Spectral Compression of Single-Cell Transcriptomes. A Proof-of-Concept FFT Framework for Scalable MRD Follow-updocx

Spectral Compression of Single-Cell Transcriptomes. A Proof-of-Concept FFT Framework for Scalable MRD Follow-updocx | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Method Article Spectral Compression of Single-Cell Transcriptomes. A Proof-of-Concept FFT Framework for Scalable MRD Follow-updocx Solomon Tessega, MD This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7803408/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Single-cell RNA sequencing (scRNA-seq) enables high-resolution profiling of cellular heterogeneity, yet its high dimensionality and sparsity remain major challenges for downstream analysis and visualization. This study introduces a framework that applies the Fast Fourier Transform (FFT) to scRNA-seq expression profiles, converting gene-level signals into frequency-domain representations. By selectively retaining low-frequency components, the method reduces dimensionality while preserving biologically meaningful structure and suppressing technical noise. The approach facilitates clustering, visualization, and annotation tasks. Applied to the PBMC10k dataset, FFT-based compression achieved performance comparable to conventional methods while offering modularity, transparency, and computational efficiency. This framework provides an interpretable and biologically grounded preprocessing strategy for scalable single-cell analysis across transcriptomic and other omics modalities Pathology Bioinformatics Single-cell RNA sequencing (scRNA-seq) Fast Fourier Transform (FFT) Spectral compression Minimal residual disease (MRD) Cell type annotation Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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