Rectangle: robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data
Abstract
Bulk RNA-seq enables effective profiling of large cohorts and complex experimental designs, but current single-cell-informed deconvolution methods incompletely resolve closely related cell phenotypes, do not scale efficiently to large single-cell datasets, or fail to account for cellular content not represented in the reference. Here, we present Rectangle, an scverse Python framework for single-cell-informed deconvolution of bulk RNA-seq data. Rectangle combines multiscale deconvolution, capturing cellular composition across multiple resolution levels, with explicit modeling of unknown cellular content. In a diverse, cross-method benchmark, Rectangle achieved consistently strong performance across all evaluated metrics, demonstrating high accuracy, high resolution, low spillover, strong scalability and efficiency, and robustness to unknown cellular content. By bridging the resolution of single-cell transcriptomics with the scale and cost-efficiency of bulk RNA-seq, Rectangle enables cell-type and cell-state profiling at scale, supporting population-scale cellular biomarker discovery and tracking of cellular dynamics in settings impractical for comprehensive single-cell sequencing.
Lifecycle
- biorxiv v1 2026-07-09 source ↗
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#SingleCell preprints @prepub-singlecell.bsky.social · 721 followers neutral
Rectangle: robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data #SingleCell 🧪🧬🖥️ https://www.biorxiv.org/content/10.64898/2026.07.07.736950v1
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bioRxiv Bioinfo @biorxiv-bioinfo.bsky.social · 4920 followers neutral
Rectangle: robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data https://www.biorxiv.org/content/10.64898/2026.07.07.736950v1
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bioRxivpreprint @biorxivpreprint.bsky.social · 8895 followers neutral
Rectangle: robust and scalable multiscale deconvolution informed by single-cell RNA sequencing data https://www.biorxiv.org/content/10.64898/2026.07.07.736950v1