Mitigating ambient RNA and doublets effects on single cell transcriptomics analysis in cancer research

Abstract

In cancer biology, where understanding the tumor microenvironment at high resolution is vital, ambient RNA contamination becomes a considerable problem. This hinders accurate delineation of intratumoral heterogeneity, complicates the identification of potential biomarkers, and decelerates advancements in precision oncology. To solve this problem, several computational approaches are created to determine the ambient RNA contribution from scRNA-seq datasets. Techniques like SoupX and DecontX assist in assessing and eliminating ambient RNA contamination from primary gene expression profiles. Practical solutions like CellBender employ deep learning techniques to concurrently address ambient RNA contamination and background noise, offering a contemporary end-to-end strategy for data preparation. This high-quality, reliable data enables clinicians and researchers to make effective decisions that will help ensure interventions are rooted in reproducible evidence, giving hope for developing more effective targeted therapies.