Year: 2025
Type: preprint
Abstract: Modern synthetic data generators and deconvolution methods rely heavily on single-cell (sc) RNA- seq data. Aggregated scRNA-seq (pseudo-bulk) is commonly assumed to closely match true bulk RNA-seq, making it a dependable benchmark for developing and evaluating new bioinformatics methods. Here, we investigated paired bulk and scRNA-seq datasets using machine learning techniques to assess the fidelity of pseudo-bulk profiles. Our results demonstrate that pseudo-bulks differ substantially from bulk RNA-seq in both analytic metrics and biological processes.
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Funder: Bioinformatics Institute
OpenAlex ID: W4411895504