McCluskey, A. | Search bioRxiv

bioRxiv2026

Cell DiffErential Expression by Pooling (CellDEEP) highlights issues in differential gene expression in scRNA-seq

Accurate identification of differentially expressed genes (DEGs) in single-cell RNA sequencing (scRNA-seq) data remains challenging. Single-cell-specific statistical models often report large numbers of candidate genes but can exhibit inflated false positive rates, whereas pseudobulk approaches improve false discovery control at the cost of reduced sensitivity. To overcome the noise and bias that other tools have, and allow the user to have more control of the DEG process, we present CellDEEP, which uses a cell aggregation (metacell) approach. This tool provides a framework for flexible selection of pooling strategies and parameterisation for differential expression analysis (DE). Benchmarking on simulated and real datasets, including COVID-19 and rheumatoid arthritis, shows that CellDEEP often outperforms other methods, consistently reduces false positives compared to single-cell methods and recovers more true positives than pseudobulk methods. Our work shifts the focus from selecting a single "best" method to an approach that reduces cell-level noise while preserving biological signal, together with transparent validation framework, advancing more reliable differential-expression analysis in single-cell transcriptomics. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=189 HEIGHT=200 SRC="FIGDIR/small/710522v1_ufig1.gif" ALT="Figure 1"> View larger version (35K): org.highwire.dtl.DTLVardef@14692f9org.highwire.dtl.DTLVardef@5b37d6org.highwire.dtl.DTLVardef@aece11org.highwire.dtl.DTLVardef@5ade3d_HPS_FORMAT_FIGEXP M_FIG C_FIG

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bioRxiv2025

BLASE: Bulk Linkage Analysis for Single Cell Experiments - Teasing Out the Secrets of Bulk Transcriptomics with Trajectory Analysis

1MotivationscRNA-seq experiments can capture cell process trajectories. Bulk RNA-seq is more practical, however does not have the granularity to elucidate cell-type specific trajectories. Deconvolution methods can estimate cell-types in RNA-seq data, but there is a need for methods characterising their pseudotime. ResultsWe show that our method, BLASE, can identify the progress of an RNA-seq sample through a trajectory in a scRNA-seq reference. ConclusionBLASE can be used to a) annotate scRNA-seq data from existing RNA-seq, b) identify progress of RNA-seq data through a process based on scRNA-seq data, and c) be used to correct developmental differences in RNA-seq differential expression analysis.

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