Generate a feature matrix.
Usage
gen_feat_mat(
data_path,
ppm_range = get_ppm_range(data_path),
si_size_real_spectrum = length(data_path$y_values),
scale_factor_x = 1000,
warn = TRUE
)Arguments
- data_path
A list of deconvoluted spectra as returned by
generate_lorentz_curves(). In older versions, this could also be the path passed togenerate_lorentz_curves(), but this is deprecated and will trigger a warning. See 'Details' for more information.- ppm_range
The ppm range over which your signals are distributed.
- si_size_real_spectrum
Number of data points in your spectra.
- scale_factor_x
The x scale factor used during the deconvolution.
- warn
Whether to print a warning in case a file path is passed to
data_pathinstead of a list of deconvoluted spectra.
Value
A list with the following elements:
data_matrix: A data.frame where each row corresponds to one spectrum and
each column to one data point, i.e. for 10 input spectra
with 131072 data points each data_matrix would have
dimensions 10 x 131072.
peakList: A list of vectors, where each vector contains the indices of
the peaks in the corresponding spectrum. The indices increase
from left to right, i.e. the smallest index corresponds to the
highest ppm value, as the ppm values decrease from left to
right.
w: A list of vectors where each vector contains the "position parameter"
of the peaks in the corresponding spectrum.
A: A list of vectors where each vector contains the "area parameter" of
the peaks in the corresponding spectrum.
lambda: A list of vectors where each vector contains the "width
parameter" of the peaks in the corresponding spectrum.
Details
Before version 1.2 of 'metabodecon', the deconvolution functions
generate_lorentz_curves and MetaboDecon1D wrote their output partially as
txt files to their input folder. Back then, gen_feat_mat() used those txt
files as input to generate the feature matrix. Since version 1.2 these txt
files are no longer created by default, to prevent accidental modifications
of the input folders. Therefore, the recommended way to pass the required
information to gen_feat_mat() is to directly pass the output of
generate_lorentz_curves() to gen_feat_mat(). However, to stay backwards
compatible, the name of parameter data_path was not changed and passing an
actual path to data_path is still possible, but will result in a warning
(unless warn is set to FALSE).
Examples
sim_subset <- metabodecon_file("sim_subset")
decons <- generate_lorentz_curves_sim(sim_subset)
obj <- gen_feat_mat(decons)
str(obj, 2, give.attr = FALSE)
#> List of 5
#> $ data_matrix: num [1:2, 1:2048] 0.0122 0.0105 0.0122 0.0106 0.0123 ...
#> $ peakList :List of 2
#> ..$ sim_01: num [1:27] 486 529 560 630 702 ...
#> ..$ sim_02: num [1:30] 552 590 636 691 766 ...
#> $ w :List of 2
#> ..$ sim_01: num [1:27] 1.56 1.52 1.49 1.42 1.35 ...
#> ..$ sim_02: num [1:30] 1.5 1.46 1.41 1.36 1.28 ...
#> $ A :List of 2
#> ..$ sim_01: num [1:27] -0.000158 -0.060544 -0.001124 -0.155302 -0.019516 ...
#> ..$ sim_02: num [1:30] -0.00377 -0.05931 -0.00255 -0.14508 -0.01957 ...
#> $ lambda :List of 2
#> ..$ sim_01: num [1:27] -0.00713 -0.00763 -0.008 -0.00838 -0.00732 ...
#> ..$ sim_02: num [1:30] -0.01002 -0.00794 -0.01318 -0.00856 -0.00757 ...