MicroRaman

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• Authors: Frederiek-Maarten Kerchkof, Cristina Garcia Timermans, and Ruben Props.

• Contributors: Benjamin Buysschaert, Jasmine Heyse, and Peter Rubbens.

The goal of this package is to provide a standardized and automated workflow for Raman spectra analysis.

If you use this package, please consider citing the original publication in which is was first used:

García-Timermans, C., Rubbens, P., Kerckhof, F. M., Buysschaert, B., Khalenkow, D., Waegeman, W., Skirtach, A. G. & Boon, N. (2018). Label-free Raman characterization of bacteria calls for standardized procedures. Journal of microbiological methods, 151, 69-75.

García‐Timermans, C., Rubbens, P., Heyse, J., Kerckhof, F.‐M., Props, R., Skirtach, A.G., Waegeman, W. and Boon, N. (2020), Discriminating Bacterial Phenotypes at the Population and Single‐Cell Level: A Comparison of Flow Cytometry and Raman Spectroscopy Fingerprinting. Cytometry. doi:10.1002/cyto.a.23952

Install the package:

library("devtools")
install_github("CMET-UGent/MicroRaman", build_vignettes = TRUE)

For exploring the functionalities, take a look at the vignette:

vignette("Demo", package = "MicroRaman")

Core functions

Functions	Description	Functional?
hs_import	Import Thermo Galactic's spc file format data into the R environment	YES
hs_preprocess	Preprocesses the data using the Garcia-Timermans et al. (2020) workflow	YES
hs_resample	Resample hyperSpec object to a requested number of spectra	YES
hs_contrast	Calculate contrasts between spectra of specified groups of cells	YES
hs_hclust	Hierarchical clustering of Raman spectra (with or without bootstrap support)	YES
hs_hclust_cutoff	Visualization of distance cut-off in hclust plots	YES
hs_type	Clusters spectra using partitioning around medoids	YES
hs_PCA	Principal Component Analysis of Raman spectra	YES
hs_tsne	t-distributed stochastic neighbor embedding of Raman spectra	YES
hs_phenoRam	Calculation of Hill diversity numbers for each individual Raman spectrum	YES
hs_coll_curve	Checks sensitivity of Hill diversity calculations under various sample sizes	YES
hs_RF	Train Random Forest classifier to distinguish between groups of cells	YES
hs_RF_pred	Predict using Random Forest classifier on new data	YES
hs_SCAdiss	Calculates the spectral contrast angle (SCA) between all cells in a hyperSpec object	YES

Convenience functions

Functions	Description	Functional?
hs_conv_mq	Converts a hyperSpec::hyperSpec object directly to a MALDIquant::MassSpectrum object	YES
mq_conv_hs	Converts a MALDIquant::MassSpectrum object directly to a hyperSpec::hyperSpec object	YES
hs_tidy_filenames	Tidies up hyperspec spectral IDs	YES
hs_SCA_conv_itol	Convert SCA dissimilarity matrix to itol-compatible object	NO
mq_plot		NO
mq_baseline_plot		NO
mq_iter_plot		NO
intervalplot		NO
model_fit_stats		NO
pred_r_squared		NO
PRESS		NO
SCA	Calculates the spectral contrast angle between two vectors	YES
wlcutter		NO

Available datasets

Some datasets are included in the package. They allow the examples and vigenttes to be run. They can be loaded using:

library("MicroRaman")
data("<name of dataset>")

Dataset name	Data contents
hs_example	Hyperspec object contaning single-cell data of 64 GFP expressing yeast cells