tidySpectR

Introduction

The aim of this package is to provide tidy methods for the analysis of spectral dataset in order to improve accessibility and reproducibility of chemometric analysis.

This package is meant to fill the gap between data acquisition and preprocessing (phasing, baseline correction) and modelling with the tidymodels metapackage.

It provides a framework for processing and analysis of spectral data:

• A collection class storing spectra informations and providing methods for spectral region selection, normalization and bucketting.
• A processing_template class storing processing steps and allowing to repeat these steps on other collection objects.
• A few step_ methods implementing some data preprocessing methods for modelling and/or multivariate data analysis, to use with the tidymodels recipe package.

Installation

This package is not yet on CRAN, install from github:

require("devtools")
install_github("CVUA-RRW/tidySpectR")

Quickstart

Processing spectral data

library(tidyverse)
library(tidySpectR)
library(tidymodels)

data(fa_nmr)

print(fa_nmr)
#> Spectra collection containing 6 entries.
#> Number of bins: 2250
#> Limits: -2.123166e-05 8.999979
#> Labels: conventional organic
#> 
#> Processing:

processed <- fa_nmr %>% 
             # Trimming edges
             extract(from = 0.5, to = 7.2) %>% 
             # Masking water peak
             mask(from = 4.58, to = 4.68) %>% 
             # Masking MeOD peak
             mask(from = 3.33, to = 3.38) %>% 
             # Bucketting with a bucket size of 0.04 ppm
             bucket_uniform(width = 0.04) %>% 
             # Normalizing with the intensity of the methylen group of phospahtidylcholin
             normalize_internalStandard(from = 3.58, to = 3.64) 

print(processed)
#> Spectra collection containing 6 entries.
#> Number of bins: 166
#> Limits: 0.4999788 7.203979
#> Labels: conventional organic
#> 
#> Processing:
#> Step 1 / 5 : extract
#> Step 2 / 5 : mask
#> Step 3 / 5 : mask
#> Step 4 / 5 : uniform_binning
#> Step 5 / 5 : internalStandard_normalization

Reproducing processing on new data

# Export processor
processor <- export_processor(processed)

# Apply processor on new data
new_data <- process(processor, fa_nmr)

print(new_data)
#> Spectra collection containing 6 entries.
#> Number of bins: 166
#> Limits: 0.4999788 7.203979
#> Labels: conventional organic
#> 
#> Processing:
#> Step 1 / 5 : extract
#> Step 2 / 5 : mask
#> Step 3 / 5 : mask
#> Step 4 / 5 : cutsom_bucketting
#> Step 5 / 5 : factor_normalization

Streamlining to tidymodels

# Exporting processed spectra as a data matrix
df <- tidy(processed)

print(df[,1:5]) 
#> # A tibble: 6 x 5
#>   id         label      `0.51997876833874… `0.55997876833874… `0.59997876833874…
#>   <chr>      <fct>                   <dbl>              <dbl>              <dbl>
#> 1 201981241… organic                 0.676              0.658               4.63
#> 2 201981241… organic                 0.722              0.711               5.20
#> 3 201981241… organic                 0.588              0.544               3.90
#> 4 201993059… conventio…              0.631              0.657               4.74
#> 5 201993062… conventio…              0.565              0.540               4.11
#> 6 201995046… conventio…              0.689              0.671               5.31

# Pre-processing for data analysis using the `recipe` package
nmr_preprocessing <- recipe(df, label ~.) %>%
                     update_role(id, new_role = "ID") %>% 
                     # Pareto-VAST scaling - Implemented in this package
                     step_vast(all_predictors(), scaling = 'pareto') %>%
                     # PCA transformation (from the recipes package)
                     step_pca(all_predictors(), num_comp = 5)

# Check the data 
nmr_preprocessing %>% prep() %>% juice()
#> # A tibble: 6 x 7
#>   id          label            PC1    PC2    PC3    PC4    PC5
#>   <fct>       <fct>          <dbl>  <dbl>  <dbl>  <dbl>  <dbl>
#> 1 20198124123 organic       -0.234   8.81 10.4   -0.197  4.37
#> 2 20198124124 organic      -39.6     9.30 -9.34  -2.50   0.787
#> 3 20198124125 organic       32.6    -1.86  0.172 -9.63  -2.44
#> 4 20199305928 conventional   5.01    6.60  2.28   6.68  -5.72
#> 5 20199306281 conventional  37.7    -5.63 -6.97   5.22   3.38
#> 6 20199504645 conventional -35.4   -17.2   3.43   0.440 -0.371

Contributions

Contributions are very welcome! If you would like to see new functionnalities submit an issue directly on github or start a PR.