,
Guilherme L. Alexandrino [aut]
,
Noroska G.S. Mogollón [aut] | Title: | Preprocessing and Multivariate Analysis of Bidimensional Gas Chromatography Data |
|---|---|
| Description: | Toolbox for chemometrics analysis of bidimensional gas chromatography data. This package import data for common scientific data format (NetCDF) and fold it to 2D chromatogram. Then, it can perform preprocessing and multivariate analysis. In the preprocessing algorithms, baseline correction, smoothing, and peak alignment are available. While in multivariate analysis, multiway principal component analysis is incorporated. |
| Authors: | Cristian Quiroz-Moreno [aut, cre]
,
Guilherme L. Alexandrino [aut]
,
Noroska G.S. Mogollón [aut] |
| Maintainer: | Cristian Quiroz-Moreno <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.2.0 |
| Built: | 2025-02-26 03:45:34 UTC |
| Source: | https://github.com/danielquiroz97/rgcxgc |
Subclass aligned_GCxGC are contained in raw_GCxGC super class. It is not contained in the prepec_GCxGC due to raw chromatograms can be aligned without a previous preproccesing technique. Although, it can improve the performance of the aligment, but it is not mandatory.
You can perform the aligment after some preprocessing technique as: baseline correction, or signal smoothing to imporve the performance of the alignment function.
'baseline_corr' provides a two-dimensional baseline correction by using the asymmetric least squares algorithm.
baseline_corr(chromatogram, ...)baseline_corr(chromatogram, ...)
chromatogram |
a raw_GCxGC object. |
... |
other parameters passed to |
This function takes a raw two-dimensional chromatogram and performs
the baseline correction with the implemented function in
baseline.corr (Eilers 2004).
Eilers PH (2004). “Parametric Time Warping.” Analytical Chemistry, 76(2), 404–411.
library(colorRamps) chrom_name <- system.file("extdata", "08GB.cdf", package = "RGCxGC") chrom_2D <- read_chrom(chrom_name, 5L) chrom_bsline <- baseline_corr(chrom_2D) plot(chrom_bsline, nlevels = 150, color.palette = matlab.like)library(colorRamps) chrom_name <- system.file("extdata", "08GB.cdf", package = "RGCxGC") chrom_2D <- read_chrom(chrom_name, 5L) chrom_bsline <- baseline_corr(chrom_2D) plot(chrom_bsline, nlevels = 150, color.palette = matlab.like)
'batch_2DCOW' perform two-dimensional correlation optimized warping alignment in batch.
batch_2DCOW(reference, sample_chroms, segments, max_warp, add_ref = FALSE)batch_2DCOW(reference, sample_chroms, segments, max_warp, add_ref = FALSE)
reference |
a GCxGC chromatogram which will be taken as the reference chromatogram. |
sample_chroms |
a named list with the sample chromatograms which will be aligned against to the reference chromatogram. |
segments |
a two integer vector with the number of segments which the first and second dimension will be divided, respectively. |
max_warp |
a two integer vector with the maximum warping parameter for the first and second dimension |
add_ref |
a logical indicating if the reference chromatogram will be joined together with the sample chromatograms. By the fault add_ref = F. If add_ref is set to T, the provide reference chromatogram will be included as another sample chromatogram in the downstream analysis. |
The first argument is the reference chromatogram which other chromatograms will aligned against to. Then, a named list is needed for the sample_chroms argument. Each chromatogram in this list will be aligned using the reference chromatogram. By default, the reference chromatogram will be not included in the subsequent analysis, such as MPCA. If you would like to add the reference chromatogram, then add_ref = T.
This is an adaptation of two-dimensional COW alignment, firstly implemented in MATLAB. This function takes a set of samples chromatogram to be aligned against to the reference. The argument [segment] will be used to split the whole chromatogram in n and m parts in the first and the second dimension, respectively. The [max_warp] argument provides the maximum tolerance of the signal transformation for the first and the second dimension, respectively.
