Spatial transcriptomic technologies are transforming our ability to profile gene expression, by mapping it to the native architecture of the tissues. However, the inherent complexity and multimodality of the problem demand robust analytical computational tools. In this study, we benchmarked seven state-of-the art clustering algorithms that cover different methodological approaches-conventional statistics, Machine Learning, and Deep Learning-based on their capacity to integrate gene expression, histology, and spatial proximity to identify spatial domains in spatial transcriptomic data. Our comparative analysis implemented on a wellannotated human brain reference dataset shows that algorithms that integrate spatial adjacency (GraphST and BayesSpace) significantly outperform the others, with GraphST achieving the best performance. As a proof of principle, we applied GraphST to a highgrade serous ovarian cancer sample. The model revealed transcriptionally distinct subregions within tumor and stromal compartments not distinguishable by histological assessment alone. Furthermore, by integrating cell-type deconvolution with spatial T-cell receptor (TCR) clonotyping, we discovered heterogeneous immune niches, characterized by domain-specific clonal expansion. Tumor cores exhibited highly diverse TCR repertoires, while stromal margins were dominated by a few expanded clones, suggesting differential immune-selection pressure. These findings highlight the potential of spatially informed Deep Learning methods to dissect the tumor microenvironment and identify actionable targets for immune-based anticancer therapies.

MOTIVATION: Recent advances in multiplex immunostaining and multispectral cytometry have opened the door to simultaneously visualizing an unprecedented number of biomarkers both in liquid and solid samples. Properly unmixing fluorescent emissions is a challenging task, which normally requires the characterization of the individual fluorochromes from control samples. As the number of fluorochromes increases, the cost in time and use of reagents becomes prohibitively high. Here, we present a fully unsupervised blind spectral unmixing method for the separation of fluorescent emissions in highly mixed spectral data, without the need for control samples. To this end, we extend an existing method based on non-negative Matrix Factorization, and introduce several critical improvements: initialization based on the theoretical spectra, automated selection of 'sparse' data and use of a re-initialized multilayer optimizer. RESULTS: Our algorithm is exhaustively tested using synthetic data to study its robustness against different levels of colocalization, signal to noise ratio, spectral resolution and the effect of errors in the initialization of the algorithm. Then, we compare the performance of our method to that of traditional spectral unmixing algorithms using novel multispectral flow and image cytometry systems. In all cases, we show that our blind unmixing algorithm performs robust unmixing of highly spatially and spectrally mixed data with an unprecedently low computational cost. In summary, we present the first use of a blind unmixing method in multispectral flow and image cytometry, opening the door to the widespread use of our method to efficiently pre-process multiplex immunostaining samples without the need of experimental controls. AVAILABILITY AND IMPLEMENTATION: https://github.com/djimenezsanchez/Blind_Unmixing_NMF_RI/ contains the source code and all datasets used in this manuscript. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.