, Mannion Joseph
[ctb], Granger Caroline [ctb], Battelle Savannah River Alliance
[cph], NNSA, US DOE [fnd]| Title: | GCxGC Preprocessing and Analysis |
|---|---|
| Description: | Provides complete detailed preprocessing of two-dimensional gas chromatogram (GCxGC) samples. Baseline correction, smoothing, peak detection, and peak alignment. Also provided are some analysis functions, such as finding extracted ion chromatograms, finding mass spectral data, targeted analysis, and nontargeted analysis with either the 'National Institute of Standards and Technology Mass Spectral Library' or with the mass data. There are also several visualization methods provided for each step of the preprocessing and analysis. |
| Authors: | Stephanie Gamble [aut, cre]
, Mannion Joseph
[ctb], Granger Caroline [ctb], Battelle Savannah River Alliance
[cph], NNSA, US DOE [fnd] |
| Maintainer: | Stephanie Gamble <[email protected]> |
| License: | GPL (>= 3) |
| Version: | 1.0.1 |
| Built: | 2024-12-18 06:54:58 UTC |
| Source: | CRAN |
align aligns peaks from samples to a reference sample's
peaks.
align(data_list, THR = 1e+05)align(data_list, THR = 1e+05)
data_list |
a list object. Data extracted from each cdf file, ideally the output from extract_data(). |
THR |
a float object. Threshold for peak intensity. Should be a number between the baseline value and the highest peak intensity. Default is THR = 100000. |
This function aligns the peaks from any number of samples. Peaks are aligned to the retention times of the first peak. If aligning to a reference or standard sample, this should be the first in the lists for data frames and for the mass data. The function comp_peaks() is used to find the corresponding peaks. This function will return a new list of TIC data frames and a list of mass data. The first sample's data is unchanged, used as the reference. Then a TIC data frame and mass data for each of the given samples containing the peaks and time coordinates of the aligned peaks. The time coordinates are aligned to the first sample's peaks, the peak height and MS is unchanged.
A list object. List of aligned data from each cdf file and a list of peaks that were aligned for each file.
file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") file2 <- system.file("extdata","sample2.cdf",package="gcxgclab") file3 <- system.file("extdata","sample3.cdf",package="gcxgclab") frame1 <- extract_data(file1,mod_t=.5) frame2 <- extract_data(file2,mod_t=.5) frame3 <- extract_data(file3,mod_t=.5) aligned <- align(list(frame1,frame2,frame3)) plot_peak(aligned$Peaks$S1,aligned$S1,title="Reference Sample 1") plot_peak(aligned$Peaks$S2,aligned$S2,title="Aligned Sample 2") plot_peak(aligned$Peaks$S3,aligned$S3,title="Aligned Sample 3")file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") file2 <- system.file("extdata","sample2.cdf",package="gcxgclab") file3 <- system.file("extdata","sample3.cdf",package="gcxgclab") frame1 <- extract_data(file1,mod_t=.5) frame2 <- extract_data(file2,mod_t=.5) frame3 <- extract_data(file3,mod_t=.5) aligned <- align(list(frame1,frame2,frame3)) plot_peak(aligned$Peaks$S1,aligned$S1,title="Reference Sample 1") plot_peak(aligned$Peaks$S2,aligned$S2,title="Aligned Sample 2") plot_peak(aligned$Peaks$S3,aligned$S3,title="Aligned Sample 3")
batch_eic calculates the mass defect for each ion, then finds
each listed EICs of interest.
batch_eic(data, MOIs, tolerance = 5e-04)batch_eic(data, MOIs, tolerance = 5e-04)
data |
a list object. Data extracted from a cdf file, ideally the output from extract_data(). |
MOIs |
a vector object. A vector containing a list of all masses of interest to be investigated. |
tolerance |
a double object. The tolerance allowed for the MOI. Default is 0.0005. |
Extracted Ion Chromatogram (EIC) is a plot of intensity at a chosen m/z value, or range of values, as a function of retention time. This function uses find_eic() to find intensity values at the given mass-to-charge (m/z) values, MOIs, and in a range around MOI given a tolerance. Calculates the mass defect for each ion, then finds the specific EICs of interest. Returns a data frame of time values, mass values, intensity values,and mass defects.
eic_list, list object, containing data.frame objects. Data frames of time values, mass values, intensity values, and mass defects for each MOI listed in the input csv or txt file.
file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) mois <- c(92.1397, 93.07058) eics <- batch_eic(frame, MOIs=mois ,tolerance = 0.005) for (i in 1:length(eics)){ print(plot_eic(eics[[i]], title=paste("EIC for MOI",mois[i]))) print(plot_eic(eics[[i]], title=paste("EIC for MOI",mois[i]), dim=2)) }file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) mois <- c(92.1397, 93.07058) eics <- batch_eic(frame, MOIs=mois ,tolerance = 0.005) for (i in 1:length(eics)){ print(plot_eic(eics[[i]], title=paste("EIC for MOI",mois[i]))) print(plot_eic(eics[[i]], title=paste("EIC for MOI",mois[i]), dim=2)) }
batch_ms Finds batch of mass spectra of peaks.