# Read Sample chromatogram GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") MTBLS08 <- read_chrom(GB08_fl, mod_time = 5) # Read reference chromatogram GB09_fl <- system.file("extdata", "09GB.cdf", package = "RGCxGC") MTBLS09 <- read_chrom(GB09_fl, mod_time = 5) # Create a named list # MTBLS08 will be repeated for exemplification # Considerer that chromatograms are renamed considering the list names batch_samples <- list(Chrom1 = MTBLS08, Chrom2 = MTBLS08) # Perform batch 2DCOW alignment # Add the reference chromatogram as another sample batch_alignment <- batch_2DCOW(MTBLS09, batch_samples, c(10, 40), c(1, 10), add_ref = TRUE) # Exclude the reference chromatogram in the sample chromatogram set batch_alignment <- batch_2DCOW(MTBLS09, batch_samples, c(10, 40), c(1, 10))# Read Sample chromatogram GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") MTBLS08 <- read_chrom(GB08_fl, mod_time = 5) # Read reference chromatogram GB09_fl <- system.file("extdata", "09GB.cdf", package = "RGCxGC") MTBLS09 <- read_chrom(GB09_fl, mod_time = 5) # Create a named list # MTBLS08 will be repeated for exemplification # Considerer that chromatograms are renamed considering the list names batch_samples <- list(Chrom1 = MTBLS08, Chrom2 = MTBLS08) # Perform batch 2DCOW alignment # Add the reference chromatogram as another sample batch_alignment <- batch_2DCOW(MTBLS09, batch_samples, c(10, 40), c(1, 10), add_ref = TRUE) # Exclude the reference chromatogram in the sample chromatogram set batch_alignment <- batch_2DCOW(MTBLS09, batch_samples, c(10, 40), c(1, 10))
Subclass batch_2DCOW are contained in raw_GCxGC super class. batch_2DCOW contains multiple aligned chromatograms, which the first one is the reference chromatogram.
You can perform the alignment after some preprocessing technic as: baseline correction, or signal smothing to improve the performance of the aligment function, or with the raw chromatogram.
'dephase_chrom' shifts the retention time in the second dimension of the two-dimensional chromatogram. This procedure is usually applied in cases when part of peaks is splited in at the final and beginning of the second dimension. Also, the solvent effect and column bleeding can be removing by dephasing the chromatogram. The dephasing procedure is performing by splitting the chromagram with the relative value provided.
dephase_chrom(Object, rel_dephase) ## S4 method for signature 'GCxGC' dephase_chrom(Object, rel_dephase)dephase_chrom(Object, rel_dephase) ## S4 method for signature 'GCxGC' dephase_chrom(Object, rel_dephase)
Object |
a GCxGC class object |
rel_dephase |
a numeric value from 0 to 100 with the relative dephasing position. |
library(colorRamps) GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") GB08 <- read_chrom(GB08_fl, 5L) plot(GB08, nlevels = 150, color.palette = matlab.like, main = "No dephased chromatogram") GB08_d25 <- dephase_chrom(GB08, 25) plot(GB08_d25, nlevels = 150, color.palette = matlab.like, main = "25% dephased chromatogram")library(colorRamps) GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") GB08 <- read_chrom(GB08_fl, 5L) plot(GB08, nlevels = 150, color.palette = matlab.like, main = "No dephased chromatogram") GB08_d25 <- dephase_chrom(GB08, 25) plot(GB08_d25, nlevels = 150, color.palette = matlab.like, main = "25% dephased chromatogram")
Class GCxGC defines the superclass of two-dimensional comprehensive gas chromatographic data.
The validity function evaluates if the provided file can be readed as a
NetCDF file. The validation function employs the function
open.nc to check if the provided file inherits to
the NetCDF class.
namethe name of a NetCDF file where the data will be retrieved from.
mod_timea integer with the modulation time for the second dimension.
