ArticleNature communications2025
Transcriptome size matters for single-cell RNA-seq normalization and bulk deconvolution.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Single-cell multi-omic profiling of heterogeneous PROCRiScience · 2026Article
- DeOPUS: cellular deconvolution via optimized power-transformed unmixing with shrinkage.Briefings in bioinformatics · 2026Article
- iDCF: Interpretable deconvolution of cell fractions via biologically-informed deep learning using scRNA-seq data.PLoS computational biology · 2026Article
- Patient-derived resources for decoding and targeting brain metastases ecosystems.EMBO molecular medicine · 2026Review
- Multi-task spatial distillation reveals cell-type-resolved programmed cell death landscapes in the human kidney.Briefings in bioinformatics · 2026Article
- Cell-Type Deconvolution of Equine BALF RNA-Seq: A Critical Comparison with Matched Single-Cell Data.Genes · 2026Article
- Transcriptomic Meta-Analysis as a Framework for Robust Cross-Study Biological Inference.International journal of molecular sciences · 2026Review
- Single-cell analysis reveals cellular heterogeneity and limits of marker-based assessment in retinal ganglion cell-enriched organoid cultures.Molecular medicine (Cambridge, Mass.) · 2026Article
- Dermal fibroblasts respond to IL-4 and IL-13 and promote T cell recruitment in atopic dermatitis.The Journal of clinical investigation · 2026Article
- omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data.Genome biology · 2026Article
- In Silico Analysis of the Dual Role of Tumor Microenvironment on Colon Cancer Subtypes.Cancer informatics · 2026Article
- Identification of the body fluid donors from mixture stains using bulk transcriptomes data.Briefings in bioinformatics · 2025Article
- MOADE: a multimodal autoencoder for dissociating bulk multi-omics data.Genome biology · 2025Article
- Approaching the holistic transcriptome-convolution and deconvolution in transcriptomics.Briefings in bioinformatics · 2025Review
- Transcriptome size matters for single-cell RNA-seq normalization and bulk deconvolution.Nature communications · 2025Article
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Authors and funding
7 authors.
Funding
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.
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