Transcriptome size matters for single-cell RNA-seq normalization and bulk deconvolution

Songjian Lu, Jiyuan Yang, Lei Yan, Jingjing Liu, Judy Jiaru Wang, Rhea Jain and Jiyang Yu ()
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Songjian Lu: St. Jude Children’s Research Hospital
Jiyuan Yang: St. Jude Children’s Research Hospital
Lei Yan: St. Jude Children’s Research Hospital
Jingjing Liu: St. Jude Children’s Research Hospital
Judy Jiaru Wang: St. Jude Children’s Research Hospital
Rhea Jain: St. Jude Children’s Research Hospital
Jiyang Yu: St. Jude Children’s Research Hospital

Nature Communications, 2025, vol. 16, issue 1, 1-17

Abstract: Abstract The variation of transcriptome size across cell types significantly impacts single-cell RNA sequencing (scRNA-seq) data normalization and bulk RNA-seq cellular deconvolution, yet this intrinsic feature is often overlooked. Here we introduce ReDeconv, a computational algorithm that incorporates transcriptome size into scRNA-seq normalization and bulk deconvolution. ReDeconv introduces a scRNA-seq normalization approach, Count based on Linearized Transcriptome Size (CLTS), which corrects differential expressed genes typically misidentified by standard count per 10 K normalization, as confirmed by orthogonal validations. By maintaining transcriptome size variation, CLTS-normalized scRNA-seq enhances the accuracy of bulk deconvolution. Additionally, ReDeconv mitigates gene length effects and models expression variances, thereby improving deconvolution outcomes, particularly for rare cell types. Evaluated with both synthetic and real datasets, ReDeconv surpasses existing methods in precision. ReDeconv alters the practice and provides a new standard for scRNA-seq analyses and bulk deconvolution. The software packages and a user-friendly web portal are available.

Date: 2025
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DOI: 10.1038/s41467-025-56623-1

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