'get_metadata' retrieves the metadata contained in a joined_chrom object.
get_metadata(chroms) ## S4 method for signature 'joined_chrom' get_metadata(chroms)get_metadata(chroms) ## S4 method for signature 'joined_chrom' get_metadata(chroms)
chroms |
a joined_chrom object created by joined_chrom function. |
This function accesses to the groups slot created by the joined_chrom function. The Names are the names of the chromatograms.
data(Myrothecium) myr_data <- get_metadata(Myrothecium) myr_datadata(Myrothecium) myr_data <- get_metadata(Myrothecium) myr_data
'join_chromatograms' saves chromatograms in a named list slot. Also, it saves information like metadata and retention times.
join_chromatograms(x, y, groups, ...)join_chromatograms(x, y, groups, ...)
x, y
|
a GCxGC object, either single or batch chromatograms. |
groups |
a data.frame containing the metadata. It must have a column named as Names to merge with the imported chromatograms. |
... |
other GCxGC objects to be merged |
GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") GB09_fl <- system.file("extdata", "09GB.cdf", package = "RGCxGC") GB08 <- read_chrom(GB08_fl, 5L) GB09 <- read_chrom(GB09_fl, 5L) join_gc <- join_chromatograms(GB08, GB09) metadata <- data.frame(Names = c("GB08", "GB09"), Type = c("Control", "Treatment")) join_metadata <- join_chromatograms(GB08, GB09, groups = metadata)GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") GB09_fl <- system.file("extdata", "09GB.cdf", package = "RGCxGC") GB08 <- read_chrom(GB08_fl, 5L) GB09 <- read_chrom(GB09_fl, 5L) join_gc <- join_chromatograms(GB08, GB09) metadata <- data.frame(Names = c("GB08", "GB09"), Type = c("Control", "Treatment")) join_metadata <- join_chromatograms(GB08, GB09, groups = metadata)
Class joined_chrom defines the superclass to gather single chromatogram, as well as batch chromatograms into a single list, prior to multiway principal compoment analysis or unfolding them.
chromatogramsa named list with all chromatograms.
timethe time range of the chromatographic run
groupsa data.frame containing the experiment metadata with a column named as Names.
mod_timemodulation time of the second dimension
'm_prcomp' Performs a multiway principal components analysis on a given
two-dimensional chromatograms and returns the results as object of class
MPCA.
Before to perform the calculation, each given chromatogramas are unfolded
to a single dimension. All chromatograms are merged and principal component
analysis is performed with the built-in prcomp function.
The print method for these objects prints the summary of the analysis.
This algorithm was first presented by (Wold et al. 1987).
m_prcomp(chrom, center = FALSE, scale = FALSE, npcs = 3, ...)m_prcomp(chrom, center = FALSE, scale = FALSE, npcs = 3, ...)
chrom |
Multiple chromatograms read or batch aligned |
center |
A logical value indicating whether the variables should be shifted to be zero centered. FALSE is set by default. |
scale |
a logical value indicating whether the variables should be scaled to have unit variance before the analysis takes place. The default is FALSE. |
npcs |
an integer indicating how many principals components are desired to maintain. The default is 3 principal components. |
... |
Other arguments passed to |
MPCA returns a list whit class "MPCA" containing the summary of the analysis, the scores matrix, unfolded loadings, and the metadata if it was provided when chromatograms were joined.
Wold S, Geladi P, Esbensen K, Öhman J (1987). “Multi-way principal components- and PLS-analysis.” Journal of Chemometrics, 1(January 1986), 41–56.
data(MTBLS579) # Perform multiway principal component analysis MTBLS579_mpca <- m_prcomp(MTBLS579, center = TRUE) # Print MPCA summary print(MTBLS579_mpca) # Retrieve MPCA scores scores(MTBLS579_mpca) # Plot bidimensional scores plot_loading(MTBLS579_mpca)data(MTBLS579) # Perform multiway principal component analysis MTBLS579_mpca <- m_prcomp(MTBLS579, center = TRUE) # Print MPCA summary print(MTBLS579_mpca) # Retrieve MPCA scores scores(MTBLS579_mpca) # Plot bidimensional scores plot_loading(MTBLS579_mpca)
'make_loading' method takes the loading matrix obtained by a mixOmixs package and fold them into two-dimensional matrix
make_loadings(floadings, time, mod_time, acq_rate)make_loadings(floadings, time, mod_time, acq_rate)
floadings |
a numeric matrix with foreign loadings. With variables in columns and eigenvalues as rows. |
time |
a vector of length two with the time range of the chromatographic run |
mod_time |
the modulation time of the second dimension. |
acq_rate |
the acquisition rate of the mass analyzer. The acquisition rate is printed when read_chrom function is performed |
We strongly recommend to use the plsda function in the mixOmics package to perform partial least squares-discriminant analysis. The result of this model is a list containing a loading matrix. The method retrieves a matrix A with m and n dimensions. Where m is the eigenvalues and n is the number of loadings which the model returns.