batch_ms(data, t_peaks, tolerance = 5e-04)batch_ms(data, t_peaks, tolerance = 5e-04)
data |
a list object. Data extracted from a cdf file, ideally the output from extract_data(). |
t_peaks |
a vector object. A list of times at which the peaks of interest are located in the overall time index for the sample. |
tolerance |
a double object. The tolerance allowed for the time index. Default is 0.0005. |
This function uses find_ms() to find the mass spectra values of a batch list of peaks in intensity values of a GCxGC sample at overall time index values specified in a txt or csv file. It outputs a list of data frames, for each peak, of the mass values and percent intensity values which can then be plotted to product the mass spectra plot.
A list object of data.frame objects. Each a data frame of the mass values and the percent intensity values.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mzs <- batch_ms(frame, t_peaks = peaks$'T'[1:5]) for (i in 1:length(mzs)){ print(plot_ms(mzs[[i]], title=paste('Mass Spectrum of peak', i))) }file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mzs <- batch_ms(frame, t_peaks = peaks$'T'[1:5]) for (i in 1:length(mzs)){ print(plot_ms(mzs[[i]], title=paste('Mass Spectrum of peak', i))) }
batch_preprocess performs full preprocessing on a batch of
data files.
batch_preprocess( path = ".", mod_t = 10, shift = 0, lambda = 20, gamma = 0.5, subtract = NULL, THR = 10^5, images = FALSE )batch_preprocess( path = ".", mod_t = 10, shift = 0, lambda = 20, gamma = 0.5, subtract = NULL, THR = 10^5, images = FALSE )
path |
a string object. The path to the directory containing the cdf files to be batch preprocessed and aligned. |
mod_t |
a float object. The modulation time for the GCxGC sample analysis. Default is 10. |
shift |
a float object. The number of seconds to shift the phase by. Default is 0 to skip shifting. |
lambda |
a float object. A number (parameter in Whittaker smoothing), suggested between 1 to 10^5. Small lambda is very little smoothing, large lambda is very smooth. Default is lambda = 20. |
gamma |
a float object. Correction factor between 0 and 1. 0 results in almost no values being subtracted to the baseline, 1 results in almost everything except the peaks to be subtracted to the baseline. Default is 0.5. |
subtract |
a data.frame object. Data frame containing TIC data from a background sample or blank sample to be subtracted from the sample TIC data. |
THR |
a float object. Threshold for peak intensity for peak alignment. Should be a number between the baseline value and the highest peak intensity. Default is THR = 100000. |
images |
a boolean object. An optional input. If TRUE, all images of preprocessing steps will be displayed. Default is FALSE, no images will be displayed. |
This function performs full preprocessing on a batch of data files. Extracts data and performs peak alignment and performs smoothing and baseline correction.
A data.frame object. A list of pairs of data frames. A TIC data frame and an MS data frame for each file.
folder <- system.file("extdata",package="gcxgclab") frame_list <- batch_preprocess(folder,mod_t=.5,lambda=10,gamma=0.5,images=TRUE)folder <- system.file("extdata",package="gcxgclab") frame_list <- batch_preprocess(folder,mod_t=.5,lambda=10,gamma=0.5,images=TRUE)
bl_corr performs baseline correction of the intensity values.
bl_corr(data, gamma = 0.5, subtract = NULL)bl_corr(data, gamma = 0.5, subtract = NULL)
data |
a list object. Data extracted from a cdf file, ideally the output from extract_data(). |
gamma |
a float object. Correction factor between 0 and 1. 0 results in almost no values being subtracted to the baseline, 1 results in almost everything except the peaks to be subtracted to the baseline. Default is 0.5. |
subtract |
a list object. Data extracted from a cdf file, ideally the output from extract_data(). |
This function performs baseline correction and baseline subtraction for TIC values.
A data.frame object. A data frame of the overall time index, the x-axis retention time, the y-axis retention time, and the baseline corrected total intensity values.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) sm_frame <- smooth(frame, lambda=10) blc_frame <- bl_corr(sm_frame, gamma=0.5) plot_chr(blc_frame, title='Baseline Corrected')file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) sm_frame <- smooth(frame, lambda=10) blc_frame <- bl_corr(sm_frame, gamma=0.5) plot_chr(blc_frame, title='Baseline Corrected')
comp_nist compares the MS data from a peak to the NIST MS
database.
