RT Journal Article
SR Electronic
T1 Benchmarking supervised signature-scoring methods for single-cell RNA sequencing data in cancer
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.06.29.450404
DO 10.1101/2021.06.29.450404
A1 Nighat Noureen
A1 Zhenqing Ye
A1 Yidong Chen
A1 Xiaojing Wang
A1 Siyuan Zheng
YR 2021
UL http://biorxiv.org/content/early/2021/07/02/2021.06.29.450404.abstract
AB Quantifying the activity of gene expression signatures is common in analyses of single-cell RNA sequencing data. Methods originally developed for bulk samples are often used for this purpose without accounting for contextual differences between bulk and single-cell data. More broadly, these methods have not been benchmarked. Here we benchmark four such supervised methods, including single sample gene set enrichment analysis (ssGSEA), AUCell, Single Cell Signature Explorer (SCSE), and a new method we developed, Jointly Assessing Signature Mean and Inferring Enrichment (JASMINE). Using cancer as an example, we show cancer cells consistently express more genes than normal cells. This imbalance leads to bias in performance by bulk-sample-based ssGSEA in gold standard tests and down sampling experiments. In contrast, single-cell-based methods are less susceptible. Our results suggest caution should be exercised when using bulk-sample-based methods in single-cell data analyses, and cellular contexts should be taken into consideration when designing benchmarking strategies.Competing Interest StatementThe authors have declared no competing interest.