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

## TL;DR

This paper introduces a benchmark scRNA-seq dataset using lung cancer cell lines with known driver genes to help improve cancer heterogeneity analysis methods.

## Contribution

The novel contribution is a controlled scRNA-seq benchmark dataset with defined cancer heterogeneity for validating bioinformatics tools.

## Key findings

- The dataset includes lung cancer cell lines with seven driver genes (EGFR, ALK, MET, ERBB2, KRAS, BRAF, ROS1).
- It provides a framework for developing and testing methodologies to analyze cancer heterogeneity.
- The dataset supports cell annotation and tumour subpopulation identification in scRNA-seq data.

## Abstract

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.