comp_nist(nistlist, ms, cutoff = 50, title = "Best NIST match")comp_nist(nistlist, ms, cutoff = 50, title = "Best NIST match")
nistlist |
a list object, a list of compound MS data from the NIST MS Library database, ideally the output of nist_list(). |
ms |
a data.frame object, a data frame of the mass values and the percent intensity values, ideally the output of find_ms(). |
cutoff |
a float object, the low end cutoff for the MS data, determined based on the MS devices used for analysis. Default is 50. |
title |
a string object. Title placed at the top of the head-to-tail plot of best NIST Library match. Default title "Best NIST match". |
This function takes the MS data from an intensity peak in a sample and compares it to the NIST MS Library database and determines the compound which is the best match to the MS data.
a data.frame object, a list of the top 10 best matching compounds from the NIST database, with their compounds, the index in the nistlist, and match percent.
comp_peaks compares peaks of two samples.
comp_peaks(ref_peaks, al_peaks)comp_peaks(ref_peaks, al_peaks)
ref_peaks |
a data.frame object. A data frame with 4 columns (Time, X, Y, Peak), ideally the output from either top_peaks() or thr_peaks(). |
al_peaks |
a data.frame object. A data frame with 4 columns (Time, X, Y, Peak), ideally the output from either top_peaks() or thr_peaks(). |
This function find compares the peaks from two samples and correlates the peaks by determining the peaks closest to each other in the two samples, within a certain reasonable distance. Then returns a data frame with a list of the correlated peaks including each of their time coordinates.
A data.frame object. A data frame with 8 columns containing the matched peaks from the two samples, with the time, x, y, and peak values for each.
extract_data Extracts the data from a cdf file.
extract_data(filename, mod_t = 10, shift_time = TRUE)extract_data(filename, mod_t = 10, shift_time = TRUE)
filename |
a string object. The path or file name of the cdf file to be opened. |
mod_t |
a float object. The modulation time for the GCxGC sample analysis. Default is 10. |
shift_time |
a boolean object. Determines whether the Overall Time Index should be shifted to 0. Default is TRUE. |
This function opens the specified cdf file using the implemented
function nc_open from ncdf4 package, then extracts the
data and closes the cdf file using the implemented function
nc_close from ncdf4 package
(Pierce 2021). It then returns a list of two data frames. The
first is a dataframe of the TIC data, the output of create_df(). The second
is a data frame of the full MS data, the output of mass_data().
A list object. A list of the extracted data: scan acquisition time, total intensity, mass values, intensity values, and point count.
Pierce D (2021). “Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files.” CRAN. https://cirrus.ucsd.edu/~pierce/ncdf/index.html.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) plot_chr(frame, title='Raw Data', scale="linear") plot_chr(frame, title='Log Intensity')file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) plot_chr(frame, title='Raw Data', scale="linear") plot_chr(frame, title='Log Intensity')
find_eic calculates the mass defect for each ion, then finds
the specific EICs of interest.
find_eic(data, MOI, tolerance = 5e-04)find_eic(data, MOI, tolerance = 5e-04)
data |
a list object. Data extracted from a cdf file, ideally the output from extract_data(). |
MOI |
a float object. The mass (m/z) value of interest. |
tolerance |
a double object. The tolerance allowed for the MOI. Default is 0.0005. |
Extracted Ion Chromatogram (EIC) is a plot of intensity at a chosen m/z value, or range of values, as a function of retention time. This function finds intensity values at the given mass-to-charge (m/z) values, MOI, and in a range around MOI given a tolerance. Calculates the mass defect for each ion, then finds the specific EICs of interest. Returns a data frame of time values, mass values, intensity values, and mass defects.
eic, a data.frame object. A data frame of time values, retention time 1, retention time 2, mass values, intensity values, and mass defects.
file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) eic <- find_eic(frame, MOI=92.1397,tolerance=0.005) plot_eic(eic,dim=1,title='EIC for MOI 92.1397') plot_eic(eic,dim=2,title='EIC for MOI 92.1397')file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) eic <- find_eic(frame, MOI=92.1397,tolerance=0.005) plot_eic(eic,dim=1,title='EIC for MOI 92.1397') plot_eic(eic,dim=2,title='EIC for MOI 92.1397')
find_ms Finds mass spectra of a peak.
find_ms(data, t_peak, tolerance = 5e-04)find_ms(data, t_peak, tolerance = 5e-04)
data |
a list object. Data extracted from a cdf file, ideally the output from extract_data(). |
t_peak |
a float object. The overall time index value for when the peak occurs in the GCxGC sample (the 1D time value). |
tolerance |
a double object. The tolerance allowed for the time index. Default is 0.0005. |
This function finds the mass spectra values of a peak in the intensity values of a GCxGC sample at a specified overall time index value. Then outputs a data frame of the mass values and percent intensity values which can then be plotted to product the mass spectra plot.
