Unsupervised single-cell analysis in triple-negative breast cancer: A case study

Abstract

This paper demonstrates an unsupervised learning approach to identify genes with significant differential expression across single-cell subpopulations induced by therapeutic treatment. Identifying this set of genes makes it possible to use well-established bioinformatics approaches such as pathway analysis to establish their biological relevance. Then, a biologist can use his/her prior knowledge to investigate in the laboratory, a few particular candidates among the subset of genes overlapping with relevant pathways. Due to the large size of the human genome and limitations in cost and skilled resources, biologists benefit from analytical methods combined with pathway analysis to design laboratory experiments focusing on only a few significant genes. As an example, we show how model-based unsupervised methods can identify a small set of genes (1% of the genome) that have significant differential expression in single-cells and are also highly correlated to pathways (p-value < 1E − 7) with anticancer effects driven by the antidiabetic drug metformin. Further analysis of genes on these relevant pathways reveal three candidate genes previously implicated in several anticancer mechanisms in other cancers, not driven by metformin. Identification of these genes can help biologists and clinicians design laboratory experiments to establish the molecular mechanisms of metformin in triple-negative breast cancer. In a domain where there is no prior knowledge of small biologically significant data, we demonstrate that careful data-driven methods can infer such significant small data to explain biological mechanisms.