RamanSPy enables automated pipelining of spectral preprocessing protocols. Experimental RS data is susceptible to non-specific signal artefacts (e.g., cosmic rays, autofluorescence background, variability in instrumentation), which can severely affect downstream analyses. Preprocessing is therefore a critical step in any spectroscopic analysis workflow . Yet, due to a lack of standardisation and frameworks for general-purpose pipelining , researchers tend to utilise variable preprocessing protocols, often dispersed across different software systems, thus affecting reproducibility and validation . . b, An exemplar spectrum from the raw volumetric Raman data (taken from the centre of the layer in d). The fingerprint region (700-1800 cm -1 ) shaded in red was used for the analysis. c, Volumetric data at the 1008 cm -1 band (characteristic of proteins) after preprocessing. d-g, Spectral unmixing analysis reveals the distribution of components within the cell: lipids (violet), nucleus (blue), cytoplasm (green), and background (yellow). d, A merged reconstruction of the sixth depth layer (10 in total) of the THP-1 cell determined via spectral unmixing. e, Four endmembers derived with N-FINDR characterised via peak assignment. f, Fractional abundance maps calculated with FCLS for the sixth depth layer. g, Fractional abundance maps for the entire volume. RamanSPy