First, RamanSPy allows the seamless integration of standard Python AI/ML methods (e.g., from scikit-learn , PyTorch and tensorflow ) as tools for RS analysis (Fig. ). As an illustration, we use RamanSPy to construct a deep learning denoising procedure based on the one-dimensional ResUNet model -a fully convolutional UNet neural network with residual connections . To do this, we simply wrap within RamanSPy the pre-trained neural network (trained on spectra from MDA-MB-231 breast cancer cells, available at ) as a custom denoising method. Once wrapped, the denoiser is automatically compatible with the rest of RamanSPy and can be readily employed for different applications. For instance, we replicate the results in , and show in Fig. that the application of this deep-learning denoiser to the low signal-to-noise ratio (SNR) test set from consistently outperforms the commonly-used Savitzky-Golay filter , as quantified by various metrics also coded within RamanSPy (e.g., mean squared error (MSE), spectral angle distance (SAD) and spectral information divergence (SID) ). Applying this pipeline to new data only Users can assemble built-in and in-house methods into complete preprocessing pipelines, which are fully compatible with data integrated within RamanSPy and can be saved, reused and shared. RamanSPy also provides access to a library of already assembled preprocessing pipelines. b, Two raw spectra from the THP-1 data from are used to compare the effect of different preprocessing pipelines. c-e, The results of three preprocessing pipelines built within RamanSPy, demonstrating the need for standardisation. Note on preprocessing methods: fingerprint region is 700-1800 cm -1 ; ASLS -Asymmetric Least Squares ; asPLS -Adaptive Smoothness Penalized Least Squares ; AUC -area under the curve; cosmic rays removed with algorithm from . is integrated as a preprocessing module within RamanSPy to investigate its performance against the Savitzky-Golay (SG) filter . b, Denoising of a spectrum from , where the low-SNR (purple) is the input and the high-SNR (green) is the target. The data is denoised with a SG filter of polynomial order 3 and kernel size 9, SG(3, 9) (blue), and with the implemented deep-learning denoiser (yellow). c, The results on the test set from involves changing the data source. Taking advantage of this transferability, we test the denoiser on unseen volumetric Raman data from another cell line (THP-1 ), with added Gaussian noise (see SI). In this case, Fig. shows improved performance especially according to the MSE metric, which is dependent on normalisation, but with lower significance according to scale-invariant and information-theoretic metrics, also available in RamanSPy. This example emphasises the importance of incorporating robust validation criteria within data analysis workflows.