A data.frame object. A data frame of the mass values and the percent intensity values.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mz <- find_ms(frame, t_peak=peaks$'T'[1]) plot_ms(mz) plot_defect(mz,title="Kendrick Mass Defect, CH_2")file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mz <- find_ms(frame, t_peak=peaks$'T'[1]) plot_ms(mz) plot_defect(mz,title="Kendrick Mass Defect, CH_2")
gauss Defines the 1D Gaussian curve function.
gauss(a, b, c, t)gauss(a, b, c, t)
a, b, c
|
are float objects. Parameters in R^1 for the Gaussian function. |
t |
a float object. The independent variable in R^1 for the Gaussian function. |
This function defines a 1D Gaussian curve function.
A float object. The value of the Gaussian function at time t, given the parameters input a,b,c.
gauss_fit fits data around a peak to a Gaussian curve.
gauss_fit(TIC_df, peakcoord)gauss_fit(TIC_df, peakcoord)
TIC_df |
a data.frame object. Data frame with 4 columns (Overall Time Index, RT1, RT2, TIC), ideally the output from create_df(), or the first data frame returned from extract_data(), $TIC_df. |
peakcoord |
a vector object. The two dimensional time retention coordinates of the peak of interest. c(RT1,RT2). |
This function fits data around the specified peak to a Gaussian curve, minimized with nonlinear least squares method nls() from "stats" package.
A list object with three items. The first data.frame object. A data frame with two columns, (time, guassfit), the time values around the peak, and the intensity values fitted to the optimal Gaussian curve. Second, a vector object of the fitted parameters (a,b,c). Third, a double object, the area under the fitted Gaussian curve.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) gaussfit <- gauss_fit(frame$TIC_df, peakcoord=c(peaks$'X'[1], peaks$'Y'[1])) message(paste('Area under curve =',gaussfit[[3]], 'u^2')) plot_gauss(frame$TIC_df, gaussfit[[1]])file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) gaussfit <- gauss_fit(frame$TIC_df, peakcoord=c(peaks$'X'[1], peaks$'Y'[1])) message(paste('Area under curve =',gaussfit[[3]], 'u^2')) plot_gauss(frame$TIC_df, gaussfit[[1]])
gauss2 Defines the 2D Gaussian curve function.
gauss2(a, b1, b2, c1, c2, t1, t2)gauss2(a, b1, b2, c1, c2, t1, t2)
a, b1, b2, c1, c2
|
are float objects. Parameters in R^1 for the Gaussian function. |
t1, t2
|
are float objects. The independent variables t=(t1.t2) in R^2 for the Gaussian function. |
This function defines a 2D Gaussian curve function.
A float object. The value of the Gaussian function at time t=(t1,t2) given the parameters input a,b1,b2,c1,c2.
gauss2_fit fits data around a peak to a 2D Gaussian curve.
gauss2_fit(TIC_df, peakcoord)gauss2_fit(TIC_df, peakcoord)
TIC_df |
a data.frame object. Data frame with 4 columns (Overall Time Index, RT1, RT2, TIC), ideally the output from create_df(), or the first data frame returned from extract_data(), $TIC_df. |
peakcoord |
a vector object. The two dimensional time retention coordinates of the peak of interest. c(RT1,RT2). |
This function fits data around the specified peak to a 2D Gaussian curve, minimized with nonlinear least squares method nls() from "stats" package.
A list object with three items. The first data.frame object. A data frame with three columns, (time1, time2, guassfit), the time values around the peak, and the intensity values fitted to the optimal Gaussian curve. Second, a vector object of the fitted parameters (a,b1,b2,c1,c2). Third, a double object, the volume under the fitted Gaussian curve.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) gaussfit2 <- gauss2_fit(frame$TIC_df, peakcoord=c(peaks$'X'[1], peaks$'Y'[1])) message(paste('Volume under curve =',gaussfit2[[3]],'u^3')) plot_gauss2(frame$TIC_df, gaussfit2[[1]])file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) gaussfit2 <- gauss2_fit(frame$TIC_df, peakcoord=c(peaks$'X'[1], peaks$'Y'[1])) message(paste('Volume under curve =',gaussfit2[[3]],'u^3')) plot_gauss2(frame$TIC_df, gaussfit2[[1]])
mass_list creates a list of atomic mass data
mass_list()mass_list()
This function creates a data frame containing the data for the atomic weights for each element in the periodic table (M. and et al. 2012).
A data.frame object, with two columns, (elements, mass).
M. W, et al. (2012). “The Ame2012 atomic mass evaluation.” Chinese Phys. C, 36 1603.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mz <- find_ms(frame, t_peak=peaks$'T'[1]) masslist <- mass_list() non_targeted(masslist, mz, THR=0.05)file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mz <- find_ms(frame, t_peak=peaks$'T'[1]) masslist <- mass_list() non_targeted(masslist, mz, THR=0.05)
nist_list creates a list of the data from the NIST MS
database.
nist_list(nistfile, ...)nist_list(nistfile, ...)
nistfile |
a string object, the file name or path of the MSP file for the NIST MS Library database. |
... |
additional optional string objects, the file names or paths of the MSP file for the NIST MS Library if the data base is broken into multiple files. |
This function takes the MSP file containing the data from the NIST MS Library database and creates a list of string vectors for each compound in the database.
