds initial regions for finding real peak containing regions

Usage

SCCharacterizePeaks$add_regions()

Method run_splitting()

Does initial region splitting and peak finding in scans

Usage

SCCharacterizePeaks$run_splitting()

Method new()

Creates a new SCCharacterizePeaks class

Usage

SCCharacterizePeaks$new(
  in_file,
  metadata_file = NULL,
  out_file = NULL,
  temp_loc = tempfile("scpcms"),
  frequency_fit_description = NULL,
  mz_fit_description = NULL,
  filter_remove_outlier_scans = NULL,
  choose_single_frequency_model = NULL,
  sc_peak_region_finder = NULL,
  calculate_peak_area = FALSE
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

SCCharacterizePeaks$clone(deep = FALSE)

Arguments

Examples

## Not run: 
  lipid_sample = system.file("extdata",
     "lipid_example.mzML",
     package = "ScanCentricPeakCharacterization")
  sc_char = SCCharacterizePeaks$new(lipid_sample)

  # prep data and check model
  library(ggplot2)
  library(patchwork)
  sc_char$load_file()
  sc_char$generate_filter_scan_function()
  sc_char$generate_choose_frequency_model_function()
  sc_char$prepare_mzml_data()
  sc_char$check_frequency_model()

  # run characterization
  save_loc = "test.zip"
  sc_char = SCCharacterizePeaks$new(lipid_sample,
                                    out_file = save_loc)
  sc_char$run_all()

## End(Not run)

Description

mzML mass spectrometry data container with some useful methods.

Details

Provides our own container for mzML data, and does conversion to frequency, filtering scans, choosing a single frequency regression model, and generating the frequency data for use in the peak characterization.

Public fields

Methods

Public methods

• SCMzml$import_mzml()

• SCMzml$extract_mzml_data()

• SCMzml$predict_frequency()

• SCMzml$convert_to_frequency()

• SCMzml$choose_frequency_model()

• SCMzml$generate_choose_frequency_model_function()

• SCMzml$filter_scans()

• SCMzml$generate_filter_scan_function()

• SCMzml$check_frequency_model()

• SCMzml$get_instrument()

• SCMzml$get_frequency_data()

• SCMzml$new()

• SCMzml$clone()

Method import_mzml()

import the mzml file defined

Usage

SCMzml$import_mzml(
  mzml_file = self$mzml_file,
  ms_level = self$ms_level,
  memory_mode = self$memory_mode
)

Arguments

Method extract_mzml_data()

get the mzml data into data.frame form so we can use it

Usage

SCMzml$extract_mzml_data(remove_zero = self$remove_zero)

Arguments

Method predict_frequency()

predict frequency and generate some summary information. This does regression of frequency ~ m/z for each scan separately.

Usage

SCMzml$predict_frequency(
  frequency_fit_description = self$frequency_fit_description,
  mz_fit_description = self$mz_fit_description
)

Arguments

Method convert_to_frequency()

actually do the conversion of m/z to frequency

Usage

SCMzml$convert_to_frequency()

Method choose_frequency_model()

choose a frequency model using the previously added function

Usage

SCMzml$choose_frequency_model()

Method generate_choose_frequency_model_function()

generate a frequency model choosing function and attach it

Usage

SCMzml$generate_choose_frequency_model_function(f_function = NULL)

Arguments

Details

Creates a new function that access the scan_info slot of an SCMzml object after conversion to frequency space, and chooses a single model based on the information there.

Method filter_scans()

filter the scans using the previously added function

Usage

SCMzml$filter_scans()

Method generate_filter_scan_function()

generate a filter function and attach it

Usage

SCMzml$generate_filter_scan_function(
  rtime = NA,
  y.freq = NA,
  f_function = NULL
)

Arguments

Details

Creates a new function that accesses the scan_info slot of an SCMzml object, filters the scans by their retention-time and y-frequency coefficients, tests for outliers in the y-frequency coefficients, and denotes which scans will be kept for further processing.