Subclass MPCA subclass for Multiway Principal Component Analysis object
scoresA matrix with the eigenvalues of projected chromatograms into principal components space.
The dataset was retrieved from MetaboLights with the identifier number MTBLS79 https://www.ebi.ac.uk/metabolights/MTBLS579. Two groups from the entire study was downloaded: control and S. typhi carriage. The name files of control group are: 08GB, 09GB, and 14GB, which has the following native name 08_GB.cdf, 14_GB.cdf, and 09_GB.cdf in the MetaboLights database. For the S. typhi group the names are: 34GB, 24GB, 29GB, which has the native name of 34_GB.cdf, 24_GB.cdf and 29_GB.cdf in MetaboLights database.
Due to the large size of chromatograms, these data is a subset of the whole chromatograms from 7 min to 18 min of chromatografic run. If you would like to acces the whole formated chromatograms, please go to https://github.com/DanielQuiroz97/MTBLS579.
The original study was developed by Näsström et al. (2018).
data(MTBLS579)data(MTBLS579)
A joined_chrom object containing four slots:
A named list with the two-dimensional chromatograms
The metadata containing two varaibles and six observations
The retantion time range of the chromatographic run
The modulation time
https://www.ebi.ac.uk/metabolights/MTBLS579
Näsström E, Jonsson P, Johansson A, Dongol S, Karkey A, Basnyat B, Tran Vu Thieu N, Trinh Van T, Thwaites GE, Antti H, Baker S (2018). “Diagnostic metabolite biomarkers of chronic typhoid carriage.” PLoS Neglected Tropical Diseases, 12(1), 1–15.
This object contains six chromatograms of the microbial metabolic kinetics. Myrothecium sp. were cultured in CMA. Inoculation was made from Petri dishes with the fully-grown fungal cells and sterile distilled water (to wash the surface of the plate). The plate was scraped with a sterile glass handle to obtain the spore suspension. The suspension was liquated and the concentration of 2.4 x 105 spores/mL was determined using a Neubauer chamber and an optical microscope. Then, 50 uL of the suspension was inoculated in a flow chamber into the tubes containing the culture medium. Tubes were kept at 25 celsius degree in a growth chamber with 12h of photoperiod.
A solid-phase microextraction (SPME) assay containing a DVB / CAR / PDMS (Divinylbenzene / Carboxene / Polymethylsiloxane 50/30 mm) fiber was placed into the tube headspace.
A set of columns consisting of HP-5MS 30m x 0.25mm x 0.25 um connected to a Supelcowax 1m x 0.10mm × 0.10um with a 1m x 0.25mm deactivated silica capillary being allocated between them. In these tests, a modulation period of 5s was used.
For GCxGC-QMS data acquisition, GCMSsolution version 5.3 software was used. The temperature program were 60-165 celcius at 3 celcius/min; 165 celcius - 260celcius at 20 celcius/min; 260 celcius (5 min); flow rate was 0.6 mL/min (Helium 5.0 carrier gas); splitless injection mode, ion source temperature 200 celcius, interface temperature 260 celcius; voltage 0,9 kV; mass range 50-380 m/z; acquisition rate 25Hz and electron ionization (70eV).
The original study was developed by Quiroz-Moreno et al. (2020).
data(Myrothecium)data(Myrothecium)
A joined_chrom object containing four slots:
A named list with the two-dimensional chromatograms
The metadata containing two varaibles and six observations
The retantion time range of the chromatographic run
The modulation time
Quiroz-Moreno C, Furlan MF, Belinato JR, Augusto F, Alexandrino GL, Mogollón NGS (2020). “RGCxGC toolbox: An R-package for data processing in comprehensive two-dimensional gas chromatography-mass spectrometry.” Microchemical Journal, 156, 104830.
