Built-in decomposition methods

In this example, we will use RamanSPy to perform Principal Component Analysis (PCA) to decompose a Raman spectroscopic image into its constituent components.

To do that, we will use the volumetric Volumetric cell data available in RamanSPy. In particular, we will decompose the fourth layer of the provided volumetric dataset.

import ramanspy

dir_ = r'../../../../data/kallepitis_data'

volumes = ramanspy.datasets.volumetric_cells(cell_type='THP-1', folder=dir_)

cell_layer = volumes[0].layer(5)  # only selecting the fourth layer of the volume

Let’s first plot a spectral slice across the 1008 cm ^-1 band of the image to visualise what has been captured in the image.

cell_layer.plot(bands=[1008])

<Axes: title={'center': 'Raman image'}>

We can also visualise a specific spectrum within the image.

cell_layer[30, 30].plot()

<Axes: title={'center': 'Raman spectra'}, xlabel='Raman shift (cm$^{{{-1}}}$)', ylabel='Intensity (a.u.)'>

We may need to first preprocess the spectral image to improve the results of our consecutive analysis.

preprocessing_pipeline = ramanspy.preprocessing.Pipeline([
    ramanspy.preprocessing.misc.Cropper(region=(500, 1800)),
    ramanspy.preprocessing.despike.WhitakerHayes(),
    ramanspy.preprocessing.denoise.SavGol(window_length=7, polyorder=3),
    ramanspy.preprocessing.baseline.ASLS(),
    ramanspy.preprocessing.normalise.MinMax(pixelwise=False),
])
preprocessed_cell_layer = preprocessing_pipeline.apply(cell_layer)

To check the effect of our preprocessing protocol, we can re-plot the same spectral slice as before

preprocessed_cell_layer.plot(bands=[1008])

<Axes: title={'center': 'Raman image'}>

as well as the same spectra we visualised before.

preprocessed_cell_layer[30, 30].plot()

<Axes: title={'center': 'Raman spectra'}, xlabel='Raman shift (cm$^{{{-1}}}$)', ylabel='Intensity (a.u.)'>

We will then perform PCA with 4 components using RamanSPy.

pca = ramanspy.analysis.decompose.PCA(n_components=4)

projections, components = pca.apply(preprocessed_cell_layer)

Having derived the PCA components and the corresponding projections, we can use RamanSPy’s ramanspy.plot.spectra() and ramanspy.plot.image() methods to visualise them.

ramanspy.plot.spectra(components, preprocessed_cell_layer.spectral_axis, plot_type="single stacked", label=[f"Component {i + 1}" for i in range(len(components))])

<Axes: title={'center': 'Raman spectra'}, xlabel='Raman shift (cm$^{{{-1}}}$)', ylabel='Intensity (a.u.)'>

ramanspy.plot.image(projections, title=[f"Projection {i + 1}" for i in range(len(projections))])

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[<Axes: title={'center': 'Projection 1'}>, <Axes: title={'center': 'Projection 2'}>, <Axes: title={'center': 'Projection 3'}>, <Axes: title={'center': 'Projection 4'}>]