nistlist, a list object, a list of string vectors for each compound in the database.
non_targeted compares the MS data from a peak to atomic mass
data.
non_targeted(masslist, ms, THR = 0.1, ...)non_targeted(masslist, ms, THR = 0.1, ...)
masslist |
a list object, a list of atomic weights, ideally the output of mass_list(). |
ms |
a data.frame object, a data frame of the mass values and the percent intensity values, ideally the output of find_ms(). |
THR |
a double object. The threshold of intensity of which to include peaks for mass comparison. Default is 0.1. |
... |
a vector object. Any further optional inputs which indicate additional elements to consider in the compound, or restrictions on the number of a certain element in the compound. Should be in the form c('X', a, b) where X = element symbol, a = minimum number of atoms, b = maximum number of atoms. a and b are optional. If no minimum, use a=0, if no maximum, do not include b. |
This function takes the MS data from an intensity peak in a sample and compares it to combinations of atomic masses. Then it approximates the makeup of the compound, giving the best matches to the MS data. Note that the default matches will contain only H, N, C, O, F, Cl, Br, I, and Si. The user can input optional parameters to indicate additional elements to be considered or restrictions on the number of any specific element in the matching compounds.
A list object, a list of vectors containing strings of the matching compounds.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mz <- find_ms(frame, t_peak=peaks$'T'[1]) masslist <- mass_list() non_targeted(masslist, mz, THR=0.05)file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mz <- find_ms(frame, t_peak=peaks$'T'[1]) masslist <- mass_list() non_targeted(masslist, mz, THR=0.05)
phase_shift shifts the phase of the chromatogram.
phase_shift(data, shift)phase_shift(data, shift)
data |
a list object. Data extracted from a cdf file, ideally the output from extract_data(). |
shift |
a float object. The number of seconds to shift the phase by. |
This function shifts the phase of the chromatogram up or down by the specified number of seconds.
A data.frame object. A list of two data frames. A TIC data frame and an MS data frame.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) shifted <- phase_shift(frame, -.2) plot_chr(shifted, title='Shifted')file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) shifted <- phase_shift(frame, -.2) plot_chr(shifted, title='Shifted')
plot_chr plots TIC data for chromatogram.
plot_chr(data, scale = "log", dim = 2, floor = -1, title = "Intensity")plot_chr(data, scale = "log", dim = 2, floor = -1, title = "Intensity")
data |
a list object. Data extracted from a cdf file, ideally the output from extract_data(). |
scale |
a string object. Either 'linear' or 'log'. log refers to logarithm base 10. Default is log scale. |
dim |
a integer object. The time dimensions of the plot, either 1 or 2. Default is 2. |
floor |
a float object. The floor value for plotting. Values below floor will be scaled up. Default for linear plotting is 0, default for log plotting is 10^3. |
title |
a string object. Title placed at the top of the plot. Default title "Intensity". |
This function creates a contour plot using of TIC data vs the x and
y retention times using ggplot from ggplot2 package
(Wickham 2016).
A ggplot object. A contour plot of TIC data plotted in two dimensional retention time.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) plot_chr(frame, title='Raw Data', scale="linear") plot_chr(frame, title='Log Intensity')file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) plot_chr(frame, title='Raw Data', scale="linear") plot_chr(frame, title='Log Intensity')
plot_defect Plots Kendrick Mass Defect of a peak.
plot_defect(ms, compound_mass = 14.01565, title = "Kendrick Mass Defect")plot_defect(ms, compound_mass = 14.01565, title = "Kendrick Mass Defect")
ms |
a data.frame object. A data frame of the mass values and the percent intensity values, ideally the output of find_ms(). |
compound_mass |
a float object. The exact mass, using most common ions, of the desired atom group to base the Kendrick mass on. Default is 14.01565, which is the mass for CH_2. |
title |
a string object. Title placed at the top of the plot. Default title "Kendrick Mass Defect". |
This function produces a scatter plot of the Kendrick mass defects
for mass spectrum data. Plotted using ggplot from
ggplot2 package (Wickham 2016).
A ggplot object. A line plot of the mass spectra data. The mass values vs the percent intensity values as a percent of the highest intensity.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mz <- find_ms(frame, t_peak=peaks$'T'[1]) plot_ms(mz) plot_defect(mz,title="Kendrick Mass Defect, CH_2")file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mz <- find_ms(frame, t_peak=peaks$'T'[1]) plot_ms(mz) plot_defect(mz,title="Kendrick Mass Defect, CH_2")
plot_eic Plots the EICs
plot_eic(eic, title = "EIC", dim = 1)plot_eic(eic, title = "EIC", dim = 1)
eic |
a data.frame object. A data frame of the times and intensity values of the EIC of interest, ideally the output of find_eic(). |
title |
a string object. Title placed at the top of the plot. Default title "EIC". |
dim |
a integer object. The time dimensions of the plot, either 1 or 2. Default is 1. |
This function produces a scatter plot of the overall time index vs
the intensity values at a given mass of interest using
ggplot from ggplot2 package
(Wickham 2016).
