A single cell RNAseq benchmark experiment embedding "controlled" cancer heterogeneity.

Autor: Arigoni M; Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy., Ratto ML; Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy., Riccardo F; Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy., Balmas E; Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy., Calogero L; Department of Electronics and Telecommunications (DET), Politecnico di Torino, Torino, Italy., Cordero F; Department of Computer Science, University of Torino, Torino, Italy., Beccuti M; Department of Computer Science, University of Torino, Torino, Italy., Calogero RA; Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy. raffaele.calogero@unito.it., Alessandri L; Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.
Jazyk: angličtina
Zdroj: Scientific data [Sci Data] 2024 Feb 02; Vol. 11 (1), pp. 159. Date of Electronic Publication: 2024 Feb 02.
DOI: 10.1038/s41597-024-03002-y
Abstrakt: Single-cell RNA sequencing (scRNA-seq) has emerged as a vital tool in tumour research, enabling the exploration of molecular complexities at the individual cell level. It offers new technical possibilities for advancing tumour research with the potential to yield significant breakthroughs. However, deciphering meaningful insights from scRNA-seq data poses challenges, particularly in cell annotation and tumour subpopulation identification. Efficient algorithms are therefore needed to unravel the intricate biological processes of cancer. To address these challenges, benchmarking datasets are essential to validate bioinformatics methodologies for analysing single-cell omics in oncology. Here, we present a 10XGenomics scRNA-seq experiment, providing a controlled heterogeneous environment using lung cancer cell lines characterised by the expression of seven different driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1), leading to partially overlapping functional pathways. Our dataset provides a comprehensive framework for the development and validation of methodologies for analysing cancer heterogeneity by means of scRNA-seq.
(© 2024. The Author(s).)
Databáze: MEDLINE
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