PC-index, a composite metric for gene expression in single-cell RNA-seq

Single-cell RNA sequencing enables analysis of gene expression heterogeneity, but summarizing gene expression across large cell populations remains challenging. Gene expression is commonly described using average expression (counts per 10,000 transcripts, CP10K) and cellular prevalence (the fraction of cells expressing a gene), which can be difficult to interpret jointly. Here, we introduce the PC-index, a single, intuitive metric that integrates expression magnitude and prevalence. The PC-index is defined as the largest value X such that at least X% of cells express a gene at [≥]X/10 CP10K. Using human adipocytes from GTEx single-nucleus RNA-seq as an illustrative example, we show that adipocyte genes exhibit distinct PC-index profiles. For example, adiponectin has a PC-index of 21, indicating that at least 21% of adipocytes express it at [≥]2.1 CP10K. Conceptually, the PC-index reflects both percentage and CP10K, yielding a stable and interpretable single-number summary of gene expression behavior in single-cell data.