NA means no filtering will be done, one-sided limits, eg. ⁠(NA, 10)⁠ or ⁠(10, NA)⁠ implies to filter <= or >=, respectively.

Method check_frequency_model()

check how well a given frequency model works for this data

Usage

SCMzml$check_frequency_model(scan = NULL, as_list = FALSE)

Arguments

Method get_instrument()

get instrument data from associated mzml file metadata

Usage

SCMzml$get_instrument()

Method get_frequency_data()

get the frequency data to go into the next steps of analysis.

Usage

SCMzml$get_frequency_data()

Method new()

Usage

SCMzml$new(
  mzml_file,
  frequency_fit_description = c(a.freq = 0, x.freq = -1, y.freq = -1/2, z.freq = -1/3),
  mz_fit_description = c(a.mz = 0, x.mz = -1, y.mz = -2, z.mz = -3),
  metadata_file = NULL,
  scan_range = NULL,
  rtime_range = NULL,
  mz_range = NULL,
  remove_zero = FALSE,
  ms_level = 1,
  memory_mode = "inMemory"
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

SCMzml$clone(deep = FALSE)

Arguments

See Also

choose_single_frequency_model_default()

Examples

## Not run: 
  lipid_sample = system.file("extdata", "lipid_example.mzML",
  package = "ScanCentricPeakCharacterization")

## End(Not run)

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

Methods

Public methods

• SCPeakRegionFinder$add_regions()

• SCPeakRegionFinder$reduce_sliding_regions()

• SCPeakRegionFinder$split_peak_regions()

• SCPeakRegionFinder$remove_double_peaks_in_scans()

• SCPeakRegionFinder$normalize_data()

• SCPeakRegionFinder$find_peaks_in_regions()

• SCPeakRegionFinder$model_mzsd()

• SCPeakRegionFinder$model_heightsd()

• SCPeakRegionFinder$indicate_high_frequency_sd()

• SCPeakRegionFinder$add_data()

• SCPeakRegionFinder$summarize_peaks()

• SCPeakRegionFinder$add_offset()

• SCPeakRegionFinder$sort_ascending_mz()

• SCPeakRegionFinder$characterize_peaks()

• SCPeakRegionFinder$summarize()

• SCPeakRegionFinder$peak_meta()

• SCPeakRegionFinder$new()

• SCPeakRegionFinder$clone()

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

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

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

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

Method summarize()

Summarize everything for output to the zip file after completion.

Usage

SCPeakRegionFinder$summarize(
  package_used = "package:ScanCentricPeakCharacterization"
)

Arguments

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

Method clone()

The objects of this class are cloneable with this method.

Usage

SCPeakRegionFinder$clone(deep = FALSE)

Arguments

Description

Holds all the peak region data

Holds all the peak region data

Details

This reference class represents the peak region data.

Public fields

Methods

Public methods

• SCPeakRegions$set_min_scan()

• SCPeakRegions$add_data()

• SCPeakRegions$new()

• SCPeakRegions$clone()

Method set_min_scan()

sets the minimum number of scans to use

Usage

SCPeakRegions$set_min_scan()

Method add_data()

Adds the data from an SCMzml object to the SCPeakRegion.

Usage

SCPeakRegions$add_data(sc_mzml)

Arguments

Method new()

Creates a new SCPeakRegions object

Usage

SCPeakRegions$new(
  sc_mzml = NULL,
  frequency_multiplier = 400,
  scan_perc = 0.1,
  max_subsets = 100
)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

SCPeakRegions$clone(deep = FALSE)

Arguments

Description

Represents the zip mass spec file

Represents the zip mass spec file

Details

This reference class represents the zip mass spec file. It does this by providing objects for the zip file, the metadata, as well as various bits underneath such as the mzml data and peak lists, and their associated metadata. Although it is possible to work with the SCZip object directly, it is heavily recommended to use the SCCharacterizePeaks object for carrying out the various steps of an analysis, including peak finding.

