A single cell RNAseq benchmark experiment embedding “controlled” cancer heterogeneity

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,...

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Bibliographic Details
Published in:Scientific data 2024-02, Vol.11 (1), p.159-159, Article 159
Main Authors: Arigoni, Maddalena, Ratto, Maria Luisa, Riccardo, Federica, Balmas, Elisa, Calogero, Lorenzo, Cordero, Francesca, Beccuti, Marco, Calogero, Raffaele A., Alessandri, Luca
Format: Article
Language:English
Subjects:
631/114/2114
692/699/67/1612/1350
Algorithms
Antibiotics
Benchmarking
Bioinformatics
Cell Line, Tumor
Cells
Data Descriptor
Datasets
Design
Embedding
ErbB-2 protein
Experiments
Gene Expression Profiling - methods
Genomics
Humanities and Social Sciences
Humans
Kinases
Lung cancer
Lung Neoplasms - genetics
Medical research
multidisciplinary
Mutation
Oncology
Proto-Oncogene Proteins - genetics
Science
Science (multidisciplinary)
Sequence Analysis, RNA - methods
Single-Cell Gene Expression Analysis
Tumor cell lines
Tumors
Citations: Items that this one cites
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Summary: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.
ISSN:2052-4463
2052-4463
DOI:10.1038/s41597-024-03002-y