Tina Han (@tingfordha.bsky.social)

Benchmarking of automated cancer cell annotation methods for scRNA-seq data reveals Consensus annotation as the preferred method Targeted cancer therapies have shown therapeutic advantages due to tumor-specific drug activity. Single-cell RNA-sequencing has been widely used in cancer studies to define different cellular identities. However, accurate identification of tumor vs other normal cells is essential to define novel tumor-specific targets. Recent methods have been developed to perform the task of tumor cell annotation, which can be divided into two categories: CNV-based methods, which use transcriptome measurements to infer copy number variations and identify cells with alterations as tumor, and Reference-based methods which train a classifier using previously annotated tumor data and use it to annotate new datasets. We benchmarked the state-of-the-art method of each category, SCEVAN and scATOMIC, respectively, together with Consensus annotation method where a cell is considered tumor if both methods agree on that. Across 20 cancer datasets spanning 9 cancer types with a total of 379 samples, the Consensus https://www.biorxiv.org/content/10.1101/2025.01.13.632750v1