'plot' plot the two-dimensional chromatogram as a contour plot.
plot(Object, type = "f", ...) ## S4 method for signature 'GCxGC' plot(Object, type = "f", ...)plot(Object, type = "f", ...) ## S4 method for signature 'GCxGC' plot(Object, type = "f", ...)
Object |
a GCxGC chromatogram, it can be a raw, or preprocessed chromatogram. |
type |
a character indicating the type of chromatogram representation.
By default, type = "f" for |
... |
Other parameters passed to |
This plot function employs the built-in contour function. As mentioned in Reichenbach et al. (2004), interpolation is used to display non-native GCxGC data.
Reichenbach SE, Ni M, Kottapalli V, Visvanathan A (2004). “Information technologies for comprehensive two-dimensional gas chromatography.” Chemometrics and Intelligent Laboratory Systems, 71(2), 107–120.
library(colorRamps) chrom_name <- system.file("extdata", "08GB.cdf", package = "RGCxGC") chrom_2D <- read_chrom(chrom_name, 5L) plot(chrom_2D, nlevels = 150, color.palette = matlab.like) plot(chrom_2D, type = "c", nlevels = 50, col = matlab.like(30))library(colorRamps) chrom_name <- system.file("extdata", "08GB.cdf", package = "RGCxGC") chrom_2D <- read_chrom(chrom_name, 5L) plot(chrom_2D, nlevels = 150, color.palette = matlab.like) plot(chrom_2D, type = "c", nlevels = 50, col = matlab.like(30))
'plot_loading' plot the loading of the a MPCA object.
plot_loading(Object, type = "n", pc = 1, thresh, ...) ## S4 method for signature 'projected' plot_loading(Object, type = "b", pc = 1, thresh, ...)plot_loading(Object, type = "n", pc = 1, thresh, ...) ## S4 method for signature 'projected' plot_loading(Object, type = "b", pc = 1, thresh, ...)
Object |
a MPCA object |
type |
the value type of loadings, p for positive, n for negative, and b for negative and positive loading values. |
pc |
the principal component to plot. |
thresh |
numerical value. A threshold to remove low loading values. |
... |
Other parameters passes to |
This function takes the loadings of MPCA and eval if a certain variable was removed previous compute de MPCA and it fills the removed variables with zero. Then, the loadings are plotted considering one principle component at a time as a two-dimensional chromatogram.
library(colorRamps) data(MTBLS579) # MPCA with mean-centered and scaled data MTBLS579_mpca <- m_prcomp(MTBLS579) # Negative loadings of the first principal component plot_loading(MTBLS579_mpca, type = "n", pc = 1, color.palette = matlab.like) # Positive loadings of the first principal component plot_loading(MTBLS579_mpca, type = "p", pc = 1, color.palette = matlab.like)library(colorRamps) data(MTBLS579) # MPCA with mean-centered and scaled data MTBLS579_mpca <- m_prcomp(MTBLS579) # Negative loadings of the first principal component plot_loading(MTBLS579_mpca, type = "n", pc = 1, color.palette = matlab.like) # Positive loadings of the first principal component plot_loading(MTBLS579_mpca, type = "p", pc = 1, color.palette = matlab.like)
Class PLSDA defines the class to import foreign results of partial least squares-discriminant analysis performed with mixOmics package.
Subclass preproc_GCxGC are contained in raw_GCxGC super class. It contains a dedicated slot to storage the preprocessed two-dimensional chromatogram.
After reading a two-dimensional chromatogram, you can perform serveral preprocessing techniques such as smoothing or baseline correction. This action will create an object of a preproc_GCxGC subclass.
