Congratulations to former visiting student Chia-Jung "Charlene" Chang (PhD candidate in biomedical engineering at National Cheng Kung University), research assistant professor Chih-Yuan Hsu, and professors Qi Liu and Yu Shyr on the publication of "VICTOR: Validation and inspection of cell type annotation through optimal regression." The article on this new method appeared online ahead of print on October 15 and will go to press as part of Computational and Structural Biotechnology Journal's December issue. As described in the paper's abstract:
Single-cell RNA sequencing provides unprecedent opportunities to explore the heterogeneity and dynamics inherent in cellular biology. An essential step in the data analysis involves the automatic annotation of cells. Despite development of numerous tools for automated cell annotation, assessing the reliability of predicted annotations remains challenging, particularly for rare and unknown cell types. Here, we introduce VICTOR: Validation and inspection of cell type annotation through optimal regression. VICTOR aims to gauge the confidence of cell annotations by an elastic-net regularized regression with optimal thresholds. We demonstrated that VICTOR performed well in identifying inaccurate annotations, surpassing existing methods in diagnostic ability across various single-cell datasets, including within-platform, cross-platform, cross-studies, and cross-omics settings.
Figure 1 in the paper provides an " One example in diagnosing the reliability of cell annotations. a) diagnostic performance of singleR, scmap, SCINA, scPred, CHETAH, scClassify, and Seurat. b) diagnostic performance of VICTOR when applied to annotations from singleR, scmap, SCINA, scPred, CHETAH, scClassify, and Seurat.