A ggplot object. A scatter plot of the overall time index vs the intensity values at a given mass of interest.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) eic <- find_eic(frame, MOI=92.1397,tolerance=0.005) plot_eic(eic,dim=1,title='EIC for MOI 92.1397') plot_eic(eic,dim=2,title='EIC for MOI 92.1397')file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) eic <- find_eic(frame, MOI=92.1397,tolerance=0.005) plot_eic(eic,dim=1,title='EIC for MOI 92.1397') plot_eic(eic,dim=2,title='EIC for MOI 92.1397')
plot_gauss Plots a peak with the fitted Gaussian curve.
plot_gauss(TIC_df, gauss_return, title = "Peak fit to Gaussian")plot_gauss(TIC_df, gauss_return, title = "Peak fit to Gaussian")
TIC_df |
a data.frame object. Data frame with 4 columns (Overall Time Index, RT1, RT2, TIC), ideally the output from create_df(), or the first data frame returned from extract_data(), $TIC_df. |
gauss_return |
a data.frame object. The output from guass_fit(). A data frame with two columns, (time, guassfit), the time values around the peak, and the intensity values fitted to the optimal Gaussian curve. |
title |
a string object. Title placed at the top of the plot. |
This function plots the points around the peak in blue dots, with a
line plot of the Gaussian curve fit to the peak data in red, using
ggplot from ggplot2 package
(Wickham 2016).
A ggplot object. A plot of points around the peak with a line plot of the Gaussian curve fit to the peak data.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) gaussfit <- gauss_fit(frame$TIC_df, peakcoord=c(peaks$'X'[1], peaks$'Y'[1])) message(paste('Area under curve =',gaussfit[[3]], 'u^2')) plot_gauss(frame$TIC_df, gaussfit[[1]])file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) gaussfit <- gauss_fit(frame$TIC_df, peakcoord=c(peaks$'X'[1], peaks$'Y'[1])) message(paste('Area under curve =',gaussfit[[3]], 'u^2')) plot_gauss(frame$TIC_df, gaussfit[[1]])
plot_gauss2 Plots a 3D peak with the fitted Gaussian curve.
plot_gauss2(TIC_df, gauss2_return, title = "Peak fit to Gaussian")plot_gauss2(TIC_df, gauss2_return, title = "Peak fit to Gaussian")
TIC_df |
a data.frame object. Data frame with 4 columns (Overall Time Index, RT1, RT2, TIC), ideally the output from create_df(), or the first data frame returned from extract_data(), $TIC_df. |
gauss2_return |
a data.frame object. The output from guass_fit(). A data frame with two columns, (time, guassfit), the time values around the peak, and the intensity values fitted to the optimal Gaussian curve. |
title |
a string object. Title placed at the top of the plot. |
This function plots the points around the peak with a
contour plot of the Gaussian curve fit to the peak data, using
ggplot from ggplot2 package
(Wickham 2016).
A ggplot object. A contour plot of the Gaussian curve fit to the peak data.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) gaussfit2 <- gauss2_fit(frame$TIC_df, peakcoord=c(peaks$'X'[1], peaks$'Y'[1])) message(paste('Volume under curve =',gaussfit2[[3]],'u^3')) plot_gauss2(frame$TIC_df, gaussfit2[[1]])file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) gaussfit2 <- gauss2_fit(frame$TIC_df, peakcoord=c(peaks$'X'[1], peaks$'Y'[1])) message(paste('Volume under curve =',gaussfit2[[3]],'u^3')) plot_gauss2(frame$TIC_df, gaussfit2[[1]])
plot_ms Plots the mass spectra of a peak.
plot_ms(ms, title = "Mass Spectrum")plot_ms(ms, title = "Mass Spectrum")
ms |
a data.frame object. A data frame of the mass values and the percent intensity values, ideally the output of find_ms(). |
title |
a string object. Title placed at the top of the plot. Default title "Mass Spectrum". |
This function produces a line plot of the mass spectra data. The
mass values vs the percent intensity values as a percent of the highest
intensity using ggplot from ggplot2 package
(Wickham 2016).