'print_mpca' call the MPCA object to print the summary of this analysis.
print_mpca(Object) ## S4 method for signature 'MPCA' print_mpca(Object)print_mpca(Object) ## S4 method for signature 'MPCA' print_mpca(Object)
Object |
a MPCA object |
The plot function employs the built-in print function and a precomputed MPCA summary to display the explained and cumulative variance for each principal component.
data(MTBLS579) MTBLS_mpca <- m_prcomp(MTBLS579, center = TRUE) print_mpca(MTBLS_mpca)data(MTBLS579) MTBLS_mpca <- m_prcomp(MTBLS579, center = TRUE) print_mpca(MTBLS_mpca)
The projected class defines the superclass for projection methods, specially for multiway principal component analysis and discriminant analysis based on partial least squares. The class represents the convergence of in-package results (m_prcomp) and the foreing building model (PLS-DA) procedure.
loadingsThe eigenvectors of each principal component.
timeThe time range of chromatographic run
mod_timemodulation time of the second dimension
Subclass raw_GCxGC are contained in GCxGC super class. It contains a dedicated slot to storage the folded two-dimensional chromatogram.
In the first creation of a raw_GCxGC object, the slot for the
chromatogram is not created yet. To read and fold the chromatogram
use the read_chrom function.
chromatograma numeric matrix.
timea vector of two elements with the range of the first dimenstion run time
'read_GCxGC' returns a raw_GCxGC with the sample name, the modulation time, the chromatographic time range and the two-dimensional chromatogram.
read_chrom( name, mod_time, sam_rate, per_eval = 0.1, x_cut = NULL, y_cut = NULL, verbose = TRUE )read_chrom( name, mod_time, sam_rate, per_eval = 0.1, x_cut = NULL, y_cut = NULL, verbose = TRUE )
name |
a name of the NetCDF file where the data is allocated. |
mod_time |
a integer, the modulation time of the chromatographic run. |
sam_rate |
a integer, the sampling rate of the equipment. If sam_rate is missing, the sampling rate is calculated by the dividing 1 by the difference of two adjacent scan time. |
per_eval |
a double between 0 and 1, with the percentage of the run time records to be evaluated if the sampling rate is homogeneous. |
x_cut |
a vector with two elements representing the retention time range to be mantained in the first dimension. |
y_cut |
a vector with two elements representing the retention time range to be mantained in the second dimension. |
verbose |
a logical indicating if the information of chromatogram is printted in the console. |
This function reads the NetCDF file and retrieves values in the total_intensity array. Then, with the provided sampling rate and modulation time, the chromatogram is folded into a numerical matrix, representing the two-dimensional chromatogram. This function is an adaptation of the presented routine by Skov and Bro (2008).
Skov T, Bro R (2008). “Solving fundamental problems in chromatographic analysis.” Analytical and Bioanalytical Chemistry, 390(1), 281–285.
GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") GB08 <- read_chrom(GB08_fl, 5L)GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") GB08 <- read_chrom(GB08_fl, 5L)
'reference_chrom' makes a reference chromatogram by calculating a statistic of multiple chromatograms.
reference_chrom(chromatograms, stat = "mean")reference_chrom(chromatograms, stat = "mean")
chromatograms |
a joined_chrom object. |
stat |
a character with the name of the mathematical function that pixels will be subjected to. By default, (stat = "mean") the new reference chromatogram will be the result of the provided mathematical function. |
The aim of this function is to create a consensus chromatogram to be used as a reference in the peak alignment process. In other words, multiple chromatograms will be subjected to a mathematical function, such as min, max, or mean in order to create a representative chromatogram. Then, the new chromatogram will be used as a template and the other chromatograms will be aligned against it. This function overlap pixels with the same chromatogram index and computes a desired mathematical function for each pixel.
# Read chromatogram 1 GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") MTBLS08 <- read_chrom(GB08_fl, mod_time = 5) # Read chromatogram 2 GB09_fl <- system.file("extdata", "09GB.cdf", package = "RGCxGC") MTBLS09 <- read_chrom(GB09_fl, mod_time = 5) # Join chromatograms joined <- join_chromatograms(MTBLS08, MTBLS09) reference <- reference_chrom(joined, stat = "mean") plot(reference, main = "Reference chromaogram")# Read chromatogram 1 GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") MTBLS08 <- read_chrom(GB08_fl, mod_time = 5) # Read chromatogram 2 GB09_fl <- system.file("extdata", "09GB.cdf", package = "RGCxGC") MTBLS09 <- read_chrom(GB09_fl, mod_time = 5) # Join chromatograms joined <- join_chromatograms(MTBLS08, MTBLS09) reference <- reference_chrom(joined, stat = "mean") plot(reference, main = "Reference chromaogram")
'scores' exports the scores matrix of a MPCA object.
