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

Area of Science:

• Genomics
• Computational Biology

Background:

• Transcriptome size variation is a critical yet often overlooked factor in single-cell RNA sequencing (scRNA-seq) data normalization.
• This variation also affects the accuracy of bulk RNA sequencing (RNA-seq) cellular deconvolution methods.

Purpose of the Study:

• To introduce ReDeconv, a novel computational algorithm that integrates transcriptome size into scRNA-seq normalization and bulk deconvolution.
• To enhance the precision of RNA sequencing data analysis, particularly for rare cell types.

Main Methods:

• Developed Count based on Linearized Transcriptome Size (CLTS) for scRNA-seq normalization, correcting misidentified differentially expressed genes.
• Incorporated transcriptome size variation, gene length effects, and expression variances into the ReDeconv algorithm.
• Validated ReDeconv using both synthetic and real-world datasets.

Main Results:

• CLTS normalization corrects standard normalization errors and improves bulk deconvolution accuracy by preserving transcriptome size variation.
• ReDeconv demonstrates superior precision compared to existing methods in bulk RNA-seq deconvolution.
• The algorithm effectively mitigates gene length effects and models expression variances, enhancing outcomes for rare cell types.

Conclusions:

• ReDeconv offers a new standard for scRNA-seq analysis and bulk deconvolution by accounting for transcriptome size variation.
• The algorithm improves data normalization and deconvolution accuracy, especially for rare cell populations.
• ReDeconv is available via software packages and a web portal, facilitating its adoption.