A ggplot object. A line plot of the mass spectra data. The mass values vs the percent intensity values as a percent of the highest intensity.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mz <- find_ms(frame, t_peak=peaks$'T'[1]) plot_ms(mz)file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) mz <- find_ms(frame, t_peak=peaks$'T'[1]) plot_ms(mz)
plot_nist Plots the mass spectra of a NIST compound.
plot_nist(nistlist, k, ms, title = "NIST Mass Spectrum")plot_nist(nistlist, k, ms, title = "NIST Mass Spectrum")
nistlist |
a list object, a list of compound MS data from the NIST MS Library database, ideally the output of nist_list(). |
k |
a integer object, the index of the NIST compound in the nistlist input. |
ms |
a data.frame object, a data frame of the mass values and the percent intensity values, ideally the output of find_ms(). |
title |
a string object. Title placed at the top of the plot. Default title "Mass Spectrum". |
This function produces line plot of the mass spectra data from the
sample on top, and the mass spectrum from a NIST compound entry on the
bottom. The mass values vs the percent intensity values as a percent of the
highest intensity using ggplot from ggplot2 package
(Wickham 2016).
A ggplot object. A line plot of the mass spectra data. The mass values vs the percent intensity values as a percent of the highest intensity.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
plot_peak plots peaks on a chromatograph plot.
plot_peak( peaks, data, title = "Intensity with Peaks", circlecolor = "red", circlesize = 5 )plot_peak( peaks, data, title = "Intensity with Peaks", circlecolor = "red", circlesize = 5 )
peaks |
a data.frame object. A data frame with 4 columns (Time, X, Y, Peak), ideally the output from either thr_peaks() or top_peaks(). |
data |
a list object. Data extracted from a cdf file, ideally the output from extract_data(). Provides the background GCxGC plot, created with plot_chr(). |
title |
a string object. Title placed at the top of the plot. Default title "Intensity with Peaks". |
circlecolor |
a string object. The desired color of the circles which indicate the peaks. Default color red. |
circlesize |
a double object. The size of the circles which indicate the peaks. Default size 5. |
This function circles the identified peaks in a sample over a
chromatograph plot (ideally smoothed) using ggplot
from ggplot2 package (Wickham 2016).
A ggplot object. A plot of the chromatogram heatmap, with identified peaks circled in red.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) plot_peak(peaks, frame, title="Top 20 Peaks")file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) plot_peak(peaks, frame, title="Top 20 Peaks")
plot_peakonly plots the peaks from a chromatograph.
plot_peakonly(peak_df, title = "Peaks")plot_peakonly(peak_df, title = "Peaks")
peak_df |
a data.frame object. A data frame with 4 columns (Time, X, Y, Peak), ideally the output from top_peaks() or thr_peaks(). |
title |
a string object. Title placed at the top of the plot. Default title "Peaks". |
This function creates a circle plot of the peak intensity vs
the x and y retention times using ggplot from ggplot2
package (Wickham 2016). The size of the circle indicates the
intensity of the peak.
A ggplot object. A circle plot of peak intensity in 2D retention time.
Wickham H (2016). ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. ISBN 978-3-319-24277-4, https://ggplot2.tidyverse.org.
file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) plot_peakonly(peaks,title="Top 20 Peaks")file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) plot_peakonly(peaks,title="Top 20 Peaks")
preprocess performs full preprocessing on a data file.
preprocess( filename, mod_t = 10, shift = 0, lambda = 20, gamma = 0.5, subtract = NULL, images = FALSE )preprocess( filename, mod_t = 10, shift = 0, lambda = 20, gamma = 0.5, subtract = NULL, images = FALSE )
filename |
a string object. The file name or path of the cdf file to be opened. |
mod_t |
a float object. The modulation time for the GCxGC sample analysis.Default is 10. |
shift |
a float object. The number of seconds to shift the phase by. Default is 0 to skip shifting. |
lambda |
a float object. A number (parameter in Whittaker smoothing), suggested between 1 to 10^5. Small lambda is very little smoothing, large lambda is very smooth. Default is lambda = 20. |
gamma |
a float object. Correction factor between 0 and 1. 0 results in almost no values being subtracted to the baseline, 1 results in almost everything except the peaks to be subtracted to the baseline. Default is 0.5. |
subtract |
a data.frame object. Data frame containing TIC data from a background sample or blank sample to be subtracted from the sample TIC data. |
images |
a boolean object. An optional input. If TRUE, all images of preprocessing steps will be displayed. Default is FALSE, no images will be displayed. |
This function performs full preprocessing on a data file. Extracts data and performs smoothing and baseline correction.
A data.frame object. A list of two data frames. A TIC data frame and an MS data frame.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- preprocess(file,mod_t=.5,lambda=10,gamma=0.5,images=TRUE)file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- preprocess(file,mod_t=.5,lambda=10,gamma=0.5,images=TRUE)
smooth performs smoothing of the intensity values.
smooth(data, lambda = 20, dir = "XY")smooth(data, lambda = 20, dir = "XY")
data |
a list object. Data extracted from a cdf file, ideally the output from extract_data(). |
lambda |
a float object. A number (parameter in Whittaker smoothing), suggested between 0 to 10^4. Small lambda is very little smoothing, large lambda is very smooth. Default is lambda = 20. |
dir |
a string object. Either "X", "Y", or "XY" to indicate direction of smoothing. "XY" indicates smoothing in both X (horizontal) and Y (vertical) directions. Default "XY". |
This function performs smoothing of the intensity values using
Whittaker smoothing algorithm whit1 from the ptw package
(Eilers 2003).