scores(Object) ## S4 method for signature 'MPCA' scores(Object)scores(Object) ## S4 method for signature 'MPCA' scores(Object)
Object |
a MPCA object |
This function takes the whole MPCA object and retrieves the score matrix.
data(MTBLS579) # MPCA with mean-centered and scaled data MTBLS579_mpca <- m_prcomp(MTBLS579, center = TRUE) # Export scores matrix scores(MTBLS579_mpca)data(MTBLS579) # MPCA with mean-centered and scaled data MTBLS579_mpca <- m_prcomp(MTBLS579, center = TRUE) # Export scores matrix scores(MTBLS579_mpca)
'set_metadata' fill metadata slot of a joined_chrom object.
set_metadata(Object, metadata) ## S4 method for signature 'joined_chrom,data.frame' set_metadata(Object, metadata)set_metadata(Object, metadata) ## S4 method for signature 'joined_chrom,data.frame' set_metadata(Object, metadata)
Object |
a joined_chrom object |
metadata |
a data.frame containing the metadata. It must have a column named as Names to merge with the chromatograms. |
GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") GB09_fl <- system.file("extdata", "09GB.cdf", package = "RGCxGC") GB08 <- read_chrom(GB08_fl, 5L) GB09 <- read_chrom(GB09_fl, 5L) extra_info <- data.frame(Names = c("GB08", "GB09"), Type = c("Control", "Treatment")) join_chrom <- join_chromatograms(GB08, GB09) join_metadata <- set_metadata(join_chrom, metadata = extra_info)GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") GB09_fl <- system.file("extdata", "09GB.cdf", package = "RGCxGC") GB08 <- read_chrom(GB08_fl, 5L) GB09 <- read_chrom(GB09_fl, 5L) extra_info <- data.frame(Names = c("GB08", "GB09"), Type = c("Control", "Treatment")) join_chrom <- join_chromatograms(GB08, GB09) join_metadata <- set_metadata(join_chrom, metadata = extra_info)
This is an adaptation of two-dimesional COW alignment, first implemented in MATLAB (Tomasi et al. 2004). This functions takes a sample chromatogram to be aligned against a reference. The argument [segment] will be used to split the whole chromatogram in n and m parts the first and the second dimension, respectively. The [max_warp] argument provides de maximum tolerance of the signal transformation for the first and the second dimension (Dabao Zhang et al. 2008).
twod_cow(sample_chrom, ref_chrom, segments, max_warp)twod_cow(sample_chrom, ref_chrom, segments, max_warp)
sample_chrom |
A GCxGC class chromatogram imported by read_chrom function or a preprocessed chromatogram. |
ref_chrom |
A representative GCxGC chromatogram chosen to be the template which sample_chrom will be aligned. |
segments |
A two integer vector with number of segments which the first and second dimension will be divided, respectively. |
max_warp |
A two integer vector with the maximum warping parameter. |
Dabao Zhang, Xiaodong Huang, Fred E. Regnier, Min Zhang (2008).
“Two-dimensional correlation optimized warping algorithm for aligning GCxGC-MS data.”
Analytical Chemistry, 80(8), 2664–2671.
Tomasi G, Van Den Berg F, Andersson C (2004).
“Correlation optimized warping and dynamic time warping as preprocessing methods for chromatographic data.”
Journal of Chemometrics, 18(5), 231–241.
GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") GB09_fl <- system.file("extdata", "09GB.cdf", package = "RGCxGC") GB08 <- read_chrom(GB08_fl, 5L) GB09 <- read_chrom(GB09_fl, 5L) GB09_al <- twod_cow(sample_chrom = GB09, ref_chrom = GB08, segments = c(20, 40), max_warp = c(2, 8))GB08_fl <- system.file("extdata", "08GB.cdf", package = "RGCxGC") GB09_fl <- system.file("extdata", "09GB.cdf", package = "RGCxGC") GB08 <- read_chrom(GB08_fl, 5L) GB09 <- read_chrom(GB09_fl, 5L) GB09_al <- twod_cow(sample_chrom = GB09, ref_chrom = GB08, segments = c(20, 40), max_warp = c(2, 8))
'unfold' converts the two-dimensional chromatograms into a one dimensional vector. Then, all chromatograms are joined into a matrix.
unfold_chrom(Object)unfold_chrom(Object)
Object |
a batch_2DCOW or joined_chrom objects. |
This function takes a single argument, batch_2DCOW or joined_chrom objects and extracts each chromatogram and then it is unfolded into a one-dimensional vector. Then, each one dimensional vector is joined in a single matrix, where each row represent an observation or a chromatogram and each column represent a variable, in our case, each retention time. Also, in order to keep information about chromatographic runs, the retention times for both dimensions are also exported.
data(Myrothecium) # Unfold 2D chromatogram chrom_1D <- unfold_chrom(Myrothecium) # Retrieve retention time for the first dimension time_1D <- chrom_1D$time # Retrieve the modulation time modulation <- chrom_1D$mod_timedata(Myrothecium) # Unfold 2D chromatogram chrom_1D <- unfold_chrom(Myrothecium) # Retrieve retention time for the first dimension time_1D <- chrom_1D$time # Retrieve the modulation time modulation <- chrom_1D$mod_time
'wsmooth' weighted whittaker smoothing.
wsmooth(chromatogram, penalty = 1, lambda = 1, min_int = 0)wsmooth(chromatogram, penalty = 1, lambda = 1, min_int = 0)
chromatogram |
raw_GCxGC or preproc_GCxGC object with name and mod_time slots. |
penalty |
an integer of the order of the penalty. Only penalty of first (penalty = 1) and second (penalty = 2) order are allowed. By default, the smooth function is performed with first penalty order. |
lambda |
smoothing parameter: larger values lead to more smoothing. |
min_int |
minimum intensity value. The smoothing routine usually creates low intensity artifacts which can obscure other compounds signals. The min intensity value replace signals bellow the given value with 0. For quadrupole mass detector this artifacts may range from 0-100, while for TOF mass analyzers it can be 0-1e3. |
This function takes a raw two-dimensional chromatogram and performs the weighted wittaker smoothing. It smooths the signal with linear or quadratic penalty, depending on the provided penalty, along side the first dimension, based on Whittaker smoother (Eilers 2003).
Eilers PH (2003). “A perfect smoother.” Analytical Chemistry, 75(14), 3631–3636.
chrom_name <- system.file("extdata", "08GB.cdf", package = "RGCxGC") chrom_2D <- read_chrom(chrom_name, 5L) chrom_smooth <- wsmooth(chrom_2D, penalty = 1, lambda = 1e1) plot(chrom_smooth, nlevels = 150, color.palette = colorRamps::matlab.like, main = expression(paste(lambda, "= 10, penalty = 1")) ) # Remove intensities bellow 1.75e5 (too high) chrom_smooth2 <- wsmooth(chrom_2D, penalty = 1, lambda = 1e1, min_int = 1.75e5) plot(chrom_smooth2, nlevels = 150, color.palette = colorRamps::matlab.like, main = expression(paste(lambda, "= 10, penalty = 1, min_int = 1.75e5")) )chrom_name <- system.file("extdata", "08GB.cdf", package = "RGCxGC") chrom_2D <- read_chrom(chrom_name, 5L) chrom_smooth <- wsmooth(chrom_2D, penalty = 1, lambda = 1e1) plot(chrom_smooth, nlevels = 150, color.palette = colorRamps::matlab.like, main = expression(paste(lambda, "= 10, penalty = 1")) ) # Remove intensities bellow 1.75e5 (too high) chrom_smooth2 <- wsmooth(chrom_2D, penalty = 1, lambda = 1e1, min_int = 1.75e5) plot(chrom_smooth2, nlevels = 150, color.palette = colorRamps::matlab.like, main = expression(paste(lambda, "= 10, penalty = 1, min_int = 1.75e5")) )