Single-cell differential abundance detection: A new angle on dissecting tumor heterogeneity

Figure 1 Overview of single-cell differential abundance detection. A: The general workflow of single-cell differential abundance (DA) analysis. Data obtained from single-cell sequencing are preprocessed and then subjected to DA detection. The results of DA detection are often validated experimentally or integrated with multi-omics data to facilitate deeper insights into the dataset; B: Single-cell DA methods can be broadly categorized based on whether they rely on clustering strategies; C: Common statistical models and typical output metrics used in single-cell DA detection. DA: Differential abundance; GLM: Generalized linear models; logFC: Log2 fold change; FDR: False discovery rate.