Public fields

Methods

Public methods

• SCZip$load_mzml()

• SCZip$load_sc_peak_region_finder()

• SCZip$save_json()

• SCZip$save_sc_peak_region_finder()

• SCZip$load_peak_list()

• SCZip$compare_mzml_corresponded_densities()

• SCZip$new()

• SCZip$show_temp_dir()

• SCZip$write_zip()

• SCZip$cleanup()

• SCZip$finalize()

• SCZip$add_peak_list()

• SCZip$clone()

Method load_mzml()

Loads the mzML file

Usage

SCZip$load_mzml()

Method load_sc_peak_region_finder()

Loads the SCPeakRegionFinder object

Usage

SCZip$load_sc_peak_region_finder()

Method save_json()

Save the jsonized summary out to actual json files

Usage

SCZip$save_json()

Method save_sc_peak_region_finder()

Saves the SCPeakRegionFinder binary object

Usage

SCZip$save_sc_peak_region_finder()

Method load_peak_list()

loads just the peak list data-frame instead of peak region finder

Usage

SCZip$load_peak_list()

Method compare_mzml_corresponded_densities()

compare peak densities

Usage

SCZip$compare_mzml_corresponded_densities(
  mz_range = c(150, 1600),
  window = 1,
  delta = 0.1
)

Arguments

Method new()

Create a new SCZip object.

Usage

SCZip$new(
  in_file,
  mzml_meta_file = NULL,
  out_file = NULL,
  load_mzml = TRUE,
  load_peak_list = TRUE,
  temp_loc = tempfile("scpcms")
)

Arguments

Method show_temp_dir()

Show the temp directory where everything is being worked with

Usage

SCZip$show_temp_dir()

Method write_zip()

Write the zip file

Usage

SCZip$write_zip(out_file = NULL)

Arguments

Method cleanup()

delete the temp directory

Usage

SCZip$cleanup()

Method finalize()

delete when things are done

Usage

SCZip$finalize()

Method add_peak_list()

Add peak list data to the temp directory

Usage

SCZip$add_peak_list(peak_list_data)

Arguments

Method clone()

The objects of this class are cloneable with this method.

Usage

SCZip$clone(deep = FALSE)

Arguments

See Also

SCMzml

SCPeakRegionFinder

Description

Allows the user to set which mapping function is being used internally in the functions.

Usage

set_internal_map(map_function = NULL)

Arguments

Details

by default, the package uses purrr::map to iterate over things. However, if you have the furrr package installed, you could switch it to use furrr::future_map instead.

Examples

## Not run: 
 library(furrr)
 future::plan(multicore)
 set_internal_map(furrr::future_map)

## End(Not run)

Description

Allow the user to turn progress messages to the console and off. Default is to provide messages to the console.

Usage

show_progress(progress = TRUE)

Arguments

Description

Does a single pass of normalizing scans to each other.

Usage

single_pass_normalization(
  scan_peaks,
  intensity_measure = c("RawHeight", "Height"),
  summary_function = median,
  use_peaks = NULL,
  min_ratio = 0.7
)

Arguments

Value

scan_peaks list

Description

Given a region that should contain signal, and the point data within it, find the peaks, and return the region, and the set of points that make up each peak from each scan.

Usage

split_region_by_peaks(
  region_list,
  min_points = 4,
  metadata = NULL,
  calculate_peak_area = FALSE
)

Arguments

Value

list

Description

returns the sum of squares residuals from an lm object

Usage

ssr(object)

Arguments

Value

numeric

Description

given a set of original and fitted values and a transform, return a set of transformed residuals.

Usage

transform_residuals(original, fitted, transform = exp)

Arguments

Value

numeric

Description

given a zip file, list the contents

Usage

zip_list_contents(zip_file)

Arguments