A data.frame object. A list of two data frames. A TIC data frame and an MS data frame.
Eilers PH (2003). “A perfect smoother.” Analytical Chemistry, 75, 3631-3636.
file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) sm_frame <- smooth(frame, lambda=10) plot_chr(sm_frame, title='Smoothed')file <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file,mod_t=.5) sm_frame <- smooth(frame, lambda=10) plot_chr(sm_frame, title='Smoothed')
targeted performs targeted analysis for a batch of data
files, for a list of masses of interest.
targeted( data_list, MOIs, RTs = c(), window_size = c(), tolerance = 0.005, images = FALSE )targeted( data_list, MOIs, RTs = c(), window_size = c(), tolerance = 0.005, images = FALSE )
data_list |
a list object. Data extracted from each cdf file, ideally the output from extract_data(). |
MOIs |
a vector object. A vector containing a list of all masses of interest to be investigated. |
RTs |
a vector object. An optional vector containing a list of retention times of interest for the listed masses of interest. Default values if left empty will be at the retention time of the highest intensity for the corresponding mass. |
window_size |
a vector object. An optional vector containing a list of window sizes corresponding to the retention times. Window will be defined by (RT-window_size, RT+window_size). Default if left empty will be 0.1. |
tolerance |
a float object. The tolerance allowed for the MOI. Default is 0.005. |
images |
a boolean object. An optional input. If TRUE, all images of the found peaks will be displayed. Default is FALSE, no images will be displayed. |
This function performs targeted analysis for a batch of data files, for a list of masses of interest.
a data.frame object. A data frame containing the areas of the peaks for the indicated MOIs and list of files.
file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") file2 <- system.file("extdata","sample2.cdf",package="gcxgclab") file3 <- system.file("extdata","sample3.cdf",package="gcxgclab") frame1 <- extract_data(file1,mod_t=.5) frame2 <- extract_data(file2,mod_t=.5) frame3 <- extract_data(file3,mod_t=.5) targeted(list(frame1,frame2,frame3),MOIs = c(92.1397, 93.07058), RTs = c(6.930, 48.594), images=TRUE)file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") file2 <- system.file("extdata","sample2.cdf",package="gcxgclab") file3 <- system.file("extdata","sample3.cdf",package="gcxgclab") frame1 <- extract_data(file1,mod_t=.5) frame2 <- extract_data(file2,mod_t=.5) frame3 <- extract_data(file3,mod_t=.5) targeted(list(frame1,frame2,frame3),MOIs = c(92.1397, 93.07058), RTs = c(6.930, 48.594), images=TRUE)
thr_peaks finds all peaks above the given threshold.
thr_peaks(TIC_df, THR = 1e+05)thr_peaks(TIC_df, THR = 1e+05)
TIC_df |
a data.frame object. Data frame with 4 columns (Overall Time Index, RT1, RT2, TIC), ideally the output from create_df(), or the first data frame returned from extract_data(), $TIC_df. |
THR |
a float object. Threshold for peak intensity. Should be a number between the baseline value and the highest peak intensity. Default suggestion is THR = 100000. |
This function finds all peaks in the sample above a given intensity threshold.
A data.frame object. A data frame with 4 columns (Time, X, Y, Peak) with all peaks above the given threshold, with their time coordinates.
file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) thrpeaks <- thr_peaks(frame$TIC_df, 100000) plot_peak(thrpeaks, frame, title="Peaks Above 100,000") plot_peakonly(thrpeaks,title="Peaks Above 100,000")file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) thrpeaks <- thr_peaks(frame$TIC_df, 100000) plot_peak(thrpeaks, frame, title="Peaks Above 100,000") plot_peakonly(thrpeaks,title="Peaks Above 100,000")
top_peaks finds the top N highest peaks.
top_peaks(TIC_df, N)top_peaks(TIC_df, N)
TIC_df |
a data.frame object. Data frame with 4 columns (Overall Time Index, RT1, RT2, TIC), ideally the output from create_df(), or the first data frame returned from extract_data(), $TIC_df. |
N |
int object. The number of top peaks to be found in the sample. N should be an integer >=1. Default suggestion is N = 20. |
This function finds the top N peaks in intensity in the sample.
A data.frame object. A data frame with 4 columns (Time, X, Y, Peak) with the top N peaks, with their time coordinates.
file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) plot_peak(peaks, frame, title="Top 20 Peaks") plot_peakonly(peaks,title="Top 20 Peaks")file1 <- system.file("extdata","sample1.cdf",package="gcxgclab") frame <- extract_data(file1,mod_t=.5) peaks <- top_peaks(frame$TIC_df, 5) plot_peak(peaks, frame, title="Top 20 Peaks") plot_peakonly(peaks,title="Top 20 Peaks")